Towards an improved methodology for Energy Return on ...

Towards an improved methodology for Energy

Return on Investment (EROI) for electricity supply

Graham Palmer

July, 2018

ORCID:0000-0002-7667-4189

School of Earth Sciences

The University of Melbourne

Melbourne, Australia

Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy

https://orcid.org/0000-0002-7667-4189

Abstract

The challenge of decarbonising electricity grids is urgent, but pathways towards decarbonisation are

uncertain. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs)

are used to project future low emission scenarios to provide policy guidance. Cost minimization is

a key objective function. Although economic analyses can identify pathways that are economically

infeasible, they cannot identify those that are energetically infeasible. Net-energy analysis (NEA),

using the energy return on investment (EROI) metric, has the potential to supplement conventional

economic and environmental models by providing an energetic valuation of energy supply. However, a

lack of methodological consistency has limited the utility of this approach. This thesis provides a foun-

dation for incorporating NEA into energy modelling by addressing the methodological inconsistencies,

specifically in relation to electricity-based EROI analyses.

The research method is to approach the methodological problem from two ‘directions’, based on

the conceptual idea of hybrid-life cycle assessment (LCA). Firstly, using a top-down approach for

systematic completeness; and a bottom-up approach for a more detailed technology-specific analysis.

The top-down approach includes an environmentally extended input-output analysis (EEIOA) of

the Australian electricity supply industry. This is the first study to calculate the EROI of electricity at

a national level, with a calculated EROI of 40:1. The industry is energetically economic, in the sense

that a relatively small energy investment leverages primary fuels to generate a much greater magnitude

of electricity generation and distribution. However, the leveraging has been achieved at the expense

of a high feedstock extraction rate and commensurate emissions. The result confirms the common

assumption that EROI is not a major factor with respect to the current configuration of the Australian

electricity supply industry. Yet, the study also validates the biophysical economists’ intuition that

a shift away from the existing generation mix risks significantly increasing the energetic costs of the

system, and consequently lowering the system EROI below an energetically viable threshold.

The bottom-up approach includes two technology-focused studies. The first of these introduces

a framework for implementing a consequential EROI analysis of variable renewable energy (VRE)

and storage. Since the underlying function of electricity grids is to ensure that supply meets demand

at a prescribed reliability level, a power based functional unit of 1 kW is adopted to supplement

i

the conventional 1 kWh of energy delivered to the grid. This is the first renewable and storage

based EROI study to undertake a power-based analysis, and provides a framework for comparing the

energetic costs of VRE and electricity storage with the value that storage provides.

The second is an evaluation of the factors that contribute to a divergence in the renewables EROI

literature. This is the first comprehensive evaluation of these factors, and establishes a framework for

incorporating process-based LCA into a consequential EROI assessment.

There are two key conclusions from this thesis. Firstly, a technology-specific EROI analysis needs

to establish the impact on the overall system EROI in order to establish the energetic viability of a

transition pathway. Such an approach is termed a consequential analysis. An attributional analysis,

in itself, is insufficient for assessing the viability of an individual technology.

Secondly, where the EROI of an electricity system is estimated to be higher than about 20:1, an

EROI approach may not provide additional guidance about the viability of a transition pathway. In

this case, EROI may be a non-constraining factor. However, much of the energy modelling literature

utilizes electricity generation and storage technologies that have significantly lower EROI ratios than

the typical incumbent system. In those cases, the modelled system may be energetically infeasible

even though it represents a cost-optimised solution.

This thesis provides a foundation for incorporating NEA into integrated, and energy system opti-

misation models.

ii

Declaration

This is to certify that:

1. the thesis comprises only my original work towards the PhD except where indicated;

2. due acknowledgement has been made in the text to all other material used; and

3. the thesis is less than 100,000 words in length, exclusive of tables, maps, bibliographies and

appendices.

iii

Acknowledgements

Special thanks to my supervisors and advisory committee Roger Dargaville, Robert Crawford, and

David Byrne.

Thanks to the reviewers for many excellent comments and valuable suggestions.

Thanks to Josh Floyd, Sam Alexander and John Wiseman for introducing me to the Energy and

Climate scholarships, and especially to Josh for co-authoring a NEA paper and for valuable discussions

that contributed to this thesis.

Thanks to many people for discussions that contributed to this work. Many people provided

different perspectives, gave valuable feedback, reviews, and answered my questions, especially Ajay

Gupta, Andrea Bunting, Carey King, Charles Hall, Dave Murphy, Eric Lee, Gail Tverberg, Garvin

Boyle, Gene Preston, George Mobus, Igor Bashmakov, Jack Albert, Jessica Lambert, John Morgan,

John Schramski, Kent Klitgaard, Manfred Lenzen, Marco Raugei, Michael Carbajales-Dale, Pedro

Prieto, Reynir Atlason, Steve Keen, Ted Trainer, Tom Biegler, and Wayne Qu.

Thanks to all those at the Australian German College of Climate and Energy, especially Malte

Meinshausen and Jen Drysdale, and fellow PhD candidates and staff, Adrian, Alex, Alex, Alexei,

Alister, Anita, Anne, Belle, Caro, Cathy, Chang, Cienna, Dimitri, Dylan, Elisabeth, Ellycia, Fiona,

Kate, Kate, Kenny, Kieran, Laura, Madhu, Mandy, Martin, Matthew, Nick, Philip, Rachelle, Raif,

Seb, Skye, Sonya, Steph, Stephen, Tash, Tim, Win, Yann, and Zeb.

Thanks to Greg, Kevin and Steve for encouragement and support.

Finally, I would like to acknowledgement the love and support of Kylie, Lachlan and Sarah.

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Preface

This dissertation is submitted for the degree of Doctor of Philosophy at the University of Melbourne.

The research described herein was conducted under the supervision of Dr Roger Dargaville and Dr

Robert Crawford in the School of Earth Sciences and Architecture respectively, University of Mel-

bourne, between December 2014 and March 2018.

This work is to the best of my knowledge original, except where acknowledgements and references

are made to previous work. Neither this nor any substantially similar dissertation has been or is being

submitted for any other degree, diploma or other qualification at any other university.

I am a recipient of an Australian Postgraduate Award (APA) scholarship – STRAPA 2014.

v

Publications Resulting from Work Reported in this Thesis

This thesis contains five papers that have been published in Energy, Energies, and BioPhysical Eco-

nomics and Resource Quality, including:

• Palmer, G. 2017, ‘An input-output based net-energy assessment of an electricity supply indus-

try’, Energy, vol. 141, pp. 1504-1516.

• Palmer, G. 2017, ‘Energetic Implications of a Post-industrial Information Economy: The Case

Study of Australia’, BioPhysical Economics and Resource Quality, vol. 2, no. 2, p. 5.

• Palmer, G. 2017, ‘A Framework for Incorporating EROI into Electrical Storage’, BioPhysical

Economics and Resource Quality, vol. 2, no. 2, p. 6.

• Palmer, G. 2018, ‘A biophysical perspective of IPCC integrated energy modelling’, Energies,

vol. 11, no. 839

The next paper was co-authored, with the lead author contributing 70%:

• Palmer, G. & Floyd, J. 2017, ‘An Exploration of Divergence in EPBT and EROI for Solar

Photovoltaics’, BioPhysical Economics and Resource Quality, vol. 2, no. 4, p. 15.

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Contents

Abstract i

Declaration iii

Acknowledgements iv

Preface v

Publications Resulting from Work Reported in this Thesis vi

Table of contents vii

Nomenclature xiv

List of figures xviii

List of tables xx

Introduction 1

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1 Literature Review 9

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.1.1 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.1.2 The purpose of EROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

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1.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.1 Definition of biophysical economics . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.2 Definition of EROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.3 Historical context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.3.1 Thermodynamic origins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.3.2 Contributors to biophysical economics . . . . . . . . . . . . . . . . . . . . . . . 15

1.3.3 The notion of energy surplus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.4 Energy and economic valuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4.2 Contrasting theories of value . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.5 Net-energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.5.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.5.2 EROI as a distinct field of inquiry . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.5.3 Bio-ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.5.4 Electricity generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2 EROI as a supplement to the price system 28

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.1.1 An outline of the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.1.2 EROI as an energy productivity indicator . . . . . . . . . . . . . . . . . . . . . 29

2.1.3 EROI as a subset of biophysical economics . . . . . . . . . . . . . . . . . . . . 31

2.1.4 The EROI hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2 Contrasting the circular and linear flow economies . . . . . . . . . . . . . . . . . . . . 32

2.2.1 Neoclassical perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.2.2 BPE perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.2.3 Integrating biophysical with neoclassical perspectives . . . . . . . . . . . . . . . 33

2.3 Limits of energy substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.3.2 Stylised isoquant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.3.3 Energy cost shares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.3.4 Oil price shocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.4 Relationship between energy and the economy . . . . . . . . . . . . . . . . . . . . . . . 38

2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.4.2 The Australian context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.5 The EROI metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

viii

2.5.1 Difference between the output elasticity of energy and its factor share . . . . . 44

2.5.2 The minimum EROI of society . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.5.3 Resource depletion versus technical change . . . . . . . . . . . . . . . . . . . . 46

2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3 Energetic Implications of a Post-industrial Information Economy: The Case Study

of Australia 48

3.1 Overview and context of chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.1.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.3 ICT-Enabled Remote Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.4 ICT and the Sharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.5 Dematerialization Driven by ICT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.6 The Energy Intensity of Australia’s Economy . . . . . . . . . . . . . . . . . . . . . . . 52

3.7 Real-World End-Use Energy Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.8 Unmeasured gains from ICT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4 An input-output based net-energy assessment of an electricity supply industry 59


4.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59


4.1.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.1.4 Study challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.1.5 Study code and data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.3.1 Net-energy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62


4.3.3 Previous studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.3.4 Motivation and context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.4 Overview of the Australian electricity supply industry . . . . . . . . . . . . . . . . . . 63

4.5 Environmentally extended input-output analysis methodology . . . . . . . . . . . . . . 63

ix

4.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.5.2 Monetary flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.5.3 Energy flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.5.4 Calculation of individual energy pathways . . . . . . . . . . . . . . . . . . . . . 64

4.5.5 Feedstock fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.5.6 Primary energy factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.5.7 Limitations of methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.6 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.6.1 Monetary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.6.2 Energy data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.6.3 Solar generation data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.6.4 Other data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.6.5 Consumer classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.7 Reconciliation of data for the electricity supply industry . . . . . . . . . . . . . . . . . 68

4.7.1 Allocating energy to all electricity supply . . . . . . . . . . . . . . . . . . . . . 68

4.7.2 Double counting due to on-selling . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.7.3 Coal mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.7.4 Revision of electricity use by the electricity supply industry . . . . . . . . . . . 68

4.8 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.8.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.8.2 Largest components of operational energy . . . . . . . . . . . . . . . . . . . . . 70

4.8.3 Tier analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.8.4 Sankey diagram of the electricity supply industry . . . . . . . . . . . . . . . . . 71

4.8.5 Low energy intensity cost components . . . . . . . . . . . . . . . . . . . . . . . 71

4.8.6 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.8.7 Differences to other energy sources . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.8.8 Implications and further work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5 A Framework for Incorporating EROI into Electrical Storage 75


5.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75


5.1.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

x

5.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78


5.3.3 Storage Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.3.4 Renewable Simulation Literature Review . . . . . . . . . . . . . . . . . . . . . 80

5.3.5 Storage Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.3.6 Goal Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.4 Goal and Scope of the Present Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.4.1 Different Roles of Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.4.2 Energy Capital Substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.4.3 Reformulation of Storage and EROI . . . . . . . . . . . . . . . . . . . . . . . . 81

5.5 Historical Precedents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.5.1 Food Storage as a Pivotal Development . . . . . . . . . . . . . . . . . . . . . . 82

5.5.2 Value as the Vector of Energy Surplus and Storage . . . . . . . . . . . . . . . . 83

5.6 The Role of Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.6.1 Conventional Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.6.2 Connecting the Pre-Industrial with the Contemporary Role of Storage . . . . . 83

5.6.3 Availability Versus Capacity Factor . . . . . . . . . . . . . . . . . . . . . . . . 84

5.6.4 Role for VRE Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.7 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.7.1 EROI Assessments Have Focused on Net-Energy Only . . . . . . . . . . . . . . 85

5.7.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.7.3 Reliability Metrics for Conventional Generation . . . . . . . . . . . . . . . . . . 85

5.7.4 Storage Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.8 The Analysed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.8.1 Reliability of Variable Renewable Generation . . . . . . . . . . . . . . . . . . . 86

5.8.2 Charging Storage with VRE Overbuild . . . . . . . . . . . . . . . . . . . . . . . 86

5.8.3 Marginal Returns to Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.8.4 ERCOT Regional Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.8.5 Embodied Energy of VRE and Storage . . . . . . . . . . . . . . . . . . . . . . . 88

5.9 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.9.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.9.2 The Embodied Energy and Marginal Embodied Energy Curves . . . . . . . . . 90

xi

5.9.3 Key Differences Between VRE-Storage and Conventional Generation . . . . . . 90

5.9.4 Consequence of Diminishing Returns . . . . . . . . . . . . . . . . . . . . . . . . 92

5.9.5 100% VRE Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6 An exploration of divergence in EPBT and EROI for solar photovoltaics 97


6.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97


6.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

6.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

6.3.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

6.3.2 Definition of EROI and EPBT . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6.4 Goal definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6.4.1 Defining LCA Goal and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6.4.2 Goal Definition in the Context of NEA . . . . . . . . . . . . . . . . . . . . . . . 100

6.4.3 Analysis Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

6.5 Detailed Investigation of Factors Contributing to Divergence . . . . . . . . . . . . . . 101

6.5.1 Life-Cycle Assessment Methodologies . . . . . . . . . . . . . . . . . . . . . . . . 101

6.5.2 Age of Primary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

6.5.3 PV Cell Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

6.5.4 Treatment of Intermittency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

6.5.5 Equivalence of Investment and Output Energy Forms . . . . . . . . . . . . . . 108

6.5.6 Differences Between Assumed Values Real-World Performance . . . . . . . . . 110

6.6 Discussion of Overall Implications for EROI and EPBT Metrics . . . . . . . . . . . . . 114

6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

7 A biophysical perspective of IPCC integrated energy modelling 119


7.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119


7.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

7.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

xii

7.3.1 Scenario modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

7.3.2 Biophysical economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

7.4 Integrated assessment models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

7.5 Detailed exploration of assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

7.5.1 Total factor productivity (TFP) and GDP growth . . . . . . . . . . . . . . . . 123

7.5.2 Declining energy intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

7.5.3 Life-cycle assessment methodologies . . . . . . . . . . . . . . . . . . . . . . . . 126

7.5.4 Steel and cement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

7.5.5 Biofuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

7.5.6 EROI constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

7.5.7 Fossil fuel resource availability . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

7.6 Discussion and recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

7.6.1 A proposed net-energy feedback model . . . . . . . . . . . . . . . . . . . . . . . 129

7.6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

7.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

7.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

7.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

8 Conclusions and future work 138

8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

8.2 Electrical storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

8.3 What would a low-emission scenario of Australian electricity look like if it was optimised

for EROI? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

8.4 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

References 146

xiii

Nomenclature

ABS Australian Bureau of Statistics

AC alternating current electricity

AEMO Australian Energy Market Operator

ALCA attributional life-cycle assessment

AR5 IPCC Fifth Assessment Report

BP British Petroleum

BPE Biophysical Economics

BREE Australian Bureau of Resources and Energy Economics

CAD computer aided design

CDF cumulative distribution function

CDE energy augmented Cobb Douglas function

CdTe cadmium telluride

CED cumulative energy demand

CIGS copper indium gallium selenide

CLCA consequential life-cycle assessment

CO2eq carbon dioxide equivalent

DC direct current electricity

EC European Commission

EEIOA environmentally extended input-output analysis

EEO Australian Energy Efficiency Opportunities Program

EIA US Energy Information Administration

EPBT energy payback time

EROI energy return on investment

xiv

EV electric vehicle

FOR forced outage rate

GDP gross domestic product

GEA Global Energy Assessment

GEER gross external energy ratio

GEPR gross external power ratio

GHI global horizontal insolation

GJ gigajoule

GPS global positioning system

IAM Integrated Assessment Model

ICT information and communications technology

IEA International Energy Agency

IEA-PVPS International Energy Agency Photovoltaic Power Systems Programme

IIASA International Institute for Applied Systems Analysis

I/O input-output analysis

IPCC Intergovernmental Panel on Climate Change

ISO International Organization for Standardization

kJ kilojoule

kW kilowatt

kWh kilowatt-hours

LCA Life cycle Assessment or Analysis

LOLE loss-of-load-expectation

LOLP loss-of-load-probability

MW megawatts

xv

MWh megawatt-hours

MJ megajoule

NEM Australian National Electricity Market

NEPR net external power ratio

NER net energy ratio

NGER National Greenhouse and Energy Reporting

NWIS North West Interconnected System

OPEC Organization of the Petroleum Exporting Countries

PDF probability distribution function

PE primary energy

POLES Prospective Outlook on Long-term Energy Systems

PJ petajoule

PHS pumped hydro storage

PR performance ratio

PV solar photovoltaic

RCP Representative Concentration Pathways

RE renewable energy

SETAC The Society of Environmental Toxicology and Chemistry

SRES Special Report on Emissions Scenarios

SWIS South West Interconnected System

TDOS electricity transmission, distribution, and on-selling

UNEP United Nations Environment Programme

VRE variable renewable energy

WEC World Energy Council

xvi

WEO IEA World Energy Outlook

WITCH World Induced Technical Change Hybrid

xvii

List of Figures

1 Schematic of research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.1 Net-energy cliff depicting difference between gross energy and net-energy. . . . . . . . 12

2.1 Stylised depiction of standard circular flow of the economy . . . . . . . . . . . . . . . . 34

2.2 Substitution and indirect energy requirements . . . . . . . . . . . . . . . . . . . . . . . 36

2.3 Entropy boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.4 Primary energy consumption and GDP Australia . . . . . . . . . . . . . . . . . . . . . 40

2.5 Change in per-capita primary energy, GDP, and energy productivity . . . . . . . . . . 41

2.6 Sources of per-capita growth Australia, 1940 to 2016 . . . . . . . . . . . . . . . . . . . 43

2.7 Real price of electricity Australia, 1955–2015 . . . . . . . . . . . . . . . . . . . . . . . 43

3.1 Australia primary energy consumption and real GDP 1900–2014 . . . . . . . . . . . . 53

3.2 Australian industry sectors - actual and projected - 1800–2020 . . . . . . . . . . . . . 53

3.3 Persons employed in selected manufacturing industries - post-War . . . . . . . . . . . 54

3.4 Passenger vehicles per capita and average floor area of new detached dwellings . . . . 54

3.5 Real price of electricity 1955–2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.1 Streamlined energy systems diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2 Australia’s four major electricity systems. . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.3 Direct versus indirect energy for electricity generation. . . . . . . . . . . . . . . . . . . 65

4.4 ‘Black box’ depiction of the electricity supply industry 2013–14. . . . . . . . . . . . . . 70

4.5 Largest 16 energy pathways disaggregated by generation and TDOS. . . . . . . . . . . 70

4.6 Total electricity supply industry for 2013–14. . . . . . . . . . . . . . . . . . . . . . . . 71

4.7 Sankey diagram of Australian electricity supply industry 2013–14. . . . . . . . . . . . 71

4.8 ABS 4604.0 net-use of electricity by the electricity supply sector. . . . . . . . . . . . . 72

5.1 Stylised graph of energy surplus versus storage. . . . . . . . . . . . . . . . . . . . . . . 82

xviii

5.2 Graph of capacity factor versus availability factor. . . . . . . . . . . . . . . . . . . . . 84

5.3 Stylised effect of storage on baseload and variable renewable energy. . . . . . . . . . . 84

5.4 Available capacity curve and forced outage rate . . . . . . . . . . . . . . . . . . . . . . 85

5.5 Stylised probability density function for a year. . . . . . . . . . . . . . . . . . . . . . . 86

5.6 Storage status for Preston simulation and Henning and Palzer . . . . . . . . . . . . . . 87

5.7 Embodied energy of VRE plus storage for PHS . . . . . . . . . . . . . . . . . . . . . . 90

5.8 Stylised representation of reliability measurement . . . . . . . . . . . . . . . . . . . . . 91

6.1 Definition of boundaries for solar PV life-cycle assessments . . . . . . . . . . . . . . . 102

6.2 Reported cumulative energy demand (CED) . . . . . . . . . . . . . . . . . . . . . . . . 105

6.3 Sankey diagram of annual global energy flows . . . . . . . . . . . . . . . . . . . . . . . 111

6.4 Population GDP per capita and global horizontal insolation by latitude . . . . . . . . 112

6.5 Installed capacity and final annual yield . . . . . . . . . . . . . . . . . . . . . . . . . . 113

6.6 Stylised depiction of the scaling impact of methodological factors . . . . . . . . . . . . 115

7.1 Proposed biophysical economic feedback model . . . . . . . . . . . . . . . . . . . . . . 130

xix

List of Tables

1.1 Branches of economics within Ecological Economics . . . . . . . . . . . . . . . . . . . 11

1.2 Comparison of energy versus exergy efficiency . . . . . . . . . . . . . . . . . . . . . . . 13

1.3 Comparison of objectivist theories of value and stores of value. Compare subjectivist

theories, show in table 1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.4 Comparison of subjectivist theories of value. . . . . . . . . . . . . . . . . . . . . . . . . 23

4.1 Primary energy factors for this study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.2 ABS 4604 fuel types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.3 Shares of electricity supply output and net capital expenditure . . . . . . . . . . . . . 68

4.4 Feedstock fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.5 Operational fuels and electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.6 Costs and energy intensity of selected cost components . . . . . . . . . . . . . . . . . . 72

4.7 Consumption of fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.1 Reference VRE plus storage model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

5.2 Assumptions applied to embodied energy . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.3 Aggregate embodied energy over 50-year time frame . . . . . . . . . . . . . . . . . . . 89

5.4 Summary of 100% VRE with storage based on simulation model . . . . . . . . . . . . 89

6.1 Direct energy inputs for major production processes for crystalline Si . . . . . . . . . . 102

6.2 Comparison of hybrid versus process-based LCA . . . . . . . . . . . . . . . . . . . . . 104

6.3 Methods of adjusting for investment and output energy equivalence . . . . . . . . . . . 110

6.4 Methodological factors affecting EPBT and EROI . . . . . . . . . . . . . . . . . . . . 114

xx

Introduction

Summary

Cost minimization is a key objective function of integrated and scenario modelling. The standard

economics perspective is that the price system embeds all of the relevant information, and therefore

differences between ‘energetic’ and ‘economic’ valuations are irrelevant. The biophysical economics

(BPE) perspective is that energy is a primary factor of production, with a lower bound to substitu-

tion, and therefore understanding the energetic value of energy supply technologies is important for

establishing whether energy transition pathways are energetically feasible.

A clear example of the divergence between the standard and BPE perspective is the treatment

of biomass energy with carbon dioxide capture and storage (BECCS) in the IPCC Fifth Assessment

Report (AR5). The report found that a carbon price of USD 100 per tonne CO2-eq would be sufficient

to drive large scale deployment of BECCS (see chapter 7). BECCS is not economically viable without

a carbon price, but the proper pricing of externalities is presumed to remedy the price disadvantage

relative to incumbent energy sources. Yet from a BPE perspective, the EROI of BECCS is estimated

to be less than 3:1, and therefore below the minimum threshold of around 10:1 required to support

an advanced society. At the margins, the production of high utility and economically useful liquid

fuels with BECCS may be useful, however BECCS is only viable within the context of the incumbent

system. The assumption that BECCS can be scaled up to substitute for fossil fuels ignores biophysical

constraints. A carbon price can alter the relative price of energy substitutes but cannot alter the

underlying energetic value.

But equally, a focus on energetic valuation alone may lead to anomalous results. Such an approach

is conceptually similar to an ‘energy theory of value’, which sets energy as the universal standard of

value. For example, much of the cost of nuclear power is related to quality assurance, regulatory

compliance, and financing costs, all of which are financially costly but energetically inexpensive.

Similarly, electricity networks are costly to build and maintain but not biophysically constrained.

Ideally, EROI should provide a means to supplement conventional economic and environmental

analysis, and inform and shape energy transition analysis. However, a lack of methodological consis-

tency has led to contestation of net-energy analysis’ (NEA) relevance to energy transition feasibility

assessment. Furthermore, although the idea of energetic valuation may be simple enough, a closer ex-

amination shows more detail and complexity. Identifying and codifying this complexity is an objective

of this thesis.

This thesis addresses these issues in relation to electricity supply technologies. Nearly all electricity-

based EROI analyses have focused on electricity supply technologies, measured as the ratio of the life-

1

time electricity generated, and the energy investments required to produce that electricity. However,

evaluating a particular component of electricity supply based on lifetime electricity generation is far

too coarse-grained if the goal is transition feasibility assessment. Electricity supply is delivered by

complex real-time systems, and much of the cost of electricity supply is related to distribution and

system costs.

The research method is to approach the methodological problem from two ‘directions’. Firstly,

using a top-down approach for systematic completeness; and a bottom-up approach for a more detailed

technology-specific analysis. Taken together, these approaches provide a foundation for building

electricity-based EROI assessments that can provide meaningful guidance in relation to transition

feasibility assessment.

Motivation

The motivation for this thesis is to better understand the challenge of decarbonising electricity grids in

biophysical terms, and apply this to decarbonisation planning. The long-term objective is to achieve

decarbonisation as economically and efficiently as practically possible. Although cost-minimized op-

timisation is a valuable research tool, it generally ignores energetic cost constraints and net-energy

considerations. A motivation is to develop a more consistent methodology for electricity-based EROI,

which can be adopted as a constraint, or an additional optimisation factor within scenario models.

Context

Australia’s electricity supply industry is undergoing a transition towards lower greenhouse (GHG)

emission electricity generation. The primary driver is GHG emission abatement targets, but tech-

nological and economic changes are also driving a transition away from coal-fired generation. Wind

power is currently the cheapest form of large-scale generation in Australia. Similar drivers are occur-

ring world-wide.

Unfortunately, there is no ready-made template for defining low-emission pathways. The historical

adoption of hydro and/or nuclear power enabled several regions or nations, including Brazil, France,

New Zealand, Norway, Ontario, and Sweden to substantially decouple GHG emissions from electricity,

but the further expansion of both of these technologies is country specific, and face geographic,

economic or social hurdles. Although these technologies generate low-emission electricity, they are

still embedded in, and dependent on the broader fossil fuelled energy sectors, in particular, petroleum

fuels.

Some form of carbon pricing is widely considered essential, but has been difficult to implement in

Australia and internationally. Until the 1990s, the standard model for electricity delivery was vertically

2

integrated monopoly under public ownership. Like many nations, Australia implemented competition

reforms that included liberalization, reorganization, and disaggregation of electricity functional units.

These reforms placed greater emphasis on prices and markets to drive GHG abatement.

Both of the principle modelling approaches – integrated assessment modelling (IAM), and energy

system optimisation modelling (ESOM) – seek a least cost solution based solely on economic prices.

Aim

The aim of this thesis is to improve the methodological consistency of electricity-based EROI studies

to enable net-energy analysis to be incorporated into energy transition analysis.

3

Thesis outline

(i) Abstract

(ii) Introduction

1. Literature review

Chapter 2 provides a high level overview of EROI. It frames the EROI metric as being based,

in part, on an energy theory of value. It address the most common criticism of net-energy analysis

(NEA), which is the claim that the price system is a more reliable indicator of value and scarcity. It

makes the argument that the price system alone may be insufficient for describing energy transitions,

and that a focus on the underlying physical system offers important insights.

It contrasts the standard economic ‘circular flow of the economy’ conception with the biophysical

linear flow conception, and seeks to integrate the two approaches. It explores the limits of energy

substitution using stylised isoquants, and introduces theories of energy expenditure cost shares.

It includes a case study that links energy consumption and economic growth to make the case

for the primacy of energy as a primary factor of production. The case study is based on a historical

overview of Australia.

The EROI metric is explored in more detail using a narrative approach, and placed in a historical

context. Finally, the role of EROI in describing the tension between resource depletion and technical

change is explored.

2. EROI as a supplement to the price system

The next two chapters adopt a national perspective to energy.

Chapter 3 builds on the previous chapter with an original article that explores the correlation

between Australian energy consumption and gross domestic product for the period 1900 to 2014. It

focuses on the shift to a service-based economy and explores why the shift to an economy based on

low-energy intensity services hasn’t led to ‘strong decoupling’ of energy and economic activity. In this

context, weak decoupling is defined as a relative reduction in energy consumption per unit of GDP,

whereas strong decoupling is also an absolute reduction in national energy consumption. The aim

of the chapter is to test the hypothesis that energy is a primary factor of production, and whether

energy can be displaced by technology.

4

The chapter introduces some of the theories that underlie biophysical economics, including: the

relationship between energy consumption and social complexity; the role of primary industries as

economies evolve towards service economies; the relationship between energy consumption and in-

formation and communications technology (ICT); and how demand for end-use energy services has

evolved.

The study finds that ICT is enabling productivity gains and new business models, but does not

significantly weaken the demand for end-use energy services. The conclusion is that a shift to an

ICT-enabled service economy has not historically enabled strong decoupling of energy and economic

activity.

Chapter 4 continues with a national-scale exploration of Australian energy consumption. It applies

a ‘top-down’, environmentally extended input-output analysis (EEIOA) of Australian electricity sup-

ply for the period 2009-10 to 2013-14. EEIOA provides a method for evaluating the linkages between

economic activities and environmental impacts, including direct and indirect energy consumption.

One technique is to combine the national monetary use-table with the national energy account.

The purpose of the chapter is to calculate the EROI of the Australian electricity supply industry.

This is the first study to assess the EROI of electricity at a system or national level. The EROI

was calculated at 40:1 for 2013-14. The electricity generation industry is energetically economic, in

the sense that a relatively small energy investment leverages a large magnitude of primary energy to

generate a large magnitude of electricity. However, the leveraging has been achieved at the expense of

a high feedstock extraction rate and commensurate emissions. Unlike comparable studies of oil and

gas production, the calculated EROI was relatively high. Much of the economic cost of electricity

supply is related to low energy intensity costs. It is concluded that the industry is cost constrained

rather than EROI constrained.

3. Energetic Implications of a Post-industrial Information Economy: The Case Study of Australia.

Article published (Biophys Econ Resource Qual.)

4. An input-output based net-energy assessment of an electricity supply industry. Article published

(Energy).

The study shifts from a ‘top-down’ national scale to a ‘bottom-up’ technology focus. Two chapters

consider the EROI of renewable energy, storage, and system-level effects.

Chapter 5 adopts a consequential life-cycle assessment (LCA) to electricity-based net-energy anal-

ysis. It applies a functional unit of 1 kW of power delivered to the grid, which is contrasted with the

5

standard functional unit of 1 kWh of energy delivered to the grid. The study introduces a framework

for electricity-based consequential analysis, and applies the framework to a suite of high-penetration

renewable scenarios.

It begins with a discussion of food and energy storage as a historical development, and how

this connects with the contemporary role of storage. It then develops a framework for measuring and

contrasting VRE plus storage, and conventional generation with the use of electrical reliability indices.

Finally, it uses a case study to apply the framework.

Chapter 6 identifies and explains the factors that have contributed to a divergence in variable re-

newable energy (VRE) net-energy analyses. It provides a link between the conventional attributional,

process-based approach, and a consequential approach that includes the systems-engineering qualities

of electricity systems.

The goal of these chapters is to provide a framework for comparing and evaluating EROI studies

and introduce a systems-engineering perspective into net-energy analysis.

5. A Framework for Incorporating EROI into Electrical Storage. Article published (Biophys Econ

Resour Qual)

6. An exploration of divergence in EPBT and EROI for solar photovoltaics. Article published

(Biophys Econ Resour Qual)

Chapter 7 explores the IPCC Fifth Assessment Report (AR5) energy scenario modelling, including

Integrated Assessment Models (IAMs).

Analysis of the costs and benefits of climate mitigation involve extrapolation into the future. Since

the long time horizons of interest extend well beyond the range of standard economic-development

scenarios, ‘transformation pathways’ are derived from integrated assessment models (IAMs). Since the

future evolution of demography, socio-economic development, and technology are highly uncertain,

scenarios have been developed that can be described by the Kaya identity.

A hypothesis of BPE is that the Kaya ‘driving forces’ are not independent variables, but interact

in complex ways. This study explores the assumptions that underlie IAMs. A preliminary proposal

for incorporating NEA into IAMs is presented.

7. A biophysical perspective of IPCC integrated energy modelling. Article published (Energies)

8. Conclusions and further work

6

Research method

This section provides an overview of the research method. Further detailed descriptions are given in

each chapter. Figure 1 outlines the research method schematically.

Chapter 4Input-output analysis

of the Australianelectricity supply industry

Engineering-systemsaspects of electricity

generation

Chapter 6Review of methodologies

for variablerenewable energy

Chapter 5Demand-based

consequential LCAapproach

Chapter 7Need to endogenize EROIin integrated assessment

models (IAMs)

Environmental, energy, economic analysis

EROI-basedenergetic valuation

Process-basedLCA for

electricity generation

Reliability indices

Top-down

Bottom-up

Otherconsequential LCA

approaches

Not incorporated intoIPCC AR5 mitigation

scenarios

Integrated assessmentmodels (IAMs)

LCA is identified but not incorporated intoIPCC AR5 mitigation

scenarios

Figure 1: Schematic of research method

This thesis addresses the two major weakness of NEA in relation to electricity supply. Firstly,

process-based analysis results in a truncated boundary, and therefore understates the indirect embod-

ied energy. In LCAs, there is no requirement to ensure that an analysis meets a prescribed level of

‘completeness’. The international standards for LCAs require that stages, unit processes or inputs are

followed until they ‘lack significance’ within the given scope. This can be problematic when LCAs are

applied to energy transition exercises since a high level of ‘completeness’ is often assumed by modellers

applying LCA data.

Secondly, nearly all NEA studies adopt an attributional approach, which takes into account the

embodied energy of the technology in question, but doesn’t take into account the broader impacts that

might result from the decision to adopt or install a specific technology. In contrast, a consequential

approach considers the broader impacts. However, it can be methodologically difficult to evaluate

consequential changes to energy systems with LCA approaches. Furthermore, the systems-engineering

aspects of electricity supply are generally treated as lying outside the domain of conventional life-cycle

research.

The paucity of consequential NEA studies has precluded the incorporation of consequential analysis

7

into integrated assessment models (IAMs) and IPCC mitigation reports. Other factors that limit the

use of LCA and NEA in integrated modelling include substantial variability in published LCA/EROI

results, and differences in LCA/EROI technique.

Although this thesis is not a hybrid-LCA study, the research method draws on the conceptual

idea of hybrid-LCA. Hybrid analysis brings together a top-down, input-output (I/O) analysis and

a bottom-up, process-LCA. An I/O analysis has the benefit of systematic completeness, but lacks

precision. On the other hand, process-LCA provides a more specific, detailed analysis, but is subject

to truncation error. The respective benefits of each can be combined with hybrid-LCA.

The I/O element of hybrid-LCA is typically used to estimate higher-order embodied energy re-

quirements of a product or service. However, in this thesis, there are two important differences.

Firstly, I/O is being used to calculate the embodied energy on an entire electricity industry, rather

than a specific product or service. Secondly, the primary energy extractions, which are usually the

main parameters of interest in LCA, are deducted to derive the energy-industry-own-use (EIOU).

Whereas the detailed element of hybrid-LCA is usually a process-based analysis, in this thesis

the detailed element is intended to provide a more detailed exploration of a consequential approach,

including the systems-engineering of renewable electricity. It comprises two studies (chapters 5 &

6), which explore a consequential LCA approach to renewable energy and storage, and a detailed

exploration of the factors that contribute to a divergence in the NEA literature.

In order to place the research within a policy-relevant context, the final chapter is a proposal to

supplement and support IAMs, and provides a useful reference point to ‘bookend’ the thesis.

Research questions

The research questions posed by this thesis are:

1. What can NEA contribute to our understanding of low-emission electricity transformation path-

ways?

2. What tools or approaches are appropriate?

3. What are the differences between a net-energy analysis and a conventional financial analysis?

8

Chapter 1

Literature Review

1.1 Introduction

1.1.1 Outline

This chapter reviews the EROI literature. The sections are organised as follows:

1. Introduction to the purpose of EROI.

2. EROI-related definitions.

3. A historical narrative to frame the emergence of net-energy analysis in the 1970s.

4. A brief overview of alternative theories of value. Since EROI relies, in part, on an objectivist

‘energy theory of value’, it is useful to place it into context.

5. A post-1970s overview of the EROI literature,

6. An overview of the current electricity-based EROI literature.

1.1.2 The purpose of EROI

The seminal paper by Cleveland, Costanza, Hall, and Kaufmann, titled Energy and the US Economy:

A Biophysical Perspective (Cleveland et al. 1984) provides a useful opening statement of the role of

EROI:

We approach macroeconomics from a thermodynamic perspective that emphasizes the pro-

duction of goods, rather than the neoclassical perspective that emphasizes the exchange of

goods according to subjective human preferences. Production is the economic process that

9

upgrades the organizational state of matter into lower entropy goods and services. Those

commodities are allocated according to human wants, needs, and ability to pay. Upgrading

matter during the production process involves a unidirectional, one-time throughput of low

entropy fuel that is eventually lost (for economic purposes) as waste heat. Production is

explicitly a work process during which materials are concentrated, refined, and otherwise

transformed. Like any work process, production uses and depends on the availability of

free energy. The laws of energy and matter control the availability, rate, and efficiency of

energy and matter use in the economy and therefore are essential to a comprehensive and

accurate analysis of economic production.

Changes in natural resource quality affect the ease and cost of fuel and matter through-

put in human economies because lower quality resources nearly always require more work

directly and indirectly to upgrade them into goods and services. Technological change

can counter changes in natural resource quality to varying degrees, but historically, many

technical advances that have lowered unit labor costs have been realized by increasing

the quantity of fuel used directly and indirectly to perform a specific task. The degree

to which technological change can offset declining resource quality as some basic natural

resources are depleted (for example, fuel and metal ores) and/or mismanaged (some biotic

resources) is an empirical question and cannot be easily predicted. Nevertheless, such

resource changes have important implications for what is and is not possible in the econ-

omy. Economic theory and policy must incorporate the physical properties of resources if

economic predictions are to be accurate and economic policies effective.

Reflecting the perspective of Cleveland et al. (1984), some of the issues explored in this thesis

include:

1. Cleveland et al. are arguing for a comprehensive and accurate analysis of economic production,

which includes a proper treatment of energy and matter. The application of ‘the laws of energy’

to economic valuation implies a partial form of ‘energy theory of value’. It is therefore useful to

explore how an energy theory of value fits into standard economics.

2. How should we think about ‘value’ in the context of energy, and how does this relate to EROI?

3. How do we assess the tension between declining resource quality and technological change, and

how does resource decoupling link into this?

4. What are methodological approaches to EROI and what is needed to improve them?

5. What are the practical applications of electricity-based EROI measurements?

10

1.2 Definitions

1.2.1 Definition of biophysical economics

‘Biophysical’ refers to the material world, and can be contrasted with an anthropocentric perspective,

of which mainstream economics is a subset (Hall & Klitgaard 2011, p.1). A useful working defini-

tion of biophysical economics (BPE) is ‘the study of the ways and means by which human societies

procure and use energy and other biological and physical resources to produce, distribute, consume

and exchange goods and services, while generating various types of waste and environmental impacts’

(Chevallerau 2017). While BPE pays attention to monetary prices, it is focused on the physical laws

that impose constraints and interact with ‘anthropocentric’ systems.

Peet (1992) classifies the major strands of economic-ecological thought by how they approach the

interactions within, or between, human society and nature. Standard economics is usually concerned

with interactions within human society, while ecology is concerned with interactions within nature. On

the other hand, resource economics and environmental economics are concerned with the interactions

between society and nature.

Biophysical economics is a term most closely associated with Charles Hall and colleagues (see Hall

& Klitgaard (2011)). The emphasis of BPE is understanding the role of energy in society, with a focus

on the tension between resource depletion and technology. Both BPE and Industrial Ecology utilise

the tools of life-cycle assessment (LCA), input-output (I/O) analysis, and environmentally extended

input-output analysis (EEIOA).

To human society To nature

From human Standard economics Environmental economicssociety Ecological economics

Biophysical economics (BPE)From nature Resource economics Ecology

Industrial ecology

Table 1.1: Branches of economics within Ecological Economics. Based on Peet (1992)

1.2.2 Definition of EROI

Net energy analysis (NEA) refers to a class of methods that are physically based, which are used to

determine the efficiency or productivity of energy supply technologies (Brandt & Dale 2011). Results

are presented in the form of energy return ratios (ERR), of which EROI is the most commonly used.

EROI is a unitless ratio, defined as the ratio of the gross flow of energy Eg over the lifetime of the

11

Perc

ent

EROI

Net-energy available

for consumption

Energy industryown-use

60

20

40

100

80

45 40 35 30 25 20 15 10 5 050

0

Figure 1.1: Net-energy cliff depicting difference between gross energy and net-energy. Derived fromMearns (2008)

project, and the sum of the energy for construction Ec, operation Eop, and decommissioning Ed

(Murphy & Hall 2011, Eq. 1). The energy includes the indirect, or embodied energy, and the direct

energy. Murphy & Hall (2010) state that ‘EROI is the ratio of how much energy is gained from an

energy production process compared to how much of that energy (or its equivalent from some other

source) is required to extract, grow, etc., a new unit of the energy in question’.

EROI =Eg

Ec + Eop + Ed(1.1)

The use of a simple ratio leads to the mathematical outcome that significance can be attached

only when the ratio is small (i.e. < 10:1). This can be graphically depicted as the so-called energy

cliff, shown in figure 1.1. A shift from, say, 5:1 to 4:1 carries more significance than a shift from 50:1

to 40:1. This is consistent with the focus of most EROI discourse, which is to explore underlying

energetic barriers of energy supply systems. Understanding and measuring the ‘energy industry own

use’ in figure 1.1 has multiple challenges, especially related to system boundaries and aggregating

different fuels, and is the prime focus of the EROI literature.

EROI is not a universal substitute for conventional energy productivity metrics and should be

considered a specific type of productivity metric that can be useful for exploring underlying limits or

constraints in relation to energy supply. Daly’s (1986) observation in relation to the entropy law is

apt, where he notes that the entropy law should be viewed as a constraint, not as an independent,

sufficient explanation of value.

12

1.3 Historical context

1.3.1 Thermodynamic origins

Although coal had been utilised much earlier for heat, at least from the Chinese Han Dynasty (206

BC to 220 AD) (Deng 2011, p.42), it was not until the early eighteenth century that the heat released

from coal could practically be converted to motion. The study of thermodynamics had proceeded in

response to the development of steam power, and throughout the industrializing period of the nine-

teenth century, physical scientists were calling attention to the underpinnings of thermodynamic laws.

Clausius (1867), Carnot (1825), Kelvin (1852) and others formalized the laws of thermodynamics.

These principles became embedded in the first and second laws, referring to the law of conservation of

energy, and the irreversibility of heat processes respectively. More precisely, the ‘Clausius statement’

that ‘heat cannot by itself pass from a colder to a warmer body’ (Clausius 1867, p.117) states that

work can be completely converted into heat, but that it is impossible to convert all heat back into

work. In any heat engine, the maximum theoretical efficiency is defined by the Carnot temperature

ratio (1−T2/T1) – see table 1.2. The Carnot equation is a re-statement of the entropy law – any heat

process must increase the total entropy. A localised decrease must be accompanied by an increase

somewhere else. These laws provided a theoretical base for the enhancement and further development

of heat and steam engines through the nineteenth century.

Thermodynamic law Parameter Equation

First Energy efficiencyη =

energyoutenergyin

Second Exergy efficiency

ε =useful workoutuseful workin

ε = η

(1 − T2

T1

)

Table 1.2: Comparison of energy versus exergy efficiency. T2 is temperature of energy source in degreeskelvin, T1 is sink temperature, η is efficiency. Source: (Brockway et al. 2014)

13

At a microscopic level, the second law can be statistically described as the state of order within

a system; processes will tend to proceed towards states of disorder through a process of random

commingling, rather than states of structured order by spontaneous separation.

The ‘state variable’, entropy, and its associated term, exergy, derive from the second law and

provide a useful metric for available energy. When applied quantitatively to steam and refrigeration

cycles, entropy and exergy were to become useful metrics; the area enclosed within a pressure-volume

integral equals energy, the area enclosed within a temperature-entropy integral equals heat. The second

law, also commonly referred to as the entropy law, provides an iron law of nature that underlies the

study of energetics. Eddington (1928, p.37) wryly observed —

The law that entropy always increases – the second law of thermodynamics – holds, I

think, the supreme position among the laws of Nature. If someone points out to you that

your pet theory of the universe is in disagreement with Maxwell’s equations – then so

much the worse for Maxwell’s equations. If it is found to be contradicted by observations

– well, these experimentalists do bungle things sometimes. But if your theory is found to

be against the second law of thermodynamics I can give you no hope; there is nothing for

it but to collapse in deepest humiliation.

Entropy can also be understood at a macro scale – linear or rotary mechanical motion represents

a highly ordered, low-entropy state since all the molecules in the moving mass are moving together. It

is easy, in a thermodynamic sense, to extract energy from ordered motion, leaving disordered motion

as a waste by-product.

A practical example may be useful here. Electricity is pure exergy since it represents a highly

ordered transmission of electrons, which can be readily converted into mechanical motion, heat or

light. But once converted, it is impossible to completely revert the heat back into electricity. For

example, if 1 kWh of electrical energy is used to heat a quantity of water from 20 to 80oC using a

resistance heater, the final exergy of the water in the tank calculates to 0.17 kWh using the Carnot

equation since the maximum efficiency of an ideal heat engine is 17% for this set of conditions. Hence,

the conversion from electricity to low-grade heat has destroyed 0.83 kWh of exergy, although no energy

has been gained or lost. In an electrical conductor, an external voltage sets up a highly ordered electric

field that propagates through the conductor. On the other hand, the molecules in warm water are

undergoing rapid, random motion at a microscopic scale, and only a small fraction of that motion can

be usefully extracted.

The distinction between the generic term ‘energy’ and the more specific term ‘exergy’ is important

in net-energy analysis. In the case of combustion-based electricity generation, a significant proportion

of the chemical energy is unavoidably lost as part of the energy upgrading process. In the case of

14

renewable technologies, the upgrading of diffuse and variable energy incurs an embodied energy cost

that must be accounted for in net-energy analyses.

1.3.2 Contributors to biophysical economics

The appeal to the second law of thermodynamics encouraged a range of theories that focused on energy.

One of the most notable was Alfred Lotka (1922), a mathematical biologist, who sought to explain

the biological evolutionary process in terms ‘energy gradients’ and the second law of thermodynamics.

In any isolated system, the second law requires that the equilibrium state will be maximum dis-

order. Yet ordered structures, such as crystalline or biological structures, seem to defy the second

law. Prigogine et al. (1972) brought attention to the nature of open systems in contact with a heat

reservoir, such as the earth, which radiates heat into space while absorbing radiation from the sun.

Such systems were termed non-equilibrium open systems, which could be contrasted with the closed

systems of practical interest, such as heat engines.

More recently, Schneider & Kay (1994) and Schneider & Sagan (2005) connected Lotka’s hypothesis

with complexity theory, postulating that free energy may have driven the chemical cocktail from

which biological processes evolved. According to their hypothesis, systems that are moved away from

equilibrium will tend to resist externally applied energy gradients. Schneider is a marine geologist and

ecological thermodynamicist, and gives the example of isolated deep sea ecosystems that derive their

energy from the temperature and chemical gradients emanating from the sea floor. He argues that the

combination of warm waters entering the cold deep ocean, along with the chemical and acid/alkaline

gradients derived from mineral releases, provided energy gradients from which chemical reactions

could take place. According to his hypothesis, systems will tend to evolve additional complexity since

more complex structures are more effective at dissipating energy within their ecosystem.

The astrophysicist, Chaisson (2011), derives a complexity metric he terms the ‘energy rate den-

sity’, which is defined as the energy flow per second, per gram of material found in a given system

(joule/s/kg) – or equivalently, the power density in watts per kg. When the energy rate density

for systems, including galaxies, the sun, earth, plants, animals, and society, are plotted over time,

a correlation between scale and energy rate density is observed. Chaisson connects the metric with

complexity, arguing that the availability of free energy is a common underlying factor in evolution,

diversity, and complexification.

Chaisson’s conception of energy and complexity has similarities with the anthropologists Joseph

Tainter (Tainter 1990), Leslie White (White 1943), and Ian Morris (Morris 2015). According to

Tainter’s hypothesis, societies become more complex as a response to ‘problem solving’. Social and

political stratification is an example of complexification, and includes new layers of specialisation,

15

bureaucracy, and social class. Tainter notes that whereas hunter-gatherer societies may have contained

up to a few dozen distinct social personalities, modern European censuses recognize 10,000 to 20,000

unique occupational roles. But specialization also has a cost. Investment in education is growing,

more students undertake advanced education, the productive phase of graduates is delayed, and post-

education specialization carries further costs. Although specialisation has been a driver of productivity,

it is nonetheless subject to declining marginal returns.

White places technology at the centre of cultural development, and was the originator of ‘White’s

Law’, which states that ‘culture develops when the amount of energy harnessed by man per capita

per year is increased; or as the efficiency of the technological means of putting this energy to work is

increased; or, as both factors are simultaneously increased.’ (White 1943, p.338).

Morris argues that cultural values, including fairness and equality, have been conditioned by differ-

ent stages of ‘energy extraction’. Morris identifies foraging, farming, and fossil fuels as defining stages

during which societies held different values (Morris 2015). For example, the shift from foraging to

farming entailed the development of hierarchies in which the notion of equality shifted from a highly

egalitarian notion, to an acceptance of social division. Foraging was non-hierarchical

The shift from farming to fossil fuels disrupted the power relationships and politics of societies

(Mitchell 2009). In organic societies, political power and available energy were tied together. The

primary source of focused energy was human labour, and those that controlled that energy, such

as through slavery or aristocracy, also maintained control of the power structures of society. But

fossil fuels broke this basic link. The rise of the industrial class in Europe was a direct result of the

availability of a large quantity of energy, disrupting the traditional hierarchical systems. Mitchell

(2009, p.27) argued that working people in the industrialised West acquired a power that would have

seemed impossible before the late nineteenth century. The logistics of coal permitted coordinated acts

of workers to interrupt flow in order to meet political aims.

Tainter (1990) used twenty-four historical examples of the collapse of civilisations (including the

Roman, Mayan and Cacoan) to argue that increasing complexity carries a greater energy cost. Tainter

posits that voluntary ‘simplification’ (i.e. reverting to a less energy intensive society) is rare, and that

collapse has been historically more common.

For example, as a solar energy based society, the Roman Empire was subject to what Wrigley (2010)

terms the ‘organic limit’. Not only was food derived from the land, but also industrial production,

such as shoes, textiles, bronze, and construction timber. In all cases, the primary source of energy

was the sun, hence the physical and biological limits of production were set by the energy captured

and stored by plants, and to a lesser extent, the solar energy extracted by the wind or flow of water.

In most cases, the seasonal cycle defined the production cycle. Although the availability of mineral

16

ores were not defined by solar energy, the labour embedded in their mining and processing, and the

energy in smelting and processing, remained tied to the solar cycle. For example, iron manufacture

depended on vast quantities of wood for conversion to charcoal to use as a reducing agent and energy

source.

The Roman empire was able to appropriate the stored products of solar energy – precious metals,

works of art, and people (Taylor & Tainter 2016). At the limits of expansion, the empire managed

to sustain itself by consuming its capital resources, but ultimately, these measures were bounded.

The financing of the empire shifted from consuming stored solar energy, to consuming annual solar

energy harvested by agriculture. Coinage was debased, shifting a greater economic burden to the

future. Eventually the Western Empire collapsed, with the Eastern Empire surviving as the much

weaker and less stratified Byzantine Empire. Tainter (2011) suggests that the transition from the

Eastern Roman to Byzantine Empire represents the only example where ‘a large, complex, society

systematically simplified, and reduced thereby its consumption of resources’.

As systems evolve towards greater complexity, they rely on more productive energy supply systems.

But energy supply is also subject to diminishing returns and eventually becomes a constraining factor.

Less developed nations consume a greater proportion of total expenditure on energy, while in the

advanced economies, there seem to be energy cost share thresholds above which economies reconfigure

or contract (Bashmakov 2007, King 2015b). The evolution of national economies towards greater

specialisation and diversification is important in projecting net-energy into the future and covered in

more detail in chapter 3.

Podolinsky (1883), a Ukrainian socialist, was the first to explicitly link energy and economic pro-

cesses. Podolinsky was concerned with the distribution of labour and tried to reconcile the labour

theory of value with thermodynamic principles, concluding that the socialist model was flawed. In

much the same way that the modern limits-to-growth movement argues that globalisation and cap-

italism run counter to resource scarcity, Podolinsky was arguing that ‘scientific socialism’ similarly

failed to account for resource scarcity.

Beginning the twentieth century, a growing body of literature was exploring the connection between

energy and economic processes. In the main, the research lay outside the orthodox economics field,

and much of it offered critiques of standard economic theory. Ostwald (1909) was the first to argue

for something approaching an ‘energy theory of value’ when he suggested that energy was the ‘sole

universal generalization’.

The most significant of the early contributors was Soddy (1933), who sought to explain elements

of economic development with the use of thermodynamics. Soddy was a nuclear chemist, but applied

physical laws to a critique of standard economic theory, debt, and financial practice. Soddy argued

17

that debts are ‘subject to the laws of mathematics rather than physics’ but ‘wealth is subject to the

laws of thermodynamics’ (Soddy 1933, chpt.IV). He noted the contradiction between financial debts,

which grow exponentially, and the ‘real’ economy, which relies on the consumption of exhaustible

stocks of fossil fuels.

Perhaps the most radical example of the application of an energy theory of value was the ‘Tech-

nocrats’, a movement led by professional engineers in the US and Canada in the 1920s and 1930s

(Buenstorf 2004, Cleveland 1987). The technocrats were influenced by Soddy, but also Frederick Tay-

lor’s scientific management, and Thorstein Veblen’s radical political economy (Buenstorf 2004). The

Technocrats proposal was to adopt energy as a universal measure of value – engineers would calculate

the energy content of all materials and work, and use these to derive tradable energy certificates. The

Technocrats argued that the conventional price system was ‘complex, unstable and arbitrary’, but

that ‘engineers, capable of making measurements free from the distorting interests of economics and

politics, would organize society better than politicians.’ (Taylor 1988).

More recently, ‘energy theory of value’ proponents have included ‘no growth’ or steady-state propo-

nents, who argue that a finite world requires a fundamental shift from conventional growth economics.

For example, Sgouridis (2014) proposes a radical shift to energy-backed or energy-referenced currencies

as a means of ‘realigning the economic system to the thermodynamic limits of the physical world’.

Although all single-value theories suffer from an inability to incorporate the complexity of human

interactions, Hau & Bakshi (2004) identify several attractive properties of the principle of energy

based valuations:

1. They provide a bridge that connects economic and ecological systems.

2. They compensate for the inability of money to value non-market inputs in an objective manner.

3. An energy valuation model is scientifically sound and shares the rigor of thermodynamic meth-

ods.

4. The adoption of a common physically-based unit allows all resources to be compared, in principle,

on a ‘fair’ basis 1.

5. Energy analysis provides a more holistic alternative to many existing methods for environmen-

tally conscious decision making.

1 Although the joule (and equivalent) is the common unit of energy, there is no universal measure that permits acomparison of energy across all contexts. The thermal-equivalence approach of the major energy agencies is usuallythe default comparator, but has limitations. Chapter 6 - ‘Equivalence of Investment and Output Energy Forms’provides a more detailed examination.

18

1.3.3 The notion of energy surplus

The first use of the concept of ‘surplus energy’, which is the forerunner to the EROI term, was

the American sociologist W. Fred Cottrell (1955) in Energy and Society. Cottrell linked the idea

of energy quality with economic and social development. He identified the capability of energy to

being able to magnify labour and therefore increase labour productivity. He explored the relationship

between energy quality and surpluses, and social and cultural life. For example, he noted that bulky

raw materials, such as farm produce and timber were often produced in the hinterland and shipped

downstream to cities, where they were processed into more valuable goods, but that the wealth did

not flow back ‘uphill’ (Cleveland 1987).

Similarly, Allen (2012) showed that the ready availability of energy, and the accompanying labour

productivity gains, significantly boosted the real wage of workers in London in the eighteenth century,

such that the typical purchasing power was two to five times higher than other comparable cities.

The application of energy through tools or machines magnified the work that a single worker could

perform.

Although the concept of ‘energy surplus’ had been introduced by Cottrell and others, it was

the work of Hall (1972) that sought to more precisely define EROI with measurable data. In his

early research paper on fish migration in North Carolina, under the tutelage of Howard Odum, Hall

measured ecosystem productivity and discovered some clear patterns. There was a trade-off between

fish investing some of their energy in upstream migration, from which they could feed from more

productive waters, compared to the downstream waters that were less productive, but more stable.

Hall found that fish populations that migrated would return at least four calories for every calorie they

invested in the process of migration. The connection to energy in other areas of ecological became

evident.

In building on the ideas of Lotka, Odum & Pinkerton (1955) developed the ecological ‘maximum

power principle’, which sought to provide a complementary explanation of biological evolution. The

principle was earlier developed in electrical engineering by Jacobi. The electrical Ohm’s Law provides

a demonstration of the relationship between maximum power and maximum efficiency. In electronic

devices, the output resistance of the power source should match the input resistance of the load to

maximise the power throughput. However, the point of optimised power does not match the point of

optimised efficiency. In the case of loudspeakers, for example, the maximum sound is achieved when

the impedance (AC resistance) of the speakers matches the output impedance of the amplifier. Under

this condition, half of the energy is dissipated in the speakers and half in the amplifier. Loudspeakers

with a higher impedance will possess a higher system efficiency but at a lower maximum sound level

volume, requiring a larger amplifier to reproduce an equivalent sound level. In practice, the respective

19

impedances are selected to provide a trade-off between power transfer and efficiency.

Using a forest as an ecological example, Hall (2004) described the relationship between a tree’s

leaf area index (LAI) and the energy capture. The highest efficiency is achieved with a relatively low

LAI since the topmost leaves capture the most sunlight, but each leaf is energetically expensive to

maintain. But the benefit of high efficiency is offset by the limited leaf area and therefore total energy

capture; an efficient plant would be short and out-competed in a forest. Hall showed that there exists

a diminishing return between maximising energy throughput (power) and efficiency – at too great a

leaf area, the energy cost of maintaining the leaves is greater than the useful benefit.

Odum applied the maximum power principle to the economic realm, suggesting that systems tend

to select for processes that maximize their use of available energies. Similarly, Boyle (2016) argued

that business supply chains that consume matter and energy at the greatest rate will tend to persist

and dominate markets.

Another of Odum’s insights was that natural processes can only be properly understood by applying

a systems analysis. Odum was a student of the ecologist G. Evelyn Hutchinson, who delivered a

paper titled Circular Causal Systems in Ecology in 1946. Hutchinson argued that biological and

physical processes were tightly linked (Taylor 1988). He constructed carbon budgets for the biosphere,

phosphorus budgets for lakes, and attempted to balance the respective material flows.

In the tradition of Soddy (1933) and others who connected thermodynamic principles and economic

growth, Herman E. Daly (1972) articulated a view that was to form the basis for the steady-state

economy movement. In Steady-State Economics, Daly argued that the ‘circular flow of exchange value’,

implicit in standard economics, is incompatible with the unidirectional and linear matter-energy flows

of the physical world. This fundamental distinction was to remain as one of the key foundations of

biophysical economics, and is covered in more detail in chapter 2. Daly argued that stocks of physical

wealth needed be held constant by controlling the consumption rate of energy and matter.

1.4 Energy and economic valuations

1.4.1 Introduction

Net-energy analysis implies an objectivist approach to energy since energy possesses intrinsic prop-

erties that are not readily substitutable (covered in more detail in chapter 2). Furthermore, energy

possess quality differences, described in exergy analysis, and quantification differences, related to the

way primary energy is evaluated. In order to contextualise net-energy within the broader economics

field, it is useful to frame net-energy as adopting, in part, an energy theory of value. This section

briefly identifies theories of value.

20

but it has quality differences (exergy) and quantification problems (among practitioners).

1.4.2 Contrasting theories of value

Prior to the so-called marginalist revolution, wealth was believed to derive from intrinsic or objective

qualities. Historically, the principle ‘objectivist’ factors, or primary factors of production, were land

and labour. Many other factors have been suggested as possessing an intrinsic value independent of

price. Similarly, various stores of value have been proposed, such as coinage, gold, or salt. These, and

the later ‘subjectivist’ interpretations are shown in tables 1.3 and 1.4.

From the early modern period, the objectivist notion of value had been undermined by the ‘water

versus diamonds‘ paradox (Hall & Klitgaard 2011, p.110). Smith (1776, p.103) argued that value can

have two different meanings – ‘value in use’ and ‘value in exchange’. Water is essential for life but is

often abundant, and therefore may not command a high price. In contrast, a diamond has little ‘use

value’ but ‘a very great quantity of other goods may frequently be had in exchange for it.’

A resolution to the paradox occurred in the late nineteenth century and came to be termed the

‘marginalist revolution’. It is generally ascribed to Jevons (1871), Menger (1871), and Walras (1896).

Marginalism is the idea that value or utility is related to ‘the next unit’ of a product or service, and

is connected to scarcity and exchange value rather than some intrinsic quality. Standard economics

emerged from the marginalist revolution. Although the adoption of market valuation seemed to resolve

the longstanding paradox, the complete rejection of intrinsic value also led to a major shortcoming

– an inability to properly value non-renewable natural resources, or fully value the cost of wastes and

pollution. The idea of ‘marginalism’ also made it much harder to correctly identify the role of natural

resources, especially energy, in economic growth. Instead, beginning with Solow (1957), ‘technical

innovation’ came to be adopted as the primary explanation of economic growth.

1.5 Net-energy

1.5.1 Context

In the postwar period, economic growth was enabled by rapidly increasing energy resources, with the

link between oil supply and the economy being strongly connected (Hamilton 1983). Up until the

late 1960s, the US had been the pre-eminent supplier of oil. For the period of World War II, the

US supplied 6 out of the 7 billion barrels of oil consumed by the Allies (Miller 2001). Japan and

Germany’s deficiency in oil were key factors in the Allied victories (Jacobs 1998, p.159).

However, US domestic supply peaked in 1970, leaving it vulnerable to supply shocks. The 1973

OPEC oil embargo, which led to a quadrupling of the world oil price, was to be a watershed in

21

Theory Proponent/s Comments

Objectivist theories

Electricity Beaudreau A production function based on capital, labour andelectric power provides a closer correlation to eco-nomic growth in the US, Germany and Japan (Beau-dreau 1999, 2005).

Energy Technocrats, Sgouridis Value is determined by the energy content of all ma-terials and work (Buenstorf 2004, Cleveland 1987,Sgouridis 2014). May include an energy-based cur-rency.

Gold Austrian School of Eco-nomics

Adherence to a gold standard reduces price inflation,and reduces exchange rate volatility.

Human action Graeber Intentional or productive action aimed at a certaingoal produces social relations and in doing so trans-forms the producers themselves (Graeber 2001, p.59).

Information Kurzweil There is a rapidly increasing knowledge and informa-tion content in products and services, and that theseare not constrained by material resources. Economiesare driven by data and knowledge (Kurzweil 1999,p.124).

Land Petty, Quesnay, Cantil-lon, Physiocrats

Land and sunlight are the origins of wealth. Manu-facturing and commerce are ‘unproductive’ or ‘sterile’(Murphy 1993).

Labour Smith, Ricardo, Marx Physical capital embodies previous labour (Smith1776, Ricardo 1891). Ricardo (1891, p.12) noted ‘Pos-sessing utility, commodities derive their exchangeablevalue from two sources: from their scarcity, and fromthe quantity of labour required to obtain them.’

Money Sumner A stable relationship between money and the economyrequires a predictable or modest controlled expansionof nominal GDP (Sumner 2015).

Social relations Strathern, Dodd Objects (e.g. a pig or shell) embody social relations(Strathern 1992). For Dodd, money is a social linkthrough which social relations of interdependence andconflict are resolved (Dodd 2016, p.45).

Sunlight Odum Solar emergy is the available solar energy used up di-rectly and indirectly to make a service or product (Hau& Bakshi 2004, Odum 1996).

Table 1.3: Comparison of objectivist theories of value and stores of value. Compare subjectivisttheories, show in table 1.4

22

Theory Proponent/s Comments

Subjectivist theories

Utility, scarcity, con-sumer preferences

Most economists Capital and labour are the primary factors pf pro-duction. Solow’s residual (i.e. total factor produc-tivity) drives economic growth. Marginal productsequal to factor shares. Energy is important but a mi-nor share of national economic activity, and is substi-tutable with capital.

Capital and labour sup-plemented by energy

Nordhaus Energy’s marginal productivity set to its factor share

Hybrid theories

Capital, labour, energy,and knowledge

Kummel, Ayres, Keen Energy’s marginal productivity much greater than itsfactor share. (Ayres & Warr 2010, Kummel 2011,Kummel et al. 2010, Keen & Ayres 2017)

Table 1.4: Comparison of subjectivist theories of value.

national energy policy for many oil importing states (Scott 1994). Nations adopted supply and

demand strategies, including an expansion of coal and nuclear energy, much greater investments in

alternative energy and synthetic fuels, greater focus on energy efficiency, energy information sharing,

and a strategic oil reserve policy. The supply shock was moderated in Australia by the development

of the Gippsland Basin, and surplus coal resources. At the time, Australia was 60% self-sufficient in

oil, and the oil price was subject to a regulated fixed price of $A2.06/bbl. Nonetheless, as a small

trading economy, Australia soon imported the indirect effects of the oil price rise – inflation, rising

unemployment, and recession (Marks 1986).

This was a formative period for energy research. It was driven by the tangible effects of oil supply

shocks and its responses, along with concerns about ‘limits to growth’. One of the fields of inquiry

that emerged was ‘net-energy’, which sought to explain energy supply from an underlying physical

perspective. Several strands of activity emerged, including:

1. Establishing the energy costs of goods and services with input-output analysis (Bullard & Heren-

deen 1975, Bullard 1975).

2. Assessing the rising per-barrel energy investment of the US oil extraction industry (Hall &

Cleveland 1981, Cleveland et al. 1984).

3. Assessing the degree to which Britain’s expanded nuclear power program reduced reliance on

imported oil (Chapman 1975a,b, Department of Energy England 1975).

4. Conducting a broad-based analysis of fossil fuel extraction in the Western United States (Melcher

et al. 1976).

23

5. Incorporating the principle of net-energy into energy policy – ‘net-energy’ was itemised as one of

several ‘governing principles’ in the US Federal Nonnuclear Energy Research and Development

Act of 1974 (US EPA 1974, sec.5.(a).(5)).

Building on the work of Bullard and others (e.g. Bullard et al. (1978)), who had introduced formal

methods for net-energy, several researchers including Cleveland, Costanza, Hall, and Kaufmann began

to incorporate empirical data on petroleum exploration and production into resource models. Much

of the literature was beginning to incorporate the EROI metric. Although the net-energy concept

was already in use, the EROI statistic was formally introduced by Hall (1972) and elaborated on by

Melcher et al. (1976). The work on petroleum was exploring various metrics as a means of assessing

future energy prospects. For example, Hall & Cleveland (1981) estimated the discovery rate for

petroleum in the United States, using the metric ‘per foot of drilling effort’. They found that the

metric was declining at about 2% per year, implying that greater effort was required to secure a given

quantity of oil and natural gas.

In their seminal paper, Cleveland et al. (1984) presented an argument for energy return on invest-

ment (EROI). The four key hypotheses of biophysical economics were identified:

1. A strong link exists between fuel use and economic output, particularly when adjustments are

made for energy quality.

2. A large component of the increased labour productivity through the twentieth century can be

attributed to the increased use of fuel.

3. Inflation levels can be partly attributed to changes in the cost of obtaining fuel.

4. The general trend is towards an increase in the cost of locating, extracting and refining fuel,

although technical change mitigates this trend to some degree.

Although early research centred on fossil fuels, especially oil, the tension between resource depletion

and technological advances (item 4 above) was to become an important area of research in relation

to electricity generation technologies. It was clear that the depletion rate of non-renewable resources

would reduce the future availability of resources. Furthermore, even renewable energy technologies

were dependent on the incumbent energy system for their production and system support, and none

of the prospective renewable energy alternatives offered the same suite of energy qualities as oil

and natural gas. But it was also apparent that technological advances and learning-by-doing made

previously uneconomic resources available or reduced the cost of renewable electricity technologies.

These competing factors, which could be framed as Malthusian versus cornucopian, were typified

by the ‘Club of Rome’ versus Julian Simon respectively. In The Limits to Growth, Meadows et al.

24

(1972) argued that exponential growth of natural resource extraction is incompatible with a finite

world in the long run. On the other hand, in The Ultimate Resource, Simon (1981) argued that the

capacity for humans to ‘invent and adapt’ outstrips any natural resource constraints.

The question of natural resource depletion and the potential for substitution was being widely

explored. Speaking from a conventional economics perspective, Solow (1974) identified the issue,

noting –

If it is very easy to substitute other factors for natural resources, then there is in principle

no ‘problem’. The world can, in effect, get along without natural resources, so exhaustion

is just an event, not a catastrophe. If, on the other hand, real output per unit of resources is

effectively bounded – cannot exceed some upper limit of productivity, which is in turn not

too far from where we are now, then catastrophe is unavoidable. In-between there is a wide

range of cases in which the problem is real, interesting, and not foreclosed. Fortunately,

what little evidence there is suggests that there is quite a lot of substitutability between

exhaustible resources and renewable or reproducible resources, though this is an empirical

question that could absorb a lot more work than it has had so far.

By the early 1990s, the price of world oil had fallen from its oil-crisis highs. Inflation had subsided

and the world economy returned to trend growth of about 3% a year, leading to a 20-year hiatus

from energy concerns (Hall & Klitgaard 2011, p.26). It wasn’t until the release of the IPCC Third

Assessment Report (TAR), emergence of debates around the efficacy of corn ethanol, and the run

up in oil prices in the 2000s that net-energy re-emerged as a field of inquiry. Energy researchers

were engaged in explorations of energy transitions, with debate centred on the efficacy of fossil fuel

substitutes and the relationship between energy and economies.

1.5.2 EROI as a distinct field of inquiry

By 2011, several elements of EROI had been formalized methodologically (Murphy et al. 2011). Four

key areas of inconsistency were addressed:

1. System boundaries.

2. Energy quality corrections.

3. Energy-economic conversions.

4. Alternative EROI statistics.

Furthermore, Murphy & Hall (2011) had identified the major strands of EROI research as:

25

1. The efficacy of corn ethanol as a net energy producer.

2. A summary of the state of EROI for most major fuels and electricity generators.

3. Alternative applications of EROI, such as energy return on water invested.

4. The relationship between EROI and the economy.

5. The minimum EROI for a sustainable society.

1.5.3 Bio-ethanol

The United States and Brazil have been producing fuel ethanol since the 1970s, however, attention

turned to the efficacy of US corn-based bio-ethanol as a petroleum substitute (Solomon et al. 2007).

Pimentel was an early protagonist of the biofuel efficacy debate in the United States (Hall et al.

2011). He drew attention to the energy intensity of the ethanol life cycle and questioned whether the

net-energy was positive Pimentel (1991). A negative result would undermine the value of the produc-

tion process as a large scale petroleum substitute, although the high utility of the liquid fuel would

somewhat complicate the assessment. Some of the most significant energy inputs included: nitrogen

fertilizer, irrigation, embodied energy of machinery, drying, and on-farm diesel. Hammerschlag (2006)

provides an overview of the literature, with most estimates falling between 1.3:1 to 1.7:1, a range of

figures that are mostly above Pimentel but well below the minimum useful energy surpluses required

for society (Hall et al. 2009).

1.5.4 Electricity generation

Unlike ethanol, which is a direct substitute for petroleum fuels, the role of electricity generators

within an electricity grid is more complex. Different generator types may not be direct substitutes.

Liquid fuels are storable and fungible, but electricity is only useful within the context of a complete

system. Defining the specific role of each component is essential to estimating their respective value.

Furthermore, much of the cost to end users is associated with transmission and distribution, rather

than generation. Yet nearly all electricity EROI studies focus exclusively on generation.

Electricity systems share the same attributes as other utilities and public services, such as water,

telephone, and passenger rail in being built to meet peak demand, requiring expensive and long-lived

infrastructure. Once constructed, costs are sunk and marginal unit costs are small. Utility costs have

traditionally been recovered with unit costing, such as per litre, per phone call, or per ride. Some

utilities include a fixed component, such as a daily or access charge.

In the case of commuter trains for example, consumers pay per ride, but in nearly all major systems,

most of the cost is met through public finances (Walker 2011). Much of the social, environmental,

26

and broader economic benefits of public transport (the so-called ‘public good’) are captured outside

the system (Walker 2010). Monetising these benefits is possible but challenging. The discussion from

Hopkinson (1892) on the challenges of equitable and prudent cost recovery in electricity provision is

still relevant today. Hopkinson observed that a reliance on unit costing for low-use consumers would

provide insufficient revenue to cover the high fixed costs of servicing those consumers.

EROI for electricity generation has been widely studied, especially coal-fired electricity, including

carbon capture (Wu et al. 2016, White & Kulcinski 2000), wind power (Kubiszewski et al. 2010),

solar photovoltaics (Bhandari et al. 2015, Koppelaar 2016, Louwen et al. 2016) and gas-fired genera-

tion (Moeller & Murphy 2016). The boundaries and types of analysis vary between studies, but all

adopt the electricity busbar or inverter output as the EROI numerator2 – electricity distribution and

management of the grid system as a whole is typically excluded from the analysis boundary.

Net-energy analyses usually adopt the tools of life-cycle assessment to establish the lifetime embod-

ied energy. The principal benefit of standard guidelines is that they permit like-for-like comparison.

Many of the differences in findings between conventional analyses can be accounted for via meta-

analyses that harmonise for key performance parameters.

The limitation of a standardised methodology is that studies are then restricted to answering the

range of research questions to which that methodology is suited. An emphasis on improved harmoni-

sation between studies may come at the cost of excluding energy investments that are important for

a comprehensive assessment of energy supply technologies. Furthermore, considerations relating to

the engineering-systems view of electricity supply, which are critical to establishing the value of some

forms of generation at higher grid penetration, are generally treated as lying outside the domain of

conventional life-cycle research.

Some variable renewable energy (VRE) studies include storage and RE overbuild within the anal-

ysis boundary. The problem is that defining the magnitude of RE overbuild and storage that provides

an ‘always available’ role is fraught, and understates the value of VRE in other contexts. Most studies

don’t consider system-level requirements within a broader suite of generation, including geographic

and technology diversity, along with engineering services. These system-level engineering services

ensure quality, stability and reliability of supply. They include parameters such as inertia, spinning

reserve, and rapid ramping. No single generator is required to provide these services, but there must

be sufficient supply of these at a system level.

2 See equation 1.1. The numerator is the energy supplied by the system in question. It is typically defined as the energysupplied over the assumed lifetime.

27

Chapter 2

EROI as a supplement to the price

system

2.1 Introduction

2.1.1 An outline of the problem

A common question posed to proponents of Energy Return on Investment (EROI) is — what will

energy analysis reveal that isn’t already apparent from a conventional financial analysis? The price

system already embeds all of the energy and non-energy costs into the cost of production of energy

supply firms. While it is true that non-market costs1 distort the ‘true cost’ of energy supply, this

doesn’t alter the argument that the price system is the most reliable guide to the value of fuels and

electricity.

The challenge for proponents of EROI is to explain how a hypothesis based, in part, on an ‘energy

theory of value’2 can be reconciled with the empirical observation that market prices regularly reflect

relative scarcity unrelated in any meaningful sense to energy resources. Clearly, energy is only one of

many scarce factors of production. The market price of a van Gogh is a stark example3. Furthermore,

while it is possible to make the claim that without energy, there would be no economic activity, it is

also possible to make the same claim for labour, water, air, land and to a lesser extent many other

factors of production. What makes energy unique?

1 Such as externalities, regulations, differential tax rates, and subsidies.2 ‘Energy theory of value’ is being used here to identify EROI as being grounded in an objectivist conception of energy,

discussed later. A better descriptor may be an energy-based analysis of a physical constraint.3 The sale of a painting is a zero-sum transaction and not measured in GDP, nor are capital gains generally. Hence, the

exchange value of artworks may not immediately contradict an ‘energy theory of value’ as it relates to value addingin GDP measurement. However, capital gains tax and broker fees, for example, would be included in GDP.

28

Since these are fundamental and contested issues, this chapter explores these in detail.

2.1.2 EROI as an energy productivity indicator

Definition of energy productivity

A common indicator of energy productivity is the ratio of economic activity to energy consumption,

or its inverse, energy intensity (Stanwix et al. 2015). The output is expressed in monetary units, such

as dollars. Energy use is expressed in physical units such as megajoules. In 2015, Australia consumed

5,831 PJ of primary energy (BREE 2015, table 1.3) and produced $1,637B of GDP (ABS 2015a),

giving an energy productivity of $0.28 per MJ of primary energy, or equivalently, an energy intensity

of the national economy of 3.6 MJ per dollar of net output.

A related, but distinct metric, end-use service energy intensity, is the ratio of energy consumed,

to end-use service delivered in physical units. An example is road freight energy intensity, expressed

as the diesel consumption per tonne-kilometre (IEA 2017c, p.83). In Australia for 2014, the road

transport sector consumed 488 PJ of diesel fuel (Office of the Chief Economist 2015b) to deliver 196

billion tonne-km of road freight (expressed as the product of the freight weight and the distance

travelled) (ABS 2014b), giving 2.5 MJ per tonne-km, or 0.065 litres of diesel per tonne-km, using the

diesel conversion factor from Australian Department of the Environment (2015).

Fuel price and energy productivity use the same units

The price of energy or fuels is expressed in the same units as energy productivity – dollars per physical

unit of energy. Petroleum fuels are priced in dollars per litre, electricity in dollars per megawatt-hour,

natural gas in dollars per gigajoule. In principle, the energy productivity of an entire economy should

be much greater than the price of energy, otherwise the economy would be producing only as much

output to purchase energy and nothing else. A nation with a high energy productivity is said to be

relatively decoupled 4. For example, Australia’s energy productivity of $0.28 value added per MJ of

primary energy can be contrasted with fuel prices of $0.038 per MJ ($1.30 per litre of petrol) and

$0.07 per MJ of retail electricity ($250 per MWh) 5.

EROI as a specialised energy productivity metric

Whereas energy productivity and related efficiency measurements are related to energy consumption,

EROI is applied to energy supply. The usual way to think about energy supply is to consider i) fuel

4 Although the activity mix of the economy and overseas trade influence energy productivity.5 Note that one unit of electricity requires about 2.9 units of primary energy in Australia with the current generation

mix, giving $0.024 per MJ primary energy.

29

price; and ii) fuel utility. The utility is the usefulness of the fuel in regards to the end-use service

required. For example, electricity and natural gas are substitutes for building space heating. On the

other hand, liquids fuels have the highest utility for transport applications, and therefore petroleum

fuels can command a price premium over less readily substitutable fuels, including natural gas and

electricity.

EROI is a specialised energy productivity metric, in which both the numerator and denominator are

expressed in units of energy, giving a unitless ratio 6. Just as the ‘road freight energy intensity’ given

above is a physical metric, EROI similarly does not directly include monetary costs7. As such, both

metrics reflect underlying physical properties. The EROI ratio provides an energetic valuation of the

fuel or electricity supply technology, which may, or may not, correlate with the conventional economic

valuation. An energetic valuation of fuels and electricity, based on EROI, conveys information about

the sustainability, and the trend of the sustainability indicators, of the energy supply chain that may

not be obvious from an economic valuation. The principle weakness of economic valuation is that

market prices reflect the cost of bringing the next unit of energy to market8, which may be a poor

indication of long-run sustainability and prosperity.

Overlap between energetic and economic valuations

There may be overlap between energetic and economic valuations, determined by the capital and

energy intensity of the energy supply system (King et al. 2015a). For example, the price of oil and gas

production has been shown to be inversely related to EROI (King & Hall 2011). On the other hand,

the price of solar photovoltaics may be a poor proxy for its energetic evaluation (Palmer & Floyd 2017).

Canadian oil sands is both energetically and economically expensive (Wang et al. 2017), while United

States shale oil has been shown to be less energetically expensive than oil sands (Moeller & Murphy

2016) although the economic costs are high due to a scarcity of drilling and support resources serving

the fracking industry (Hughes 2018). Biofuels generally perform poorly from an energetic perspective

(Ketzer et al. 2017, Carneiro et al. 2017), but differential tax treatment and other incentives may

obscure the underlying costs when assessed by an economic evaluation alone.

Nuclear power is subject to economic, regulatory and social constraints (von Hippel et al. 2012,

Lang 2017), rather than underlying biophysical constraints. Much of the cost of modern electricity

supply is related to network infrastructure, retail margins, and other administrative costs that have

a low energy intensity, and therefore the economic valuation may be more pertinent (Fares & King

2017, Palmer 2017b).

6 EROI is usually discussed as an energy ratio, but may also be expressed as a power ratio when applied to annualenergy flows.

7 Although monetary costs are often used to estimate the physical energy consumed.8 Market prices are ideally based on competitive markets, economic equilibrium and rational expectations.

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2.1.3 EROI as a subset of biophysical economics

Net-energy will be explored through the lens of biophysical economics (BPE). ‘Biophysical’ refers to

the material world and can be contrasted with an anthropocentric perspective, of which mainstream

economics is a subset (Hall & Klitgaard 2011, p.1). A useful working definition of BPE is ‘the study

of the ways and means by which human societies procure and use energy and other biological and

physical resources to produce, distribute, consume and exchange goods and services while generating

various types of waste and environmental impacts’ (Chevallerau 2017). While BPE pays attention

to monetary prices, it is focused on the physical laws that impose constraints and interact with

‘anthropocentric’ systems.

2.1.4 The EROI hypotheses

The argument for EROI is summarised in the suite of hypotheses as follows:

1. Economically useful fuels and electricity are always derived from primary energy resources ex-

tracted from nature 9. But, with few exceptions, energy resources in nature are not suitable for

providing energy services until they have been processed or upgraded to energy products, suit-

able for downstream purposes10. By definition, commercial energy products are ‘commercial’

because some value-adding processes have been undertaken.

2. A corollary to hypothesis 1 is that the upgrading of natural energy resources occurs within

the economic system, but the natural resources themselves are appropriated from outside the

economic system.

3. Energy is a primary factor of economic production, and energy consumption and economic

development are highly correlated. Relative decoupling between energy and economic growth

can be observed in some developed countries over some time intervals, but doesn’t alter the

general pattern across regions and through historical time (Stern et al. 2014).

4. A corollary to hypothesis 3 is that energy is substitutable with capital and labour, but there is a

lower bound to substitution. Certain types of economic activity, such as knowledge production

and personal services are only weakly linked to energy. However, all physical processes are

governed by physical and thermodynamic laws. In respect of energy, civilisation is like any other

physical process; that is, as an open, non-equilibrium thermodynamic system that sustains itself

with the consumption and dissipation of energy (Garrett 2014).

9 Primary energy is defined as energy mined or extracted from nature. This includes non-renewable resources, such ascrude oil and coal; and renewable energy sources, such as sunlight and wind.

10 Upgrading includes extraction, processing, refining and transport. A counter-example is drying clothes outdoors,which is free. It is an example of direct use of energy from nature, not requiring any conversions.

31

5. A further corollary to hypothesis 3 is that the output elasticity of energy is far greater than its

factor share11 (Ayres 2001, Stern & Kander 2012, Lindenberger & Kummel 2011, Keen & Ayres

2017, Kummel et al. 2015). Energy-augmented aggregate production functions have been shown

to provide a closer approximation to output than those relying on capital, labour, and ‘technical

progress’ alone.

6. Energy industries can be conceptualised as functional units that ‘lever’ primary energy from

nature, using commercial energy12, to make additional commercial energy for consumption by

industry and households. EROI is a measure of the mechanical advantage of the ‘lever’. However,

energy is only one scarce factor of production to energy supply firms.

2.2 Contrasting the circular and linear flow economies

2.2.1 Neoclassical perspective

Beginning from the work of Law, Cantillon, and most notably Quesnay’s Tableau Economique in

the eighteenth century (Murphy 1993), the concept of the circular flow economy (e.g. Samuelson &

Nordhaus (2010, fig. 5.1)) conceptualises goods and services flowing from producers to purchasers,

who direct their labour and investment back for more production. The circular flow, or closed system

model, is the basis for the accounting procedures for national accounts (Coulter 2017). All three

approaches to gross domestic product (GDP) – production, expenditure, and income – are based on

accounting rules that bring each side of an accounting ledger into balance, consistent with double

entry bookkeeping. For example, the production approach defines GDP as the sum of all gross value

added, which equates to the difference between total output and intermediate consumption. This

is consistent with treating ‘the economy’ as being purely a monetary-based system, with currency

flowing between economic actors. It is also consistent with the first law of thermodynamics, which

is the law of conservation of energy – equivalently, no money is gained or lost through economic

exchange. Furthermore, since the marginalist revolution of the late nineteenth century, the concept

of economic equilibrium, in which supply and demand curves intersect, is assumed to apply. Closed

systems are characterised by predictable end states, with defined equilibria.

The concept of circular flow was further reinforced with Bill Phillips so-called ‘MONIAC’ – a

mechanical-hydraulic machine that simulated the UK economy with a system of tubes, tanks, valves

11 The standard cost-share theorem states that the output elasticity of a production factor must be equal to its sharein total factor cost (Lindenberger et al. 2017). Hence the ‘economic power’ of energy can be no more than its factorshare.

12 In this discussion, a distinction is being made between ‘primary energy’ and ’commercial energy’ to clarify thehypotheses. In energy statistics, there is usually overlap, particularly for coal, for which the material dug out of theground is the same as the material weighed and shipped.

32

and pumps. It was first demonstrated at the London School of Economics in 1949.

Each tank represented a sector of the economy, including households, business, government, and

foreign sectors (Bollard 2016). A further tank at the bottom represented total national income. The

machine was able to be ‘tuned’ by adjusting valves to simulate real-world aggregate transactions.

At face value, the MONIAC was a brilliant machine in respect of its treatment of monetary flows

and investments. Phillips machine was successfully able to replicate characteristics of the macro-

economy. But as a model of the real world, it contained an underlying flaw – it ignored the flows of

energy and materials upon which ‘real economies’ are dependent (Raworth 2017). In a fitting analogy,

the machine only worked when an electric motor was turned on to power the pumps.

2.2.2 BPE perspective

In Paleolithic societies, energy and material flows could be conceptualised as circular flows energised

by sunlight. Humans consumed food produced by nutrient rich soil and energised by sunlight, and

breathed oxygen released by plant life. Organic wastes were returned to the soil and carbon dioxide

to plants. In a natural ecological cycle, no waste accumulates because nothing is wasted (Commoner

1971, p.126). Hence, in a pre-industrial context, the closed-system model of economic exchange may

have been able to approximately replicate natural biophysical cycles. However, once released from the

‘organic cycle’ by fossil fuels (Wrigley 2010), the entire cyclic process became disrupted.

Whereas neoclassical economics adopts capital and labour as the primary factors of production,

BPE defines energy-matter13 as the only factor that cannot be physically produced from within the

economic system – energy is the only unsubstitutable and unrecyclable input into every human activity

(Smil 2008, p.344);(Stern 2011). This implies, at least in part, an objectivist ‘energy theory of value’.

2.2.3 Integrating biophysical with neoclassical perspectives

The modern physical economy is based on the flow of energy and materials to and from the environ-

ment. The one-way flow of energy – from natural resources, upgrading, through dissipative processes,

and eventually low grade heat, pollutants and degraded materials – is an immutable natural law.

Money 14 facilitates the physical process, but does not define the process itself. Although the circular

flow model is consistent with the first law of thermodynamics, it is not consistent with the second

law, which states that energy quality must degrade when work is extracted from a thermodynamic

system.

13 Energy is not itself ‘stuff’. Energy is a property of objects that characterises their behaviour and their relationshipsto one another. Although the scientific definition is often given as ‘the ability to do work’, there is no single definitionbetween different contexts (Giampietro & Sorman 2012).

14 Not shown in figure 2.1 is the expansion of the money supply by money creation by private and central banks.So-called endogenous money creation occurs within the economic system.

33

Final goods & services

Producers

Purchasers

Productive services(labour, land, etc.)

Wages, rents, profits, etc.

Expenditures

Primaryenergy

Waste,low-grade heat

& pollutants

Energy industryown use Final

consumption

Industrialconsumption

Waste,low-grade heat

& pollutants

Energysupplyfirms

$

$

Intermediateconsumption

Figure 2.1: Stylised depiction of standard circular flow of the economy, augmented with energy.

Figure 2.1 augments the standard economic model of circular flows with the linear flows of energy,

including energy-industry-own-use. In this energy-augmented conception, energy is both a driver and a

facilitator of economic activity – causation between energy and economic activity can be bi-directional.

Biophysical economists emphasise that production functions based on an energy-augmented model

must reflect the physical reality of the economic system (e.g. Keen & Ayres (2017)). Whereas the

standard economics model is taken as a closed system in equilibrium, the biophysical model is an open

system in disequilibrium. Open systems can exhibit complex and unpredictable behaviour patterns

that are far from equilibrium.

However, standard economics does not recognize energy and materials from nature as existing

outside the economic domain, nor the complex behaviour of biophysical systems that are subject to

biophysical constraints or shocks. Rather, the relevant metrics are the capital and labour economic

costs of bringing energy and materials to market.

2.3 Limits of energy substitution

2.3.1 Introduction

Energy is substitutable with capital-labour, but the BPE hypothesis is that there is a lower limit to

energy substitution. Conventional economics assumes that there is, in principle, no limit to resource

and energy substitution, and ascribes a high price elasticity to energy in the long-run in response

34

to rising fuel prices. For example, rising oil prices in the 1970s stimulated policies to improve the

energy efficiency of end-use energy services. This contributed to increasing the capital stock of energy

consuming equipment, but at a lower direct energy consumption. A neoclassical energy-economics

analysis would usually only consider the direct energy effects of this substitution. However, higher

efficiency equipment typically embeds a higher energy content than what would be assumed taking

an economy-wide energy intensity.

2.3.2 Stylised isoquant

One way of depicting the limits of substitution is the use of a stylised isoquant15 comparing energy

on the y-axis with capital and labour on the x-axis. Figure 2.2 depicts a stylised isoquant plotted

against energy versus capital-labour, based on Stern (1997, fig.2).

E = f(K,L) is a neoclassical isoquant (blue dashed line) for a constant level of output; g(K,L)

is indirect energy of capital-labour; and addition of the direct and indirect energy costs results in the

isoquant E = h(K,L). h and g shown for different indirect energy requirements. All physical work,

heat, material transformations, and material processes are governed by physical laws that require a

minimum application of direct energy (Cullen & Allwood 2010).

Figure 2.3 is based on Kumhof & Muir (2014, fig.3); Stern (1997, fig.2); and Ayres & Nair (1984,

fig.3), and depicts the isoquant region to the right of the upward turn as an ‘unviable factor space’

since it represents a more costly region of the isoquant.

Referring to figure 2.2, in the ranges of high and intermediate energy consumption, the neoclassical

(blue) and BPE (black) approach produce a similar isoquant. However, the BPE perspective is that

energy substitution is ultimately constrained by the second law of thermodynamics. As the ‘entropy

boundary’ is approached at lower direct energy consumption (upper edge of red region in figure 2.3),

the isoquant turns upwards because of the energy embodied in energy producing, and consuming,

capital (and to a lesser extent labour)16. Standard economics would normally only consider the

direct energy flows, rather than also incorporating the indirect energy explicitly. An upward turn of

the isoquant implies that proceeding further rightward increases the total (direct plus indirect) energy

consumption, rather than decreasing it, as focusing on the direct energy only would imply. In practice,

economic activity would not be expected to proceed to the right, but rather, shift downwards and to

the left to a different isoquant at a lower level of output (i.e. economic contraction).

Although it may be possible to establish similar isoquant relationships with other scarce factors

15 In economics, an isoquant (equal output) is a contour line depicting the same output for a given set of productionfactors, typically capital and labour, or in this case energy and capital-labour. The gradient of the curve at any givenpoint depicts the elasticity of substitution between the given factors.

16 The energy of labour can be considered either the food intake, where 3,000 daily calories equals 12.5 kJ, or thecontinuously available muscle power of perhaps 100 to 200 watts. This is two orders of magnitude less than theper-capita consumption of commercial energy in the developed economies.

35

f(K,L)h (K,L)1

h 2h 3h 4 g (K,L)1g2

g4g3

Ener

gy

Capital/labour

Figure 2.2: Substitution and indirect energy requirements, based on Stern (1997, fig.2).

of production under certain circumstances, including clean water or mineral ores, a unique aspect of

energy is that it is not possible to avoid the requirement for energy in any physical process. Given

sufficient cheap energy, it is nearly always possible to produce, or substitute away, from non-energy

factors.

2.3.3 Energy cost shares

Empirical data on energy cost shares can be used to test the hypothesis for an entropy boundary

with respect to energy. Evidence in support of the hypothesis is the relatively narrow band of energy

expenditure cost share as a proportion of GDP.

Bashmakov (2007) introduced the so-called ‘Three Laws of Energy Transitions’, one of which was

the observation that the energy cost to income ratio tends to converge towards a stable long-term

ratio. When the energy costs to GDP ratio is below a given threshold, which Bashmakov defines as

less than 11% in OECD countries, energy exhibits a moderate price elasticity. However, when the

threshold is exceeded, price reactions to small changes in demand are much higher, and economic

growth is hampered. Similarly, Fizaine & Court (2016) found that the energy cost share in the United

States must be less than 11% for the national economy to exhibit a positive economic growth rate.

Fizaine & Court explicitly linked the energy cost share to EROI. Based on the current energy intensity

of the US economy, this translates to a societal EROI of around 11:1. King (2015b) collected long-run

energy expenditure data (Fouquet 2010, 2011, 2014) and found similar results.

36

Unviableregion of

factor space

Ener

gy

Capital/labour

h (K,L)1

Figure 2.3: Entropy boundary based on figure 2.2.

For a given energy cost share, the final energy services delivered are a function of a country’s

GDP, energy productivity, and energy price. This implies that wealthier nations consume more

energy services, and that nations with a higher overall energy price tend to possess a higher energy

productivity. This is borne out by international comparisons (King 2015b), and can be explained by

differential national circumstances and policies (Grubb 2014, 2017).

The asymmetry of the price elasticity of energy, with respect to the national energy cost share,

suggests that there is a lower bound to energy substitution. Energy is an primary factor of production

that is only partially substitutable.

2.3.4 Oil price shocks

The oil price shocks of the 1970s produced a rich literature examining the linkages between oil and

economic activity. Much of the analysis focused on conventional economic interpretations, such as ex-

ogenous versus endogenous oil prices, effects of monetary policy and inflation, consumer and producer

expectations, etc. (e.g. Kilian (2008, 2006)). The closest biophysical interpretation by a standard

economist is possibly Hamilton (1983, 2009), who implicated large increases in the crude oil price in

all but one of the post-World War II US recessions.

The challenge for the standard economic interpretation is that the magnitude of macroeconomic

outcomes is far greater than what the relatively minor share of oil expenditures would suggest. For

37

example, the loss of oil (a 20% decline in oil consumption over 2 years) from Cuba’s economy as a

consequence of dissolution of the Soviet Union in 1989 was devastating to the entire economy, even

though the implicit expenditure on oil was minor relative to national GDP. The Cuban socialized

model of agriculture had been sustained by the highly generous terms of trade of Cuban sugar for

Soviet oil (Sinclair et al. 2001). The Cubans did eventually learn to live with less oil (Hall et al. 2008),

and adapted to the loss of oil through decentralization, urban gardening, and a range of market based

reforms (Sinclair et al. 2001).

The hypothesis that energy is a primary factor of production, and that the output elasticity is far

greater than its factor share (Ayres 2001, Stern & Kander 2012, Lindenberger & Kummel 2011, Keen

& Ayres 2017, Kummel et al. 2015), provides a plausible and arguably more coherent explanation

than the standard economics explanation. Furthermore, it appeals to Occam’s razor, which states

that the successful model with the fewest assumptions may capture the underlying structure better.

2.4 Relationship between energy and the economy

2.4.1 Introduction

The close relationship between energy and the economy was recognized at least as early as 1926 in

Frederick Soddy’s declaration that ‘... the flow of energy should be the primary concern of economics’

(Soddy 1933, ch.3). By the 1970s, the unique role of energy had been recognized in standard economics

(e.g. Jorgenson (1984), Nordhaus (1979), Stiglitz (1974)).

Outside of the mainstream economics literature, the standard interpretations were sometimes

considered inadequate to explain the magnitude of economic effects of the oil crises of the 1970s

(Cleveland et al. 1984, Hall & Klitgaard 2011). Meanwhile, ecological (Odum 1973, Hall 1972),

geographical (Smil 2008), anthropological (Tainter 1990), historical (Wrigley 2010), and biophysical

perspectives (Stern 1993, Cleveland et al. 1984, Georgescu-Roegen 1972) drew attention to the essential

role of energy in economic production.

The stable relationship between per-capita energy consumption and per-capita income is one of

several ‘stylized facts’ of the energy-economy nexus (Stern et al. 2014). Using a sample of 33 countries,

energy consumption and GDP have been shown to be cointegrated, with a long-run dependency ratio

of 0.6 to 0.7 (Giraud & Kahraman 2014).

However, despite the large body of research exploring the energy-economy nexus, the question of

the nature of causality between energy and the economy remained contested. Recent literature reviews

(Chen et al. 2012, Kalimeris et al. 2014, Payne 2010, Ozturk 2010) have identified four hypotheses

that explore the direction of causality.

38

1. A cause-effect running from energy to economic growth

2. A cause-effect running from economic growth to energy

3. A bi-directional relationship

4. No causal relation

The ambiguity of the causal relationship was delineated in Fizaine and Court’s (2016) discussion

of motor vehicle fuel consumption per kilometre, and vehicle speed. Fizaine and Court argued that

a test of Granger causality17 should show a causal relation running from the latter variable to the

former. The relation could be proved by referring to the aerodynamic drag equation, which shows

that drag (and hence fuel consumption) increases roughly with the square of speed. Yet no-one would

dispute that fuel consumption causes forward motion of the vehicle – seemingly the opposite causal

relation.

A resolution of the paradox is that both variables are subordinated to the physics variable ‘force’.

Velocity is a function of force and aerodynamic drag. Fuel consumption is a function of force and dis-

tance. Velocity and fuel consumption are therefore linked by force, but are both dependent variables.

2.4.2 The Australian context

Introduction

The case study of Australia will be used to explore the relationship between energy and GDP. Figure

2.4 depicts Australian primary energy consumption and real GDP for the period 1900-2014. Despite

a significant shift towards a service economy over the 115 year span, primary energy consumption has

remained strongly connected to GDP, but overlaid with distinct long-run trends in energy intensity.

The distinct periods have been labelled as ‘agrarian’, ‘urbanisation’, ‘post-war industrialisation’, and

‘service economy’ respectively, and explored further in chapter 3.

In the ‘agrarian’ phase up to around 1920, energy consumption rose faster than GDP, reflecting

the substitution of labour with capital and energy to increase the productivity and output of agricul-

ture and an emerging manufacturing sector. In the ‘urbanisation’ phase in the lead-up to the second

world war and including the Great Depression, there was a gradual shift from rural to urban regions,

and energy intensity remained constant. In the ‘post-war industrialization’ phase, tariff barriers sup-

ported energy-intensive manufacturing, with energy intensity rising. Following the war, the chemical,

electrical, automotive and iron and steel industries were seen as important national industries, and

17 Granger causality testing is commonly used to test for causality in energy-economic models (Stern & Enflo 2013). Itrelies on the intuition of temporal precedence. A variable xt is said to ‘Granger cause’ yt if prediction of the currentvalue of y is enhanced by using past values of x. However Pearl (2009, p.42) cautions that temporal informationalone may be insufficient for disassociating genuine causation from spurious association.

39

-

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

RealGDP

(AUD

$2016)

Prim

aryen

ergyco

nsum

ptionbyfu

el(PJ)

Blackcoal

Browncoal

Petroleum

Naturalgas

Hydroandrenewables

Real GDP

AGRARIAN POST-WARINDUSTRIALISATION

SERVICEECONOMYURBANISATION

Figure 2.4: Primary energy consumption and GDP, Australia, 1900-2016. Sources Vamplew (1987),Office of the Chief Economist (2015a, 2017), Dyster & Meredith (1990), Butlin (1962), ABS (2017a)

the motor car became a symbol of modernity and affluence. This marked a period of expanding and

growing house size in the major cities and commensurate expansion of car use. In the current ‘service

economy’ phase, energy intensity is falling, reflecting the de-industrialisation of the economy due to

structural changes starting in the 1980s, and ongoing efficiency gains.

Role of energy in economic growth

In order to assess the sensitivity of Australia’s economy to changes in energy consumption and energy

productivity, Giraud and Kahraman’s (2014) mathematical relation was applied to time-series data

of Australia’s primary energy consumption, GDP and GDP deflator. Giraud & Kahraman (2014)

connected GDP and per-capita energy with the simple but useful relation given in equation 2.1. The

expression enables per-capita GDP growth to be decomposed into per-capita energy consumption

growth, and energy productivity improvement respectively.

YtNt

=Et

Nt× YtEt

(2.1)

40

-5%

-3%

-1%

1%

3%

5% An

nualgrowth

5yrmovingaverage changeinenergypercapita

5yrmovingaveragechangeinenergy productivity

($perGJ)

5yrmovingaveragechangeinrealGDPpercapita

POST-WARINDUSTRIALISATION

SERVICEECONOMY

Figure 2.5: Change in per-capita primary energy, per-capita GDP, and energy productivity for Aus-tralia, 1945-2016. Columns are annual change in per-capita GDP.

where

Yt is real GDP as a function of time t

Et is national primary energy consumption as a function of time t

Nt is population as a function of time t

and therefore

YtNt

is per-capita real GDP as a function of time t

Et

Ntis per-capita primary energy consumption as a function of time t

YtEt

is energy productivity of the national economy

which leads to

∆YtNt

= ∆Et

Nt+ ∆

YtEt

(2.2)

It is important to distinguish between the outcome of an algebraic relation, and causation. The

41

relation, in itself, does not prove a relation between any of the variables. Rather, the example is

intended as a case study to test whether the data can produce a sensible and intuitive outcome.

Figure 2.5 is based on equation 2.1, and depicts annual changes in per-capita primary energy,

per-capita real GDP, and energy productivity for Australia, 1945-2016. Since the year-on-year change

is volatile, the annual changes are all drawn as a 5 year moving average. From equation 2.1, the

per-capita growth in GDP is a function of the change in energy productivity and the change in per-

capita energy consumption. Therefore, under the condition of falling energy consumption, the energy

productivity must rise faster to result in an increase in per-capita GDP.

Several observations emerge from figure 2.5. Firstly, energy productivity swung above and below

the zero axis in the post-war period up to the early 1980s, after which it turned positive, except for the

period of the recession of the early 1990s. This is consistent with a shift away from energy-intensive

industries, towards a service economy.

Secondly, the per-capita energy consumption growth remained positive for the entire period up

to around 2000, except for the recession of the early 1980s. However, post-2000, the trend has been

downward, and since the global financial crisis, turned negative. From the data, and accepting the

biophysical hypothesis, the decline in per-capita GDP growth can be largely attributed to falling

per-capita energy consumption. The unresolved question is the causal factors.

In order to explore the hypothesis in more detail, figure 2.6 was constructed with the same data as

figure 2.5. The two independent and single dependent terms were grouped into decades and shown as a

column chart. The per-capita GDP growth is a product of the per-capita energy consumption and the

national energy productivity. From figure 2.6, the period from 1990 to 2016 has shown rising energy

productivity, but a decline in per-capita energy consumption. There has been significantly reduced

per-capita economic growth. The biophysical hypothesis is that much of what has been termed the

‘productivity puzzle’ (e.g. Summers (2016)) can be simply explained by falling energy consumption

without a commensurate improvement in energy productivity. A part explanation for the reduction

in energy is a significant increase in electricity costs, shown in figure 2.7.

42

!2%

!1%

0%

1%

2%

3%

4%

5%

1940!1

950

1950!1

960

1960!1

970

1970!1

980

1980!1

990

1990!2

000

2000!2

010

2010!2

016

Annualise

d6compund6growth6rate6for6p

eriod

Per!capita6GDP Energy6per6capita Energy6productivity

Figure 2.6: Sources of per-capita growth Australia for decades 1940 to 2010, and 6 years up to 2016.Change shown as annualized compound growth over the respective periods.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Real+price+$2015/kW

h

households

industrial

Figure 2.7: Real price of electricity (2005$) 1955–2015. Calculated from ABS A2325846C CPI index,ABS (2016b), Brady (1996), OECDiLibrary (2015)

43

2.5 The EROI metric

2.5.1 Difference between the output elasticity of energy and its factor

share

The large difference between the output elasticity of energy and its factor share is explained by

the fact that national economic production is a function of the total marketable energy, but energy

expenditures reflect the effort by energy supply firms of bringing energy to market, plus profit. Society

gets the use of the total marketable energy, but essentially only has to pay for the energetic-economic

cost of bringing the energy to market. Gaining access to primary energy from nature18 and disposal

of wastes19 usually comprises a small proportion of overall cost.

2.5.2 The minimum EROI of society

Introduction

A key question of EROI research is – what is the minimum EROI that is required to support an

industrial society (Hall et al. 2009, Lambert et al. 2014, Brandt 2017)? Empirically, the energy

cost share must be below 11% for economic growth in developed nations (King et al. 2015b, Fizaine &

Court 2016). Where the energetic and economic valuations approximately align, the EROI is inversely

related to the energy cost share, implying an EROI of roughly 9:1. The inverse of EROI is conceptually

equivalent to energy expenditures cost share.

Historically, societies have flourished and grown with a higher cost share, but with a different

configuration. The hierarchy of human wants and needs has evolved markedly since industrialisation.

Earlier configurations exhibited much higher proportions of primary and secondary industry sectors,

such as agriculture and mining, and much less of discretionary sectors (Hall & Klitgaard 2011, Palmer

2017c). The minimum EROI of society is defined by the configuration of society.

The lower the energy cost share, the easier it is to satisfy basic needs. In contemporary developed

societies, the direct20 energy cost share is sufficiently low that it is not a major determinant of

household discretionary expenditures. Where the energetic costs are much greater than the economic

costs, the effects may be pervasive.

18 For example, mining royalties and resource taxes.19 The obvious exception is carbon pricing, where applicable. There may also be other waste disposal or rehabilitation

fees, although these costs are minimal in relation to the cost of externalities.20 In Australia, household energy comprises 11% of non-transport energy consumption (Office of the Chief Economist

2015b), hence most energy is embodied in goods and services, or related to the provision of public goods

44

Historical overview

Historically, four main phases can be identified in relation to the prevailing societal EROI:

1. In hunter-gatherer societies, the relevant EROI metric was the caloric value of the food captured

or gathered, versus the calorific expenditure of the hunt or gathering expedition. Studies of

hunter-gatherers show an EROI of 10:1 to as high as 50:1 (Lee 1969, Glaub & Hall 2017).

However, the limited capacity for food storage and settlement was a significant impediment to

development.

2. High population and overexploitation of resources has been identified as a possible driver of early

domestication (Weitzel & Codding 2016). In pre-industrial agriculture, the EROI was 5:1 or

less (Day et al. 2018). Agriculture requires intense effort over long periods, often with variable

results. A high proportion of activity was related to production of food, fodder and fuelwood.

However, farming had the benefit of food storage (Palmer 2017a), dedicated fruit trees and crops,

established settlements, and concentrated labour (Diamond 2005). These advances permitted

surplus food production and seasonal food storage for the first time, increased population density,

and represented a prelude to the development of non-farming specialization and villages.

3. Early industrialized societies benefited from rising EROI and energy surpluses. Capital and

energy substituted for labour. Food, fodder and fuel could be provided with fewer workers,

permitting an expansion of non-primary sectors. The range of goods and services expanded. In

the United Kingdom, energy and food expenditures fell to 20% as a proportion of GDP in 1830

(Fouquet 2011, 2014) from 50 to 80% prior to the industrial revolution (Day et al. 2018).

4. In developed nations with advanced service economies, more spending is available for consump-

tion of discretionary goods and services, and more advanced activities, including advanced health

care, the arts, and higher education. (Hall & Klitgaard 2011, p.318);(King 2015a). The wellhead

EROI of global oil reached its maximum value of 50:1 in the 1930s-40s, with a current EROI of

around 10-15:1 (Court & Fizaine 2017). In general, the EROI of oil and gas extraction are in

decline (Court & Fizaine 2017), while the EROI of mine-mouth coal extraction, and the EROI of

renewable electricity are improving (Day et al. 2018). In the contemporary period, resource de-

pletion and technology can be conceptualised as opposing factors that either improve, or worsen,

the EROI. Furthermore, economic forces that may offset the natural resource constraints include

the substitution with man-made factors of production (discussed earlier), and returns to scale

(Stiglitz 1974).

45

2.5.3 Resource depletion versus technical change

Resource depletion

Energy extraction has generally conformed to the ‘best first’ principle, meaning that the largest and

most accessible natural resources tend to be the first exploited 21. The ‘Lucas 1 on Spindletop’ Texas

oil gusher was discovered in 1901 by a small group of wildcatters, and went on to produce seventy-

five thousand barrels per day (Yergin 2011, pp. 66-71). On the other hand, contemporary offshore

platforms operate in much more demanding conditions, carry more technical risk, and are costly to

build and operate (Tainter & Patzek 2011). The Berkut rig operating in the Russian Far East produces

around one-hundred thousand barrels per day at a rig cost of USD$12 billion (Cunningham 2015).

Lower-quality and difficult to access resources require more work to locate, upgrade, and refine than

their higher-quality counterparts.

Technical change

Human ingenuity continually pushes the technological frontier forward. Innovation, learning-by-doing

and incremental improvements push the cost of technologies downwards as they progress along a

learning curve. Technical knowledge is cumulative. The development of hydraulic fracturing (Moeller

& Murphy 2016), the rapid decline in production cost of solar photovoltaics (Louwen et al. 2016),

and the incremental advances in scaling, design, capacity factor, and durability of wind power (Wiser

et al. 2016), are the outstanding examples of technical change in relation to energy supply, leading to

technology-specific economic and energetic cost declines.

Future uncertainty

King (2015b) compiled energy cost share estimates for forty-four countries for the period 1978-2010,

combined these with longer-run historical data, and concluded that the period around 2000 represented

a historical nadir in global energy cost share. More recent analysis confirms that the upward trend in

cost shares since around 2000 has continued (King & Rhodes 2018).

Yet in the period in the lead up to and since 2000, there have been substantial technological de-

velopments in the energy supply technologies listed above, along with energy and electricity markets,

greater use of information and communications technologies, among many other technical develop-

ments. This implies that future outcomes of the competing factors of resource depletion and technology

are uncertain, and understanding how these factors interact is important to transitions research.

21 Although generally true, Norgaard (1990) identified the so-called ‘Mayflower Problem’, referring to the landing of thePilgrims in North America – the Pilgrims were initially unaware that richer soils lay far inland. Similarly, naturalresource development has not historically conformed to Hotelling’s theoretical model of resource scarcity. Investmentflows to projects that can be commercially and profitably developed.

46

In a review of the energy transition literature, Day et al. (2018) found that few studies discuss

the thermodynamic and biophysical implications of transitions. Critical biophysical feedbacks include

diminishing returns to resource use (Hall & Klitgaard 2011), energetic limits to build-out rates of

capital intensive energy supply infrastructure (Floyd 2016), ecosystem health (Schramski et al. 2015),

earth system dynamics (Rockstrom et al. 2009), societal stability (Ahmed 2016), the energy-complexity

nexus (Tainter 1990), and the relationship between energy costs and economic structure (King 2016).

2.6 Conclusions

The EROI ratio provides an energetic valuation of the fuel supply chain or energy supply technology,

which may, or may not, correlate with the conventional economic valuation. Where the energetic

valuation, expressed as the EROI ratio, is much higher than the conventional economic valuation, a

conventional economic approach is warranted. On the other hand, a low, or reducing, EROI may

indicate biophysical limits that may not be apparent from the price system alone. Identifying these

energetic bottlenecks is critical to assessing the efficacy of low-emission pathways.

47

Chapter 3

Energetic Implications of a

Post-industrial Information

Economy: The Case Study of

Australia

3.1 Overview and context of chapter

3.1.1 Introduction

The previous chapter made the case for the primacy of energy. However, given the long-run shift of

the composition of the Australian economy from energy-intensive primary to lower energy-intensive

service-based industries, intuitively one would expect energy to have been decoupled from economic

activity. For this study, ‘strong’ decoupling is defined as an absolute reduction in energy use, while

‘weak’ decoupling is defined as a relative reduction. Strong decoupling would undermine the primacy

of energy as a primary factor of production. Since EROI rests on several hypotheses, identified in

section 2.1.4, which link energy and economic activity, it was considered important to collect the data

and perform a time-series regression in the early stage of this thesis.

3.1.2 Research questions

The research questions posed by this chapter are:

48

1. What is the long-run correlation between energy and economic activity in Australia?

2. Has the shift towards a service economy led to strong decoupling of energy from economic

activity?

3. Do the conclusions support or refute the hypothesis that energy is a primary factor of production?

3.1.3 Research method

The question was explored by applying a time-series regression between real GDP and primary energy

consumption for the period 1900-2014. A qualitative approach was adopted to investigate the role of

energy in service and information technologies.

From inspection of the regression residuals, four phases were classified based on the changing

energy intensity of GDP. In the ‘agrarian’ phase up to around 1920, energy consumption rises faster

than GDP; during the ‘urbanisation’ phase, energy intensity remains constant; during the ‘post-war

industrialization’ phase, once again energy consumption rises faster than GDP; in the current ‘service

economy’ phase, energy intensity is falling.

49

Vol.:(0123456789)1 3

Biophys Econ Resour Qual (2017) 2:5 DOI 10.1007/s41247-017-0021-4

COMMENTARY

Energetic Implications of a Post-industrial Information Economy: The Case Study of Australia

Graham Palmer1

Received: 29 August 2016 / Accepted: 28 March 2017 © Springer International Publishing Switzerland 2017

An explanation for the long-run connection is two-fold. The evolution towards greater social and industrial com-plexity has been underpinned by the ready availability of cheap fuels. The deepening of the service economy towards the infotronics phase should be seen partly as a conse-quence of available energy supply and productive primary and secondary sectors. Several specific examples of ICT are explored to test the hypothesis, including ICT-enabled remote work, online retail, the ‘sharing economy’, and productivity-enhancing ICT applications. Second, energy consumption is driven by demand for end-use energy ser-vices, including transport, buildings and food. Demand for these services is a function of human wants, needs and income, and a changing industrial structure does not alter their underlying demand. The conclusion is that ICT is ena-bling productivity gains and new business models, but does not significantly weaken the demand for these services, and therefore does not enable strong decoupling.

ICT-Enabled Remote Work

The increasing role of ICT-driven service sectors should, in principle, offer opportunities to enable ‘strong’ decoupling by substituting ICT for real-world interactions. Australian service sectors have a much lower energy intensity (energy consumed per dollar of value added) than the primary and secondary sectors, including agriculture, mining, manu-facturing and transport (Stanwix et al. 2015). To the extent that ICT-enabled services can substitute for higher energy intensity products, energy consumption should be reduced, both in relative and absolute terms.

The case of ICT substituting for travel is one such exam-ple—passenger road transport makes up 40% of Australia’s transportation energy (Energy Information Agency (EIA)

Abstract The potential for decoupling of energy and resources from economic growth should enable economic development while improving environmental sustain-ability indicators. Relative decoupling of energy has been a characteristic of developed nations as a consequence of efficiency gains and productivity growth. The trend has strengthened in recent decades as economies have advanced further into the service economy phase. The next phase of development (the so-called ‘Infotronics’ phase) is being enabled by the rapid growth of information and com-munications technology (ICT), and artificial intelligence. The question explored in this commentary is whether the Infotronics phase will shift energy consumption in abso-lute rather than relative terms (so-called ‘strong’ vs. ‘weak’ decoupling), using Australia as a case study. In this context, weak decoupling is defined as a relative reduction in energy consumption per unit of GDP, whereas strong decoupling is also an absolute reduction in national energy consump-tion. Historic data on Australian primary energy consump-tion, gross domestic product, GDP deflator, and industrial sectors have been assembled for the period 1900–2014. A time-series linear regression between energy and real GDP was undertaken to explore the historic relationship between changes in the changing structure of the Australian econ-omy and energy consumption. Despite a significant shift towards a service economy, primary energy consumption has remained strongly connected to GDP, but overlaid with distinct long-run trends in energy intensity.

* Graham Palmer [email protected];

[email protected]

1 Australian-German College of Energy and Climate, The University of Melbourne, Melbourne, Australia

50

Biophys Econ Resour Qual (2017) 2:5

1 3

5 Page 2 of 9

2015). Indeed, the example of communications-enabled decoupling has historic precedents—the eighteenth century telegraph, later telephone, transatlantic cable, right up to the late twentieth century internet are cases in which it was hypothesised that advanced communications would substi-tute for travel (Mokhtarian 2009).

However despite early optimism for ICT-enabled remote work, the substitution effect has not been as evident as originally assumed (Van Wee et al. 2013). Email and video conferencing has not been able to fully compensate for the richness of face-to-face contact, and the collegiality of a regular work group. Whatever reduction in travel that might have been expected from remote work has been over-whelmed by the economic growth enabled by ICT. By low-ering the relative cost of information gathering, processing and enabling complementary business innovations, ICT has in fact provided a direct stimulant to business (Brynjolfs-son and Hitt 2000). The expansion of ICT has occurred at the same time as rising travel congestion—Australian met-ropolitan travel distance tripled from 1970 to 2014 (Bureau of Infrastructure Transport and Regional Economics 2016, Fig. 1), while the population doubled.

ICT and the Sharing Economy

The emergence of ‘sharing economy’ applications, such as Uber, Lyft and Airbnb, provide examples in which software is impinging on real-world activities. Andreessen (2011) describes the foray of software into the physical world as ‘software eating the world’. Once again, the substitution of low energy intensity software development, defined in the Australian National Accounts (Australian Bureau of Statis-tics (ABS) 2016a) as a low energy intensity service sector, seems to present a pathway of low energy development.

However, the significance of ICT seems to be more reflective of a business model disruption than a funda-mental change to the physical and energetic outcomes. For example, Uber has been shown to have a higher utilisation factor than traditional taxis (Cramer and Krueger 2015). Nonetheless, the Uber service consists of conventional vehicles being driven on public roads, consuming the same public infrastructure and energy as conventional vehicles, albeit possibly at a slightly higher passenger–km efficiency. In Australia, Uber was estimated to have provided 6% of taxi industry rides during 2015, with strong continuing growth (Deloitte 2016). The Australian experience is that Uber is drawing in consumers who may not have otherwise taken a cab, and in some cases is substituting for travel modes that would have carried a lower energy intensity (energy per km), such as trains. Similarly, in principle, Airbnb should be substituting for hotel rooms and increas-ing the utilisation of the built environment (i.e. fewer hotels

would need to be constructed). However, Airbnb mainly competes in the more price-elastic leisure market, which has less impact on the established hotel market (Moody’s 2016). Schor (2016) makes the observation that the produc-tivity growth of sharing economy businesses will lead to macroeconomic growth effects that may overwhelm energy reduction effects.

Dematerialization Driven by ICT

An early version of dematerialization was Buckminster Fuller’s concept of ‘Ephemeralization’—doing more and more with less and less until eventually you can do every-thing with nothing (Fuller 1973, pp. 252–259). In a con-temporary ICT-based version, Kurzweil (1999) hypoth-esised that computing power will eventually cross a critical boundary (the so-called singularity), after which dema-terialized economic growth will accelerate sharply. Kur-zweil (1999, p. 124) argued that there is a rapidly increas-ing knowledge and information content in products and services, and that these are not constrained by material resources.

Using Fuller as a backdrop, Lee (2011) uses the concrete example of the introduction of Google Maps onto smart-phones to argue that information technology is a ‘magic wand’ that ‘in one stroke, transformed millions of Android phones into sophisticated navigation devices’. In Lee’s conception, the smartphone is assumed to be a low energy footprint device that substitutes for a host of real-world products—at zero marginal cost, Google Maps is said to be substituting for paper maps and dedicated navigation devices.

But the reverse is true—Nokia, Google and Apple all have multi-billion dollar ‘real world’ investments in map-ping hardware, software development and data. Further-more, GPS piggy backs onto the large sunk investment of the Navstar GPS satellite system. Google has bundled ‘free’ maps to improve the perceived value of Android, from which it reportedly made $31 billion in revenue and $22 billion in profit during the past seven years (Curry 2016). Furthermore, GPS devices are penetrating cameras and fitness devices, far exceeding the material and energy footprint of paper-based maps and atlases. Hence far from dematerialising, the ‘magic’ of GPS-enabled devices car-ries a far reaching energy and material footprint.

Smil (2013) provides a similar example in which appar-ent ICT dematerialisation has contributed to system growth effects that exceed the direct energy and resource-saving effects. The introduction of computer-aided design (CAD) and machining significantly reduced the man-hours and office infrastructure to produce a drawing and then pro-duce the final product. But in a sort of rebound effect,

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CAD enabled orders-of-magnitude greater complexity and sophistication of advanced products, such as jetliners. Fur-thermore, the ease of translating ideas into products has contributed to an overwhelming diversity and choice of consumer and industrial products. Rather than dematerial-izing, CAD is better described as a complex form of mate-rial substitution.

The emergence of online retail demonstrates the multi-faceted effects of ICT that may be difficult to quantify. For example, online women’s fashion has grown at 15% in recent years in Australia (Magner 2016b), at the same time as the emergence of ‘fast fashion’, which has grown at 12% (Magner 2016a). Yet despite strong online growth, Austral-ian suburban shopping malls continue with major redevel-opment projects (Zhou and Robb 2016). A part explanation is that women’s fashion is being driven by social media and online shopping, exposing consumers to the latest designer fashions (Magner 2016a). Furthermore, the environmental implications of online versus conventional shopping can be difficult to estimate when a broader perspective of shopper behaviour is considered (Edwards et al. 2010).

All of these examples illustrate the inter-relationships between growing complexity, a deepening of service sec-tors, energy consumption, and ICT. At face value, ICT applications may seem to represent low marginal cost, dematerialized solutions, but are in fact examples of Taint-er’s (1990) energy–complexity spiral in which software is embedded in an increasingly complex system with a far reaching energy footprint.

The Energy Intensity of Australia’s Economy

The hypothesis that ICT is not leading to ‘strong’ decou-pling was explored with the case study of Australia. Real GDP was derived by dividing nominal GDP by the GDP deflator. Primary energy consumption refers to the con-sumption of primary feedstock fuels for end-use consump-tion and electricity generation. In the case of electricity derived from renewable fuels, there are two alternative methods to calculate their primary energy equivalent. This commentary uses the IEA methodology, which simply assumes that 1 MJ of electricity from hydro or renewables is equivalent to 1 MJ of primary fuels (Modahl et al. 2013). The alternative method adopted by the EIA is to multiply the electricity generation by three to approximate the fuels that would otherwise have been combusted if the electric-ity was derived from coal, gas or petroleum. With a rising penetration of renewable fuels, the IEA method will tend to indicate greater decoupling (i.e. one unit of renewable elec-tricity substitutes for three units of coal or gas).

Figure 1 compares the Australian real GDP and the pri-mary energy consumption for the period 1900–2014. Over

the period, the per capita real GDP increased 5.7-fold, and the per capita primary energy rose 7.6-fold. Viewed over a century, the primary energy consumption has been strongly connected to the gross domestic product, despite a strong shift towards a service economy (see Fig. 2). A time-series linear regression between real GDP as independent and primary energy as the dependent variable, with 115 annual observations, results in an R-squared of 0.973, providing support for a correlation.

A closer inspection of the regression residuals reveals four phases. In the ‘agrarian’ phase up to around 1920, energy consumption rises faster than GDP, reflecting the substitution of labour with capital and energy to increase the productivity and output of agriculture and an emerg-ing manufacturing sector. In the ‘urbanisation’ phase in the lead-up to the second world war and including the Great Depression, energy intensity remains constant. This marks a period of electricity substituting for steam power—the proportion of factory horsepower in Australia delivered by electricity had increased from 24% in 1918, to 81% in 1939 (Australian Bureau of Statistics (ABS) 1920, 1940).

In the ‘post-war industrialization’ phase, tariff barriers support energy-intensive manufacturing, with energy inten-sity rising (Fig.3). Following the war, the chemical, elec-trical, automotive and iron and steel industries were seen as important national industries, and the motor car became a symbol of modernity and affluence (Frost and Dingle 1995).

This marks a period of expanding and growing house size in the major cities and commensurate expansion of car use. In 1945, there were only 0.12 motor vehicles per person, rising threefold to 0.36 by 1968 (Frost and Dingle 1995, p. 34). Passenger vehicles per capita rose linearly until an inflection in the 1980s led to slowing trend growth (Fig. 4). The post-war spread of Australian cities up until the 1990s exceeded one million hectares (Buxton 2006). New detached housing floor area rose steadily before peak-ing in 2008/09 (Commsec 2016). Near-complete electrifi-cation wasn’t reached until around 1970 (Australian Bureau of Statistics (ABS) 1970), and for the period 1945 to 1970, national electricity consumption rose seven fold. By 1973, manufacturing consumed 38% of total primary energy. Electricity consumption continued to rise before moderat-ing post-2010.

In the current ‘service economy’ phase, energy intensity is falling, reflecting the de-industrialisation of the economy due to structural changes starting in the 1980s, and ongoing efficiency gains (Stanwix et al. 2015). Household energy consumption has stabilised, with rising building and appliance efficiency being off-set by rebound factors. In an engineering energy study, Palmer (2012) showed that despite significant and sus-tained improvements in appliance and building fabric

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Fig. 1 Australia primary energy consumption and real GDP 1900–2014. Sources: Australian Bureau of Statistics (ABS) (2015a); Butlin (1962); Dyster and Meredith (1990); Office of the Chief Economist (2015); Vamplew (1987)

Fig. 2 Australian industry sectors, actual and projected, 1800–2020, Source: author estimates from Ruthven (2013)

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Fig. 3 Persons employed in selected manufacturing industries, post-War. Sources: Australian Bureau of Statistics (ABS) (1955, 1960, 1965)

Fig. 4 Passenger vehicles (excluding commercial and other vehi-cles) per capita 1945–2016, and average floor area of new detached dwellings 1984–2016. Sources: Australian Bureau of Statistics (ABS)

(1962, 1976, 1988, 1999, 2013a, 2014, 2015b); Commsec (2016); Frost and Dingle (1995)

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efficiency, the per capita heating energy consumption in Melbourne remained stable over the studied period of 1960–2010. This was attributed to higher expectations of thermal comfort, such as larger heated areas, extended heating periods and higher thermostat temperatures, and lower per-household occupancy rates.

Since the mid-1990s, per capita energy consumption has plateaued at around 240 GJ per capita per annum. The recent fall in the total consumption for 2013 and 2014 is due to a reduction in the electricity demand as a consequence of a doubling of retail electricity prices during the period 2007–2012, which has mostly impacted coal consumption (Australian Productiv-ity Commission 2013). The price rise has accelerated the recent structural changes, including the closure of energy-intensive production, and a trend towards greater efficiency (Fig. 5). Based on a projected average annual economic growth rate of 2.7% and using an equilibrium model with ongoing efficiency gains, the growth in pri-mary energy consumption is projected at 1% per annum for the period up to 2050 (Bureau of Resources and Energy Economics 2014). The BREE projection should be taken as a scenario that satisfies an equilibrium model for a given economic growth rate rather than a forecast.

Real-World End-Use Energy Services

At face value, the low energy intensity of service sec-tors and a concentration of wealth in Australian localities associated with ICT services would seem to strengthen the decoupling hypothesis (Ruthven 2012). However, the growth of service sectors and the relative decline of energy-intensive sectors (see Fig. 2) do not seem to have led to strong decoupling. Part of the explanation is that the ongoing demand for the real-world end-use services that consume energy has not diminished. End-use ser-vices include passenger and freight transport, construction materials for buildings and other products, food, hygiene, thermal comfort, communications and illumination (Cul-len and Allwood 2010). These end-use services are a func-tion of human wants, needs and income. At a global level, a consumption-based approach to energy and material flows (vs. the conventional production approach) shows that the ‘material footprint’ is still strongly correlated with GDP (Wiedmann et al. 2015).

Furthermore, the ‘physical dimensionality’ of goods places practical limits on efficiency gains (Bithas and Kalimeris 2013). For example, the fuel economy of motor vehicles is a function of their mass, shape, and required performance. The path dependency of urban development establishes commute distances and the associated energy

Fig. 5 Real price of electricity (2005AUD) 1955–2015. Calculated from ABS A2325846C CPI index, Australian Bureau of Statistics (ABS) (2016b); Brady (1996); OECDiLibrary (2015)

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footprint. In buildings, people require the full suite of mechanical services, including heating and cooling, lifts and lighting. All of these products and services involve direct and embodied energy consumption. It is not appar-ent that service and information economies have a funda-mentally different need for these services. Indeed, even ICT services are themselves responsible for an expanding and significant magnitude of energy consumption, including the rapid growth of cloud computing and data centres (Aebi-scher and Hilty 2015, Fig. 7; Corcoran and Andrae 2013).

Unmeasured gains from ICT

An alternative postulate is that ICT is not significantly contributing to GDP but has led to unmeasured gains in welfare, and therefore material well-being is much higher than that measured by conventional metrics (Brynjolfsson and McAfee 2014). The prime example related to ICT is the claim that social media enhances welfare, but there are many measures of welfare unrelated to economic devel-opment, such as relationships and a healthy environment (Australian Bureau of Statistics (ABS) 2013b). If true, this would be equivalent to low energy intensity development, and is sometimes said to explain the so-called ‘productivity puzzle’ of post-industrial economies.

A resolution to the puzzle is that the recent smartphone developments produce non-market benefits (i.e. consum-ers are more productive in using their non-market time to consume services they value), but that the impact on con-ventional standard-of-living metrics is minor (Byrne et al. 2016). In much the same way that the earlier introduc-tion of colour television contributed to unmeasured gains in consumer surplus, social media has supplanted tradi-tional forms of entertainment and communication. From a consumer perspective, social media appears to have zero marginal cost because cellphone and internet services are often fixed monthly cost services. This tends to obscure the costs that lie behind the provision of the services and the telephony infrastructure. Furthermore, social media is not substantially substituting for real-world products such as transport, buildings and food.

Summary

A key question in sustainability research is whether ‘strong’ decoupling of energy and resource consumption from eco-nomic activity is occurring. An observation supporting the decoupling hypothesis is that national economic activity is increasingly dominated by low energy intensity ICT-driven service sectors—the Australian quaternary and quinary

service sectors now comprise 47 and 10% of the national economy, respectively.

The question was explored by applying a time-series regression between real GDP and primary energy consump-tion. The regression showed that economic activity and energy consumption has remained linked, but overlaid with distinct long-run trends in energy intensity. Two hypotheses that sought to explain the connection were presented. The first argued that the demand for energy end-use services is a function of human wants, needs and income, irrespective of the relative composition of the national economy. The second argued that ICT-enabled service sectors are driv-ing new business models but not significantly altering the underlying demand for end-use energy services. The deep-ening of the service economy towards the Infotronics phase should be seen partly as a consequence of sufficient energy supply and productive primary and secondary sectors. The conclusion is that ICT is enabling productivity gains and new business models, but does not significantly weaken the demand for these services, and therefore does not enable strong decoupling.

Acknowledgements The author would like to thank the anonymous reviewers for their valuable comments that substantially improved the commentary.

Compliance with Ethical Standards

Conflict of Interest The author states that there is no conflict of in-terest.

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Chapter 4

An input-output based net-energy

assessment of an electricity supply

industry


4.1.1 Introduction

Nearly all net-energy assessments of electricity generation have focused on specific technologies, and

adopted an attributional, process-based LCA. The functional unit is nearly always given as kWh (or

equivalently, MJ) of electricity delivered to the grid over the assumed life of the device.

However, much of the cost of delivering electricity is related to networks and ensuring sufficient

system redundancy. Hence there are system-level properties that are not addressed in technology-

specific analyses. Furthermore, nearly all EROI and LCA studies of electricity generation adopt

a process-based methodology, resulting in truncation error. Chapter 5 addresses similar issues by

adopting adopting a ‘bottom-up’ approach, using a functional unit of 1 kW of power delivered to the

grid. This study takes a ‘top-down’ approach using an input-output analysis.

A challenge with this study was that, to the best of my knowledge, there hasn’t been a study that

assessed the EROI of electricity at a system or national level. There has been progression towards

identifying the need for a broader, or consequential approach. Furthermore, some of the issues have

been widely explored, such as the costs of renewables integration, but much of the LCA or net-energy

approach has been ad-hoc and incomplete. Therefore, the initial challenge was ascertaining how to

59

approach the analysis.



1. Is it possible to calculate the EROI of a national electricity industry?

2. If so, what techniques are appropriate?

3. What data is available and what are the limitations of the data?

4. What are the highlights of the analysis?


This study adopts an environmentally extended input-output analysis (EEIOA). EEIOA provides a

method for evaluating the linkages between economic activities and environmental impacts, including

direct and indirect energy consumption. One technique is to combine the national monetary use-table

with the national energy account. The main strength of input-output (I/O) analysis is systematic

completeness – all national energy is allocated to specific industrial production or consumer end-use.

Its main weakness is homogeneity, or the assumption that each sector of the economy products a

single, homogeneous good or service.

Since the electricity supply industry produces a single product, the homogeneity weakness is partly

alleviated, although electricity costs vary across customer class, location and time. I/O also suffers

from uncertainty related to sampling, reporting and imputation. For this study, the benefit of an

economic input-output approach is that it includes all elements of electricity supply, including trans-

mission, distribution, and retailing.

The adoption of Australia as the country of interest has two benefits:

1. Most of the energy inputs are sourced within Australia. This reduces error associated with

multi-regional analyses and simplifies the analysis.

2. Australian electricity is geographically confined nationally, and therefore completely bounds the

analysis.

I/O analysis is widely adopted in several fields, especially economics. There is a also a large body

of literature on EEIOA. Most of the energy related EEIOA is related to embodied greenhouse gas

emissions and other pollutants. Since no comparable study was able to be identified, the matrix

algorithm needed to be developed. The study was conducted with Matlab.

60

4.1.4 Study challenges

There were several challenges with the study. Firstly, the Australian Bureau of Statistics (ABS) system

of national accounts treats household electricity consumption and gross fixed capital formation as

final demand. Therefore the energy intensity of electricity for each sector is calculated mostly relative

to a unit of household consumption. But this study also treated industrial/commercial electricity

consumption as final demand. This required adaptions to the Matlab algorithms to correctly allocate

energy intensities and reconcile energy flows.

The second main challenge was that Australian economic and energy statistics are not designed for

net-energy analysis. The ABS produces a ‘net-use’ energy account, but the ABS definition of net-use

differs from the BPE definition. Similarly, the IEA ‘energy industry own use’ appears to be a statistic

that can be readily incorporated into net-energy analysis. However, closer analysis of the ABS and

IEA data, and further investigations, revealed that the ‘net-use’ or ‘own use’ statistics are not directly

completely translatable to ‘net-energy’ analysis. The ABS was able to assist with clarifying specific

issue with the ‘net-use’ account.

The third main challenge was dealing with the problem of differential tariffs and costs across fuels

and end-users. The standard approach in I/O analysis is to adopt a homogenous price for a product

across industries. For example, gas consumption can be calculated from purchases from the ‘gas

supply’ industry, coal from the ‘coal mining’ industry, and so on. The average tariff is estimated as

the quotient of the fuel industry revenue and fuel sold. However, it is difficult to establish precise

estimates for fuel tariffs, there is no single tariff for all consumers, and the assumption of a fixed tariff

does not correctly reflect energy flows across industries. The approach of this study was to allocate

energy consumption for all 15 of the ABS fuel types directly to industries and final demand. This

required building fuel allocation algorithms into the Matlab code.

4.1.5 Study code and data

The Matlab code and data utilised in the model can be found at:

https://github.com/grahampalmer/I_O_study

61

https://github.com/grahampalmer/I_O_study

An input-output based net-energy assessment of an electricity supplyindustry

Graham PalmerAustralian-German College of Climate and Energy, The University of Melbourne, Parkville 3010, Australia

a r t i c l e i n f o

Article history:Received 9 May 2017Received in revised form10 November 2017Accepted 12 November 2017Available online 15 November 2017

Keywords:Net-energyEROIInput-output analysis

a b s t r a c t

Electricity systems process and upgrade crude feedstock energy from the natural environment using highquality energy inputs, including diesel and electricity. The ratio of electricity output to the energy inputsis termed the energy-return-on-investment (EROI) and may be an important metric linking energyconsumption and standard of living. Environmentally extended input-output analysis (EEIOA) can beused to determine the energy flows through an economy with a monetary use-table and a satelliteenergy account. This study applies an EEIOA analysis to the Australian electricity supply industry, dis-aggregating the feedstock from the energy inputs, and further disaggregating electricity generation fromtransmission, distribution and on-selling. We calculated the system EROI at 40:1 for 2013e14. Theelectricity generation industry is energetically economic, in the sense that a relatively small energy inputleverages a much greater magnitude of electricity generation and distribution. However, the leveraginghas been achieved at the expense of a high feedstock extraction rate and commensurate emissions. Wefind that the industry is cost constrained rather than EROI constrained. This is the first study to examinethe net-energy of electricity at a system level, and establishes a baseline for exploring future low-emissions scenarios.

© 2017 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Net-energy analysis

Energy systems process and upgrade crude feedstock energyfrom the natural environment using high quality energy inputs,such as diesel and electricity. The energy-return-on-investment(EROI) is the ratio of the net energy output to the low entropyenergy inputs that are ‘diverted from society’ [78]. Input-output (I/O) modelling can be used to determine the energy flows through aneconomy with a monetary use-table and a satellite energy account.However most I/O studies are concerned with the primary feed-stock energy extracted from the environment. Net-energy analysisrequires that the feedstock energy is subtracted from the cumula-tive energy demand (CED) [17,18].

1.2. Definition of EROI

EROI is a dimensionless ratio, defined as the ratio of the gross

flow of energy inputs Eg , over the lifetime of the project, and thesum of the energy for construction Ec, operation Eop, and decom-missioning Ed ([73]; eq. (1)). More generally, Murphy and Hall [72]state that ‘EROI is the ratio of how much energy is gained from anenergy production process compared to how much that energy (orits equivalent from some other source) is required to extract, grow,etc. a new unit of the energy in question.’

EROI is an important metric for understanding the relationshipbetween energy and standard of living [63]. At a high EROI (>20:1),EROI is not a constraint on economic activity, however at low andfalling EROI, society may contract or reconfigure to stay above someminimum [56]. Large differences in the energy intensity of energysupply technologies can result in significant differences betweeneconomic and energetic costs.

EROI is the most commonly used net-energy ratio (NER), butthere are several NERs that take slightly different mathematicalforms. This study adopts the gross external power ratio (GEPR),introduced in King et al. [58] and discussed in Ref. [48]; p.130. TheGEPR is the power-based equivalent to the life-cycle based ‘grossexternal energy return’ (GEER) ratio given in Brandt and Dale [26].A ‘gross external’ ratio has been applied because: i) this studyadopts a complete system boundary based on a top-down analysis;

E-mail address: [email protected].

Contents lists available at ScienceDirect

Energy

journal homepage: www.elsevier .com/locate/energy

https://doi.org/10.1016/j.energy.2017.11.0720360-5442/© 2017 Elsevier Ltd. All rights reserved.

Energy 141 (2017) 1504e1516

62

ii) only marketed (i.e. external) energy is included in the analysis;and iii) electricity output is expressed as gross output. A powerratio has been applied because the study is considering annual,rather than life-cycle energy flows. Given the relatively highcalculated GEPR, the equivalent ‘net’ ratio (NEPR) would be onlyslightly lower (see Fig. 1).

EROI ¼ EgEc þ Eop þ Ed

(1)

1.3. Previous studies

There is a large body of literature exploring the EROI of variousenergy sources, especially oil production, biofuels, and electricitygeneration [35,49,73]. The focus of most EROI studies is exploringthe biophysical constraints of energy technologies and whether theenergy source is energetically ‘worth doing’ [47]. Further work in-cludes exploring the relationship between net-energy and prices[57]. Electricity generation has beenwidely studied, especially coal-fired electricity, including carbon capture [87,90], wind power [62],solar photovoltaics [61] and gas-fired generation [71]. Importantly,the boundaries and types of analysis vary between studies, but alladopt the electricity busbar or inverter output as the EROInumerator e the distribution and management of the electricitysystem lies beyond the analysis boundary. Recent research suggeststhat the fixed costs of networks may be a greater constraint thanthe net-energy of conventional generation [41], however a risingpenetration of variable renewable energy, electrical storage, andbiomass will increase the energetic burden [49]. Wolfram et al. [88]adopted a hybrid-LCA to explore the carbon footprint of lowemission scenarios for Australian electricity, and were the first toadopt a full life-cycle approach to electricity generation in Australia.To the best of our knowledge, this is the first study to estimate theEROI/GEPR of electricity supply at a national level, as well as thefirst to disaggregate generation, transmission and distribution.

1.4. Motivation and context

Australia's electricity system is undergoing a transition towardslower greenhouse emission electricity generation [43]. The primarydriver is greenhouse emission abatement targets, but technologicaland economic changes are also driving a transition away from coal-fired generation. Wind power is currently the cheapest form of newlarge scale generation in Australia [22].

Several studies have explored Australian low-emission sce-narios, including AEMO [12]; Blakers et al. [24]; Elliston et al. [38];

Elliston and Riesz [39]; Jeppesen et al. [53]; Lenzen et al. [67]. Alladopt a least-cost optimisation methodology, with differing ap-proaches to incumbent generation, geographic diversity and tech-nology mix. However, all scenarios adopt a gross-energy approach,rather than a net-energy approach, and thereby overstate fuelsubstitution in cases where the net-energy is much less than theincumbent system. Financial and energetic costs should be assessedat a system level in order to capture system-level effects, such asgeographic and technology diversity, curtailment, storage, demandmanagement, and additional transmission. This study provides abaseline for exploring low-emission scenarios.

2. Overview of the Australian electricity supply industry

The Australian electricity supply industry includes the twomajor energy markets, the National Electricity Market (NEM),which covers the majority of Australia's population and businessactivity on the east coast and Tasmania, and the South WestInterconnected System (SWIS) in Western Australia (see Fig. 2).These comprise 85% and 13% of national electricity consumption,respectively [23]; Fig. 4.4. Smaller networks include the NorthWestInterconnected System (NWIS) in Western Australia and theNorthern Territory Electricity Network (NTEN).

In 2013e14, Australian electricity was generated by black coal(43%), brown coal (19%), natural gas (22%), diesel (2%), hydro (7%),and other renewables, including wind (4%), solar (2%), and biomass(1%). Mine mouth dedicated coalmines supplied all the coal-firedpower stations in the state of Victoria and some in Queensland[10]. All NSW generators and some Queensland generators sourcedcoal from a mix of open-cut and underground mines. For the studyperiod, South Australian coal-fired generators sourced coal from anopen-cut mine. Transport costs may comprise a substantial pro-portion of the delivered cost of coal. Natural gas is supplied viaextensive pipeline infrastructure from large conventional gas ba-sins and increasingly, also coal-seam gas [30].

3. Environmentally extended input-output analysismethodology

3.1. Introduction

Environmentally extended input-output analysis (EEIOA) pro-vides a method for evaluating the linkages between economic ac-tivities and environmental impacts, including direct and indirectenergy consumption. One technique is to combine the nationalmonetary use-table with the national energy account. The mainstrength of I/O analysis is systematic completeness e all nationalenergy is allocated to specific industrial production or consumer

Fig. 1. Streamlined energy systems diagram. Derived from Raugei and Leccisi [78].

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end-use. Its mainweakness is homogeneity, or the assumption thateach sector of the economy products a single, homogeneous goodor service [60]. Since the electricity supply industry produces asingle product e electricity e the homogeneity weakness is partlyalleviated, although electricity costs vary across customer class,location and time. I/O also suffers from uncertainty related tosampling, reporting and imputation. For example, in a hybrid-I/Ostudy of wind turbines, Lenzen and Wachsmann [68] calculated astandard error of 24e29%.

3.2. Monetary flows

Following Leontief [69]; Lenzen [65] and Common and Salma[34]; let y be a vector ðn� 1Þ of final demand in monetary units,where n is the number of industry sectors and Z is a matrix of in-termediate demand of industries, then the vector of total output, x,can be expressed as -

xi ¼ zi1 þ zi2 þ…þ zij þ yi (2)

A matrix ðn� nÞ of technological coefficients, A, can be given byZ and x, shown in Eq. (3).

aij ¼zijxj

(3)

From Leontief [69]; the vector x can be expressed as a function ofy, including the total direct and indirect requirements for eachsector with the ‘Leontief Inverse’, L, and technology matrix, A,shown in Eq. (4).

x ¼ ðI � AÞ�1y ¼ Ly (4)

The Leontief Inverse, L, can also be expressed as the geometricseries, shown in Eq. (5), which can be used to calculate the indi-vidual pathways (see Section 3.4).

L ¼ ðI � AÞ�1 ¼hI þ Aþ A2 þ…

i(5)

3.3. Energy flows

One technique of allocating fuel use is to connect the monetary

Fig. 2. Australia's four major electricity systems. Sources: PowerWater [77]; ERAWA [40]; AEMO [14].

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flows with the respective energy industries. Gas consumption canbe calculated from purchases from the ‘gas supply’ industry, coalfrom the ‘coal mining’ industry, and so on. However, it is difficult toestablish precise estimates for fuel tariffs, and the assumption of afixed tariff does not correctly reflect energy flows across industries[66]. This study allocates energy consumption directly to industries,and indirect energy is approximated indirectly through the mon-etary flows. The functional unit for this study is annual energyconsumption in petajoules (PJ) for the respective electricity sectors.

Let G be a matrix ðf � sÞ of the industrial consumption of f fueltypes and s industry sectors given in the Energy Account. Since theAustralian Energy Account uses a higher level of aggregation thanthe National Account ðs<nÞ, the respective industry sectors, n, ofthe National Account were grouped under respective sectors, s,given in the Energy Account. Electricity supply has its own sector inthe Energy Account. The vector for energy consumption for each ofthe Energy Account sectors, g, was allocated to respective NationalAccount sectors on a pro-rata basis based on the total output, xi,giving a matrix H ðf � nÞ.

The fuel intensity vector ðn� 1Þ for each fuel type is shown inEq. (6).

e ¼ Hx�1 (6)

The total direct and indirect fuel attributed to each industrysector, i, can be calculated as shown in Eq. (7).

Fueli ¼ be � L� by (7)

3.4. Calculation of individual energy pathways

Fig. 3 illustrates the application of direct and indirect energypathways. Stage 0 are the direct energy inputs for the two elec-tricity supply sectors (generation; transmission, distribution & on-selling). The ‘stage 1’ inputs of a process are the direct energy inputsof the respective industries supplying the electricity supply sector(e.g. coal mining, construction services, road transport), ‘stage 2’supply the ‘stage 1’ industries, and so on. The total energy for eachstage, or for all stages, can be calculated using the Leontief matrix.However, disaggregated individual pathways require an expansionroutine. Inserting the geometric series from Eq. (5) into Eq. (7)([60]; eq. 7), gives Eq. (8).

Fueli ¼ ½be � I � by� þ ½be � A� by� þ ½be � A� A� by� þ… (8)

where the first bracketed term is stage 0 (direct) energy, the secondis stage 1 (indirect), and so on.

From Eq. (8), stage 1 and higher ordered pathways are individ-ually calculated as shown in Eqs. (9)e(11).

pathwayji ¼ ej � aji � yi (9)

pathwaykji ¼ ek � akj � aji � yi (10)

pathwaylkji ¼ ek � alk � akj � aji � yi (11)

and so on.The number of possible pathways evaluates to nt , where n is the

number of industry sectors and t is the number of stages. At n of 114and t of 5, there are 19.3 billion pathways. The processing time and

Fig. 3. Direct versus indirect energy for electricity generation. Derived from Treloar et al. [85].

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size of the itemised lists is improved by applying the pruningtechnique described in Treloar [84]; which truncates subsequentpaths when a pathway is below a given threshold. The contributionto indirect energy generally lessens for each subsequent stage, suchthat five stages encompasses nearly all of the indirect energy e bytrial and error, five stages and a threshold of 0.00001 PJ were foundto provide 97.6% of total energy. The process was repeated for eachof the fuel types, f, making it possible to explore pathways by fueltype (see Table 2).

3.5. Feedstock fuels

The feedstock fuels dominate the CED of electricity generation.However net-energy analysis requires that the feedstock energy issubtracted from the CED ([27,78]; Fig. 1). For example, in 2013e14,feedstock fuels totalled around 2200 PJ, electricity generationtotalled 894 PJ, but the energy investment to build and operate thesystem was less than 100 PJ. From a net-energy perspective, allfeedstock energy inputs, including coal, natural gas, sunlight andthe kinetic energy of wind, are treated more-or-less equivalently.

3.6. Primary energy factors

After evaluating the evaluation of energy pathways, it isnecessary to apply a scaling factor based on fuel type to take ac-count of the differing economic usefulness of a fuel. For example, amegajoule of coal energy has a different value to a megajoule ofelectricity, although they both represent the same quantity of en-ergywhen expressed as the ‘first-law’ (of thermodynamics) heatingvalue. Since electricity is a secondary energy carrier, it is necessaryto determine the primary energy consumed to generate a mega-joule of electricity. The electricity conversion factor is specific forthe particular electricity system in question, and given as the ratioof the primary energy consumed to the electricity output. In the LifeCycle Assessment (LCA) literature, primary energy is expressed asthe life-cycle primary energy, including the embodied energy andenergy harvested from nature [44]. The major energy agenciesmeasure primary energy as the ‘physical energy content’ of the fuelat the point where it first becomes an economically useful energyproduct [51,52]. For grids composed primarily of fossil fuels, theratio is numerically similar to the average power plant heat rateratio of 2.6e3.3 MJ per MJ of electricity.

However, there is no consensus within the energy agencies onthe treatment of non-thermal power generation, such as hydro-power and wind power [45,70]; Section 1.A.3. The ‘substitutionmethod’ is adopted by BP, EIA, IIASA, and WEC, and adopts athermal conversion of 1 MJ electricity equals � 3 MJ primary en-ergy. The ‘direct equivalent’ method is used by the UN, IEA andEurostat, and uses a one-to-one equivalence between electricityand primary energy (i.e. 1 MJ electricity equals 1 MJ primary en-ergy). Various other approaches have been explored in relation toaggregating energy flows, including economic approaches usingprices ormarginal product, the emergy analysis approach pioneeredby Odum, and the thermodynamic approach using exergy [33,55].

Taking the annual generation for Australia for 2013e14, the‘primary energy factor’ (PEF) equates to 2.9 using the EIA non-fossilconversion, and 2.6 using the IEA non-fossil conversion factor ofunity. A prospective future grid composed of less thermal

generation would require a different conversion factor.In life-cycle analysis, primary energy factors are applied to each

fuel type. Treloar [84] applied amultiplier of 1.2 for coal, oil and gas;1.4 for petroleum and coal products; 3.4 for electricity; and 1.4 forgas. The factors used for this study are shown in Table 1.

3.7. Limitations of methodology

A single-region model has been adopted. This assumes thatimported products are produced with the same energy intensity asif they were produced domestically [79]. For products that are notproduced in Australia, or for other elaborately transformed manu-factures, the implicit energy intensity may not apply and thereforean error will be introduced. This will becomemore important as theexpenditure on renewable energy components, such as solarpanels, increases. The Australian electricity supply industry sourcesall of the electricity feedstock inputs, and much of the energyintensive capital stock locally. In contrast, fuel importing countriesrequire a multi-regional model as a minimum requirement (e.g.Brand-Correa et al. [25]).

In 2013e14, imports comprised 9% of electricity supply industryvalue added. In the case of coal-fired and conventional thermalgeneration, most of the embodied energy cost is associated withcoal mining, oil and gas extraction, and transport. Construction andmaterials of the capital stock is estimated at 5% for coal-firedgeneration [87] to 11% [90]. Since nearly all of the energy foot-print of renewable energy is embodied in the build/constructionphase, a future system with a higher penetration of importedrenewable energy components will require the use of expandedboundaries, including multi-regional tables.

4. Data sources

4.1. Monetary data

The primary economic data source was the Australian Bureau ofStatistics (ABS) ‘input-output Table 5’ from data series5209.0.55.002, ‘industry by industry flow table (direct allocation ofimports)’ [8]. The ABS has continually evolved the input-outputframework, requiring slightly different mapping of the energysatellite account, G, to the National Account. The first year availableonline (1998e99) used 106 industry sectors, 2001e02 through2005e06 used 109 sectors, 2006e07 through 2008e09 used 111sectors, and subsequent years have used 114 industries. There hasbeen continuity with most industries, but shifts must be accountedfor in a time-series analysis. In particular, the change from 109 to111 sectors in 2006e07 included some aggregation of sectors,

Table 1Primary energy factors for this study.

Coal, coke, briquettes, wood, bagasse Diesel, petrol, LPG other-refined, natural gas Electricity

1.0 1.2 2.9

Table 2ABS 4604 fuel types.

1 Black coal 9 Diesel2 Brown coal 10 Other refined3 Coke 11 LPG4 Coal by products 12 Biofuels5 Briquettes 13 Wood6 Natural gas 14 Bagasse7 Crude oil 15 Electricity8 Petrol

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especially farming-related, and an expansion of other serviceindustries.

With the introduction of 111 industries in 2005e06, ‘electricitysupply’ was split into 2 sectors e ‘electricity generation’ and‘electricity transmission, distribution, on-selling and electricitymarket operation’ (TDOS). The transmission/distribution sectorincludes purchase and sales of distribution costs as part of on-selling, hence caution must be exercised to avoid double countingwhen deriving sales and revenue data from the ‘use table’. For theperiod 2008e09 onwards, the industry spending on transmission/distribution can be calculated as the difference between ‘total in-dustry uses’ and sales of transmission/distribution to itself (i.e. thediagonal of the I/O table). However, this calculation does not workfor the period 2007e08 and earlier. An explanation is that duringthe process of privatisation and disaggregation, formerly singleentities became multiple entities [4]. Furthermore, many activitiesthat were once conducted by the single, state-owned corporation,are now carried out by specialist businesses, which are classified toother industries. These include businesses associated with con-struction, repair and maintenance. Since the ABS does not have aspecific ‘solar industry’ sector, it was not possible to disaggregatethe solar industry. The majority of enterprises that operate in theindustry are small-scale contractors and tradespeople [64], andtherefore sales and installation is allocated to the ‘service sector’.

4.2. Energy data

4.2.1. Feedstock fuelsFeedstock refers to fuel that is directly combusted for electricity

generation. The feedstock fuels were derived from BREE, Table F1,‘Australian energy consumption, by industry and fuel type, energyunits’ [74]. Four feedstock fuels were included: black and browncoal, natural gas, and diesel, which combined comprised 98.8% of‘fuels consumed’, excluding consumption of electricity. Since elec-tricity is a secondary fuel, consumption of electricity by the elec-tricity supply industry is treated as a net-use, rather than afeedstock.

Since Table F1 represents gross energy consumption, this studyassumes that nearly all of the consumption of the four fuels by theelectricity supply industry can be attributed to feedstock ratherthan net-use fuels. In recent years, brown coal has only beenconsumed as a feedstock fuel (historically, it was also a feedstockfor briquettes and syngas) and it is assumed that most direct blackcoal and natural gas consumption is also a feedstock fuel. This isconfirmed by the net-use data from ABS 4604. Diesel is consumedas both a feedstock and a direct energy input, but from inspection,around 95% of diesel consumption can be attributed to a feedstockfuel.

4.2.2. Net-use energyThe energy net-use data sources were derived from three

sources: ABS data series 4604.0, ‘Australian net use of Energy2002e03 to 2013e14’; BREE Table F1, ‘Australian energy con-sumption, by industry and fuel type, energy units’ [74]; and ABSdata series 4660.0, ‘Energy Use, Electricity Generation and Envi-ronmental Management, Australia’.

The ABS net use of energy is derived from three sources e theEnergy, Water and Environment Survey (EWES), which is con-ducted every three years; Australian Energy Statistics (AES) data;and NGER reported data. Some entries in the ABS 4604.0 energyaccount are labelled ‘np’, which refers to data that is not itemised inorder to preserve confidentiality of the reporting industries, but arenonetheless included in totals. A process of summing rows andcolumns and ‘guestimating’ permits estimates to be made of themissing entries.

Electricity net-use data was cross referenced with the publisheddata from the Australian Clean Energy Regulator for the NationalGreenhouse and Energy Reporting (NGER) scheme. The schemerequires reporting of greenhouse emissions for generators andcorporate groups that exceed a threshold of 25 kt of CO2-eq orproduction of greater than 100 TJ (27.8 GWh). This includes all ofthe major generators and nearly all electricity generation, includingwind farms with a capacity of greater than around 10 MW. Smalldistributed generators not owned by large corporate entities androoftop solar fall below the reporting threshold.

Scope 1 (direct emissions) result from the combustion of fossilfuels for electricity generation, and scope 2 (indirect) emissionsresult from the purchase and consumption of electricity. This im-plies that self-generated self-use within a facility is not included.Scope 2 emissions are calculated as the electricity multiplied by theemission factor for the state or territory in which the consumptionoccurs. Therefore, the electricity can be back-calculated given theestimated emission intensity.

From NGER reporting, the Victorian Loy Yang A facility is treatedas an integrated generation-mine operation and therefore theelectricity consumption associated with mining is reported as ageneration activity. All Victorian coal-fired generators consumemine-mouth brown coal, and operate as integrated open-cut-generator facilities [10]. Loy Yang B is separately owned and pur-chases coal from the owner-operator of Loy Yang A. From 2002, theYallourn mine was operated by a joint venture company [82]. Forthis study, it is assumed that the Victorian coal-fired generators arevertically integrated generator-mines, which report as ‘generators’.All other generators in other states purchase fuel from the ‘coalmining’, ‘natural gas supply’, or ‘oil & gas extraction’ industriesrespectively.

A check of the ABS 4604.0 data related to mining and oil and gasextractionwas enabled by cross-referencing ABS data to the EnergyEfficiency Opportunities (EEO) Program. The EEO was a FederalGovernment program running from 2006 to 2014. It required cor-porations that consumed greater than 0.5 PJ per annum to report atleast 80% of their total energy use by the end of the first five-yearcycle. Several reports covering industry groups were published in2013 covering the period 2006e11 [19,20].

Since NGER only applies to generators, it should be possible tosubtract generator estimates of electricity consumption from theABS 4604 data to derive an estimate for TDOS. However, since it isassumed that generation is much more electricity intensive thanTDOS, and it was not possible to directly disaggregate purchasedelectricity by TDOS, an alternative method was applied. An elec-tricity intensity per dollar of value added was estimated from thecalculated intensity of the non-energy intensive industry sectorsfrom ABS 5204.5, Table 5. This study uses an intensity of 50 MWhper $million gross value added based on a range of around 20 to 60,giving 0.9 TWh for 2013e14.

4.2.3. Electricity generation and loss dataElectricity generation data were sourced from BREE table O1

[75]. Further electricity supply data was derived from AEMO [13]archived generation data, including the supply of electricity fromAustralia's three pumped-hydro schemes (Wivenhoe, Shoalhavenand Jindabyne). Transmission and distribution losses of 2.5% and3.5% respectively were estimated from Australian data from WorldBank [89] and ABS [5].

4.3. Solar generation data

For the period of this analysis, the contribution of rooftop solarwas small enough that Australian PV Institute estimates areadequate. However future expansion will require more detailed

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assessments. For the period of the study, nearly all installed solarwas rooftop. Since rooftop solar is embedded within the low-voltage distribution network and partly consumed on the house-holder side of the meter, self-consumed solar generation isunmetered and treated as a demand reducer from the perspectiveof the electricity supply industry. Feed-in tariffs are based onmetered net-exports rather than gross generation. Gross-meteringwould permit more precise estimates. However only a limitednumber of systems in NSW and ACT use a dual meter [54] and theinformation is not publicly accessible. Most of the estimated solargeneration is based on system capacity and postcode data from theClean Energy Regulator, which can be calibrated against knownperformance data from gross-metered systems [11]. The AustralianPV Institute website provides estimated real-time performancedata based on solar insolation and selected in-field solar systemsvia the ‘http://pvoutput.org’ portal. Johnston and Egan [54] calcu-lated a national weighted average annual solar output from theRenewable Energy Certificate registry of 1400 kWh/kW, or3.8 kWh/kW per day. In relation to monetary data, the ABS does notdisaggregate the Australian solar industry, however industry-basedreports are available which enable some estimates to be made,including Ledovskikh [64] and Solar Business Services [81].

4.4. Other data

A set of time-series data, including electricity consumption,prices, feedstock fuels and GDP, was built up from several historicand contemporary sources including Vamplew [86]; Butlin [31];Dyster and Meredith [37]; ABS [2,3]; Office of the Chief Economist[74,75]. Further disaggregation of the ‘transmission, distribution,and on-selling’ sector was provided by IBIS World reports. IBISpublishes industry reports based on firm-level and other data. Thisincludes separate reporting of the transmission [42], distribution[15], and retail electricity sectors [16]. The capital depreciationmetric was used as a proxy for the proportion of energy expendi-ture for each of the 3 non-generation sub-sectors, with therespective proportions calculated as 26% transmission, 62% distri-bution, 12% on-selling.

4.5. Consumer classes

Some jurisdictions apply several consumer classes to energyconsumption, including: industrial; commercial; government; andresidential/households. For this study, only 2 consumer classes aredisaggregated based on ABS reporting: industrial, comprising allbusiness and government; and residential/households.

5. Reconciliation of data for the electricity supply industry

5.1. Allocating energy to all electricity supply

Since this study is considering the energy inputs into the elec-tricity industry, several adaptions to the basic methodology wereadopted to reconcile energy consumption. The ABS system of na-tional accounts treats household electricity consumption and grossfixed capital formation as final demand. Therefore the energy in-tensity of electricity for each sector will be calculated mostly rela-tive to a unit of household consumption. But this study is alsotreating industrial/commercial electricity consumption as finaldemand. This was remedied by allocating all direct energy inputs tothe electricity supply industries directly, rather than just the energyattributed to final demand. The problem this creates is that non-energy purchases by the electricity supply industry, from sectorsthat have purchased from the electricity supply industry, becomedouble counted inputs. This was remedied by directly allocating

only the remainder of total electricity after processing the energypathway routine.

5.2. Double counting due to on-selling

A further issue relates to the on-selling of electricity in the‘transmission, distribution and on-selling’ sector. During the periodof state-owned utilities, electricity was sold directly to consumersby vertically integrated entities. However, the functional separationof retailing post-privatisation meant that electricity becamecounted twice in the National Account e firstly as purchases by theretail sector, then as sales plus markup by the retail sector to finalconsumers. This is a characteristic of the accounting systemadopted by the ABS. The IBIS report on electricity retailing [16] alsoshares this characteristic. It only applies from 2008 to 09 onwards.

The issue this creates is that it increases the intermediate use ofthe TDOS sector relative to the generation sector, and therefore agreater proportion of the direct energy of the entire ‘electricityindustry’ is incorrectly attributed to TDOS when direct energy isallocated on a pro-rata basis (see Section 3.3). In order to betteralign the monetary flows with the physical flow of electricity, theapproach taken in this study was to subtract the estimated costs oftransmission and distribution from the diagonal of the intermedi-ate matrix, Z, as though retailing was vertically integrated withTDOS. This subsequently reduced the total output, xi, of the‘transmission, distribution and on-selling’ sector and brought therespective electricity supply components back into the typicalrelative proportion given in the National Accounts in 2012, shownin Table 3.

5.3. Coal mining

Diesel consumption by the coal mining sector is the singlelargest indirect pathway. From the monetary use table, the pur-chase of ‘coal mining’ product by the ‘electricity generation’ in-dustry made up 3.6% of total supply. But from the energy account,the consumption of black coal for electricity generation constituted8.6% of total production, with exports constituting 89% of produc-tion. This implies that ‘electricity generation’ is purchasing blackcoal at a much lower price than exported coal, and/or there areadditional costs associated with exported coal, including transport.There may also be a cost difference associated with the quality ofcoal, whereby local generators can utilise relatively inferior de-posits at lower cost, but presumably at the same, or higher, energycost of mining and processing. Furthermore, product that is sup-plied for local generation is usually offered at a discount due to thestability of long-run contracts [10]. For this study, the indirect en-ergy of ‘coal mining’ that was attributed to ‘electricity generation’was adjusted upwards two-fold, equating to 7.2%.

The GEPR of coal supply was estimated from data from theEnergy Efficiency Opportunities Program. Direct energy consump-tion for the coal mining industry for 2010e11 included: diesel 50 PJ;and electricity 12 PJ [19]. From BREE [28]; total production of blackcoal was 9215 PJ in 2010e11. Applying a primary energy multiplierof 1.0 for coal, 1.2 for diesel and 2.9 for electricity equates to a GEPR

Table 3Shares of Electricity supply output and net capital expenditure by ANZSIC group,2006e2007, source [6].

Industry value added (%) Net capital expenditure (%)

Generation 35 30Transmission 11 18Distribution 47 48On-selling 7 4

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of 97:1 for direct energy inputs only, which comprise most of theenergy footprint [90].

5.4. Revision of electricity use by the electricity supply industry

The ABS data series 4604.0 ‘net-use’ includes electricity con-sumption by the electricity supply industry itself. It includes: self-generated electricity for own use; purchased electricity for ownuse; electricity losses due to transmission and distribution; andelectricity purchased to charge pumped hydro storage (PHS) [9].The IEA [51,52] Energy Balance data for ‘electricity own use’ ap-pears to be derived from the ABS data, and therefore does not addadditional information. Some studies (e.g. Brand-Correa et al. [25];King et al. [59] have used IEA ‘energy industry own use’ (EIOU) datato calculate EROI/GEPR of energy at a national level, but the highlevel of aggregationmay not properly reflect the boundaries usuallyadopted for the EROI denominator.

Generator self-use is referred to as auxiliary load or parasiticuse. This includes drive power components such as pumps, fans,and conveyors for fuel handling, furnace draft, and feedwaterpumping; power conversion, protection and distribution compo-nents such as transformers; and instruments and control systems.In total, thesewill typically consume around 7e15% of generation insteam power plants ABB [1]; down to around 2% in gas-firedcombined-cycle plants [46]. Anti-pollution devices in pulverisedcoal plants, including precipitators, can consume 5% of generationoutput. Prospective estimates for coal with carbon capture are25e35% [1]; p.338. From ABS [9]; it is estimated that the ABS hasused an estimate of 2.8% across all generation, however it is notclear which components have been included. The re-charging ofpumped hydro constituted around 0.1% of Australian electricitysupply and could be ignored. The estimated losses in PHS charge-discharge cycling were estimated at 20%.

Self-generated electricity for own use within an energy supplyfacility can be treated in two ways e either as a loss within thefacility that effectively reduces the efficiency of production but isnot added to the denominator on the EROI equation; or treated as adiversion of energy ‘from society’ that needs to be added to thedenominator [48,71]; pp.130e131. Consistent with an ‘external’approach Brandt and Dale [26]; this study uses the former.

For 2013e14, ABS4604.0 reports electricity consumption by theelectricity supply industry as 96 PJ. Taking transmission and dis-tribution losses at 6% calculates to 55 PJ; gross inputs for PHS isestimated at 0.6 PJ; ownuse electricity for generators only is esti-mated at 25 PJ (from personal contact with [9]); leaving 15 PJ ofpurchased electricity, which was allocated mostly to generation. Itwas subsequently found that the last figure is the most significantcomponent of the overall energy footprint, and therefore the studyaccuracy is dependent upon procuring unpublished data. Unfor-tunately, the ABS net-use account is not designed for EROI analysis.

6. Results and discussion

6.1. Overview

Tables 4 and 5 summarise the results derived from the methodsdescribed in detail above.

These results provide the primary data for depicting the entireelectricity system as a ‘black box’ that shows all inputs and outputse feedstocks, operational fuels, electricity output and waste (heat).That ‘black box’ is shown schematically in Fig. 4.

One can see that in 2013e14, 894 PJ (248 TWh) of electricity wassupplied by the Australian electricity supply industry. Spending onelectricity was estimated at $29.8 B or 1.9% of GDP for industrialconsumers, and $14.4B or 0.9% of GDP for households. The GEPR has

been calculated as 40:1 for 2013e14 using primary energy equiv-alent scaling, or 29:1 using unity scaling for all fuel types andelectricity.

6.2. Largest components of operational energy

Fig. 5 itemises the 16 largest net-use energy pathways. Thelargest net-use is the purchase of electricity by the electricitygeneration sector. This refers to purchased electricity and does notinclude generator self-use (see Section 5.4). Using primary energyequivalent scaling, electricity comprises 75% of the operationalenergy. The second largest component is diesel consumption by thecoal mining industry for transport and machinery. ‘Other refined’,including bunker oil, is consumed by the water and pipeline in-dustry for the generation sector. Other industry sectors that areresponsible for a significant energy footprint supplying the elec-tricity supply industry include oil and gas extraction, constructionservices, gas supply, and road transport.

6.3. Tier analysis

Fig. 6 itemises the aggregated energy pathway totals for the first4 stages, for the 6 most significant fuels, where stage 0 is the directenergy inputs into the electricity supply industry. Natural gas,diesel and ‘other refined’ are dominated by stage 1 inputs. Themostsignificant consumption of natural gas includes: oil and gasextraction; gas supply; coal mining; and construction services.Diesel consumption is dominated by the coal mining industry fol-lowed by road transport. ‘Other refined’ includes water and pipe-line transport and coal mining. Most of the crude oil and petrolconsumption relates to purchases by the electricity sectorsthemselves.

6.4. Sankey diagram of the electricity supply industry

Fig. 7 presents the Sankey diagram representation of the results.The figure graphically depicts the feedstock fuels, operational en-ergy inputs, and annual energy flows of the all fuels consumed inthe Australian economy. With the exception of exported crude oil,exports are not included in the diagram since these have no directimpact on the electricity supply industry. Major exports for2013e14 include black coal (9485 PJ), liquefied natural gas (1303 PJ)and uranium oxide (given as 3944 PJ) [29]. The results for trans-mission, distribution and on-selling have been disaggregated usingthe capital depreciation metric discussed in Section 5.2.

6.5. Low energy intensity cost components

A characteristic of the EEIOA methodology is that energy isallocated on an industry-by-industry basis, whether directly to theelectricity supply industry, or indirectly through purchases by theelectricity supply industry. Using this framework, payments to la-bour, taxes and gross profit margins have, by definition, a zeroenergy intensity since these payments re-enter the economy asconsumption or capital purchases. Labour compensation includesremuneration in cash or in kind, and employers' social contribu-tions [7]. Some studies argue that an energy intensity should beapplied to labour [48]; pp.139e40, however labour is usually

Table 4Feedstock fuels, 2013-14.

Black coal Brown coal Natural gas Hydro & RE Diesel

1018 622 531 133 39

G. Palmer / Energy 141 (2017) 1504e1516 1511

69

explicitly excluded in life cycle assessments [80]. Using thisframework, the application of an energy intensity to labour wouldresult in double counting since the same energy would be allocated

to household consumption.Other significant low-intensity cost components include finance

and insurance. The energy intensity of service industries includes

Table 5Operational fuels and electricity >0.01 PJ, 2013-14.

Black coal Natural gas Crude oil Petrol Diesel Other refined LPG Biofuels Wood Bagasse Elect.

0.1287 2.2856 1.0916 1.5179 6.4177 2.1667 0.0701 0.0691 0.0640 0.0101 16.8802

Fig. 4. ‘Black box’ depiction of the electricity supply industry, 2013e14.

Fig. 5. Largest 16 energy pathways, disaggregated by generation and TDOS. Petroleum includes diesel, petrol, ‘other refined’, and LPG.

G. Palmer / Energy 141 (2017) 1504e15161512

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the direct consumption of electricity (e.g. for office buildings) andpetroleum (e.g. to run vehicles), along with the indirect energycosts of those industries respectively. Significant costs for thefinance and insurance industries include computer systems design,

internet services, employment services, and travel agency services.From Table 6 it is apparent that much of the cost of electricity can beattributed to low energy intensity costs that have no bearing on thebiophysical limitations of energy supply.

Fig. 6. Total electricity supply industry for 2013e14. Direct (left/blue), and the first 3 stages of indirect, for the most significant 6 fuel types. (For interpretation of the references tocolour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7. Sankey diagram of Australian electricity supply industry 2013e14, figures shown without primary energy equivalent scaling.

G. Palmer / Energy 141 (2017) 1504e1516 1513

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6.6. Sensitivity analysis

Since the studied period was a single year, a sensitivity analysiswas undertaken to establish whether the period was representa-tive. The I/O analysis was repeated for the years 2009e10 and2012e13 which have an identical use-table structure to 2013e14.The ABS did not produce input-output tables for 2010e11 and2011e12, and 2013e14 is the most recent year for which I/O tablesare available. The four most significant fuels, excluding electricity,are shown in Table 7. By inspection, these are sufficiently close tosuggest that 2013e14 was representative for the 5-year time span.The ABS 4604.0 reported net-use of electricity consumption for theperiod 2003e03 through 2013e14 is plotted in Fig. 8. By inspection,there has been a slight downward trend since 2011-12 but it is notclear whether this is an artefact of the ABS accounts or a distincttrend.

6.7. Differences to other energy sources

The cost structure of the electricity system differs from otherenergy sources in several important ways. Firstly, Australian

electricity supply is geographically constrained and therefore notsubject to direct international cost competition. Second, unlike allliquid and solid fuels, electricity cannot be stored directly andtherefore the infrastructure must be built to meet peak demand.The high reliability standards imposed by Australian regulatorsnecessitate quality engineering, redundancy, and regulatory su-pervision, all of which add to cost. By definition, this lead to sub-optimal usage of infrastructure since system capacity far exceedsaverage utilisation. Third, since electricity network services arenatural monopolies, there is little scope for direct competition [21];p.65. Network businesses are compensated through rate of returnregulation, which may suffer from the Averch-Johnson effect e

firms have incentives to over-invest to increase the capital basefrom which they derive a return [83]; p.19). These factors lead toover investment, regulatory, administration, and other service coststhat carry a low energy intensity.

6.8. Implications and further work

Scenario analyses typically adopt least-cost optimisationmethods [50]. All of the published Australian studies adopt a gross-energy approach, which implicitly assumes that the energy in-tensity of the modelled system is commensurate with the incum-bent system. However, if the EROI of substituting electricity supplycomponents is much less than the incumbent system, the net-energy flows may be much less than anticipated, and thereforethe scenario analyses are overstating the degree to which low-emission substitution is occurring.

Nearly all EROI studies focus on a specific generation type, and

Table 6Costs and energy intensity of selected low energy intensity cost components, 2013-14.

Costs or flows ($M) Energy intensity (MJ/$)

Generation TDOS Diesel N/gas Electricity

Labour compensation 1618 5009 0 0 0Gross operating surplus 3157 9343 0 0 0Taxes 2897 2949 0 0 0Auxiliary Finance and Insurance Services 1285 1975 0.01 0.01 0.02Finance 1063 1907 0.01 0.01 0.02

Table 7Consumption of fuels, excluding electricity.

Diesel Petrol Natural gas Other refined

2009e10 6.33 1.00 3.46 3.912012e13 5.10 0.98 2.46 2.782013e14 6.42 1.52 2.29 2.17

Fig. 8. ABS 4604.0 net-use of electricity by the electricity supply sector. See discussion Section 5.4.

G. Palmer / Energy 141 (2017) 1504e15161514

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adopt the electricity busbar or inverter output as the EROInumerator. However, scenario analyses reveal system level char-acteristic that are not easily evaluated at the level of a specifictechnology. For example, geographic and technology diversity, anddemand management are important tools for optimising a suite ofvariable renewable generation.

The benefit of evaluating EROI at a system level is that synergies,trade-offs and system-level properties can be explored. Forexample, from this study, transmission is not a significant factor inthe system-wide embodied energy, and therefore geographic di-versity may be a strategy that can be implemented withoutadversely affecting the system-wide EROI. On the other hand,electrical storage [76] and biofuels [32]; Fig. 8 generally incur ahigher embodied energy debt, and may constrain low-emissionscenarios. The adoption of coal with CCS will increase both theprimary energy extraction, and the embodied energy [87,90].

Future work should include estimating the EROI/GEPR of lowemission scenarios to assess potential energetic constraints.Another approach is to include an EROI constraint in optimisationalgorithms to ensure that a minimum EROI is met e for exampleDupont et al. [36] calculated a global wind energy potential atvarious minimum EROI constraints.

7. Conclusions

The EROI/GEPR of energy supply technologies is an importantmetric linking energy consumption and standard of living. Thisstudy aimed to fill a global gap in net-energy related to the treat-ment of electricity at a systems level. An environmentally extendedinput-output analysis was undertaken to calculate the grossexternal power ratio (GEPR) of the Australian electricity supplyindustry. We calculated the GEPR at 40:1 for 2013e14. The highGEPR implies that the industry is not EROI/GEPR constrained.

The results of this study differ from similar studies on oil supplyand biofuels, which show a stronger inverse relationship betweenEROI and price. A part explanation is that much of the cost ofproviding reliable electricity comprises low energy intensity costsassociated with networks, regulation and administration. Largecost increases in recent years are due to network and other lowenergy intensity costs, which are not reflected in a significant fall inGEPR/EROI.

This work establishes a EROI/GEPR baseline for Australia. Futurework should include overlaying low emission scenarios with anEROI/GEPR analysis to assess potential energetic constraints.

Conflicts of interest

Wewish to confirm that there are no known conflicts of interestassociated with this publication and there has been no significantfinancial support for this work that could have influenced itsoutcome.

Acknowledgements

The author gratefully acknowledges Roger Dargaville, RobertCrawford, Josh Floyd, Carey King, and Tom Biegler for helpfulcomments and feedback. Thanks also to the anonymous reviewersfor helpful comments and feedback.

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Chapter 5

A Framework for Incorporating

EROI into Electrical Storage


5.1.1 Introduction

This study is intended as one variant of a consequential approach to electricity-based net-energy

analysis.

Nearly all electricity-based net-energy and LCA studies adopt a functional unit of 1 kWh (or equiv-

alent) of electrical energy delivered to a grid. This study appraised the limitation of this functional

unit, and argued that a functional unit of 1 kW of power delivered to the grid is more useful in the

context of large-scale transitions. The main rationale for testing this hypothesis is that all electricity

systems are built and managed to meet maximum power ; annual energy supplied is important for

revenue for much less important from an operational perspective.



1. There is a lack of consensus in the EROI literature as to how to incorporate electrical storage

into EROI assessments. Is it possible to formulate a repeatable and consistent framework?

2. What are the conceptual differences between conventional electricity generation and variable

renewable electricity (VRE)?

75

3. Is it possible to normalize EROI estimates for the different classes of generation?

4. What are the roles of electrical storage in electricity systems?

5. How feasible are high-penetration VRE studies that rely on electrical storage?


Organisation

The paper was conceptualized as three parts.

1. A discussion of food and energy storage as a historical development, and how this connects with

the contemporary role of storage.

2. The use of electrical reliability indices to provide a repeatable and consistent method of mea-

suring and contrasting VRE plus storage, and conventional generation.

3. An application of the framework to a suite of renewable simulations to demonstrate the frame-

work.

Storage as an intrinsic quality

The origins of the paper were based in trying to understand the concepts of energy (and food) stocks

and flows in human society. Much of the early drafting of the paper was focused on the ‘Historical

Precedents’ section, with an anthropological perspective. This led to the conclusion that energy (or

food) storage is an intrinsic property, from which value is derived. It is argued that food storage was

pivotal to the evolution of human society and culture. Similarly, the value of fossil fuels has been due

to their high energy density, utility, and the capability of producing power on demand.

The formulation of the framework

The second part derived a technical framework. It was built on the principles derived in the first part,

but using electrical power system reliability indices to measure and contrast VRE plus storage.

It is commonly understood that conventional generation is ‘dispatchable’, but there are also a

diverse range of technical specifications between different generator types. The challenge was in for-

mulating a framework that enables VRE plus storage to be compared with conventional generation

that captured the intrinsic value of respective configurations or sub-systems. Since no single conven-

tional generator is perfectly reliable, it was necessary to consider reliability metrics at a system level.

Similarly, it is not necessary for individual renewable sub-systems to possess the same dispatchable

76

qualities as conventional generation to contribute value to a system. Some characteristics can be

understood at a system level, such as geographic and technology diversity.

The proposed framework has the benefit of trading off the embodied energy of VRE-overbuild and

storage, with the additional value that it provides. Conceptually, this results in an optimal magnitude

of storage in a given context. In much the same way that an economic analysis may be intended to

evaluate an optimal solution to reduce costs, it is possible to evaluate an optimal solution based on

‘energetic costs’.

Applying the framework to VRE simulations

In order to demonstrate the framework, it was necessary to collate a suite of demand-balance models.

The third part consisted of combining LCA data with a suite of simulations to demonstrate the

framework.

Discussion

The framework was able to produce sensible comparisons of VRE-storage sub-systems versus conven-

tional generation. The first units of storage are the most valuable, with a diminishing return with

additional storage. Using the framework, it is apparent that an optimal solution based on an energetic

evaluation would evaluate to much less storage than anticipated in some high-penetration renewable

scenarios, and that some scenarios may be infeasible from an energetic perspective.

77

Vol.:(0123456789)1 3

Biophys Econ Resour Qual (2017) 2:6 DOI 10.1007/s41247-017-0022-3

ORIGINAL PAPER

A Framework for Incorporating EROI into Electrical Storage

Graham Palmer1

Received: 24 July 2016 / Accepted: 11 April 2017 © Springer International Publishing Switzerland 2017

was modelled as a limiting case of VRE plus storage, and is therefore not intended as a comprehensive cost-optimised solution to high-penetration VRE. A shift from an elec-trical system based mostly on energy stocks to one based mostly on natural flows is constrained by the quantity of storage required, and the quantity of VRE overbuild to charge the stores. The application of the framework shows that the value of electrical storage and overbuild exhibits a marked diminishing returns behaviour at rising VRE pen-etration and therefore the first units of storage are the most valuable. The framework is intended to stimulate further research into using EROI to better understand the role of VRE and storage in prospective energy transitions.

Keywords Storage · EROI · Renewables · Energy surplus

Introduction

Overview

Regardless of climate goals, the contribution from renew-able energy is projected to increase significantly over the medium to long term (Krey and Clarke 2011). The IPCC AR5 450 ppm suite of energy scenarios (Edenhofer et al. 2014, p. 12) are characterised by rapid improvements in energy efficiency, and a significant scaling up of the share of low-carbon energy supply. Nearly all substantially increase the deployment of renewables, many deploy an increasing share from nuclear and/or fossil fuels with car-bon capture and sequestration (CCS), and some include the possibility of bioenergy with carbon capture and storage (BECCS). All of these options have net-energy implica-tions, and some (e.g. nuclear and CCS) face additional bar-riers and risks (Edenhofer et al. 2014, pp. 20–22).

Abstract The contribution from variable renewable energy (VRE) to electricity generation is projected to increase. At low penetration, intermittency can usually be accommodated at low cost. High-penetration VRE will displace conventional generation, and require increased grid flexibility, geographic and technology diversity, and the use of electrical storage. Energy return on investment (EROI) is a tool that gives greater weight to the principles of energetics over market prices, and may provide a long-term guide to prospective energy transitions. The EROI of electrical storage may be critical to the efficacy of high-penetration renewable scenarios. However, there is no generally agreed upon methodology for incorporating stor-age into EROI. In recent years, there have been important contributions to applying net-energy analysis to storage, including the development of storage-specific net-energy metrics. However, there remains uncertainty as to how to apply these metrics to practical systems to derive useful or predictive information. This paper will introduce a frame-work for evaluating storage at a system level. It introduces the surplus energy-storage synergy hypothesis as a general principle for exploring the role of storage. It is argued that the useful energy available to society is determined by both the net-energy of the energy source and the stored energy as stocks. This hypothesis is translated across to electric-ity systems with the use of electrical reliability indices to evaluate the value of storage. A case study applies the framework to a suite of VRE simulations. The case study

* Graham Palmer [email protected];

[email protected]

1 Australian-German College of Energy and Climate, The University of Melbourne, Melbourne, Australia

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At low penetration, intermittency can usually be accom-modated at low cost (Gross et al. 2006). High-penetration RE will displace conventional generation, and require increased grid flexibility, geographic and technology diver-sity, and the use of electrical storage (Denholm and Hand 2011).

The need for storage increases the energy burden of elec-tricity supply systems, and thus lowers the EROI (Carba-jales-Dale et al. 2014). However, there is ongoing debate as to how much storage will be required in a shift to an elec-trical system based mostly on VRE, and whether this rep-resents an additional but affordable cost, or a fundamental constraint on VRE penetration (Morgan 2014). Although there are estimates of the embodied energy of storage from the life-cycle literature, there is no agreed methodology on how to apply these estimates to a comprehensive estimate of EROI (Pickard 2014a).

Definition of EROI

EROI is a unitless ratio, defined as the ratio of the gross flow of energy Eg over the lifetime of the project, and the sum of the energy for construction Ec, operation Eop, and decommissioning Ed (Murphy et al. 2011, Eq. 1). More generally, Murphy and Hall (2010) state that “EROI is the ratio of how much energy is gained from an energy produc-tion process compared to how much of that energy (or its equivalent from some other source) is required to extract, grow, etc., a new unit of the energy in question”.

Although there is usually general agreement on the con-cept of EROI (i.e. energy out/energy in), there is a diver-gence of objectives and methodologies (Carbajales-Dale et al. 2015; Pickard 2014a). The divergence is perhaps most acute with VRE, compared to say oil, because VRE does not substitute one-for-one with conventional generation, with uncertainty related to the allocation of energy costs for integration and storage. Unlike oil, which is pervasive and substitutable, VRE produces electricity, which has high utility but is not globally fungible and currently makes up only 18% of global total final energy consumption (Interna-tional Energy Agency (IEA) 2016, p. 28).

Storage Literature Review

Much of the literature that explores the value of storage has focused on the relationship between storage and markets, and the potential for storage to add value within incumbent systems [e.g. McConnell et al. (2015); Salles et al. (2016)]. Some of the analyses explore a strategic network role (i.e.

(1)EROI =Eg

Ec + Eop + Ed

supporting networks rather than contributing to bulk gener-ation) and the potential role of technological disruption of distributed solar and storage, although much of the disrup-tion is occurring within markets, rather than in transforma-tions of the physical systems.

There have been a number of approaches taken to include the embodied energy of storage into a system-level EROI analysis. Much of the analysis has attempted to iso-late storage devices to determine a device-specific EROI. The main weakness to date has been establishing a dynamic function for incorporating storage into EROI.

Barnhart and Benson (2013) defined the metric Energy stored on invested (ESOI), defined as the ratio of electri-cal energy stored over the lifetime of a storage device, to the embodied primary energy required to build the device. In contrast, Weißbach et al. (2013) considered the storage capacity of the storage device (in this case, pumped hydro storage) required to provide an equivalent baseload role for VRE. Both of these approaches try to ascertain the embod-ied energy debt of storage, but may not reflect the differ-ing value of storage depending on their role and context. Weißbach assumed a baseload role with 10 days of storage; however, it is not clear why 10 days should be a reference and a high-VRE scenario would value other characteristics, especially flexibility. Furthermore, Raugei (2013) noted some methodology inconsistencies that reflect differing approaches to net-energy analysis.

Barnhart et al. (2015) provided a broader context for the role of storage by exploring the various trade-offs between curtailment, storage, and greenhouse emissions. The trade-off was illustrated with the use of a graphic that depicted energy intensity (as a proxy for EROI) versus carbon inten-sity. The plot encompassed both generation and storage devices. It was dissected into four quadrants; ‘do today’, reflecting options that are worthwhile; ‘reduce emissions’ and ‘improve EROI’, as prospective options requiring fur-ther development; and ‘avoid’, reflecting options that have a poor EROI and poor emission performance.

Using the specific context of off-grid rooftop solar, Palmer (2013) considered the role of storage within the context of an overall system. In this case, the role of storage and surplus solar capacity (to ensure adequate winter sup-ply) in a rooftop solar system was evaluated to establish the lifetime EROI of the complete system.

Denholm and Kulcinski (2004) is representative of stud-ies that are measuring the embodied energy of storage devices, but not attempting to account for the relative value that storage provides. In this case, pumped hydro storage (PHS) was evaluated, along with compressed air storage (CAES) and large-scale batteries. Denholm and Kulcinski were concerned with establishing the life-cycle parameters for a given power capacity (GW) rather than a given stor-age capacity (GWh).

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Renewable Simulation Literature Review

This study will apply the framework to a renewable energy simulation. Although there are many published regional VRE simulations [e.g. Budischak et al. (2012); Jacobson et al. (2015)], few apply or publish a loss-of-load proba-bilistic assessment, or explicitly include reserve margins. Where a reserve margin has been applied, it is usually applied as a simple exogenous multiplier rather than emerg-ing from the probabilistic assessment [e.g. (MacDonald et al. 2016b, pg. 32)]. These are described as ‘first order’ demand-balance simulations (see Hart et al. (2012)) and could be regarded as high-level, exploratory studies rather than the ‘second-order’ system-level reliability analyses typically carried out by electrical system operators.

Storage Requirements

Storage requirements of VRE are dominated by stretches of low-wind and solar resource (the ‘big gaps’ problem) (Len-zen et al. 2016). The ratio between the average monthly solar insolation between summer and winter varies greatly across geographic regions (PV Education 2016). Low lati-tude regions, such as Singapore, show a relatively small summer/winter ratio of 1.3–1.7, rising to 3–4 in the mid-latitudes such as Nevada USA, and above 10 for higher lati-tudes, such as London. Cloud cover is also region depend-ent, with clear skies and low aerosols typically located in the latitudes from 15° to 40° north or south (International Energy Agency (IEA) 2014). Concentrated solar thermal requires high direct normal radiation (DNI) and is usu-ally located in arid regions in the subtropical band. Winter insolation and extended cloudy periods define the storage requirements for systems reliant on solar.

In the case of wind, geographic dispersion, the physical inertia of wind machines, and aggregation provide smooth-ing of bulk wind power at sub-hourly scales (Archer and Jacobson 2007). At an hourly and above scale, wind speeds are highly correlated within wind regimes, which may span distances of hundreds to thousands of kilometres (Huva et al. 2016). Seasonal variation is usually much less than for solar but multi-day low-wind conditions appear to be a characteristic of many regions (MacKay 2008, p. 187; Oswald et al. 2008), and these low-wind stretches define the quantity of storage or backup capacity required.

In a review of VRE storage, Pickard (2014b) assumed that around 7 days of storage would be required in a high-penetration VRE scenario. In a simulation for the PJM network in the US, Budischak et al. (2012) used between 9 and 72 h storage, with 3-times VRE overbuild, which included maintaining a significant share of the legacy fos-sil fuel capacity, operating as infrequent reserve capacity. Abdulla et al. (2014) found that 58 h of storage (calculated

at 1500 GWh and 25.5 GW demand) would be required for a high PV scenario in Australia in an explorative assess-ment. Oswald et al. (2008) found that around 6 days of stor-age would be required to cope with continental scale low-wind conditions in Europe.

In a 100% renewable simulation, including sector cou-pling (i.e. heat and electricity) for Germany, 55 GWh of battery storage, 60 GWh of PHS, and 62,000 GWh of methane storage was used in the ‘medium’ scenario (Palzer and Henning 2014, Fig. 3.4). At 500 TWh annual con-sumption, the storage capacity represents around 45 days of full-load electrical capacity (equated at average annual demand). Likewise for North America (Canada, USA, and Mexico), Aghahosseini et al. (2016) used around 3-times VRE overbuild, around 2000 GWh of battery storage and 174,000–230,000 GWh of methane storage. At 6059 TWh annual consumption, the storage capacity represents around 14 days of full-load electrical capacity.

Much of the scenario literature avoids the problem of large-scale storage by maintaining a significant share of conventional capacity [e.g. Budischak et al. (2012)] or assuming the ready availability of large-scale biomass. For example, Lenzen et al. (2016, Table 2) did not employ conventional electrical storage but used concentrated solar thermal with 15 h storage (equating to a theoretical 917 GWh of storage), and generated 16,500 GWh with bio-fuelled powered gas turbines in a simulation for Australia. Assuming the biofuels were used for seasonal storage equates to 21 days of full-load electrical capacity (equated at average annual demand). In a simulation covering the eastern populated regions of Australia, Elliston et al. (2012) employed a similar generation suite, with biofuelled tur-bine capacity set at 71% of annual peak demand but at low capacity factor. The resulting 28,000 GWh of electricity generated from biofuels equates to 50 days full-load electri-cal capacity, assuming seasonal storage. Both of these sim-ulations set the minimisation of biofuels as an objective.

This study is not seeking to prescribe a storage type and only considers the role of electrical storage. Sector cou-pling (e.g. power-to-heat, power-to-gas, power-to-desal-ination, vehicle-to-grid), and other forms of storage (e.g. thermal storage in concentrated solar thermal) may offer opportunities to substitute for electrical storage.

Goal Definitions

Carbajales-Dale et al. (2015) identifies three distinct goal definitions for EROI as it applies to VRE: a descrip-tive assessment of a particular technology; a compara-tive assessment of alternative energy technologies; and an exploration of the viability of emerging technologies to completely substitute for the incumbent system.

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As a tool for comparative assessment, net-energy analy-sis is frequently used to assess the degree to which VRE displaces fossil fuel consumption. For example, Raugei et al. (2012) derived a ‘primary energy equivalent’ multi-plier in conjunction with EROI to describe the equivalent primary energy that is ‘virtually returned to society’ (i.e. coal or gas that is preserved for alternative uses). Raugei et al. argue that a unit of energy invested in solar signifi-cantly reduces the depletion of fossil fuels over the lifetime of the solar system. This is consistent with much of the discussion of VRE as an abatement tool within an incum-bent system. From this perspective, it is assumed that the problem is that there is too much readily available fossil fuel and that without policy intervention the IPCC ‘repre-sentative concentration pathways’ (RCPs) at the high-end are more likely (e.g. RCP 6.0, RCP 8.5). Although RCP 8.5 should not be taken as a no-climate-policy reference sce-nario (Moss et al. 2010), it nonetheless assumes continuing growth in fossil fuel demand (Riahi et al. 2007).

Goal and Scope of the Present Study

Different Roles of Storage

This study is focused on the use of storage to buffer or complement VRE, modelled as natural renewable flows. But to date, most of the use of storage in electricity grids has been for arbitrage and time-shifting in conjunction with baseload (Yang and Jackson 2011). It will be argued that the role of storage as a complement to VRE is different to its traditional role with baseload. Furthermore, the output of electrical storage is usually directly substitutable with energy stocks, especially flexible generation (Denholm and Hand 2011).

Storage can also be reduced to the extent that renewable flows coincide with demand, or demand can be modulated with demand management (Schreiber et al. 2015). Further-more, geographic diversity (Grossmann et al. 2015; Huva et al. 2016; MacDonald et al. 2016a) and resource diversity (Bogdanov and Breyer 2016) provide a quasi-storage role by raising the effective availability factor.

Energy Capital Substitution

This study will frame EROI in the context of energy capi-tal substitution, rather than fuel substitution (i.e. to what degree can VRE plus storage replace the capital stock of fossil fuel extraction and conversion rather than just sup-plant the flow of fossil fuels). It is more concerned with assessing the possibility of future energy systems being able to both sustain themselves, and provide society with sufficient surplus energy (Hall et al. 2009).

A justification for focusing on substitution of capital is the German Energiewende. The term ‘Energiewende’ can be dated to a study by the German Öko-Institut in 1980 (Krause et al. 1981), with the title ‘Energy turnaround, growth and prosperity without oil and uranium’ (Joas et al. 2016; Maubach 2014). Between the starting point of the EEG in 2003 and 2014, total installed power gen-eration capacity grew by 51%, although total annual gen-eration was virtually unchanged (Bundesministerium für Wirtschaft und Technologie 2015).

The Energiewende has been accompanied by grid expansions and tariff increases, although wholesale spot prices have declined (Poser et al. 2014). Hence the Ener-giewende is reducing the direct fuel load and CO2 emis-sions, at a cost, but not replicating the substitution of infra-structure that has typically accompanied historic transitions (i.e. the incentive policies encourage a process of adding to the capital stock rather than substituting away from the leg-acy capital stock). The tariff increases need to be balanced against the broader societal costs of energy, including sub-stantial German fossil fuel and nuclear subsidies (Morris and Jungjohann 2016; van der Burg and Pickard 2015). One interpretation is that the Energiewende is succeeding in reducing the environmental burden of electricity genera-tion, but that the shift is reliant on the strength of the Ger-man economy, the relatively high economic output per unit of energy, and the high EROI of the global energy system.

Reformulation of Storage and EROI

The contribution of this study is to reformulate the analysis of storage. Firstly, it will be argued that energy storage is a fundamental property of net-energy. An understanding of the transition from hunter-gatherer, through pastoralism and agriculture, the Industrial Revolution, and twentieth cen-tury economic development, can be enhanced through sup-plementing the concept of net-energy with the concept of the energy surplus-storage synergy. This approach follows the precedent of applying behaviours observed in natural ecology to industrial ecology [e.g. Brown et al. (2011)]. In the pre-industrial context, energy referred mostly to food and fodder, but by the twentieth century, energy included a broader range of traded energy sources and biomass (King et al. 2015).

Secondly, the cost structure of electricity systems is dominated by capital-intensive infrastructure and fixed operating costs (Brown and Faruqui 2014). It is the nature of all utilities (e.g. electricity, water, telecommunications) and many public services (e.g. passenger rail) that the infrastructure must be built to meet peak demand. Some services, such as urban transit systems and water supply systems, can dampen peak demand by permitting conges-tion or using built-in storage. For example, trains can queue

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passengers on platforms during peak periods, allowing con-gestion to buffer peak demand without the train system fail-ing; the pressure of water supply can fall during summer heat waves, permitting a stressed system to remain func-tional but at a lower quality of service.

In contrast, the real-time flow limitation of electrical sys-tems does not permit ‘soft’ congestion management during peak periods. Furthermore, unlike gas or water pipelines, which may store several hours (or days) of product within distribution networks that can be readily drawn down, AC networks do not possess an equivalent ‘draw down’ capa-bility without storage devices. Instead, infrastructure must be maximally sized to meet reliability standards, although infrastructure requirements could be marginally reduced if reliability standards were relaxed (Australian Productivity Commission 2013).

Finally, a framework for incorporating storage into EROI is presented. This requires calculating EROI of the VRE and storage as a sub-system, and measuring the resulting EROIVRE+storage relative to the displaced capacity of conventional generation. The benefit of measuring the result relative to the displaced capacity is that it is possible to trade-off the rising availability factor with the diminish-ing returns of marginal storage, thereby overcoming the limitation of arbitrarily setting the quantity of storage and RE overbuild. The result reflects the energy investment to substitute for energy infrastructure, rather than displacing fossil fuel consumption.

Historical Precedents

Food Storage as a Pivotal Development

The contemporary value of energy storage is not unique, and a historical parallel can be drawn from the Neolithic transition, beginning around 10,000 years ago. Prior to this period, Palaeolithic man lived a subsistence life of hunting and gathering, governed by the diurnal and seasonal cycles (Diamond 2005).

In the transition, humans began domesticating plants and animals (Hibbs and Olsson 2004). There are different hypotheses for why humans chose to adopt agriculture in the first place (Diamond 2005). But it gradually led to spe-cialised crop cultivation, land clearing, and basic irrigation. These advances permitted seasonal food storage for the first time, increased population density, and represented a prel-ude to non-farming specialists, villages and later, cities.

The decisive break came from collecting of grain, then the evolution of grain, and cereal farming. The cru-cial advantage of grains and cereals was that it permitted agricultural surpluses to be converted into seasonal stor-age. But in the early period at least, the relative calorific

return from agriculture may have been less than tradi-tional foraging and hunting, and it is not obvious that farming would have been worth the effort. Agriculture requires intense effort over long periods, often with vari-able results.

The capacity for storage was to prove crucial. On the one hand, grains have several useful properties. They are hard and dry, and do not readily rot and go bad. Since they are dry, they have a high calorific-to-weight ratio, making them both storable and readily transportable (Laudan 2015). But on the other hand, they are excep-tionally difficult to turn into something useful and digest-ible. Most food gathered or caught by hunter-gatherers is eaten in its raw state or cooked over a fire, but grains require several processing and conversion stages. Wheat must be harvested, threshed, winnowed, and ground. Finally, the flour can be mixed with water and baked to produce unleavened bread. The case of wheat suggests that there must have been a strong evolutionary advan-tage to adopting agriculture and perhaps derive an easily storable product.

The history of salt provides another example of the importance of food storage. Salt was used since antiquity for curing foods such as beef, pork, fish, and later butter. Such was its importance that it was sometimes a strate-gic commodity and was used as an early form of currency (Cowen 2005).

Fig. 1 Stylised graph of energy surplus versus storage. ‘Value’ is defined as the vector of surplus and storage

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Value as the Vector of Energy Surplus and Storage

The evolution of storage is depicted in Fig. 1 in which ‘value’ is defined as the vector of energy surplus and stor-age. The early adoption of farming may have had a lower calorific return than foraging but storage contributed to a greater vector of value than with no storage.

In the early industrial era, steam engines were extremely inefficient in converting chemical energy to mechanical motion, and the net-energy of early steam must have been remarkably low by contemporary standards. Newcomen’s early engines were around 1% efficient and Watt’s early innovations lifted this to 2–4% (Buenstorf 2004). None-theless, as ‘stored sunlight’, coal was able to provide copi-ous quantities of power on demand. Coal and steam power evolved within a virtuous cycle to leverage the power out-put of steam, and were to break the ‘organic limit’ imposed by reliance on solar energy flows (Wrigley 2010).

The Role of Energy Storage

Conventional Storage

Nearly all electrical storage to date has been pumped hydro storage (PHS), which makes up 97% or 142 GW of global power capacity for electrical storage (United States Depart-ment of Energy (DOE) 2016). The three leading PHS coun-tries are Japan with 26 GW, China at 24 GW, and the US at 22 GW. The Eurelectric region comprising the 34 Euro-pean countries that are part of the five European synchro-nous regions has a total installed capacity of 35 GW. These figures relate to power but comprehensive data on storage capacity (GWh) are less readily available.

From facility-level author calculations, the storage capacity of most PHS facilities in the US, Japan, and China range from 8 to 25 GWh per GW of installed capacity, cor-responding to a typical daily arbitrage cycle with spare capacity. In Europe, the storage capacity of 2500 GWh is dominated by Spain with 1530 GWh in 17 PHS plants comprising around 4.8 GW (Pirker et al. 2011).

From Pickard (2012), the ten largest capacity facilities in the US total 13.4 GW with 332 GWh of storage capacity, equating to 25 GWh per GW. If the storage ratio is extrap-olated to all 36 facilities, the storage capacity equates to 545 GWh.

PHS has historically operated in unison with coal and nuclear baseload. In the US, the deployment of PHS was relatively slow until the 1960s, but developed in parallel with nuclear during the 1960s and 1970s, and subsequently slowed in the 1980s when nuclear deployment came to a standstill (Yang and Jackson 2011). Baseload-PHS usu-ally operates with a daily arbitrage cycle between inflexible

overnight off-peak and daytime peak. The daily cycling maximises energy throughput for a given storage capacity and underpins the economic return for PHS, while provid-ing a demand sink for surplus off-peak baseload (Barnes and Levine 2011). Since the deregulation of electricity markets, the use of pumped hydro has expanded to cover a range of additional services, including rotational inertia, load following, frequency control, spinning reserve, and voltage regulation (MWH 2009).

At a global scale, other utility scale storage includes thermal storage (e.g. concentrated solar thermal) at 1.7 GW, which assuming 6 h storage equates to around 10 GWh. Other storage includes electro-mechanical (e.g. flywheel) at 1.4 GW, battery at 0.75 GW, and hydrogen at 0.003 GW (United States Department of Energy (DOE) 2016).

Connecting the Pre‑Industrial with the Contemporary Role of Storage

The question is—how to connect the pre-industrial or organic conception of surplus-storage with the role of storage within a modern electricity system? In the pre-industrial context, the value of storage was in guaranteeing survival during winter or times of poor harvest. Survival during austere times was more important than feasting dur-ing good harvests—favourable harvests were valuable to the extent that food could be stored for later consumption. This aligns with a marginalist interpretation of energy sup-ply (i.e. the first units of energy are the most valuable but there is a diminishing return to surplus energy).

In the contemporary interpretation, the guarantee of electricity supply is provided by the built infrastructure and a high ‘availability factor’ from a suite of generators. Modern electricity markets are founded on the concept of marginal cost pricing, in which there is always a surplus of available power. Generators with the lowest short-run marginal cost (SRMC) bid into the market first, with higher marginal cost generators providing load following.

Most of the value to residential and industrial consum-ers, and the cost of providing that service, is in the guar-anteed connection and available power flow, rather than the cumulative energy consumption per se. For example, the electrification of the United States during the twentieth century was a major contributor to rising productivity and output (Jorgenson 1984; Schurr 1990). Much of the produc-tivity gain from industry was a result of the ‘fact of elec-trification’ and the associated re-organisation of industrial production. In the early industrial period, the shift away from line-drive permitted organisations to adopt flexible and more efficient production processes (Rosenberg 1994). Electricity was a key enabler of innovation in industrial and consumer devices through the twentieth century.

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The value of storage is therefore proportional to the degree that storage contributes to the assurance of demanded power flow. Fortunately, the electricity supply industry has a suite of probabilistic reliability indices that fall under the broad categories of availability factor and forced outage (Billinton and Allan 1996). The availability factor defines the proportion of nameplate power that is available on demand, or in the case of VRE, the quantity of dispatchable power that is displaced (Preston 2015b).

Availability Versus Capacity Factor

The value of stored energy is evident in the availability factor versus capacity factor graph (see Fig. 2). Conven-tional generators convert stocks of energy into an elec-trical power flow. Providing the stocks are available, the energy converters (generators) can be dispatched in real-time, subject to technological and physical constraints, such as ramp-rate limits (the time rate of change of power output). In contrast, the capacity factor (often described as the full-load hours) describes the usage of the genera-tor, and is driven mostly by the short-run marginal cost (SRMC) for dispatchable generators, or the availability of the natural flows in the case of VRE (Tasman 2009).

From the perspective of an individual generator, con-ventional generation can be considered to have access to unlimited fuel storage within a planning time frame (i.e. sufficient fuel at all times), and therefore possess high availability factors. In contrast, solar PV without storage has a low availability factor, except to the extent that solar insolation coincides with annual peak demand (Denholm et al. 2015). The rising availability of the vari-ous types of concentrated solar is a direct result of solar tracking, solar field multipliers, built-in storage, and natural gas backup, which are reflected in progressively higher costs (International Renewable Energy Agency (IRENA) 2012).

Role for VRE Storage

From an operational viewpoint, the purpose of storage is to increase operational flexibility. From the perspective of VRE, the role of storage is to boost the availability—that is, a greater proportion of the nameplate power of the VRE is considered ‘firm’ when power is demanded. From the perspective of large thermal generators, the role of storage is to increase utilisation during off-peak periods (see Fig. 3).

Fig. 2 Graph of capacity factor versus availability factor with typi-cal ranges shown as whisker plots. Availability and capacity factor representative figures from Sims et al. (2011, Table 8.1), Sims et al. (2007), Bashmakov et al. (2014), Australian Energy Market Opera-tor (AEMO) (2012, Table 6), US Energy Information Administra-tion (EIA) (2015), International Renewable Energy Agency (IRENA) (2012, Table 1)

Fig. 3 Stylised effect of storage on baseload and variable renewable energy

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Methods

EROI Assessments Have Focused on Net‑Energy Only

In EROI assessments, storage is rarely, if ever, explicitly incorporated. This is probably due to the universality of storage being embedded within fossil fuels - the notion of stored sunlight is simply built into the energy sources (Schramski et al. 2015). Many analyses have incorpo-rated ‘energy quality’, or exergy, as a scaling factor in EROI analysis (e.g. Ayres and Warr (2010); Murphy et al. (2011)) to differentiate between the economic use-fulness of energy in its various states—a megajoule of electricity is more useful than a megajoule of coal.

Overview

The proposed framework requires calculating the embodied energy of the VRE-storage sub-system rela-tive to the displaced capacity of conventional generation. The methodology can be applied at any point along an energy transition pathway. In this context, conventional generation refers to thermal generation or hydro. The key concept is that the EROI of conventional genera-tion substitutes should be compared against the capacity they are replacing. In contrast, the conventional abate-ment approach is to consider the cost of displacing fossil fuel consumption as a consequence of applying environ-mental taxes or regulation on energy (Enevoldsen et al. 2009).

Reliability Metrics for Conventional Generation

The availability of conventional generation is approxi-mately proportional to the inverse of the forced outage rate (Billinton and Allan 1996, chpt.11). The forced outage rate is defined as the proportion of operating time that a unit is out of service due to unexpected problems or fail-ures. Since forced outages are usually uncorrelated between conventional generators (i.e. the distribution functions are independent random variables), the system reliability at a given power asymptotes towards 100% as additional gen-erators are added to the system.

To illustrate this, Fig. 4 plots the cumulative density function (CDF) (left) and probability density function (PDF) of a given capacity being available in any hour for a hypothetical suite of 10 generators, each 100 MW with a forced outage rate (FOR) of 0.1 (or equivalently, an avail-ability of 0.9). An FOR of 0.1 means that there is a 10% probability of an outage in a given hour. The plot has been calculated using the recursive convolution algorithm given in Preston (2016a). The left plot indicates that there is a 93% probability of at least 800 MW being available. The right plot indicates that there is 19% probability of exactly 800 MW being available. The CDF of annual demand is often referred to as the ‘load duration curve’. The electric-ity supply industry uses many indices to describe availabil-ity, including seasonal derating factors and planned mainte-nance [see IEEE (2007)]. In this context, availability refers to effective availability factor (EAF) in IEEE (2007).

The PDF, such as the right side of Fig. 4, can be com-bined with the projected demand function, to produce a probabilistic reliability assessment for each hour. The

Fig. 4 Available capacity curve for 10 generators, each 100 MW, and forced outage rate (FOR) of 0.1. Left—cumulative density function (CDF), right—probability density function (PDF)

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probability of demand exceeding supply for each hour is termed the loss-of-load-probability (LOLP). The LOLPs for every hour are added to give the loss-of-load-hours (LOLH) over the projected time frame (e.g. 1 year). Alter-natively, the LOLP for the peak hour only for each day can be added to give the loss-of-load-expectation (LOLE). There may be slight differences in methodology across regional operators. For example, LOLE can be expressed as either days/year, or hours/year (OFGEM 2013). An alterna-tive energy-based metric is the expected unserved energy (EUE), which is the sum of the unserved energy over all hours in a year.

Figure 5 illustrates this graphically for a stylised exam-ple of the Texas ERCOT grid over a year. The intersection of the areas bounded by the demand and supply curves gives the probabilistic LOLH. Note that this does not imply that demand will exceed supply but that there is a small but finite probability of unmet demand.

Since surplus generator capacity is costly, and outages are also costly, the optimal arrangement is to install only as much capacity as is required to provide a given (small) probability of unmet demand. In economics terminology, this can be framed as the intersection of the reliability-cost and reliability-worth curves (Billinton and Allan 1996, Fig. 1.3).

The standard criteria for generator reserve margin plan-ning vary between jurisdictions. The United States standard is a LOLE of ‘one day in ten years’ (North American Elec-tric Reliability Corporation (NERC) 2011). The ‘one day in ten years’ means that an outage (of any duration) should only occur on one day in 10 years on average. For compari-son, a LOLE of 2.9 h per year is used within the reliability

standards used by France, Ireland, and Belgium (OFGEM 2013). Australia applies an EUE standard of 0.002% of annual consumption (Australian Energy Market Operator (AEMO) 2013, Table 1). In this context, ‘outage’ refers to the adequacy of the bulk system and excludes outages due to local network interruptions due to storms etc.

The Analysed System

Reliability of Variable Renewable Generation

Renewable converters (e.g. solar panels, wind turbines) convert natural flows into electrical power flows, but have limited capability of controlling power on demand (although turning down is an obvious mode of control). Since renewable converters within a region are highly cor-related (i.e. it is sunny or relatively windy everywhere), VRE cannot be modelled as random independent variables using a convolution algorithm. Instead, they must be mod-elled as load reducers (Preston 2015b). The distinction between ‘adding to supply’ and ‘subtracting from demand’ may appear subtle, but is important because the value of VRE depends on both the supply and demand functions. VRE can be conceptualised as changing the shape of the PDF of demand (left side of Fig. 5). The degree to which VRE changes the LOLE is related to the degree that the rightward tail of the distribution is shifted leftward (i.e. the reliability value of VRE is dominated by the co-incidence of VRE with annual peak loads).

The first units of wind (or solar) produce the highest availability factor, with additional wind power subject to

Fig. 5 Stylised probability den-sity function (PDF) for Texas ERCOT system for a year. LOLE is equal to the intersec-tion of the areas bounded by the demand and supply curves

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diminishing returns. The reason for this is that if the first units of wind reduce peak loads, those hours are no longer peak hours, and the system reliability is dominated by other load hours not well served by wind (Preston 2015b). Therefore additional wind during those (now) non-peak hours reduces net-demand but does not contribute further to peak demand mitigation. This general property applies regardless of the composition of the grid. The sub-system of interest could also be composed of multiple VRE types (e.g. wind + solar PV), which would yield a composite den-sity function.

Therefore, the availability of a suite of VRE is defined as the reduction in net-demand as a proportion of nameplate capacity, at a given probabilistic reliability. For example, if 100 MW of wind power permits an additional 10 MW of demand at a given reliability level, the availability is expressed as 10% or 10 MW.

Charging Storage with VRE Overbuild

A key question is the quantity of storage and the VRE overbuild that is required to substitute for generation over and above inflexible or baseload generation. Storage must be accompanied by VRE overbuild in order to charge stores. Overbuild is defined as capacity that is surplus to demand—it can be defined at both a local or system level. For example, at a household level, rooftop solar that pro-duces surplus solar power could be either stored locally or exported into the grid. At a system level, wind power that exceeds a given usable proportion of system supply could be exported to an adjoining grid, stored, or curtailed (Den-holm and Hand 2011).

Marginal Returns to Storage

The first units of storage and overbuild are the most use-ful. The reason is that the PDF (i.e. the histogram describ-ing the number of annual hours for each ‘bin’ of power) for typical electricity systems is approximately bell shaped with a tail extending rightward representing short duration peak loads (see Fig. 5). Therefore a relatively modest quan-tity of storage can shift the PDF of generator availability leftward (right side of Fig. 5), ‘filling’ the highest peak loads that make up the rightward tail of the demand distri-bution. These peak loads are often the most valuable and easiest to fill, particular if the peaks tend to correlate with VRE power (e.g. solar power and air conditioning). How-ever, progressively greater amounts of energy must be used to fill the (new) peaks after the first peaks are filled. The marginal returns to storage aligns with market-based esti-mates of the value of storage [e.g. McConnell et al. (2015)] and net-energy-based assessments [e.g. Barnhart and Ben-son (2013)].

ERCOT Regional Grid

The framework will be applied to a suite of renewable simulations from Preston (2015a, 2016b), with reliability methodology described in detail in Preston et al. (1997), a simplified procedure in Preston (2015b) and detailed out-put files at http://egpreston.com/cases.htm. The simulations are for the Texas ERCOT network. ERCOT is one of nine independent system operators (ISO) in the US. It supplies electric power to around 24 million Texas customers. The ERCOT region has abundant wind and solar resources.

Fig. 6 Storage status for Preston simulation (left) for 1 Jan 2010–31 Dec 2010 and Henning and Palzer (2014, Fig. 4.5) gas storage (right). Nor-malised as days of full-load storage, based on annual average load

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The Preston simulations were modelled as time-series, demand-balance simulations using Fortran, optimised for least cost but constrained to VRE plus electrical stor-age, with a further reliability constraint (LOLE). Since the simulations were modelled with higher cost seasonal storage, they are not intended as a comprehensive cost-optimised solution to high-penetration VRE. Indeed, commercially available electrical storage is economic only for storage up to several hours (Luo et al. 2015, Table 12). Since this study is concerned with VRE plus storage, they provide a valuable tool to establish refer-ence points to demonstrate the framework. Furthermore, the magnitude of seasonal storage (of any type) at high-VRE penetration is consistent with the published sce-nario literature in relation to the days of full-load storage required (see “Storage requirements” Section and Fig. 6).

The simulations provide a base case, two intermedi-ate steps, and a 100% renewable simulation (see Table 1). They are based on projected demand in 2017, using his-torical wind and solar data from NREL, and hourly loads from 2010 to 2012. Wind and solar data were derived from NREL datasets. Wind was located along the coast, Panhandle, and west Texas. Solar sites included cen-tral Texas (Austin), south central Texas (San Antonio), and west Texas (Pecos). They are optimised for the US standard LOLE of 0.1. The intermediate steps and LOLE fit readily into the proposed framework, and provide multiple reference points for establishing framework parameters.

Preston has started with the incumbent ERCOT system, assumed zero wind power, but with 76 GW of fossil thermal generation and 5.15 GW of nuclear power (see Table 1). Each of the simulations traced 3 consecutive years with a maximum demand of around 71 GW and minimum 28 GW. Annual energy consumption is around 340 TWh. Addi-tional wind and solar PV was added, while thermal genera-tion was reduced, to maintain a LOLE of 0.1. Hence, the VRE has replaced the installed capacity of thermal plant at the given system reliability level. Nextly, storage was added while reducing thermal generation to also maintain a LOLE of 0.1. Figure 6 (left) displays the storage status for 2010

for the full year of the simulation. The right side is derived from Henning and Palzer (2014) as a comparison. In both cases, the requirement for seasonal storage is evident.

Embodied Energy of VRE and Storage

Embodied energy has been calculated for two storage types for comparison: PHS and Li-ion. These have been selected to provide low and high embodied energy references for commercially mature storage devices. PHS is the domi-nant form of electrical storage globally, providing a valu-able reference for a near lower bound for embodied energy and high round-trip efficiency. Li-ion is projected to expand and provides a distributed generation reference. In practice, PHS is not a large-scale option in Texas—Hall and Lee (2014) identified several possible PHS projects in Texas but these were relatively small. Compressed air energy storage (CAES) possesses intermediate embodied energy (Den-holm and Kulcinski 2004). Power-to-gas combined with gas turbine is a potential future seasonal storage option (Schiebahn et al. 2015), which could be assessed with the proposed framework.

In this study, embodied energy for storage is defined as the embodied energy per unit of storage (MJ per kWh of storage). There are uncertainties with Li-ion embod-ied energy, which is largely related to accessing firm-level energy consumption and production data. Furthermore, nearly all battery life-cycle assessments are carried out as process-based attributional assessments, which understate embodied energy relative to systematically complete hybrid assessments [see Crawford (2009)].

This study uses data from Ellingsen et al. (2014), and is based on primary data for a traction battery cell. Ell-ingsen’s medium ‘ASV’ value of 960 MJ/kWh was used, which is representative of the broader literature [e.g. Zack-risson et al. (2010) 790 MJ/kWh, Rydh and Sandén (2005, Table 6) 1510–1870 MJ/kWh for recycled and virgin materi-als, respectively]. The current learning rate (the cost reduc-tion following a cumulative doubling of production) of vehi-cle battery packs using Li-ion is 6–9% (Nykvist and Nilsson 2015). Barnhart and Benson (2013) explored the theoretical

Table 1 Reference VRE plus storage model from Preston (2015a)

Scenario Wind (GW) Solar PV (GW)

Storage (GW) Storage (GWh) Fossil Nuclear

Actual installed capacity at Dec 2015

16 0.3 0 0 72 5.15

Reference 0 0 0 0 76 5.15Case 4 24 24 0 0 63 5.15Case 6 68 76 50 16,500 0 5.15Case 7 76 84 54 18,970 0 0

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and resource constraints of battery manufacture and con-cluded that embodied energy of batteries could be reduced ‘at most by a factor 2 to 3’, but also note that the great-est potential for reducing the per-cycle embodied energy lies in increasing the cycle life (Barnhart and Benson 2013, Sect. 3.3). The recyclability of Li-ion packs is constrained by the complexity of cell and battery construction, and diffusion of elements throughout the anode, cathode, and electrolyte (Gaines 2014). Therefore the energetic benefit of recycling Li-ion chemistry is relatively low (Rydh and Sandén 2005, Fig. 4). Repurposing of EV battery pack cells for grid storage to extend their service life has also been proposed, with as yet uncertain commercial value (Neubauer et al. 2012). The repurposing has been described as ‘cascading reuse’ in the

life-cycle literature, and requires allocating the environmental burden across multiple uses. Various procedures have been proposed for allocation, including ‘quality based’, ‘cut-off’, and ‘50/50’ (Richa et al. 2015). The use of vehicle-to-grid storage would similarly entail allocation across multiple uses.

The embodied energy for PHS of 400 MJ/kWh is taken from Denholm and Kulcinski (2004). PHS is a mature civil engineering technology and therefore not subject to a pro-jected significant decline in embodied energy. An advantage of PHS is its long life of 60 years or greater.

The embodied energy for wind and solar PV were cal-culated from the respective assumed EROI data as shown in Table 2. For wind, a meta-review by Kubiszewski et al. (2010) estimated 19.8–25.2 in 2009. Since wind has pro-gressed in recent years, this study assumes 30:1. For solar PV, Raugei et al. (2012) calculated 5.9–11.8 for data from 2009 to 2011. Since solar PV has progressed significantly in recent years, this study takes a high-end estimate of around 25 from a review from Dale and Benson (2013). A sensitivity analysis was included with varying EROI for the wind and solar PV, with wind from 20:1 to 40:1 and solar PV from 15:1 to 40:1.

The embodied energy and resulting EROI are shown in Tables 3 and 4. In this work, the EROI is defined for the VRE-storage sub-system—EROI is not defined for storage as a stand-alone unit. EROI VRE−storage is equal to the ratio of the gross lifetime energy supplied by the subsystem E g , and the sum of the lifetime embodied energy of the VRE, E VRE and storage, E storage . The lifetime of the subsystem has been defined as 50 years. The benefit of applying this methodology is that it is possible to trade-off the energetic costs of stor-age with the value that storage provides, with different grid mixes, VRE penetration, and geographic regions.

(2)EROIVRE+storage =Eg

EVRE + Estorage

Table 2 Assumptions applied to embodied energy calculation for Texas

[1] Representative figure derived from global horizontal irradiance averaged between Austin and El Paso (The University of Texas at Austin 2016) [2] (Denholm and Kulcinski 2004) [3] (Ellingsen et al. 2014), use ‘ASV’ value [4] (Kubiszewski et al. 2010) meta-review ranged 15–30 years, most were 20 years, assume 25 [5] most studies assume 25 or 30 years, assume 30 years [6] assume 50 + years [7] Li-ion is cycled limited, cycle life is dependent on depth-of-discharge and capacity degrades with cycling, assume 10 years as high-end estimate for warranties [8] Kubiszewski et al. (2010) estimated EROI of 19.8–25.2 in 2009, assume improvement to 30:1. Embodied energy calculated from EROI data [9] taken as a high-end estimate from Dale and Benson (2013). Embodied energy calculated from EROI data

Wind Solar PV PHS storage Li-ion storage

Capacity factor 40% 18.7% [1] N/A N/AEmbodied energy 10,512 MJ/kW [8] 5911 MJ/kW [9] 400 MJ/kWh [2] 960 MJ/kWh [3]Modelled lifetime 25 years [4] 30 years [5] 50 years [6] 10 years [7]Lifetime EROI of VRE only 30:1 [8] 25:1 [9] N/A N/AAnnual energy production 3504 kWh/kW 1752 kWh/kW N/A N/A

Table 3 Aggregate embodied energy over 50-year time frame

50-year VRE embodied energy (PJ)

50-year storage embodied energy (PJ)

Wind Solar PV PHS Li-ion

Case 4 505 227 0 0Case 6 1430 718 6583 79,200Case 7 1598 794 7569 91,056

Table 4 Summary of 100% VRE with storage based on simulation model for 76 GW wind, 84 GW solar PV, 54 GW, and 18,970 GWh storage, for two types of storage, pumped hydro storage and lithium-ion

50-year embodied energy of VRE + storage (PJ)

50-year EROI of VRE + storage

PHS Li-ion PHS Li-ion

Case 7 9961 93,448 7.2:1 0.8:1

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Results and Discussion

Overview

Figure 7 depicts the embodied energy and marginal embod-ied energy for the generalised case with pumped hydro storage. Referring to the figure, the embodied energy (blue curve) below 14 GW (curtailment lower bound) was calcu-lated from Preston case 4 (zero storage) and assumed to be linear up to 14 GW. For greater than 14 GW, a trend curve was estimated from the method of least squares, applied to a second-order polynomial. The second-order polynomial was selected as the closest fit based on previous work with off-grid solar PV and storage (Palmer 2013). The R2 cor-relation was 0.9997. The solid curves are for an assumed EROI for wind of 30 and solar PV of 25 (see from Table 2). The shaded areas bounding the solid lines show sensitivity for varying wind and solar EROI, from wind at 20 and solar at 15, up to wind at 40 and solar at 40.

From the first derivative of the embodied energy curve, it is possible to estimate the marginal embodied energy, and from this, the marginal EROI. The marginal EROI refers to the EROIVRE−storage of the additional VRE storage required to provide an additional gigawatt of displaced capacity. Importantly, the marginal EROI varies depending on the penetration of VRE. Since the trend was assumed a second-order polynomial, the first derivative is necessarily linear.

As shown in Fig. 7, the marginal embodied energy per unit of displaced capacity is around four times higher at the high end-point of the line for PHS, but around 41-fold for Li-ion (not shown in graph). This implies that it is 4 to 41 times (PHS versus Li-ion) more energetically expensive to displace a gigawatt of conventional capacity at near-100% VRE versus low penetration VRE.

The sensitivity shows that the EROI of the wind and solar is much less important at high penetration since the embodied energy is dominated by the storage. This aligns with other studies that suggest it may be energetically pref-erable to curtail renewable energy at times, rather than store energy [e.g. Barnhart et al. (2013)].

The Embodied Energy and Marginal Embodied Energy Curves

From this work, the most important outcome is the shape and behaviour of the embodied energy and marginal embodied energy curves. The first units of VRE and stor-age have the lowest embodied energy (and therefore highest EROI). This implies that the first hour of storage produces the highest marginal EROIVRE+storage, the second hour, less so, and so on. Conceptually, VRE changes the shape of the ‘demand distribution’ density function (Fig. 5), and storage shifts the ‘generator availability’ density function rightward.

The ERCOT simulation is intended to demonstrate the framework, but different regional grids, assumptions, and simulations would provide different results. Furthermore, ERCOT is a single region in the US, and the simulation is not seeking to capture the benefits of geographic diversity. However, the qualitative relationships would be expected to be broadly similar—all large grids exhibit a similarly shaped load duration curve [e.g. Billinton and Allan (1996, Fig. 2.4)] giving the characteristic PDF shown in Fig. 5. Demand management technologies that address peak demand effectively ‘blunt’ the rightward tail of the ‘annual demand distribution’ (Fig. 5), shifting the right-hand tail leftwards.

Wind and solar PV exhibit broadly similar shaped CDF curves across different regions, shown in the bottom sec-tion of Fig. 8. The parameters and size will vary depending on geographic location but the characteristic shape will be similar.

Key Differences Between VRE‑Storage and Conventional Generation

There are two key differences between a VRE-storage sys-tem and a suite of conventional generators. In the case of conventional generation, the risk of outage between genera-tors is mostly uncorrelated. Therefore each generator can be treated as a random independent variable, cumulatively adding to system available capacity. In contrast, VRE of a particular type is highly correlated within a region, limiting the available firm capacity. Technology diversity and geo-graphic dispersion are two methods to de-correlate VRE availability.

Fig. 7 Embodied energy of VRE plus storage for PHS for ERCOT grid, displaced generation based on simulation by Preston (2015a)

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Fig. 8 Stylised representation of reliability measurement for system composed of conventional generation, VRE, and storage

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Nextly, conventional generation can be considered to have unlimited storage available from the perspective of an individual generator within a planning time frame. VRE does not have access to storage except through discrete storage devices. The devices can only be charged through overbuilding VRE capacity to produce a surplus at times of high wind speeds or solar irradiation.

Consequence of Diminishing Returns

Rising VRE penetration is subject to diminishing returns such that the marginal EROI may fall below the mini-mum useful EROI for society. Hall et al. (2009) identifies an EROI of around 3:1 as being the absolute lowest use-ful EROI for oil or corn-based ethanol to provide unsubsi-dised energy to society, but an EROI of 10:1 being required to deliver unsubsidised energy for a modern developed society. Lambert et al. (2014) suggest a societal EROI of 20–30:1 as being necessary for a high standard of living.

A shift from an electrical system based mostly on energy stocks to one based mostly on natural flows is handicapped by the energetic demands of surplus VRE and storage. From this case study, it is readily apparent that Li-ion bat-teries could only usefully contribute a short-term role to buffering VRE. However, the energetic requirements of PHS are sufficiently low to enable a greater penetration of VRE. Future storage devices may be energetically superior to PHS.

100% VRE Scenarios

In a prospective 100% VRE scenario, it is not necessary to consider the displaced capacity as shown in Fig. 7 since it is assumed that there is zero fossil fuel capacity. The sys-tem-wide EROIsystem is calculated as a standalone estimate for the given probabilistic reliability (e.g. LOLE = 0.1) as shown in Eq. 3, where EROIsystem is the ratio of the gross lifetime energy supplied by the entire system Eg, and the sum of the lifetime embodied energy of the VRE, EVRE and storage, Estorage.

Similarly, in scenarios that retain conventional capac-ity (e.g. hydro), the methodology can be used to assess the EROIVRE−storage of the non-conventional portion. Since demand-balance simulations in the academic literature are usually modelled without probabilistic reliability, it may be challenging to compare alternative scenarios on a like-for-like basis. Rigorous application of the methodology will require the incorporation of loss-of-load estimates into sce-nario models.

(3)EROIsystem =

Eg

EVRE + Estorage

In much the same way as scenarios can be compared based on cost (e.g. total system cost or $/kWh), they can be compared for system-wide EROI. A high EROI estimate would imply that the system is energetically viable from a net-energy perspective, and therefore capable of converting natural energy flows into demand-based electricity. A low EROI estimate (<10:1) would imply that the system is not energetically viable without access to an external source of inexpensive energy (i.e. the system is only energetically viable due to the importation of components from regions with inexpensive energy).

Availability of Storage

This study makes no assumptions about the prospec-tive availability of storage. To put the scale of the PHS into perspective, the US is the third leading country in pumped hydro storage, with an estimated 547 GWh of stor-age capacity, and Preston’s 100% VRE ERCOT scenario requires 18,970 GWh of storage. ERCOT (340 TWh) rep-resents around 9% of total annual US electrical generation (3764 TWh).

The methane or hydrogen storage option, via the power-to-gas and gas turbine pathways described in Palzer and Henning (2014), offers another prospective long-term path-way with as yet uncertain costs (Götz et al. 2016; Sterner 2009) and embodied energy. The high energy density of methane, stored either within pipelines, underground stor-age, or as liquefied LNG, potentially offers seasonal stor-age. For example, it has been estimated that the storage capacity of the German gas network is of the order of ‘hun-dreds of TWh’ (i.e. >300,000 GWh) (Sterner 2009, p. 105). The use of existing gas infrastructure would be favourable from a net-energy perspective. Götz et al. (2016) details the various technical and economic barriers that would need to be resolved for successful commercialisation, including the availability of CO2 sources, the dynamic behaviour of the various processes, a low round-trip efficiency, and high capital cost.

Conclusions

It was argued that energy storage, along with net-energy, is a fundamental property of the useful energy available to society, and can be described by the vector of surplus energy and storage. The examples of the development of agriculture, especially grains, provide an example of the value of storage. Despite the relatively high energetic demand of early agriculture, grains provided calories when they were needed—access to food during austere times was more important than feasting during good harvests. The early Industrial Revolution provides a similar lesson.

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Early coal-fired steam was extremely inefficient and ener-getically expensive but gave access to essentially unlimited ‘stored sunlight’, providing copious quantities of power on demand.

These historic lessons inform a methodology for evalu-ating the value of variable renewable energy and storage in a modern context. The methodology is based on the EROI metric, which gives greater weight to the principles of ener-getics over market prices.

Much of the contemporary global shift to variable renewable energy is adding to the capital stock rather than substituting away from legacy infrastructure. This could be contrasted with earlier transitions in which the substituting energy source replaced legacy infrastructure and enhanced productivity. The case of coal substituting for wood, and oil for coal provides early examples. However, it may not be necessary to completely substitute to provide value. The addition of VRE to existing electrical grids reduces the fos-sil fuel that would otherwise have been consumed, thereby providing emission abatement, usually measured as the marginal abatement cost in $/tonne CO2Eq.

This study argues that a long-run transition is better measured by substitution of generation capacity. Through the use of demand-balance simulations and probabilistic reliability assessments, the embodied energy of alternative VRE-storage options can be plotted against displaced gen-eration to compare their efficacy in substituting for capac-ity. In a 100% VRE scenario, the system-wide EROI is calculated as a stand-alone estimate. The most important conclusion is that rising VRE and storage exhibit marked diminishing returns, and therefore the first units of storage are the least energetically expensive. Unlike conventional generation, which has access to essentially unlimited stor-age in the form of fuels, VRE is handicapped by the ener-getic demands of surplus VRE and storage.

The rate of diminishing returns is dominated by the embodied energy of the storage device. Pumped hydro stor-age is currently the dominant form of electrical storage, and gives a much shallower diminishing return than Li-ion. It is apparent that the EROI of a system reliant on Li-ion (and other similar electro-chemical storage devices) would rapidly fall below the minimum useful EROI for society. In principle, if regional topography and water availability per-mitted, the large-scale use of pumped hydro storage would permit VRE to displace a substantial proportion of conven-tional generation capacity. Since electrical storage is not currently economic for seasonal storage, the scenario lit-erature adopts lower cost strategies, such as demand man-agement, maintaining a significant share of conventional capacity, or assuming the ready availability of large-scale biomass. In the future, other storage options may emerge, such as methane via the power-to-gas and gas turbine pathways.

The goal of this study was to introduce a framework for exploring the role of storage with net-energy and stimulate further research. Further work should include renewable simulations across different regions that are tailored to net-energy analysis and loss-of-load reliability metrics.

Acknowledgements The author would like to thank Roger Dar-gaville of the Melbourne Energy Institute at the University of Mel-bourne for valuable comments and feedback. Thanks also to the anonymous reviewers for their valuable comments that substantially improved the paper.


Conflict of interest The author declares no conflict of interest.

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Chapter 6

An exploration of divergence in

EPBT and EROI for solar

photovoltaics


6.1.1 Introduction

Solar photovoltaics (PV) is widely regarded as one of the most promising renewable energy technolo-

gies. However, there is a large divergence in EROI and energy payback time (EPBT) across studies.

This contributes to ongoing uncertainty in relation to the sustainability, and energetic value of PV.

Although this study focused on solar PV, the same general lessons hold for other renewable energy

technologies.

This study undertook a review of the primary and review literature and make an original con-

tribution to explaining the underlying methodologies. It finds that most of the apparent divergence

between studies is accounted for by six factors – life-cycle assessment methodology, age of the primary

data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy

forms, and assumptions about real-world performance. The apparent divergence in findings between

studies can often be traced back to different goal definitions, which are not usually explicitly identified.



97

1. What are the underlying reasons for the wide divergence in EROI in renewable energy studies?

2. How significant are these factors?

3. What is the magnitude of each of these factors?

4. How do these factors relate to energy transition feasibility assessments?

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Vol.:(0123456789)1 3

BioPhysical Economics and Resource Quality (2017) 2:15 https://doi.org/10.1007/s41247-017-0033-0

ORIGINAL PAPER

An Exploration of Divergence in EPBT and EROI for Solar Photovoltaics

Graham Palmer1 · Joshua Floyd2

Received: 19 September 2017 / Accepted: 16 November 2017 © Springer International Publishing AG, part of Springer Nature 2017

AbstractSolar photovoltaics (PV) is widely regarded as one of the most promising renewable energy technologies. Net energy analysis (NEA) is a tool to evaluate the energetic performance of all energy supply technologies, including solar PV. Results across studies can appear to diverge sharply, which leads to contestation of NEA’s relevance to energy transition feasibility assess-ment and contributes to ongoing uncertainty in relation to the critical issue of the sustainability of PV. This study explores how PV NEA approaches differ, including in relation to goal definitions, methodologies and boundaries of analysis. It focuses on two principal NEA metrics, energy return on investment (EROI) and energy payback time (EPBT). Here we show that most of the apparent divergence between studies is accounted for by six factors—life-cycle assessment methodology, age of the primary data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance. The apparent divergence in findings between studies can often be traced back to different goal definitions. This study reviews the differing approaches and makes the case that NEA is important for assess-ing the role of PV in future energy systems, but that findings in the form of EROI or EPBT must be considered with specific reference to the details of the particular study context, and the research questions that it seeks to address. NEA findings in a particular context cannot definitively support general statements about EROI or EPBT of PV electricity in all contexts.

Keywords Solar PV · EROI · EPBT · Net energy

Introduction

Summary

Net energy analysis (NEA) is a tool used to evaluate the energetic performance of energy supply technologies. The two net-energy metrics commonly applied to electricity generation technologies are the energy return on investment (EROI) and energy payback time (EPBT). EROI for electric-ity generation has been widely studied, especially coal-fired electricity, including carbon capture (Wu et al. 2016; White and Kulcinski 2000), wind power (Kubiszewski et al. 2010), solar photovoltaics (Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016) and gas-fired generation (Moeller and Murphy 2016). The boundaries and types of analysis vary between studies, but all those just cited adopt the electricity

busbar or inverter output as the EROI numerator—electricity distribution and management of the grid system as a whole is typically excluded from the analysis boundary.

Net energy analyses for solar photovoltaic (PV) systems have mostly conformed to mainstream life-cycle assessment (LCA) guidelines, with different values often assumed for key performance parameters, such as insolation and operat-ing life. Further reinforcement of a standard methodology was provided by the IEA PV Power Systems Programme (IEA-PVPS) guidelines (Frischknecht et al. 2016). Since the IEA-PVPS guidelines are seen by many investigators as the consensual outcome of debate over methodology, this study adopts them as its reference point for standard practice.

The principal benefit of standard guidelines is that they permit like-for-like comparison between different types of solar PV system (e.g. between systems employing different cell technologies), and assessment of the variance between different production contexts for systems of the same type. Many of the differences in findings between conventional analyses can be accounted for via meta-analyses that har-monise for key performance parameters (e.g. Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016).

* Graham Palmer [email protected]

1 Australian-German College of Climate and Energy, The University of Melbourne, Parkville 3010, Australia

2 The Rescope Project, Melbourne, Australia

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The limitation of a standardised methodology is that stud-ies are then restricted to answering the range of research questions to which that methodology is suited. An emphasis on improved harmonisation between studies may come at the cost of excluding energy investments that are important for understanding the broader socio-economic consequences of providing an increasing proportion of final energy supply via PV electricity. Furthermore, considerations relating to the engineering-systems view of electricity supply, which are critical to establishing the value of solar PV at higher grid penetration, are generally treated as lying outside the domain of conventional life-cycle research.

This study explores the differing approaches to under-stand why there is apparently such divergence in EROI find-ings. We show that much of the difference between stud-ies can be attributed to six factors—life-cycle assessment methodology, age of the primary data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance.

Definition of EROI and EPBT

EROI is a unitless ratio, defined as the ratio of the gross energy output over the operating lifetime for an energy supply system, and the sum of the energy for manufacture, construction, operation and maintenance, decommissioning and disposal/recycling over the system’s project life-cycle (Murphy et al. 2011, Eq. 1). Murphy and Hall (2010) state that ‘EROI is the ratio of how much energy is gained from an energy production process compared to how much of that energy (or its equivalent from some other source) is required to extract, grow, etc., a new unit of the energy in question.’ EPBT is the length of time, in years, for a PV system to generate the same amount of energy (in terms of primary energy equivalent) that was used to produce the sys-tem itself (Frischknecht et al. 2016, Sect. 3.4.2). The energy invested (embodied energy) is established by LCA. For a PV system, this is the same as the cumulative energy demand (CED). However, the CED of PV electricity differs markedly depending on whether the ‘energy harvested’ or the ‘energy harvestable’ concept is applied (see “Output Energy form Equivalence in the Context of PV NEA” section). Energy investment and output can be expressed in different forms depending on the goal of the study, as discussed in detail in “Equivalence of Investment and Output Energy Forms” section.

(1)EROI =Eout

Einv

(2)EPBT =Einv

Eoutyr

,

where Eout is the life-cycle electrical energy delivered by the PV system at the inverter output, Einv is the life-cycle energy investment and Eoutyr

is the annual primary energy equivalent

output (see “Equivalence of Investment and Output Energy Forms” section).

Goal Definitions

Defining LCA Goal and Scope

ISO 14040 (ISO 2006) sets out the requirements for LCA goal and scope definition. The scope definition reflects the underlying purpose of the LCA. Since EROI is defined as the ratio of ‘energy out’ to ‘energy investment’, the goal and scope may sometimes be interpreted as self-evident. Stud-ies published in the LCA-specific literature generally adopt more explicit goal and scope definitions than has been typi-cal for EROI-focused NEA studies.

Goal Definition in the Context of NEA

Carbajales-Dale et al. (2015) identified three applications of NEA with distinctly different goal definitions:

1. descriptive assessment of the viability of a particular technology (e.g., solar PV satellite);

2. comparative assessment of alternative energy technolo-gies; and

3. calculation of the (minimum) EROI to support an indus-trial society, or alternatively assessing the feasibility of some technology to (single-handedly) support an indus-trial society.

For the present study, we reframe and expand on these appli-cations and their respective goal definitions as follows:

1. Energy in, lifetime energy out. Most life-cycle-focused EROI analyses adopt the ‘basic net-energy’ goal defini-tion of ‘lifetime energy out’ versus ‘life-cycle energy in’. The functional unit is typically 1 kWh (or alternatively, 1 MJ) of AC electricity delivered to the grid. Further-more, most adopt a process-based LCA methodology, which focuses on the energy-intensive production pro-cesses. Within this broad category, there are slightly different approaches to boundaries. System-level con-siderations, such as the functional role of PV within an electricity grid treated as a whole, lie outside of the scope. Examples: Fthenakis and Kim (2011), Alsema (2000), Leccisi et al. (2016).

2. Energy in, dispatchable equivalent out. Considers an expanded role beyond the lifetime electricity generation. Includes PV overbuild and storage to provide an equiva-

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lent role to dispatchable generation such as gas turbine or hydro. Does not consider system-level requirements within a broader suite of generation, including geo-graphic and technology diversity. Defining the mag-nitude of PV overbuild and storage that provides an ‘always available’ role is fraught, and understates the value of PV in other contexts. Example: Weißbach et al. (2013), see also Weißbach et al. (2014), Raugei (2013), Raugei et al. (2015).

3. Comparative assessment in relation to substitution of generation capacity. Considers an expanded role beyond the lifetime electricity generation. Includes the embod-ied energy of storage and solar PV overbuild. Consid-ers the value of generated electricity and the degree to which solar PV substitutes for generation power capac-ity. The functional unit is 1 kWh of AC electricity deliv-ered to the grid, but concern for the energy cost of 1 kW of supply capacity is implicit in the study context. Permits a trade-off between storage capacity and power capacity substitution. Considers the role of solar PV within a suite of different generation types. Requires technical and reliability analysis of electricity systems and geographic and technology diversity. Examples: Palmer (2013, 2017).

4. Comparative assessment in relation to fossil-fuel con-sumption. Compares the life-cycle embodied energy with the fossil fuels displaced over the lifetime of the PV system. The focus is on the substitution of fuels rather than substitution of capital infrastructure. The functional unit is 1 kWh of AC electricity delivered to the grid, though this is implicitly a proxy for an equivalent quan-tity of fossil fuel displaced from conventional thermal generation. No weighting applied to energy out on the basis of system-level considerations. Examples: Raugei et al. (2012), Dale and Benson (2013).

5. Comparative assessment in relation to greenhouse gas emissions. The greenhouse gas emissions embodied in the manufacture of solar PV systems are estimated using life-cycle inventories. Most greenhouse-focused analy-ses adopt a similar framework to approach (1). Func-tional unit is CO2-equivalent emissions per kWh, with no weighting of lifetime energy out. Example: Nugent and Sovacool (2014).

6. Comparative assessment in relation to substituting for all primary energy. A conceptually and technically demand-ing goal definition, based on the functional unit of 1 MJ of final energy service (e.g. work or heat of various forms, or some mix of these) delivered to the ‘rest of the economy’ by the overall economy’s energy supply sub-system. Hypothesises the substitution of solar PV electricity for incumbent fuels and their associated sup-ply infrastructure. May include, for example, considera-tion of solar PV electricity as a transport fuel including

conversion to liquid fuels, and possibly broader electri-fication of final energy uses currently reliant on direct use of liquid, gaseous or solid fuels.

Analysis Boundaries

Figure 1 provides a conceptual overview of the range of boundary definitions used across PV NEA studies. Boundary definition varies depending on study goals. As such, it could be considered as a ‘meta-factor’ in terms of its implications for the apparent divergence in study findings. However, in terms of the range of factors identified in this study, the implications of boundary definition flow through directly to (i) the selection of life-cycle assessment methodology; and (ii) the treatment of intermittency (if this is considered at all). We therefore consider the effects of boundary definition specifically through the consequences for these two factors, rather than treating analysis boundaries as a separate factor in its own right. That said, there is no essential relationship between the analysis boundary and LCA methodology. Stud-ies with the same boundary could employ different method-ologies, and studies with different boundaries could employ the same methodology. As such, recognising that analysis boundaries differ between studies for legitimate reasons, and taking into account the specific boundary employed in any given study, is essential for accurately interpreting NEA study findings.

In this study, the ‘Level 2’ boundary from Raugei et al. (2016, Sect. 3.4.4) is adopted as the conventional frame of reference with which to compare alternative definitions. This boundary may be defined either as cradle-to-gate or cradle-to-installation, capturing the most important direct energy inputs of the solar PV panel manufacturing process chain, including the related ‘balance of system’ (BOS) com-ponents, comprising inverter, wiring and support structure, and possibly end-of-life energy inputs. This study focuses on crystalline silicon PV cell technologies because they com-prise ∼ 94% of global production (IEA 2016b, p. 5).

Detailed Investigation of Factors Contributing to Divergence

Life‑Cycle Assessment Methodologies

Process‑Based LCA

The most commonly adopted LCA technique, and that rec-ommended by the IEA-PVPS Programme, is attributional, process-based life-cycle assessment (ALCA) (Frischknecht et al. 2016, p. 6). Attributional approaches contrast with con-sequential approaches. “Attributional Versus Consequential

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LCA” section discusses the distinction between these, and the contexts for which each is most relevant.

In process analysis, the focus is on identifying the most energy-intensive stages in the process chain. In the case of crystalline silicon, the direct energy inputs for the major pro-duction processes are shown in Table 1. For example, silica sand undergoes carbothermic reduction, driven by heat and reducing agents in electric arc furnaces, to produce metal-lurgical grade silicon, consuming 11 kWh electricity per kg of product. The metallurgical grade silicon is processed via the Siemens process to produce PV grade silicon, and so on.

Alternative pathways are also available, including the Elkem pathway, which bypasses the Siemens process (Glöckner and de Wild-Scholten 2012).

The researcher steps through the process chain, identify-ing the process-specific data. Firm-level data are required but are often hard to acquire—a key objective of IEA-PVPS ‘Task 12’ is to gather and compile life-cycle inventory data (Frischknecht et al. 2015a). In the case of PV modules, each of the processes is based on the primary flow of wafer-based materials, but differences in process implementation and operational characteristics from firm to firm will result in

Silica sand

Metallurgical grade Si

Si - production mix

Czochralski- mc-Si

Solar grade Si - modified Siemens

process

Electronic grade Si

Multi-Si Single -Si

Multi-Si PV Single -Si PV cell

Multi-Si PV panel/

Single -Si PV cell

Aluminium, glass, EVA, copper, other

electricity

electricity,wood chips

coke, charcoal

electricity, natural gas

Indi

rect

ene

rgy

&

capi

tal g

oods

IEA-PVPS boundary

Inverter, etc.

PV system hardware

Admin

Insurance

Network integration

Faulty modules, inverters, tracking

OPERATION & MAINTENANCE

Operation & maintenance

Module washing

CONSTRUCTION

Inverter/busbar output

Accesses, fences

Foundations for mounts

Security & surveillance

PV Integration

electricity

electricity electricity

electricity electricity

electricity

Rights-of-way

electricityelectricity

electricity

Trades & fairs

INSTALLATION

ADMIN.

direct & indirect energy direct & indirect energy

electricity, natural gas

Electrical network extension

Offices, HVAC

Vehicles

direct & indirect energy

UP

ST

RE

AM

TR

UN

CA

TIO

N

SIDEWAYS TRUNCATION

Electrical storage

Curtailment

Thermal cycling losses

Force majeure

Faulty panels, inverters, trackers

Capacity firming

Lifetime generation

Availability- adjusted output


DO

WN

ST

RE

AM

TR

UN

CA

TIO

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Installation

Tran

spor

t

diesel, bunker fuel

Installed PV system

Distribution network

capacity = ƒ(storage)


Degradation

IEA Performance Ratio

Energy losses

END-OF-LIFE

Decommissioning

Disposal

Reduction in assumed output

Complete boundary with input-output/hybrid analysis (e.g. Yao, Zhai)

IFIA

S le

vel 3

- p

lant

Note: new Elkem process

Complete boundary including integration & storage

Cradle to gate boundary (Bhandari et al.)

Broader boundaries for installation and administration (Prieto & Hall)

+2 to 5 %

Extended booundary may include 30 to 50%

of embodied energy

Distribution losses

Aggregation error - potential for overestimating embodied energy

with I/O

Storage

Cradle to installation boundary

Delivered electricity

Fig. 1 Definition of boundaries for this study for solar PV life-cycle assessments. Green coloured regions indicate conventional boundaries rec-ommended by the IEA-PVPS Programme. Size of regions not related to magnitude of energy investment

Table 1 Direct energy inputs for major production processes, for mono- and multi-crystalline silicon wafers Source Frischknecht et al. (2015a)

Energy inputs vary depending on region, and change with technology development and production learning

Process input Process output Major process Major process energy inputs

Silica sand Metallurgical grade silicon (MG-Si) Electric arc furnace at 2000–2200 °C 11 kWh electricity per kg MG-SiMG-Si PV grade silicon (SoG-Si) Siemens process 110 kWh electricity and 185 MJ

natural gas per kg SoG-SiSoG-Si Mono-Si Czochralski process (mono-Si) 68 kWh electricity per kg mono-SiSoG-Si Multi-Si Casting and crystallisation process (multi-Si) 56 kWh electricity per kg multi-SiMono- or poly-Si Single wafers Wafering process 75–93 kWh electricity per m2 Si

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variations in material and energy flows at a more detailed level. Depending on time and effort, the researcher contin-ues with a detailed assessment for each process. Ideally, all flows should be followed until they are elementary—i.e. to the boundary between the technical system and the natural system (Tillman 2000). However, since the number of con-nections in the ‘energy flow web’ rapidly accumulates as the researcher proceeds through the upstream processes, there is a practical need to ‘prune’ sub-branches that are deemed insignificant (Suh and Huppes 2002). Furthermore, the importance of each energy flow rapidly diminishes through indirect relations, requiring greater effort for diminishing significance.

Truncation Error

The practical necessity to adopt a finite boundary leads to the omission of contributions that lie outside this bound-ary. The magnitude of these contributions is termed trunca-tion error. There are broadly three types of truncation error (Crawford 2011):

1. Upstream truncation. This includes higher-order (or background) processes, such as products further up the value chain, or capital goods, including production equipment. PV studies often include an allowance for ‘capital plant’, but process-based analyses do not com-prehensively account for capital goods.

2. Sideways truncation. This includes the omission of minor goods or services that are not part of the main process chain, such as inputs associated with office administrative costs. These costs are generally of low energy intensity and comprise a small (but in aggregate, material) proportion of the overall energy footprint. The IEA-PVPS LCA guidelines (Frischknecht et al. 2016, Sect. 3.2.3) recommend against including administra-tion, marketing, and research and development. Prieto and Hall (2013) investigated such costs, including plant accesses, administration, insurances and promotions among others, and found them to account for a signifi-cant proportion of their total inputs.

3. Downstream truncation. This is usually defined as the exclusion of processes in the ‘use’ and ‘end-of-life’ phase. In this study, ‘downstream’ is defined as the additional inputs that lie beyond the busbar or inverter. These are explored further in “Treatment of Intermit-tency” section.

Energy-intensive products generally carry the lowest trunca-tion error since most of the energy investment is embodied in a limited number of direct and first-order inputs. Services exhibit higher truncation error because much of the energy footprint is in higher-order (or background) paths. Lenzen

(2000) notes that most goods carry a truncation error of the order of 50%.

Level of ‘Completeness’

Since the ISO 14041 standard for goal and scope defini-tion does not define system boundaries as absolute, but as dependent on the goal of the study (Lenzen 2000), there is no requirement to ensure that the analysis meets a pre-scribed level of ‘completeness’. ISO (1998, Sect. 6.4.5; 2006, Sect. 5.2.3) requires that stages, unit processes or inputs are followed until they ‘lack significance’ within the given scope. This can be problematic when applied to NEA since a high level of ‘completeness’ is often assumed by NEA practitioners applying LCA data.

Benefits of Standard Boundaries and Methodology

The consistent treatment of system boundaries is useful for comparison of findings between studies with similar goals. For example, Bhandari et al. (2015) collected 232 refer-ences for PV studies published between 2000 and 2013, the vast majority of which adopted conventional LCA-based boundaries. This biases meta-analyses towards conventional boundaries.

If all products within a product class are used within a similar context, the truncation associated with defined boundaries is less important than establishing the differences between products. For example, in considering the life-cycle differences between timber and concrete railway sleepers, it may not be necessary to consider the life-cycle energy of the steel tracks or installation, since these are common to both types of sleepers. However, if the study goal was to com-pare rail freight to road freight, then much wider boundaries would be required.

The level of completeness need not be a limitation pro-vided the goal definition is stated clearly and results are presented with appropriate qualifications. However, results that have been obtained with a process-based framework are often presented as though they represent a comprehensive inventory of all energy investments. The common use of expressions such as ‘cradle-to-grave’ and ‘full life-cycle’ implies a high degree of completeness, but is only accurate in the context of the process-based methodology.

Attributional Versus Consequential LCA

The objective of ALCA is to track energy and material flows using a bottom-up accounting approach, for the purpose of attributing energy and material quantities to a unit of product or service delivered at the analysis boundary (Tillman 2000). This allows comparison of functionally equivalent products and services on the basis of embodied energy, materials and

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pollutants. Consequential life-cycle assessment (CLCA), on the other hand, involves estimating how flows to and from the environment would be affected by the adoption of par-ticular products or services, or the substitution of alternative products or services for those currently in use. This requires broader boundaries, which may overlap with other LCAs and potentially lead to double counting of energy inputs. The IEA-PVPS LCA guidelines recommend a system bound-ary ending at the inverter output (Frischknecht et al. 2016, Sect. 3.2.2). The marginal or consequential changes in net-work costs due to intermittency are not generally included (Jones et al. 2016, Sect. 3.1.2). The guidelines argue that ‘aspects of dispatchability or intermittency’ should be addressed at a system level rather than at a technology level (Frischknecht et al. 2016, p. 9). The third edition of the IEA-PVPS LCA methodological guidelines has maintained its recommendation of process-based ALCA, but has suggested that a consequential approach should be adopted for inves-tigating a large-scale, long-term energy supply transition (Frischknecht et al. 2016, Sect. 3.2.1.d).

Input–Output and Hybrid LCA

An alternative to the process-based approach is the use of economic input–output (I/O) tables with a satellite energy account, classified as environmentally extended input–out-put analysis (EEIOA) (Suh and Huppes 2005). I/O analysis connects energy flows to monetary flows using national I/O tables, which are a comprehensive account of national mon-etary flows. Analyses can be expanded with multi-region I/O tables to account for imports and exports. Since financial data are usually more commonly available than energy-based data, they enable researchers to access industry informa-tion that may otherwise be difficult to obtain. Furthermore, EEIOA is systematically complete—all energy within the region/s of the analysis is included. The main weakness is that I/O tables combine products that are heterogeneous in terms of energy inputs, introducing aggregation error. Other weaknesses of I/O analysis include excessive age of data, inconsistent classification schemes and inadequate docu-mentation (UNEP/SETAC 2011).

The respective benefits of process and I/O analysis—spec-ificity for the former and completeness for the latter—can

be combined through the use of hybrid analysis. There are three broad types of hybrid analysis (Suh and Huppes 2005):

1. tiered-hybrid approach;2. IO-based hybrid approach; and3. integrated hybrid approach.

In a tiered-hybrid approach for example, some important direct requirements are examined with a detailed process analysis, while higher-order requirements that are less easily tracked are covered with an I/O analysis (Crawford 2011, p. 53). In the PV NEA literature, very few studies have adopted a hybrid approach, and for those that do, embod-ied energy values significantly higher than for comparable process-based analyses are calculated—see Table 2.

We note here that since I/O analysis uses financial costs as the basis for determining energy inputs, results are sensi-tive to the energy intensities attributed to different cost com-ponents. It is sometimes assumed that embodied energy and PV prices (and by inference, the installed cost incurred by PV plant owners) should exhibit a strong positive correlation (Bhandari et al. 2015, p. 140). However, differences between financial costs incurred in manufacture, and prices paid by owners, can lead to anomalous results.

Several factors have contributed to declines in prices that do not reflect corresponding reductions in embodied energy. ‘Soft costs’ are defined as the costs associated with regula-tion and compliance (IEA 2016b, pp. 43, 57), and comprise from around 10% of system costs (e.g. Spain), up to around 60% (e.g. the US and Canada) (IEA 2016b, Fig. 26). These costs relate to low energy intensity administrative functions, which are independent of the PV production system and its energy investments. The greater the contribution that soft costs make to overall price reduction over time, the weaker the correlation will be between installed prices and embod-ied energy. As such, declining cost of ownership may be a poor proxy for embodied energy reduction.

A similar issue arises in relation to the effect of market distortions on PV system price. For instance, overcapacity in Chinese PV production has led to dumping in several markets, starting from around 2011 (European Commis-sion 2017, Sects. 4.7, 3.3.2). Some jurisdictions, including the EU, implemented anti-dumping measures (European

Table 2 Comparison of hybrid versus process-based LCA for the same or comparable projects

Study Hybrid LCA Process-based LCA

Building integrated photovoltaic systems Crawford et al. (2006, Table 1) 41.0 GJ (total) 18.4 GJ (total)

Major Chinese manufacturers Yao et al. (2014, Table 3) 4.7-year EPBT 1.5–2.6-year EPBT

Multi-Si PV System in 2007 Zhai and Williams (2010, Table 7) 4.4 GJ/m2 2.7 GJ/m2

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Commission 2017, Sect. 1.1). The further markets deviate from the ideal of perfect competitive behaviour, the weaker the correlation is likely to be between price and embodied energy. Comparison of experience curves for price and CED makes apparent the overall effect of considerations such as these—Louwen et al. (2016, Fig. 3) show a learning rate of 20% for selling price and around 12% for CED. If an LCA methodology makes use of an assumed relationship between cost of ownership and embodied energy in order to calculate energy inputs, different assumptions about the strength of any correlation would contribute to divergence in values for EROI and EPBT.

Primacy of Precision or Comprehensiveness Depends on Study Context

Sonnemann et al. (2013, p. 1171) differentiate between ‘traditional’ process-based data and ‘adaptive’ approaches, including I/O and hybrid. While the I/O and hybrid approaches are recognised in the IEA-PVPS LCA guidelines (Frischknecht et al. 2016, p. 6), the guidelines recommend against the I/O approach due to a lack of confidence, citing Sonnemann et al. (2013) and UNEP/SETAC (2011). How-ever, UNEP/SETAC (2011, p. 97) note that ‘LCAs should use the most appropriate datasets and modelling approaches to meet the specific goal and scope required to satisfactorily answer the questions posed.’

The IEA-PVPS LCA guidelines endorse the ‘traditional’ process-based approach, emphasising the primacy of data-sets based on ‘complete and verifiable documentation’ (Sonnemann et al. 2013, p. 1170). Furthermore, process

analysis is generally seen to be more accurate (within the given scope) and relevant. A counter argument here is that when the context for NEA is feasibility assessment for large-scale energy transition, providing a comprehensive account of the situation from a net-energy perspective may be a higher priority than data precision, especially if precision necessarily comes at the cost of narrowing the focus for data collection.

Age of Primary Data

Solar PV is a developing technology. Various manufacturing improvements have led to lower energy intensity produc-tion, including processing of metallurgical grade silicon to solar grade silicon, and more productive wafering processes. Louwen et al. (2016, Fig. 3) estimated a CED learning rate (per doubling of cumulative capacity) of between 11 and 13%. NEA findings are particularly sensitive to increases in energy output due to improvement in PV cell efficiency over time (Fthenakis, personal communication, 2 February 2017).

Since published studies utilise a mix of primary and sec-ondary data, the publication date may not reflect the age of the primary data. In some cases, studies cite secondary sources that themselves cited earlier primary data. For exam-ple, Ferroni and Hopkirk (2016) cited Kannan et al. (2006), who had adopted primary data from studies from 1997 and 2002 (i.e. the primary data were 14–19 years old at the time of publication). The use of older data (see Fig. 2) confounds the age-adjustment process for PV EROI and EPBT meta-analyses, unless the primary data sources are traced and adjusted accordingly (Koppelaar 2016).

Fig. 2 Reported cumulative energy demand (CED) data by year. Note logarithmic y-axis. Data from Louwen et al. (2016)

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Of the factors considered in this study, this stands out as potentially contributing to actual rather than apparent divergence in NEA findings, where studies are otherwise equivalent. If the purpose of a study is to consider present or future PV deployment, then the use of earlier data will lead to errors. If the purpose of a study is to investigate historical performance, then this may require the intentional selection of older data.

PV Cell Technologies

This study focuses on crystalline silicon (mono-Si, multi-Si) cell technologies, which account for ∼ 94% of global production (IEA 2016b, p. 5). Most of the remainder com-prises thin-film technologies, including amorphous silicon, copper indium gallium selenide (CIGS) and cadmium tellu-ride (CdTe). The so-called III–V semiconductor compounds with gallium arsenide (GaAs) or similar substrates allow production of high-efficiency multi-layer cells, but are cur-rently used in niche applications only. Process analyses for thin-film technologies calculate an EPBT of around half that of crystalline Si (Ito et al. 2016; Fthenakis and Kim 2011). Findings from a meta-study by Bhandari et al. (2015, Fig. 7) indicate mean harmonised PV module plus BOS EROI (fac-tory-gate boundary) for multi-Si approximately 33% higher than for mono-Si.

Treatment of Intermittency

A significant area of difference in NEA goal definition is the treatment of output power quality, specifically the dif-ference between PV electricity when an inverter bound-ary is assumed, and electricity from dispatchable sources. Since most studies consider the lifetime aggregate energy output only, issues associated with real-time electrical power characteristics are not usually assessed. If such issues are considered in the goal definition though, this can contribute significantly to the apparent divergence in findings.

Defining Reliability

The reliability of large electrical systems is defined by the loss-of-load-expectation (LOLE), which is the primary relia-bility metric for generation adequacy planning (NERC 2011; OFGEM 2013). The value of the LOLE metric for a given grid is prescribed based on priorities that are particular to the prevailing socio-political-economic values for the terri-tory in question. A developing country may place much less value on a high level of reliability than a developed country, depending on electricity consumers’ needs and expectations.

The LOLE is mostly a function of two factors: (i) the ‘availability factor’ of individual generators; and (ii) the pro-jected demand function of the power system. The availability

factor is defined as the inverse of the probability of a forced outage in a given period (Billinton and Allan 1996, Chap. 11). No single generator is completely reliable, but since forced outages are usually uncorrelated between generat-ing units (i.e. the distribution functions are independent random variables), the system reliability converges asymp-totically towards 100% with a large enough number of gen-erators. Within this conventional framework, the role of energy storage, such as pumped hydro storage (PHS), is to arbitrage between low-cost overnight baseload supply and higher-value peak load supply. The value of PHS is there-fore determined by the economics of arbitrage (Yang and Jackson 2011).

In contrast to thermal and hydro generators, PV output exhibits very strong correlation across geographic regions (i.e. it is simultaneously either day-time or night-time eve-rywhere across a region), and therefore individual PV sys-tems cannot be modelled as independent random variables. Instead, since the reliability contribution of PV is domi-nated by the correlation between PV output and demand on the peak-demand days, PV is often modelled as a demand reducer (Preston 2015b). The potential role of demand man-agement becomes more apparent in this context, since the value of PV is also dependant on the potential to voluntarily shed load or time-shift it to periods of high insolation.

The question of whether or not storage should be defined as falling within the PV system boundary arises because PV systems can be designed to exhibit an availability compara-ble with conventional generation when sufficient storage and PV overbuild is deployed. The issue then is not, as Leccisi et al. (2016, p. 4) suggest, whether a single generator can ‘single-handedly follow the dynamics of societal electricity demand’, but how PV, whether considered at the scale of individual systems in isolation, or collectively at the grid-scale, can contribute to system reliability.

Capacity Firming and Storage to Accommodate Temporal and Spatial Output Variability

Solar PV exhibits variability over timescales ranging from seconds to seasons, and at local, regional and national spatial scales (Sayeef et al. 2012). At a regional level, geographic diversity smooths short-term cloud flicker, generally reduc-ing aggregate output variability from all PV systems in a given region. However, weather systems can sometimes extend for thousands of kilometres, reducing output across entire regions (Huva et al. 2016; Sayeef et al. 2012). Sun-rise and sunset are each effectively co-incident for different locations at the regional scale. This results in almost simul-taneous diurnal ramp-up and ramp-down of output from optimally oriented PV modules across regions.

On a seasonal timescale, the ratio of the average monthly insolation between summer and winter varies greatly across

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geographic regions and latitudes (NASA 2017; PV Educa-tion 2016). Low-latitude regions, such as Singapore, show a relatively small summer-to-winter ratio of 1.3–1.7, rising to 3–4 in the mid-latitudes such as Nevada, USA, and above 10 for higher latitudes, such as London. Latitude is the primary determinant of the seasonal ratio, but regional climate fac-tors, including the cloud index, are also important.

At low penetration, variable output from solar PV is read-ily integrated into electricity systems (Gross et al. 2006). At higher penetration, maintaining system reliability is more challenging and costly, and, in addition to demand manage-ment, will require complementary flexible generation and storage (Sims et al. 2011, pp. 15–16).

Defining Integration Costs

Integration costs are usually defined as additional expenses associated with load following, the provision of ancillary services and curtailment (Kirby et al. 2003; Heptonstall et al. 2017). In most cases, the additional costs are small at low solar PV penetration, but will be material at a higher pen-etration; however, the relationship between penetration and integration costs is highly context specific (Heptonstall et al. 2017, Sect. 2.3).

Integration costs can also be conceptualised more broadly as the additional buffering, connection and ancillary service costs necessary for PV to provide a substantive role in a transition from fossil fuel to low-carbon electricity supply. The physical characteristics of conventional synchronous generators, including inertia and reactive power response, have provided some ancillary services by default, and so these have traditionally been uncosted. The retirement of synchronous generators will reduce the contribution of these uncosted services. Since solar PV and wind are asyn-chronous, the substitution of these electricity sources for synchronous generation will require some form of market mechanism or ‘security obligation’ to ensure that equivalent ancillary services are provided with rising variable renew-able energy (VRE) penetration (Finkel et al. 2017, recom-mendations 2.1, 3.3). Additional energy inputs associated with providing ancillary services by alternative means need to be included in NEA where study goals relate to large-scale energy transition.

Transmission Infrastructure

Transmission infrastructure consists of meshed networks that are shared by multiple supply and demand nodes. As such, high-voltage transmission infrastructure is usually assumed to lie outside the study boundary for electricity generation NEA [for exceptions, see Ito et al. (2008, 2016)].

Sometimes, however, dedicated transmission network extensions are required where new generation assets

(whether solar PV or otherwise) are situated geographically outside the existing grid boundary. Many of the most favour-able locations for solar PV lie in sparsely inhabited regions. For example, Ito et al. (2008) assumed that 100 km of trans-mission lines would be required to connect a 100-MW PV system to existing transmission, and found that transmis-sion infrastructure comprised between ∼ 10 and 15% of system CED over the lifetime of the project (Ito et al. 2008, Table 8). Furthermore, Ito et al. (2005) estimated losses due to transformer, reactive power compensation and transmis-sion line losses of 5.8–8.2% for a 100 km line in a hot desert.

In such situations, it is legitimate to ask whether the dedi-cated transmission infrastructure should be treated as falling inside the generation NEA boundary. LCA studies are usu-ally seeking to answer a narrower question than that posed by NEA studies, and therefore the question is resolved by simply stating the scope. However, if the goal of the NEA study is to assess the feasibility of a large-scale energy transition, then the additional transmission costs must be accounted for somewhere, whether attached to a genera-tion asset or considered as part of the broader system-level changes. The lifetimes of transmission assets are usually longer than generation assets. If included within the genera-tion boundary, this would require allocating the transmission CED across multiple PV lifetimes, mitigating the impact on EROI and EPBT.

High‑Penetration PV and Storage Scenarios

An estimate for the quantity of storage required to meet a supply–demand balance for a given period can be derived from studies with a high penetration of solar PV. However, much of the electricity system scenario literature avoids the problem of large-scale storage by maintaining a significant share of legacy thermal generation capacity at low capacity factor (Budischak et al. 2012), or by assuming the ready availability of large-scale biomass-fuelled thermal genera-tion (Lenzen et al. 2016). In the context of energy transi-tion feasibility assessment, we note that studies that reduce emissions while retaining legacy generation capacity involve fundamentally different goals to those focused on transition to 100% renewable electricity supply.

For scenarios where conventional thermal generation capacity is not retained (and where this is not replaced by biomass generation), the storage capacity required for a given level of reliability escalates rapidly with increasing VRE penetration, exhibiting a sharply diminishing return in terms of the reliability outcome obtained for each unit of energy investment (Palmer 2017). This is mostly due to the ‘big gaps’ problem of extended cloudy periods during winter (Lenzen et al. 2016). More generally, the shift from an electricity system based on ‘stored sunlight’ (i.e. fossil fuels) to the one based mostly on uncontrollably variable

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energy flows is constrained by the storage capacity required and the quantity of VRE overbuild necessary to maintain energy stores at adequate levels to ride through low insola-tion periods. Sufficient generation capacity overbuild can substitute to some extent for inter-seasonal variation in PV output. Regardless of storage medium though, in the absence of backup thermal generation capacity, for 100% renewable electricity supply with high PV penetration, some combina-tion of capacity overbuild and large-scale storage sufficient for inter-seasonal balancing of generation and demand is likely to be essential.

For example, in a 100% renewable simulation with wind and solar PV for Germany, 45 days of full-load electric-ity supply capacity (based on average annual demand) was required (Palzer and Henning 2014, Fig. 3.4). In a study encompassing Canada, USA and Mexico, Aghahosseini et al. (2016) found that around 14 days of supply capacity was required. Both of these studies assumed power-to-gas for inter-seasonal storage. Preston (2015a) modelled a set of wind, solar and storage scenarios for Texas, finding that 21 days of supply capacity was required.

Effect of Storage and Overbuild on NEA Findings

There is no single ‘best answer’ to the appropriate quantity of storage (if any) and solar PV overbuild. However, it is possible to state some general principles. Summer peaking grids benefit much more from solar PV than winter peaking grids, and a wide latitudinal diversity improves the value of solar PV when interconnected between regions. Relatively modest storage can improve the value of solar PV in some contexts, such as systems that have high air conditioning loads in summer. Palmer (2017) formulated a framework for calibrating EROI based on the generation capacity displaced, which provided a method to trade-off storage capacity versus the value it provided in a specific context. The low capacity factor and seasonality of PV results in strongly diminish-ing return with increasing solar PV penetration (i.e. the first units are the most valuable but as penetration increases, the marginal value of adding further capacity decreases). In win-ter peaking grids, the role of PV is restricted to displacing fuel consumption of thermal generators, rather than displac-ing their contribution to the generating capacity required by the overall system, unless inter-seasonal storage and/or a very high level of PV overbuild is implemented.

With reference to the goal definitions in “Goal Defini-tions” section, different study contexts explore different questions, and will arrive at different answers in relation to the quantity of storage required. Approaches (1), (4) and (5) do not consider system-level implications at all, and therefore do not consider storage. Approach (2) specifically assumes that ‘PV plus storage’ substitutes for dispatch-able generation, and therefore requires solar overbuild and

substantial storage. Weißbach et al. (2013, p. 213) adopted a 2-time solar PV overbuild, resulting in a halving of EROI. The EROI was reduced by a further ∼ 20 % due to the inclu-sion of pumped hydro energy storage.

Approach (3) takes the incumbent system as given and assumes that a supply system transition proceeds by incre-mentally substituting solar PV and storage for conventional generation capacity. For example, in summer peaking grids, at low PV penetration 2 h of battery storage without solar overbuild improves the network value of PV, but reduces the EROI by a modest 15% (based on data from Palmer 2017, Table 2). But at near 100% wind and solar penetration, the EROI of the last unit of solar PV with battery storage is reduced by 98.8% due to the problem of diminishing returns (Palmer 2017, Fig. 7).

Approach (6) considers the complete substitution of the fossil-fuel energy system by renewable sources, including for transport. As such, a range of alternative storage media may be considered, including liquid and gaseous fuels, with NEA accounting for the attendant conversion losses.

Equivalence of Investment and Output Energy Forms

Primary Energy Basis for LCA Energy Input Accounting

For all LCA methodologies, it is conventional to account for each energy input in terms of the primary energy required to make it available at the point where it enters the analysis boundary. In the broadest physical sense, primary energy is considered to be energy that is available from resources as they exist in nature: chemical energy of fossil fuels, gravita-tional potential energy of water in a reservoir, electromag-netic energy of sunlight, etc. (Nakicenovic et al. 1996). For the purpose of accounting for energy supply and use at the national and global level, the primary energy value of an energy source is typically treated as its ‘physical energy content’ at the point where it first becomes an economically useful ‘energy product’ suitable for multiple downstream purposes (IEA 2017). With combustible fuels in the form of wood, biomass and, most significantly, fossil fuels domi-nating energy trade, by convention, this type of high-level energy accounting has been recorded in thermal energy units, including BTUs or joules. For the purpose of most LCA studies on the other hand, the primary energy input from a given source is defined as that source’s life-cycle CED (Frischknecht et al. 2007b, 2015b).

Since electricity is a secondary energy carrier, each elec-tricity input is adjusted to account for the primary energy required for its supply. For instance, if a particular process in the PV production chain requires 1 kWh of electricity sourced from the grid at the point of manufacture, then this is accounted for as 1∕�grid kWh of primary energy, where

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�grid is the grid-average life-cycle efficiency. This is calcu-lated as the ratio of the annual electrical energy supplied to the total primary energy (renewable and non-renewable) har-vested from the environment for the operation of the grid in the same year (Raugei et al. 2016, p. 8). Where combustion-based thermal generation dominates supply, the life-cycle efficiency is slightly lower than the grid-average thermal efficiency (the ratio of electricity delivered to heating value of all fuel used) since combustion fuels constitute most of the life-cycle primary energy. However, grids composed of greater shares of nuclear and/or renewables may produce a markedly different grid life-cycle efficiency depending on the approach for determining the energy resource inputs.

Output Energy form Equivalence in the Context of PV NEA

While primary energy input accounting is a standard con-vention across LCA methodologies, there is no universal standard for establishing the energy content equivalence of different energy sources. Within the LCA literature, vari-ous conventions are followed for assigning primary energy values to different final energy carriers (Frischknecht et al. 2007b, pp. 31–32). In the case of solar PV, for which the natural resource is unlimited on a human time scale, but for which the conversion efficiency is low, it may be appropriate to register the amount of energy harvested rather than the amount of solar radiation ultimately required (Frischknecht et al. 2007a). Ecoinvent, a widely applied LCA databases, defines the energy harvested by a solar panel as equal to the electrical energy transmitted from the panel to the inverter (Frischknecht et al. 2007b, Sect. 2.2.1). However, several other LCA databases apply the ‘energy harvestable’ concept to solar PV, for which the renewable energy input is the amount of solar energy needed to produce the electricity generated by the PV module (Frischknecht et al. 2015b). From a LCA perspective, the most important considera-tion is to clearly state the methodology and assumptions for a given study context. However, where the study context relates to NEA, there is a potential for misalignment between NEA study goals and standard LCA practice.

Among the major agencies that report on national and global energy statistics, there are two conventions for deter-mining the primary energy equivalence of non-thermal renewables (Lightfoot 2007, Sect. 1.A.3; Grubler et al. 2012). A ‘substitution method’ is used by BP, EIA, IIASA and WEC. This is based on thermal energy equivalence and considers the fossil fuels displaced by renewables, giv-ing a conversion of 1 MJ PV electricity equals ∼ 3 MJ pri-mary energy. The ‘direct equivalent’ method is used by the UN, IEA and Eurostat. It adopts a one-to-one equivalence between electricity and primary energy (i.e. 1 MJ electric-ity equals 1 MJ primary energy). Within the LCA literature, no method corresponds exactly with the ‘direct equivalent’

method. However, the ‘energy harvested’ method used in LCA for determining the primary energy for PV electricity returns a numerically similar result.

Given that the output from solar PV systems is typically in the form of AC electricity delivered to grids supplied by a range of generation types, questions naturally arise in relation to how this output should be treated in terms of its equivalence to other energy sources. There are two distinct contexts in which questions relating to the energy content equivalence of the electricity output from PV can be consid-ered. The first relates to its equivalence to other electricity sources within the context of the grid for which a PV system is deployed. The second relates to the output electricity’s equivalence to the full range of energy sources required for manufacture and deployment of the PV system itself, and hence that are accounted for as energy inputs for the purpose of NEA. This includes a range of liquid and solid fuels, and electricity from grids other than that for which the PV sys-tem is deployed. This second context is discussed further at the end of this section.

In relation to equivalence questions arising in the first of these contexts, the IEA-PVPS Programme recognises two principal approaches: (i) accounting for the output in terms of the physical energy content of the electrical energy delivered; and (ii) accounting for the output in terms of the equivalent primary energy for the grid to which the PV system is connected (Raugei et al. 2016, p. 5). In the context of grids where combustion-based thermal genera-tion dominates, the second approach returns numerically similar results to the ‘substitution method’ described above in relation to conventions used by energy reporting agen-cies. It is, however, a methodologically distinct approach, and results diverge as the contribution of non-combustion generation sources increases. The IEA-PVPS Programme recommends the second approach, on the basis of what it describes as a ‘replacement logic’, where a unit of electric-ity from any source is considered equivalent to the primary energy required to produce a unit of electricity from the overall mix of sources for the grid in question (Raugei et al. 2016, p. 5). Where NEA indices are calculated on this basis, their values must be viewed as deployment-context specific, and findings interpreted accordingly.

In the PV NEA literature more broadly, three approaches to treating the equivalence of investment and output energy forms are recognised, as shown in Table 3. The IEA-PVPS Programme’s recommended method is shown as Method 1 (Frischknecht et al. 2016, pp. 16–17). Methods 1 and 2 are both forms of the ‘substitution method’ and give essentially the same result. Method 3 is the most common method in the wind power EROI literature (e.g. Kubiszewski et al. 2010), and is the alternative method recognised by the IEA-PVPS Programme (Raugei et al. 2016, Eq. 2, p. 8). It is also widely adopted in the PV EROI literature. Of the

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three meta-analyses considered in this study, all apply a ‘substitution method’ equivalence adjustment to either the investments or output, with primary energy to electricity conversion factors of 0.311 (Louwen et al. 2016) and 0.35 (Bhandari et al. 2015; Koppelaar 2016). A related study adopts 0.30 (Leccisi et al. 2016).

Since PV produces electricity, and most of the direct energy inputs in the manufacturing process are in the form of electricity (see Fig. 3), it could be argued that EROI should be expressed as the ratio of the electrical energy output to the electrical equivalent of all energy investments. Arithmet-ically, this requires expressing all inputs as el−eq (Method 2), or expressing the PV output as PE−eq (Method 1)—see Table 3.

On the other hand, some essential non-electrical energy inputs, while smaller in relative magnitude, may not be read-ily substituted by electricity. If an energy input is essential, but cannot in practice be provided by electricity, then a ques-tion arises as to how the scaling for an electricity-equivalent adjustment should be determined. Freight transport provides an interesting case in point. There is no imminent substitute for diesel for heavy vehicles and ocean-going shipping, or jet fuel for air transport (Sims et al. 2014, p. 615). Calculat-ing the electricity equivalent of a given quantity of liquid fuel would require the assumption of a suitable conversion pathway from electricity through to a synthetic fuel, such as hydrogen, and estimation of the ‘electricity-to-wheels’ (or equivalent, depending on end-use) efficiency for the full fuel cycle.

Implications for Interpreting PV NEA Findings

The appropriate method for treating investment and output energy form equivalence depends on the study context and the questions it seeks to answer. In light of this, our interest is in understanding the different approaches and the con-texts in which they might be applied, rather than arguing for a single ‘correct’ method. For example, goal definitions (4) and (5) from “Goal Definitions” section are exploring

the magnitude of displaced fossil fuels within an incumbent system, and therefore Method 1 (convert electricity output to ‘primary energy equivalent’) may be most appropriate. On the other hand, goal definition (6) considers the potential role for solar PV in providing all energy services, includ-ing transport, via renewably generated electricity, and the adoption of a universal energy content scaling factor may be inappropriate given the complex energy conversion path-ways involved.

Further to this, we point out that the established conven-tions discussed here treat equivalence exclusively in terms of a ‘physical energy content’ criterion. We note that, par-ticularly in light of considerations discussed in “Treatment of Intermittency” section, a broader concept of functional equivalence could provide clearer guidance with respect to the most appropriate scaling adjustment to make to the energy output from a PV system in any given situation. Closer investigation of this seems to be warranted, but is beyond the scope of the present study.

From the point of view of understanding the apparent divergence in PV NEA findings, the particular method for treating investment and output energy form equivalence adopted in any given study context clearly plays a major role. Just by adopting a different reporting convention, the findings of a study can change by a factor of three or more. Being informed about the convention adopted for a particu-lar study, and making appropriate adjustments when com-paring findings across studies, is fundamentally important for accurate interpretation of findings.

Differences Between Assumed Values for Key Performance Parameters and Real‑World Performance

Actual performance of PV systems depends on many fac-tors, including insolation of the region in question, ori-entation and shading of panels, and the actual (versus rated) performance of the PV modules. The IEA-PVPS Programme adopts a ‘performance ratio’ (PR) of 0.75 or

Table 3 Methods of adjusting for investment and output energy equivalence in the PV and related EROI literature

PE−eq primary energy equivalent, el−eq electricity equivalent, el electricity. �grid is the life-cycle energy efficiency of the grid in question (typi-cally 0.29–0.35)

Methodology Examples Equation

1 Convert electricity output to ‘primary energy equivalent’

Frischknecht et al. (2016), Raugei et al. (2012), Bhandari et al. (2015), Fthenakis and Kim (2011), Ito et al. (2016), Louwen et al. (2016)

EROIPE−eq =EoutPE−eq

Einv,

where EoutPE−eq =Eoutel

�grid

2 Convert primary energy investments to ‘electricity equivalent’

Koppelaar (2016), Dale and Benson (2013) EROIel−eq =Eoutel.

Einvel−eq, where

Einvel−eq = EinvPE × �grid

3 Adopt ‘direct equivalent’ to electricity output Ito et al. (2003), Fu et al. (2015), Moeller and Murphy (2016), Kubiszewski et al. (2010)

EROIel =Eoutel.

Einv

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0.80 to account for differences between rated performance and actual AC electricity generation (Frischknecht et al. 2016, p. 4). The PR accounts for non-optimal siting and shading, panel degradation, dust, DC to AC conversion losses and other factors. LCAs and NEAs are carried out with assumed factors that are intended to approximate real-world conditions in a given deployment context.

Insolation

The IEA-PVPS LCA guidelines (Frischknecht et al. 2016, Sect. 3.1.2) recognise three approaches for treating insola-tion, depending on the goal of the study. These are industry

average and best-case insolation; insolation with modules optimally orientated and tilted, or with single-axis track-ing; and average insolation for installed systems in a grid network. Insolation should be reported with the given ori-entation and inclination (Frischknecht et al. 2016, Sect. 3.5).

Meta-analyses (e.g. Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016) typically adopt a reference in-plane insolation of 1700 kWh/m2 year. However, there is sometimes ambiguity as to whether the insolation refers to in-plane or global horizontal insolation (GHI). Many LCA studies explicitly identify the insolation as being in-plane (e.g. de Wild-Scholten 2013; Ito et al. 2016, Table 2, Sect. 5; Louwen et al. 2016, Eq. 1). However, some studies

Coal101 EJ

Natural gas49 EJ

Oil 12 EJ

Nuclear 27 EJ(EIA method)

Hydro 14 EJ

Wind 2.2 EJ

Geothermal 2.5 EJ

Combustion &heat transformations

Biomass8.7 EJ

Solar PV 0.5 EJ

Electricity total70 EJ

Commercialheat11 EJ

Electric motor19 EJ

Electric heat 20 EJ

Cooling 11 EJ

Lighting 7 EJ

Electronics 6 EJNon-thermalconversions

Electricityend-use

Primary energyfor electricity

Conversions

Waste heat

Primary energyfor direct use

Coal60 EJ

Natural gas56 EJ

Oil167 EJ

Biomass43 EJ

Non-electricityend-use

Freight transport73 EJ

Passenger transport60 EJ

Heat194 EJ

Other motion, 3 EJ

Solar PV

109 GJ-el

[25 yrs, 0.8 PR1,500 kWh/m2/yr]

~ 75%

~ 25%Heat, transport

Motion, heat

Non-electric primary energy

330 EJ

High grade (> 400C)

Med. grade (100 to 400C)

Low grade (< 100C)

10.2 4.5 GJ-el/kW29.0 12.5 GJ-pe/kW

10.0 4.4 GJ-pe/kW

Fig. 3 Sankey diagram of annual global energy flows, focused on electricity. PV output is lifetime output Source Adapted from IEA (2016a). Data based on median from Koppelaar (2016). Relative proportion of PV inputs estimated from Frischknecht et al. (2015a). (Color figure online)

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also refer to horizontal insolation in relation to an ‘aver-age’ 1700 kWh/m2 year insolation (e.g. Fthenakis and Kim 2011; Phylipsen and Alsema 1995, p. 42). In other studies, it is unclear whether insolation values are meant to be inter-preted as in-plane or horizontal (e.g. Bhandari et al. 2015, Sect. 2.4.3; Koppelaar 2016, Sect. 2.3), although tracing references usually resolves ambiguity. Solar maps usually depict annual solar exposure as the total amount of solar radiation falling on a horizontal surface. From Breyer and Schmid (2010, Appendix), it has been estimated that the in-plane global insolation for an optimally tilted, fixed array is ∼ 145–272 kWh/m2 year greater than the GHI. The actual difference is dependent on the latitude, and the relative con-tributions from direct versus diffuse insolation at the given region in question.

In principle, for the purpose of comparing the relative performance of different PV systems, any consistently applied in-plane insolation value will suffice. However, for NEA findings to be representative of actual field per-formance, the reference insolation value must match the actual conditions at the deployment location. EROI and EPBT values must be adjusted accordingly to account for any difference between reference and site-specific values. In this respect, it is interesting to consider also how the reference in-plane insolation of 1700 kWh/m2 year com-pares with the average insolation for all PV deployed glob-ally to date, when this is weighted for conditions at the deployment location.

The geographic distribution of solar PV reflects factors including population, wealth, the availability of transmis-sion infrastructure, and historically political support and PV subsidies. Hot, arid regions generate the highest out-put but do not generally favour high population densities over widespread territories. Figure 4 provides a graphical depiction of the global relationship between installed PV capacity, population, wealth and insolation. It plots repre-sentative summer and winter GHI across latitudes versus population and GDP per capita. Insolation plots are aver-aged across longitudes and are intended to depict differences across latitude rather than between specific regions—there can be significant variation between locations at similar lati-tude. Also plotted is the estimated distribution of solar PV capacity by latitude. This was calculated from national-level reporting of the regional distribution of solar PV for the 15 leading countries by installed capacity. The distributions were extrapolated for the reported PV capacity at the end of 2015 from IEA (2016b), and for China up to the end of 2016 due to around 34 GW being installed during 2016. IEA (2016b) provides an average country final yield [i.e. annual AC electricity at inverter output in kWh per kW of installed capacity (Frischknecht et al. 2016, Sect. 3.1.3)] and installed capacity, shown in Fig. 5.

Based on these data sources, the deployment-location-weighted average final annual yield (i.e. the ratio of AC electricity out to rated PV capacity) equates to 1204 kWh/kW year. Population-weighted country insolation data from Breyer and Schmid (2010, Appendix) was used to calculate a

80 N

60 N

40 N

20 N

0

20 S

40 S

60 S

GDP per capita (2007)

Population (2005)

Winter averageglobal horiz.insolation

Summer averageglobal horiz.insolation

kWh/m -day86420

GDP (PPP) per capita (USD) 2007010,00030,000 20,000

Installed PVcapacity of largest

15 countries

Installed PV capacity (GW)30 20 10 0

California 1,900Arizona 1,900

Queensland 2,010

NSW 1,710

Victoria1,680

South Australia1,750

Italy 1,500

Castilla-La Mancha1,700

Extremadura 1,800

Castile and León 1,500

1,100east UKSouth-west UK

1,000

Gansu1,640

Hebei1,570

Inner Mongolia

1,600

Jiangsu1,460

Qinghai1,820

Xinjiang1,570

Tokyo 1,350

Kyushu 1,390

Chubu 1,390

Kansai 1,420

Nouvelle-Aquitaine 1,300

S.Korea1,500

Provence-Alpes-Côte d'Azur 1,500

Lower Saxony

2

Fig. 4 Population, GDP per capita, and winter and summer average global horizontal insolation by latitude. Sources Kummu and Varis (2011); NASA (2017). Insolation values are averaged across all lon-gitudes for 22-year period 1983–2005. Winter uses northern hemi-

sphere January/southern hemisphere July; Summer uses northern July/southern January. Orange dots indicate regions with substantial solar installations, and number is average annual insolation in kWh/m2. (Color figure online)

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corresponding deployment-location-weighted average inso-lation for fixed, optimally tilted PV installations, returning a value of 1550 kWh/m2 year. We used Breyer and Schmid’s population-weighted country insolation data, rather than the area-weighted data that they also provide, on the assumption that geographic distribution of population within countries will act as a proxy for geographic distribution of PV capac-ity. Dividing the deployment-location-weighted average final annual yield by the fixed optimally tilted insolation returns a corresponding average PR of 0.78. This is in close agree-ment with the typical PR range of 0.75 (rooftop) to 0.80 (ground-mounted) specified in the IEA-PVPS LCA Meth-odological Guidelines (Frischknecht et al. 2016, p. 4). The deployment-location-weighted average in-plane insolation (fixed, optimally tilted installations) of 1550 kWh/m2 year is 9 % below the reference in-plane insolation typically adopted for meta-analyses.

Longevity

The operational lifetime of solar panels can vary signifi-cantly. There are reported examples of earlier panels with operational lives well in excess of 25 years, but there are also reports of premature failure and abandonment after only a few years (Jordan et al. 2016). During the 1990s, 5- to 10-year product warranties were common, but almost all manufacturers now offer a 25-year performance warranty, in addition to the 5- to 10-year product failure warranty. The most common standard factory warranty for the leading inverter brands is 5 years.

We observe what seem to be two competing views on system performance. On the one hand, solar PV has proven

to be mostly robust and durable, particularly in the emerging period of high-cost panels. High-cost components placed a floor on quality and provided motivation to maintain systems.

In contrast, under-performance has emerged as a signifi-cant issue in recent years due to the proliferation of budget-priced rooftop systems, exposing structural problems in some markets (Johnston 2017; Pulsford 2016). It is not yet clear how rooftop systems will be maintained following the failure of minor parts or inverters. The use of net-metering in rooftop systems obscures actual solar generation, making it difficult to establish precise generation statistics. Commer-cial enterprises are usually better equipped to conduct due diligence and quality assurance. Profit-seeking enterprises have an interest in maintaining systems for the duration of the operational life.

Researchers legitimately hold differing perspectives in relation to the appropriate operating life to assume for PV NEA. Most studies now default to a lifetime of 25 or 30 years for PV modules (20 years was common in the past), reflecting manufacturers’ expectations. In the context of life-cycle assessment, the statistically representative oper-ating life based on actual field experience should be used, rather than the manufacturer’s anticipated operating life for an individual facility in isolation. Notably, DNV-GL report that 85% of the current installed global PV capac-ity is less than 5 years old (Meydbray and Dross 2016). Hence, it will be some decades before a clear empirical picture of statistically representative operating life can be determined for PV systems presently being deployed. In the meantime, researchers will be required to make assumptions about the long-term performance of systems.

Germany Japan China Italy

20 40 60 80 100 120 140 160 180 200

USA

Australia

Spain

Korea

FranceCanada

ThailandNetherlandsBelgium

UK

Switzerland

Final

annu

al yie

ld (kW

h/kW

-yr)

1,800

Weighted average 1,204 kWh/kW-yr

Cumulative installed capacity (GW)

1,500

1,250

1,000

Horiz

ontal

glob

al ins

olatio

n (kW

h/m2-

yr)

1,000

1,400~ 1,360 kWh/m2-yr

Fig. 5 Installed capacity and final annual yield of countries with greater than 1 GW installed PV capacity. Yield data from IEA (2016b). Horizontal insolation estimated, based on Fig. 4. Note that

countries with a higher proportion of on-ground installations and sys-tems with tracking generate a higher yield for a given insolation than countries with a higher proportion of rooftop systems

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Discussion of Overall Implications for EROI and EPBT Metrics

A summary of the methodological factors discussed in “Detailed Investigation of Factors Contributing to Diver-gence” section is shown in Table 4 and Fig. 6. The table pro-vides a heuristic for contextualising studies that investigate different questions and that adopt different methodologies and boundaries. It is not intended as a precise harmonisation

tool—the factors can, however, be compounded to compare studies that have adopted different assumptions.

It is apparent that any factor considered in isolation can alter the EROI or EPBT significantly, and that studies con-ducted in relation to essentially equivalent situations can produce markedly different results. For example, the use of primary data that were sourced 10 years prior to conducting a study would be expected to roughly halve EROI and dou-ble EPBT. Choosing between the ‘direct equivalent method’ versus the ‘substitution method’ for treatment of investment

Table 4 Methodological factors affecting EPBT and EROI

Factor Range of reported values Comments

1 LCA methodology Process-based or hybrid I/O I/O reduces sideways and upstream trunca-tion, increases EPBT ∼ 43–100% (reduces EROI ∼ 30–50 %)

2 Time between study publication and sourced data

∼ 2–19 years 10-year-old data increase EPBT ∼ 100 % (reduces EROI ∼ 50%), 20 years ∼ 400% (reduces EROI ∼ 80%) (Louwen et al. 2016)

3 PV technology Mono-Si, multi-Si, amorphous silicon, CIGS, CdTe

Crystalline silicon (mono/multi-Si) tech-nologies account for ∼ 94 % of global production (IEA 2016b, p. 5). Relative to multi-Si, mono-Si has 33 % longer EPBT (25 % lower EROI). Amorphous silicon, CIGS and CdTe, respectively, have 20, 42 and 66 % shorter EPBT than multi-Si (Bhandari et al. 2015, Fig. 7)

4a Transmission infrastructure Rarely considered. Only applicable to remote solar farms. Ito et al. (2008) considered 100 km

Inclusion of 100 km transmission to con-nect 100 MW PV, and including losses, increases EPBT 16–25 % (reduces EROI 14–20 %) over the lifetime of the PV project. Transmission lifetime is longer than PV

4b Capacity firming Zero storage up to 10 days. Battery or pumped hydro storage

2-h Li-ion storage increases EPBT ∼ 20 % (reduces EROI ∼ 17 %). With 2-day Li-ion storage and PV overbuild for off-grid solar, EPBT increases 400 % (reduces EROI ∼ 80 %) (Palmer 2013, 2017)

5 Investment and output energy form equiva-lence

‘Direct equivalent method’ or ‘Substitution method’

Applying the ‘Substitution method’ adjust-ment to either investments or output increases EROI ∼ 200 %

6a In-plane insolation 800–2344 kWh/m2 year Meta-analyses usually adopt 1700 kWh/m2 year (Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016). Deployment-location-weighted average insolation for fixed, optimally tilted installations is 1550 kWh/m2 year. Applied globally, an assumed in-plane insolation of 1700, rela-tive to 1550 kWh/m2 year, reduces EPBT 9 % (increases EROI 10%)

6b System lifetime 20–30 years Studies take into account manufacturers’ expectations and historic experience. 85% of the current installed global PV capac-ity is less than five years old (Meydbray and Dross 2016). Future performance is uncertain. An increase from 20 to 30 years reduces EPBT 33% (increases EROI 50%)

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and output energy form equivalence involves a straightfor-ward scaling of either the investments or output, but results in a threefold difference in reported values for EROI. Both methods can be legitimate in any given situation, though one may be preferred over the other depending on study purpose.

Importantly, the meta-analyses discussed in this study harmonise only for parameters related to factors 2, 3, 6a and 6b, leaving a significant gap in relation to factors 1, 4a, 4b and 5. Findings from studies that have adopted differ-ent methodologies, boundaries or investment-output energy form equivalence adjustments are therefore not directly com-parable unless appropriate adjustments are first made for these methodological factors.

Conclusions

NEA is a tool that has been widely applied to energy supply technologies to explore their present and potential future physical economic roles. However, apparent wide divergence in findings for solar PV has cast doubt on NEA’s relevance to energy transition feasibility assessment. We have shown that most of the apparent divergence between studies can be

attributed to six factors—life-cycle assessment methodology, age of the primary data, PV cell technology, treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance. The apparent divergence in findings between studies due to these factors can often be traced back to different goal definitions. NEA’s contribution to understanding PV performance, espe-cially in the context of a large-scale renewable energy transi-tion, will be improved if study purposes and goals are clearly and fully stated.

Similarly, there is a role for interpreters of NEA findings to take into account the many contextual factors that underlie NEA studies. We believe the findings of this study support the view that NEA is an essential tool for making sense of economic situations in biophysical terms. In turn, this is essential for coming to grips with questions about sustain-ability of current forms of social organisation, viability of alternatives and feasibility of transition pathways between them. We hope that this study might support increased awareness of contextual issues affecting PV NEA findings, and in doing so contribute to more widespread appreciation for the role that NEA can play in investigating energy transi-tion questions.

Modelledlifetime(years)

Age ofprimary data(years)

Investment & outputenergyequivalence

LCAmethod

30

20

20

Recent

10

2 days full-load storage & 2.5 times

PV overbuild

Process-based

I/O-b

ased

Directequivalent

method

Substitution method

1,700

2,344

800

PVtechnology

mono-Si

CdTe

multi-Si

CIGS

a-Si

25

Sca

ling

fact

or fo

r E

RO

I rel

ativ

e to

met

a-an

alys

es

3.0

0.2

1.0

1.2

0.8

Sca

ling

fact

or fo

r E

PB

T r

elat

ive

to m

eta-

anal

yses

0.4

5.0

1.0

0.8

0.6

0.4

2.0

1.5

0.6

2.0

1.5

0.9

0.7

0.5

4

In-planeinsolationkWh/m -yr2

2 hours

12 hours

Full load storage

onlyTransmission

Not included

Fig. 6 Stylised depiction of the scaling impact of methodological fac-tors from Table 4. Left scale gives approximate scaling factors for EROI for different assumptions relative to meta-analyses considered in this study. Right scale expressed in relation to EPBT. Length of columns depicts sensitivity of aggregate result to respective factors. Factors can be compounded. For example, studies that applied an insolation of 2200 kWh/m2 year have an EROI around 1.2 times (20

%) higher, but studies using data that are 10 years old calculate an EROI around 0.5 times lower (50 % lower). The meta-analyses con-sidered in this study adopted a 25- or 30-year lifetime. PV technology shown scaled relative to multi-Si. Hybrid LCA method shown with 30 to 50 % of CED falling outside process-based boundaries. Stor-age and overbuild estimated from Palmer (2017, Table 2) using Li-ion with 70 % depth-of-discharge

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Acknowledgements The authors would like to acknowledge the con-tribution of correspondence with Charlie Hall, Rembrandt Koppelaar, Pedro Prieto, Marco Raugei and Gail Tverberg, and others at the BPE Workshop: Society in Transition; and separately with Vasilis Fthena-kis. Thanks also to anonymous reviewers for valuable comments and feedback.

Funding This research was not funded by any specific grants from agencies in the public, commercial or not-for-profit sectors.


Conflict of interest The authors declare no conflicts of interest.

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Chapter 7

A biophysical perspective of IPCC

integrated energy modelling


7.1.1 Introduction

This chapter concludes the main body of the thesis with a policy focus.

Analysis of the costs and benefits of climate mitigation involve extrapolation into the future. How-

ever, the long time horizons of interest extend well beyond the range of standard economic-development

scenarios. In order to guide policy makers, ‘transformation pathways’ are derived from integrated as-

sessment models (IAMs). These models represent interactions between human and natural systems,

including energy, agriculture, the carbon cycle, and economic systems.

IAMs adopt simplified, stylised, and numerical approaches to complex systems. Since the future

evolution of demography, socio-economic development, and technology are highly uncertain, scenarios

have been developed that describe plausible alternative pathways for the key socio-economic drivers

of greenhouse gas (GHG) emissions.

The IPCC scenario series includes the SA90, IS92, and SRES, published in 1990, 1992 and 2000

respectively. Beginning with the Fifth Assessment Report (AR5), ‘representative concentration path-

ways’ (RCPs) were introduced to serve the dual purpose of new socio-economic and emissions scenar-

ios, and form the basis for new climate model simulations. The RCPs are being replaced with ‘share

socioeconomic pathways’ (SSPs).

Scenario drivers can be described by the Kaya identity. The Kaya identity includes population,

per-capita income, energy intensity of the economy, and carbon intensity of energy. Since the identity

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is multiplicative, the component growth rates are additive. Therefore, growth of per-capita income is

compatible with a decline of GHG emissions, provided energy and carbon intensity decline sufficiently

rapidly. A hypothesis of biophysical economics (BPE) is that per-capita income growth is in fact an

emergent parameter from the biophysical-economic system. Rather than being independent variables,

the Kaya parameters are interlinked in complex ways.

This study focuses on the key drivers of economic growth, how these are derived, and whether

IAMs properly reflect the underlying biophysical systems.

It proposes that GDP and productivity growth are integrated as feedbacks with an expanded

environmentally extended input output analysis (EEIOA). A preliminary proposal is presented.



1. How can the results of this thesis be incorporated into climate and energy policy?

2. What are the key drivers of economic growth in the IAM literature, and do these properly reflect

the energy-economic nexus?

3. How can IAMs be improved using the principles of biophysical economics?

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energies

Article

A Biophysical Perspective of IPCC IntegratedEnergy Modelling

Graham Palmer ID

Australian-German College of Energy and Climate, The University of Melbourne, Melbourne 3010, Australia;[email protected]; Tel.: +61-428-325-555

Received: 5 February 2018; Accepted: 2 April 2018; Published: 4 April 2018��

Abstract: The following article conducts an analysis of the Intergovernmental Panel on ClimateChange (IPCC) Fifth Assessment Report (AR5), specifically in relation to Integrated AssessmentModels (IAMs). We focus on the key drivers of economic growth, how these are derived andwhether IAMs properly reflect the underlying biophysical systems. Since baseline IAM scenariosproject a three- to eight-fold increase in gross domestic product (GDP)-per-capita by 2100, but withconsumption losses of only between 3–11%, strong mitigation seems compatible with economicgrowth. However, since long-term productivity and economic growth are uncertain, they are includedas exogenous parameters in IAM scenarios. The biophysical economics perspective is that GDPand productivity growth are in fact emergent parameters from the economic-biophysical system.If future energy systems were to possess worse biophysical performance characteristics, we wouldexpect lower productivity and economic growth, and therefore, the price of reaching emission targetsmay be significantly costlier than projected. Here, we show that IAMs insufficiently describe theenergy-economy nexus and propose that those key parameters are integrated as feedbacks with theuse of environmentally-extended input-output analysis (EEIOA). Further work is required to build aframework that can supplement and support IAM analysis to improve biophysical rigour.

Keywords: Integrated Assessment Models; economic growth; energy; biophysical; optimisation;energy return on investment (EROI)

1. Introduction

1.1. Scenario Modelling

Much of the Intergovernmental Panel on Climate Change (IPCC) Work Group 3 (Mitigation)reporting relies on prospective energy modelling based on Integrated Assessment Models (IAMs) [1].Baseline scenarios assume a three- to eight-fold increase in gross domestic product (GDP)-per-capitaby 2100 ([1], p. 426). GDP growth is composed of GDP per-capita and population growth and connected toemissions with the Kaya identity, which underlies the emissions scenario literature ([2], p. 105). The Kayaidentity states that total CO2 emissions are the product of population, GDP per-capita, energy intensityand carbon intensity, shown in Equation (1).

CO2Emissions = Population × GDPPopulation

× EnergyGDP

× CO2

Energy(1)

Since strong mitigation scenarios result in global consumption (in this context, consumptionrefers to economic goods and services) losses in 2100 of only between 3–11% relative to thebaseline ([1], p. 419), strong mitigation seems consistent with economic growth. In the IPCCFifth Assessment Report (AR5), consumption losses are based on incremental costs relative to a

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baseline ([3], p. 1221) and do not take into account co-benefits or the adverse side-effects of mitigationactions ([4], p. 1292).

However, as Stern [5] notes, with assumed growth of over 1% over a century and with modestdamages, the nature of compound growth ensures that ‘future generations are assumed to be muchbetter off’. When the assumption of growth is relaxed, the cost of mitigation, as reflected in the socialcost of carbon (SCC), can increase by orders of magnitude [6]. The challenges of the long time framesof interest in climate mitigation are reflected in the wide divergence in SCC estimates that resultfrom different assumptions in parameters such as the discount rate and ‘pure rate of time preference’(e.g., Smith [7], Pindyck [8]).

Since there are no economic-development scenarios available in the literature that extend to thelong-term time horizons required for climate scenarios, GDP growth and total factor productivityare exogenous inputs. However, the biophysical economics (biophysical economics (BPE) is definedas ‘the study of the ways and means by which human societies procure and use energy and otherbiological and physical resources to produce, distribute, consume and exchange goods and services,while generating various types of waste and environmental impacts’ [9]. ‘Biophysical’ refers to thematerial world and can be contrasted with an anthropocentric perspective, of which mainstreameconomics is a subset ([10], p. 1)) perspective is that these are in fact emergent parameters from theeconomic-biophysical system.

Furthermore, the assumption that future energy systems will possess equivalent or superiorbiophysical and economic qualities is contested within the biophysical literature [11–13]. If futureenergy systems were to possess worse biophysical performance characteristics, productivity andeconomic growth may be overstated in the IAM literature, and therefore, the price of reaching emissiontargets may be significantly costlier than projected.

Section 1.2 defines and describes biophysical economics. Section 2 briefly describes integratedassessment models. Section 3 explores assumptions in the modelling, with an emphasis on a biophysicalperspective. Section 4 identifies two approaches to connecting conventional economic approacheswith a biophysical perspective. Section 4.1 introduces a preliminary framework for supporting andsupplementing integrated models, and finally, Section 5 provides some overarching conclusions.

1.2. Biophysical Economics

Biophysical economics (BPE) is related to the field of industrial ecology, which uses the tools oflife-cycle assessment (LCA) and environmentally-extended input-output analysis (EEIOA) to explorethe full environmental assessment of the life cycle of products and services. The focus of BPE is theenergy-environment-economic nexus.

Net energy analysis (NEA) refers to a class of methods that are physically based, which are usedto determine the efficiency or productivity of energy supply technologies [14]. Results are presented inthe form of energy return ratios (ERR), of which EROI is the most commonly used (see Equation (2)).EROI is a unitless ratio, defined as the ratio of the gross flow of energy Eg over the lifetime of theproject, and the sum of the energy for construction Ec, operation Eop and decommissioning Ed ([15]Equation (1)). The energy inputs include both the direct and indirect energy. Murphy et al. [16] statethat ‘EROI is the ratio of how much energy is gained from an energy production process compared tohow much of that energy (or its equivalent from some other source) is required to extract, grow, etc.,a new unit of the energy in question’.

EROI =Eg

Ec + Eop + Ed(2)

The ratio provides an energetic valuation of the fuel, which may, or may not, correlate with theconventional economic valuation. An energetic valuation of fuels and electricity, based on EROI,conveys information on the potential benefit of using that energy source that may not be obvious fromthe price system alone.

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There may be overlap between energetic and economic valuations, determined, in part, by thecapital and energy intensity of the energy supply system [17]. For example, the cost of oil and gasproduction has been shown to be inversely related to EROI [18,19], but the market price is overlaidwith cycles and subject to many factors [20]. On the other hand, the price of solar photovoltaics maybe a poor proxy for its energetic evaluation [21]. Nuclear power is subject to economic, regulatoryand social constraints (e.g., von Hippel et al. [22]), rather than underlying biophysical constraints,depending on factors such as reactor type, ore grade and enrichment method [23].

Ideally, EROI should provide a means to supplement conventional economic and environmentalanalysis and inform and shape energy transition analysis. However, a lack of methodologicalconsistency has led to contestation of NEA’s relevance to the broader and IAM scenario literature [21].

2. Integrated Assessment Models

Stabilizing greenhouse gas emissions will require transformation of energy supply and energyend-use services ([1], p. 420). Net global CO2 energy supply emissions must eventually be broughtto, or below, zero. The main IPCC modelling tool for assessing transformation pathways areIAMs ([1], p. 420). IAMs employ a simplified and stylised approach to physical and social systems,but integrate energy, the carbon cycle and economic systems. They typically incorporate all of themajor energy sources (i.e., coal, oil, gas, nuclear, renewables), but may constrain specific sources, suchas nuclear, or coal with carbon capture.

A key use of IAMs has been to produce energy scenarios for representative concentrationpathways (RCPs) as part of the IPCC climate mitigation process. AR5 defined seven CO2eqconcentration categories (see Clarke et al. [1], Table 6.2) and selected four that corresponded with RCPpathways: RCP2.6, 4.5, 6.0 and 8.5. The RCP scenarios are considered to be plausible and illustrative,but do not have probabilities attached to them [24]. In AR5, the RCPs superseded the Special Reporton Emission Scenarios (SRES) ‘storylines’. The storylines permit harmonisation across models, and aredefined as the A1, A2, B1 and B2 scenario families ([2] Box SPM-1). GDP growth is one of several‘driving forces’, with the A1 storyline adopting the highest economic growth, followed by B1, then A2and B2. A total of 1184 scenarios was reviewed for AR5 [25], of which a quarter were baseline andthree-quarters were mitigation scenarios ([26], p. 9).

Other economic explorations of mitigation measures include energy supply cost curves, marginalabatement cost (MAC) curves (e.g., McKinsey) and cost-benefit studies (e.g., Tol [27], Nordhaus andBoyer [28]). However, AR5 has adopted integrated modelling as the preferred approach due to its highstructural detail ([29], p. 534).

Although modelling approaches and objectives differ, all models use economics as thebasis for decision making by minimizing the aggregate economic costs of achieving mitigationoutcomes ([1], p. 422; [30]). The BPE perspective is that an economic valuation of fuels and electricitymay not convey complete information about the potential benefit, or costs, of that energy source.This study is not intended as a systematic review of the IAM scenario database, but a contributionto supplementing and supporting the economic valuation perspective of IAMs with a biophysicalenergetic valuation perspective.

3. Detailed Exploration of Assumptions

3.1. Total Factor Productivity and GDP Growth

In economic texts (e.g., (Mankiw [31], pp. 249–250), three sources of economic growth areidentified: changes in the amount of capital, changes in the amount of labour and changes in totalfactor productivity (TFP). Since TFP is not directly observable, it is measured indirectly as a residual,or as the growth that remains after accounting for changes in capital and labour.

The concept of TFP can be traced to Robert Solow’s discovery that output rose faster than capitaland labour inputs in U.S. time-series data for the period 1909–1949. He appended At, calling it

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‘technical change’, to describe what appeared to be a shift in the production function (see Equation (3)).He explained that the term was meant in the broadest sense, including ‘slowdowns, speed-ups,improvements in the education of the labour force’ and ‘other things’ [32]. It was only later that it wasto become a stylized fact that ‘technical change’ (Solow later noted that ‘technical change’ was simply‘a measure of our ignorance’, which has since been variously termed ‘Solow’s residual’ [33], ‘mannafrom heaven’ [34], the ‘dark matter of growth’ [35] and most commonly ‘total factor productivity’in mainstream economics texts [31]) was the primary contribution to national economic growth.In contemporary use, the expression describes a broad range of factors, including technology, educationand the role of institutions. The importance of At is that productivity is the main driver of per-capitagrowth and a rise in the standard of living [36].

Yt = AtLαK1−α (3)

where Y, L and K are output, labour and capital. respectively, and α is labour’s factor share.Beginning with Romer [37], many variants of ‘endogenous growth theory’ were developed

that sought to explain and disaggregate At. Endogenous theory holds that the primary drivers of‘technological progress’ are human capital, innovation and knowledge, and therefore investment ineducation, research and development and knowledge production contribute spillovers and positiveexternalities. The essential feature of endogenous growth theory is the primacy of knowledge capital.For example, Buonanno et al. [38] (Equation (1)) augment the Solow equation with knowledge capital(see Equation (4)).

Yt = AtKβRLαK1−α (4)

where KR is knowledge capital and β is output elasticity of knowledge capital.Although there are many approaches for accounting for endogenous technological change [39],

nearly all energy-economic models take productivity growth as exogenous and assume averageglobal TFP growth of ∼2–3% per year ([40], Section 3.1). Since there are no economic developmentscenarios available in the literature that extend to the long-term time horizons required for climatescenarios, GDP growth and total factor productivity are exogenous in the IAM literature. In AR5,all models assume increasing per capita income ([1], p. 426). Income growth is modelled as anexogenous improvement based on productivity growth during the Twentieth Century. The averagebaseline per-capita growth rate for OECD countries is mostly clustered between 1.2 and 2.0%, whilefor non-OECD, 3–4% ([1], Figure 6.2). On average, baseline scenarios assume a three- to eight-foldincrease in global GDP-per-capita by 2100 ([1], Figure 6.1b). However, recent data on per-capita growthshow a marked decline for wealthier nations ([41], Figure 2.6), with attention turning to what has beentermed ‘secular stagnation’ and falling productivity growth (e.g., [42–44]).

‘Technical change’ also contributes to improved energy efficiency and therefore a decline in energyintensity, discussed in Section 3.2; and a reduction in the cost of energy supply technologies [40,45].In the IAM literature, ‘endogenous technological change’ is sometimes applied to energy supply,resource availability and end-use technologies through the use of learning curves. Examples includeMESSAGE-MACRO [46] and DEN21+ [47]. In modelling, induced technological change tends toreduce the costs of environmental policy and accelerates the learning rates of low-emission energysupply technologies [48].

Whereas neoclassical economics adopts capital and labour as the primary factors of production,with ‘technical change’ or ‘knowledge capital’ driving TFP growth, BPE also adopts energy as aprimary factor (e.g., Ayres [49], pp. 385–389). Energy-matter is the principle factor that cannot bephysically produced from within the economic system. With respect to energy, civilisation is like anyother physical process; that is, as an open, non-equilibrium thermodynamic system that sustains itselfwith the use and dissipation of energy [50]. The simplest energy-augmented production function,given as indexed parameters, from Lindenberger and Kümmel [51] is given as:

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Y = y0KαLβE1−α−β (5)

where Y, y0, L, K and E are output, output at time zero, labour, capital and energy, respectively, and α

and β are capital and labour’s factor share, respectively.Whereas neoclassical economics assumes that there is, in principle, no limit to the

substitution of energy, the BPE perspective is that there is a lower bound to substitution.Kumhof and Muir [52] term this lower bound the ‘entropy boundary’, alluding to the second lawof thermodynamics. Kümmel et al. [53], Kümmel [54], Giraud and Kahraman [55], Keen and Ayres [56]and Ayres and Warr [33] have modelled variations on the neoclassical Cobb–Douglas aggregateproduction function and found that the inclusion of energy accounts for around half to two-thirds ofthe observed economic growth that is usually attributed to TFP. This implies that the output elasticity,with respect to energy, is much greater than its factor share.

IAMs generally include energy as a factor of production, but implicitly set the outputelasticity as equal to the factor share, which is typically less than 10% in the developed economies(e.g., Edenhofer et al. [45], Equation (1)). If the BPE hypothesis is correct, it would require that the At

term (Scientific equations usually include dimensions for dimensional analysis. However, economicmodelling of production functions of this type usually apply dimensionless indexes. Barnett [57]argued that the use of dimensions may be either meaningless or economically unreasonable. In thiscase, the At term is dimensionless.) be endogenous and a key feedback loop in IAMs.

3.2. Declining Energy Intensity

A declining energy intensity permits higher economic growth for a given greenhouse gas (GHG)emission budget. The historical trend is towards declining energy intensity since GDP growth isgenerally greater than energy use growth. The reduction in energy intensity for AR5 baseline scenariosis ∼61–80% up to 2100 ([1], Figure 6.17), equating to an averaged ∼−1.0–−1.8%/year. However, thehistorical change in global energy intensity over the period 1970–2010 averaged −0.8%/year ([1],Figure 6.1c). EIA [58] shows a return to trend decline since a plateau around 2010 (Different primaryenergy conventions across energy reporting agencies [59] and differences in GDP measurement resultin differences in reported changes in energy intensity. For example, EIA [58] shows greater declinethan Clarke et al. [1]. EIA [60] (Table J3) adopt $PPP, while Clarke et al. [1] (Figure 6.1) adopt marketexchange rates. This study relied on Clarke et al. [1] to ensure like-for-like comparisons.).

IAMs are, on average, modelling a baseline reduction in energy intensity of 25–125% greater thanthe average for the period 1970–2010 [13,61]. Some of the bottom-up optimisation models cited in theAR5 project even greater decoupling: Teske [62] assume −3.4%/year and Jacobson and Delucchi [63]assume −3.6%/year. Clarke et al. [1] (Figure 6.2) shows 11 OECD baseline projected projectionsfor 2010–2050 at higher than ∼−2.0%/year and 11 non-OECD at higher than ∼−3.0%/year.The 530–580-ppm and 430–480-ppm CO2-eq scenarios project an additional reduction over the baselineof ∼18–45% for the period up to 2100.

In a review of the AR5 430-530 ppm scenarios, the additional annual investment for efficiencyfar exceeded the additional investment in energy supply ([3], Figure 16.3). However, models do notendogenize the increased embodied energy of the technical efficiency measures of vehicles, buildingsand energy-consuming equipment. IAMs account for costs in the construction stage, but are implicitlyassuming that the energy intensity of manufacturing, construction and energy consuming equipmentis the economy-wide average. Since service sectors are both less energy intensive and comprisea significant share of developed economies, the economy-wide intensity is typically less than formanufacturing and construction (e.g., Hertwich and Peters [64]). In some cases, the improved efficiencyduring the use stage (The ‘use stage’ is one of several stages in life-cycle assessment. Other stagesinclude raw material extraction, processing, manufacturing or production, use and maintenance andend-of-life [65]) is significantly offset by the additional embodied energy during the production stage.Examples include electric vehicles [66] and housing that conforms to the Passive House Standard [67].

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Since energy efficiency protocols almost universally apply only to energy used during the use stage,the problem can arise of ‘overshooting’ the optimal lifetime efficiency.

Projections of energy intensity are partly based on historical energy efficiency improvements.However, it can be difficult to model the human behavioural and income effects resulting fromJevon’s paradox [68]. The direct rebound effect posits that efficiency lowers the cost of an energyservice, leading to increased use of that service, termed the income effect [69]. However, the indirect,economy-wide and transformational effects are significant and pervasive in producer economiessuch as China [70]. On the one hand, the technical efficiency gains of delivering end-use energyservices are very large [71], but translating technical gains into sustained reductions has provenallusive absent sustained rises in energy prices [72]. AR5 identified the efficiency rebound effect inBruckner et al. [29] (Section 7.12.1), Kolstad et al. [73] (Section 3.9.5) and Blanco et al. [74] (Section 5.6.2);however, the adoption of exogenous energy intensity as part of the scenario ‘storylines’ essentiallyinvalidates any macro integration of rebound.

3.3. Life-Cycle Assessment Methodologies

The IPCC Special Report ‘Renewable Energy Sources and Climate Change Mitigation’ containeda chapter [75] on sustainable development, which included a broad discussion of life-cycle analysis(LCA) and net-energy. The report identified the limitations of attributional, process-based life cycleassessments (LCAs) ([75], p. 730) and the need to identify uncertainties, but nonetheless, used anattributional approach to present the main results. Since non-fossil technologies are generally morecapital intensive, the importance of indirect energy, and therefore a comprehensive LCA, will likelyincrease in the future [76].

The two main issues are, firstly, process-based analysis results in a truncated boundary andtherefore understates the indirect embodied energy [77]. In LCAs, there is is no requirement toensure that an analysis meets a prescribed level of ‘completeness’. ISO [78] (Section 6.4.5) andISO [79] (Section 5.2.3) require that stages, unit processes or inputs are followed until they ‘lacksignificance’ within the given scope. This can be problematic when LCAs are applied to energytransition exercises since a high level of ‘completeness’ is often assumed by modellers applyingLCA data [21]. Environmentally-extended input-output analysis (EEIOA) connects energy flowsto monetary flows using national input-output (I/O) tables and ensures systematic completeness.However, the assumption of heterogeneity introduces aggregation error. The respective benefits ofprocess and I/O analysis can be combined with hybrid-LCA [65].

Secondly, an attributional approach considers the embodied energy of the technology in question,but does not consider the broader impacts that might result from the decision to adopt or install aspecific technology ([75], p. 730). In contrast, a consequential approach considers the broader impacts.However, it can be methodologically difficult to evaluate consequential changes to energy systems withLCA approaches (e.g., Jones et al. [80]). Consequential analyses are generally more comprehensive,but also carry greater uncertainty and the risk of double counting due to overlap between studies.The paucity of energy technology consequential studies reflects this, and Sathaye et al. [75] (p. 730)note that the limited number of studies precludes the incorporation of consequential analysis intoIAMs. Other factors that limit the use of LCAs in integrated modelling include substantial variabilityin published LCA results and differences in the LCA technique ([75], p. 730). Some IAMs adopt asimplified approach to consequential changes related to variable renewable energy. For example,WITCH and MESSAGE adopt flexibility and capacity constraints that weight the dispatchability ofdifferent generator types [81].

The LCA literature emphasises the use of standard guidelines and the primacy of datasets basedon ‘complete and verifiable documentation’ [82]. The limitation of standard guidelines is that studies arethen restricted to answering the research questions to which that methodology is suited. In the context ofelectricity system transitions, considerations relating to the engineering-systems perspective of electricitysupply are generally treated as lying outside the domain of conventional life-cycle research.

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In the case of solar photovoltaics for instance, the choice of goal definition, methodology andboundaries can alter the EROI by an order of magnitude [21]. Since the LCA literature has tended toconverge towards a standard suite of guidelines, study results emphasise differences between studies,rather than a high level of completeness. The use of attributional, process-based LCAs may be unsuitedto the task of assessing large-scale energy transitions, unless they are combined with some form ofconsequential approach.

3.4. Steel and Cement

IAMs mostly ignore material cycles and recycling [83], although some material flows areconsidered. In the AR5 literature, life-cycle analysis is identified (e.g., Sathaye et al. [75], p. 730)but is not broadly adopted in IAMs. Similarly, material scarcity associated with low-emission energysources is identified ([29], p. 549), but not explicitly modelled in IAMs.

Some IAMs include material flows that have a significant emissions footprint, such as steel andcement; these comprise 28% and 27% of direct industrial CO2 emissions, respectively ([84], Figure 2.22).However, no IAMs fully integrate material flows into the physical system [83]. For example, IMAGE isone of the most comprehensive models for the steel cycle and its emissions, but steel demand is modelledas a function of per-capita GDP ([85], Figure 1). A more detailed approach would have been to link steelproduction to the main steel consuming sub-sectors: buildings and transportation ([83] p. 17).

Furthermore, the embodied material and energy of energy supply technologies are not modelled.Fossil fuel-derived electricity is significantly more lifecycle greenhouse intensive than renewablesources due to direct combustion ([29], Figure 7.6); [86]. On the other hand, renewable and nuclearelectricity possess a higher embodied material and energy content [86,87]. Wind, ocean and CSPrequire more steel and cement than fossil fuel plants, per unit of electricity generated ([29], p. 549).

3.5. Biofuels

AR5 devoted a chapter to bioenergy ([88], Chapter 11), which covered life-cycle emissions ofbiofuels. The life-cycle emissions are a proxy for embodied energy, but land use changes significantlyincrease life-cycle emissions relative to embodied energy ([88], Figure 11.24). Most of the biofuel-relatedLCA literature applies carbon accounting rather than energy accounting, but arrives at similarconclusions to Hall et al. [89]; for example, DeCicco et al. [90] found that U.S. corn biofuel onlyoffsets CO2 emissions by around a third of that required to achieve carbon neutrality. Much of thestudied bioenergy is marginal from a net-energy perspective ([91], Figure 2), ([92], Figure 8), but someproduction systems (e.g., Brazilian ethanol) may be favourable in some contexts [93].

Virtually all bottom-up energy scenario models that include biofuels adopt gross energy flows(e.g., Elliston et al. [94]). In IAMs, projected costs are modelled, but implicitly assume that the energyintensity of energy supply technologies is commensurate with incumbent sources. Where the EROI ofa biofuel production system is significantly less than conventional fuels, the net-energy may be muchless than assumed, thereby overstating the extent of fuel substitution.

The use of biomass energy with carbon dioxide capture and storage (BECCS), will reduce the EROIfurther since CCS is estimated to consume 25–35% of gross output ([95], p. 338). In a review of IAMs,Bruckner et al. [29] (p. 559) found that a carbon price of USD 100 per tonne CO2eq would be sufficientto drive large-scale deployment of BECCS. Yet, from a biophysical perspective, energy sources withan EROI in the range of 0.8–3:1 cannot support an advanced society [12], irrespective of carbon price.AR5 identifies several limiting factors for BECCS, including land availability, a sustainable supply ofbiomass and storage capacity ([1], p. 485), but does not identify EROI or net-energy as a constraint.

3.6. EROI Constraints

The theoretical global potential of RE sources is substantially higher than global energydemand ([96], p. 10). Studies have typically focused on economic costs, land availability and kinetic or

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radiative energy constraints. However, AR5 identified the issue of whether some of the ‘bottom up’estimates are consistent with real physical limits ([29], pp. 525–526).

One of those additional constraints is EROI. When EROI constraints are applied, the practicallimits can reduce by an order of magnitude. For example, estimates of global wind power potentialgenerally report that wind power could conceivably supply a large proportion of global energysupply [97,98]. Lu et al.’s (2009) estimate of 840 EJ annual global wind power potential, with a 20%capacity factor constraint, exceeds the 400–800 EJ global final energy use of baseline scenarios for 2050from Clarke et al. [1] (Figure 6.18). For a reference, global wind power generation in 2016 equalled3.5 EJ [99]. Conversely, models that also include an EROI constraint generally report an estimate thatis an order of magnitude lower (e.g., [100,101]). Dupont et al. [101] calculated a global wind powerpotential of 709, 536, 322 and 99 EJ/year, with an EROI constraint of 5, 8, 10 and 12, respectively,although Kubiszewski et al.’s (2010) [102] meta-analysis showed an average EROI of 20–25:1. Similarly,in an assessment of solar photovoltaics, wind power and storage, Palmer [103] found that there ismarked diminishing return to EROI at higher grid penetration.

3.7. Fossil Fuel Resource Availability

IAMs adopt differing resource availability estimates based on SRES storylines ([2], pp. 207–211).Since there is large uncertainty in resources, a range of values reflecting so-called ‘optimistic versuspessimistic’ forecasts is considered ([2], p. 134; [104], p. 435). Although baseline scenarios cover a broadrange of annual GHG emissions, Edenhofer et al. [26] (Figure SPM.4) shows the median baseline lyingroughly midway between RCP6.0 and RCP8.5, with the upper range of baseline scenarios extendingup to and beyond RCP8.5. The surplus availability of fossil fuels through the Twenty-First Century,including unconventional resources, is reflected in the SRES, which stated ‘It is evident that, in theabsence of climate policies, none of the SRES scenarios depicts a premature end to the fossil-fuelage.’ ([2], p. 208). Similarly, in the EMF27 inter-model comparison, McCollum et al. [105] concludedthat fossil fuel resource constraints are unlikely to limit GHG emissions this century. The ‘resourceoptimist’ approach is also reflected in the Global Energy Assessment (GEA) [104], which is often usedas a basis for IPCC estimates (e.g., Bruckner et al. [29], Table 7.2).

Estimates of fossil fuel availability have been drawn from several sources, including Rogner [106],which proposed the theory of learning-by-extracting (LBE). The LBE theory was based on theobservation that historically, the reserves-to-production (R-P) ratio of coal, oil and gas has beendynamic. A static concept of present technology and cost will not properly reflect the replenishmentof reserves by resources: the availability of economically-available reserves at any time relieson the dynamic interplay between geological assurance, technological possibilities and economicfeasibility ([74], p. 379).

The basis for the bias towards the upper end of RCPs reflects an assumption of a so-called ‘returnto coal’ [107], which has by far the largest resource base ([104], Table 7.1). Historically, the R-P of coalhas been expressed in hundreds of years, but more recent reviews suggest a resource-constrainedsupply peak in the first half of this century (e.g., Mohr et al. [108], Rutledge [109]). The high-coalconclusion can be traced back to scenario modelling through the 1970s, which assumed coal-to-liquidsas a backstop liquid energy supply [107]. Other ‘resource pessimists’ argue that there is greateruncertainty and significantly lower economically-recoverable resources than often assumed in IAMscenarios [108–113].

The BPE perspective is less about resource availability per se, but about the increasing workto locate, upgrade and refine lower-quality and difficult to access resources [10]. Difficult to access,or lower quality resources, lower the EROI and lead to a higher energy expenditure cost share.This would result in lower productivity and economic growth than assumed in high-resourceavailability scenarios.

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4. Discussion and Recommendations

Pauliuk et al. [83] proposed several strategies to increase the robustness of IAM scenarios, basedon an industrial ecology (IE) perspective. These included: higher levels of detail regarding materialstocks, flows and physical linkages; the explicit physical description of products, processes, transportand infrastructure; improving the link between fixed capital and material stocks; vintage tracking; andthe development of a ‘standard model’ of society’s biophysical basis.

Although not related to the IAM literature, Daly et al. [76] adopted an environmentally-extendedmulti-region input-output (EE-MRIO) model, combined with an energy system optimisation model(ESOM) to estimate the indirect CO2 emissions of current and future energy technologies for the U.K.

The broad aim of both of those approaches was to better connect the conventional economicapproaches with a biophysical perspective. The approach of this study more closely relates to themodelling approach of Daly et al., except that the metric of interest is ‘energy industry own use’ (EIOU),with a focus on how this relates to economic growth.

4.1. A Proposed Net-Energy Feedback Model

Net-energy analysis requires estimation of both the direct and indirect energy of energy supply toderive a systematically-complete EIOU for the EROI denominator (see Equation (2)). Where the EROIis sufficiently high (>~20:1) or it is assumed that future energy systems will possess an EROI at least ashigh as incumbent energy systems, a net-energy approach may not contribute additional informationto that already available in IAMs.

A provisional national estimate for EIOU can be derived from the IEA energy balance statistics.The IEA documentation states that ‘Energy industry own use contains the primary and secondaryenergy consumed by transformation industries for heating, pumping, traction, and lighting purposes,(including) for example, own use of energy in coal mines, own consumption in power plants andenergy used for oil and gas extraction.’ Some studies have used the EIOU to calculate EROI at anational level (e.g., Brand-Correa et al. [114], King et al. [115]), but the IEA metric only partly reflectsthe boundaries usually adopted for the EROI denominator [116]. In 2015, the IEA reported EIOU as8.9% of total final global consumption ([117], p. 47).

A systematically-complete method of deriving EIOU is environmentally-extended input-outputanalysis (EEIOA). One technique is to combine the national monetary use-table with the nationalenergy account [116]. EEOIA permits disaggregation of primary fuels from EIOU fuels and finaldemand and identification of indirect energy pathways. The main weakness of EEIOA is homogeneity,or the assumption that each sector of the economy produces a single, homogeneous good orservice. Depending on the dependence of imported fuels and electricity supply capital equipment,a multi-regional model may be necessary.

For scenario analysis based on an IAM, it is necessary to convert the IAM aggregate energy andeconomic system into a detailed energy model and input-output table. From this, an updated EEIOAis evaluated to calculate a system-EROI (see Figure 1).

From Section 3.1, a significant fraction of total factor productivity growth can be attributed toenergy. The proposed model requires a transfer function that links EROI (and energy expenditures’share of GDP) to growth in GDP. The transfer function is underpinned by Bashmakov’s [118]‘Three Laws of Energy Transitions’, which was the observation that the energy cost to income ratiotends to converge towards a stable long-term ratio. When the energy costs to GDP ratio is below agiven threshold, which Bashmakov defines as less than 11% in OECD countries, energy exhibits amoderate price elasticity. However, when the threshold is exceeded, price reactions to small changesin demand are much higher, and economic growth is hampered. Similarly, Fizaine and Court [119]found that the energy cost share in the United States must be less than 11% for the national economyto exhibit a positive economic growth rate. Fizaine and Court [119] explicitly linked the energy costshare to EROI. Whereas neoclassical economics makes the a priori assumption that output elasticities

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will mirror cost shares in the economy [120], the BPE perspective is that the output elasticity of energyis much larger than the factor share, and for labour, much smaller [33,121].

A further way to model the transfer function is to adopt an aggregate production functionapproach. In the macroeconomic module of IAMs, output is determined by an aggregate productionfunction, typically of a Cobb–Douglas, or constant elasticity of substitution (CES) form. For example,MACRO’s production function is a nested CES, comprising capital, labour force, electricity demandand non-electric energy demand ([122], Equation (2)).

However, the IAM choice of production function does not resolve the ‘factor share’ problem.The asymmetry of energy’s output elasticity with respect to factor share is consistent with Bashmakov’sasymmetry of economic growth with respect to energy expenditure cost share. In a recent contributionto addressing the problem, Keen and Ayres [56] developed an energy-augmented production function,including a proof that production (output) and distribution (cost shares) are no longer congruent,arguing that ‘something other than marginal products’ must determine the distribution of income.

Referring to Figure 1, finally, the ‘calculated GDP’ that has been determined by the transferfunction is compared with the ‘scenario GDP’, giving an error term, which is used as a basis torevise the energy system. We envisage an iterative process of testing IAMs and revising the energysystem accordingly.

Integrated assessmentmodel

Detailed energysystem model

Modelled input-output tables

Scenario-basedEEIO Model

EROI andenergy cost share

Aggregateenergy system

GDP growth = f EROI

ScenarioGDP

CalculatedGDP

-+

Aggregateeconomic system

Energy technologyLCA

Revise energysystem

error

Transfer function

Figure 1. Proposed biophysical economic feedback model.

4.2. Future Work

The proposed feedback model as depicted here is intended as a preliminary conceptual model.The model as described is technically demanding. IAMs operate at a regional level, but EEIOA isusually modelled at a national level. Multi-regional input output models, such as the AustralianIndustrial Ecology Virtual Laboratory (IELab) [123], facilitate regional energy-economic modellingand would provide a basis for EEIOA. The transfer function as described requires further work todemonstrate the linkages between energy supply and the economy.

5. Conclusions

Analysis of the costs and benefits of climate mitigation involve extrapolation into thefuture. However, the long time horizons of interest extend well beyond the range of standard

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economic-development scenarios. In order to guide policy makers, ‘transformation pathways’ arederived from integrated Assessment Models (IAMs). These models represent interactions betweenhuman and natural systems, including energy, agriculture, the carbon cycle and economic systems.

IAMs adopt simplified, stylised and numerical approaches to complex systems. Since the futureevolution of demography, socio-economic development and technology are highly uncertain, scenarioshave been developed that describe plausible alternative pathways for the key socio-economic driversof greenhouse gas (GHG) emissions. The drivers can be described by the Kaya identity. The Kayaidentity includes population, per-capita income, energy intensity of the economy and carbon intensityof energy. Since the identity is multiplicative, the component growth rates are additive. Therefore,growth of per-capita income is compatible with a decline of GHG emissions, provided energy andcarbon intensity decline sufficiently rapidly.

A hypothesis of biophysical economics (BPE) is that per-capita income growth is in fact anemergent parameter from the biophysical-economic system. Rather than being independent variables,the Kaya parameters are interlinked in complex ways. If future energy systems were to possess worsebiophysical performance characteristics, we would expect lower productivity and economic growth,and therefore, the price of reaching emission targets may be significantly costlier than projected.With the long time horizons of IAMs, the nature of compound growth means that relatively smalldifferences in economic growth result in a significant divergence in outcomes.

We propose that per-capita income growth is included as a feedback loop in IAMs. One approachwould be to use environmentally-extended input-output analysis (EEIOA) to link the biophysicalproperties of the modelled energy system with projected economic growth. The EEIOA would bebased on a detailed energy system model that is constructed from the aggregate model described bythe IAM. A transfer function would link the calculated energy cost share, derived from the EEIOAmodel, to economic growth. Depending on the difference between the exogenous and calculated GDP,the IAM energy system would be revised. The proposed feedback model is intended as a preliminaryconceptual model. Further work is required to build a framework that can supplement and supportIAM analysis.

Acknowledgments: Thanks to the three anonymous reviewers who have added much to the quality of this articlethrough their insightful comments. Thanks to Igor Bashmakov and the other anonymous reviewers for commentson an earlier version of the draft and to Josh Floyd for discussions and feedback. Thanks also to my supervisorsRobert Crawford and Roger Dargaville.

Conflicts of Interest: The author declares no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

AR5 IPCC Fifth Assessment ReportCO2eq Carbon dioxide equivalentCSP Concentrated solar thermal powerDNE21+ Dynamic New Earth 21 modelEEIOA Environmentally-extended input-output analysisEIOU Energy industry own useEMF27 Stanford Energy Modeling Forum Study 27EROI Energy return on investmentEV Electric vehicleGEA Global Energy AssessmentGDP Gross domestic productIAM Integrated Assessment Model

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IE Industrial EcologyIEA International Energy AgencyIMAGE Integrated Model to Assess the Global EnvironmentIPCC Intergovernmental Panel on Climate ChangeISO International Organization for StandardizationLCA Life-cycle assessment or analysisMACRO IIASA macroeconomic modelMESSAGE Model for energy supply strategy alternatives and their general environmental impactMESSAGE-MACRO Linked energy supply model (MESSAGE) and macroeconomic model (MACRO)OECD Organisation for Economic Co-operation and DevelopmentOPEC Organization of the Petroleum Exporting CountriesPOLES Prospective outlook on long-term energy systemsRCP Representative Concentration PathwaysRE Renewable energySRES Special Report on Emissions ScenariosWEO IEA World Energy OutlookWITCH World induced technical change hybrid

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Chapter 8

Conclusions and future work

8.1 Conclusions

The motivation for this thesis was to better understand the challenge of decarbonising electricity grids

in biophysical terms. Biophysical economics (BPE) uses the tools of net-energy analysis (NEA), of

which the energy return on investment (EROI) is the most common. Proponents of NEA believe that

it offers insights into the energy-economic nexus that may not be obvious from a price-based approach

alone. NEA is a tool for exploring questions about sustainability of energy systems, viability of

alternatives and feasibility of transition pathways. It is not intended to replace conventional price-

based analysis, but rather, supplement economic, environmental, and other types of analysis. The

EROI ratio provides an energetic valuation of electricity generation technologies, which may, or may

not, correlate with the conventional economic valuation, discussed in chapters 1 and 2.

Ideally, NEA should provide a means to inform and shape energy transition analysis. However,

a lack of methodological consistency has led to contestation of NEA’s relevance to energy transition

feasibility assessment. The aim of this thesis was to address the methodological inconsistency of

electricity-based NEA.

A major weakness in the literature is the failure to adequately incorporate the systems-engineering

properties of electricity supply. There are several reasons for this. Firstly, electricity generation EROI

studies were historically based on attributional, process-based life-cycle assessment (LCA) studies,

with a functional unit of 1 kWh of electricity delivered to the grid. Attributional refers to ‘attribut-

ing’ environmental impacts to the production of the generation technology, and process-based refers to

a method of evaluating the impacts by tracking the production of the generation technology through

a process tree. The conventional study goal was ascertaining whether an electricity generation tech-

nology, considered independently of the grid, ‘paid back’ the energy invested in its production, over

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the lifetime of the device. The simple payback concept implied that an EROI of 1:1 was sufficient for

establishing energetic viability. The expression ‘energy payback time’ evolved out of this conceptual

framing. Implicit in this framing was that instances of the technology would be in ‘energy debt’

for some, or most of their lifetime, and that the incumbent energy system would energetically fund

transitions.

A requirement to ‘pay back’ the energy debt is necessary, but not sufficient for establishing whether

a technology can support the energy demands of advanced economies. Furthermore, a process-based

analysis is systematically incomplete, and truncates typically one third to one half of the embodied

energy.

The contemporary configuration of society requires a system-wide EROI of around 10:1 or greater.

Historically, societies have flourished and grown with a lower EROI, but exhibited a much simpler

degree of cultural and industrial complexity. The hierarchy of human wants and needs has evolved

markedly since the beginning of industrialisation. Earlier configurations exhibited much higher pro-

portions of primary industries, including agriculture and mining, and much less of discretionary and

service sectors. An energy supply technology that is greater than 1:1 but less than roughly 10:1 is not

energetically deficient in itself, but inadequate for supporting the energy demands of contemporary

developed economies.

Since the publication of the first EROI studies in the 1970s, climate emission targets and the asso-

ciated mitigation and scenario literature has progressed significantly. Contemporary studies examine

the efficacy of energy transitions, often including high-penetration variable renewable energy (VRE).

As scenario analysis has evolved, the implicit goal and scope of NEA has shifted, discussed in chapter

6. However, the evolving goal and scope hasn’t been explicitly identified or formalized in the NEA

literature. This has become particularly problematic for electricity-based EROI analyses because elec-

tricity supply is delivered by complex real-time systems. Furthermore, the performance characteristics

of many low-emission substitutes is markedly different to conventional generators. Evaluating a par-

ticular component of electricity supply based on lifetime electricity generation is far too coarse-grained

if the goal is transition feasibility assessment.

An EROI analysis needs to establish the impact on the overall system EROI, in order to establish

the energetic viability of a transition pathway. Such an approach is termed a consequential analysis.

For non-dispatchable electricity sources, the difference between the isolated-case and the system-case

is much wider than for dispatchable sources. An example of a wide divergence is solar photovoltaics

(PV). Based on an implicit isolated-case, some studies calculate an EROI above 20:1. However, due

to the daily and seasonally dominated supply curve, the contribution of PV to system-EROI can be

reduced to below 5:1.

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In some cases, an ad-hoc approach has been taken to address the weaknesses of the attributional

approach. But paradoxically, many studies that correctly identified weaknesses of an attributional

approach introduced other shortcomings because the study goals and boundaries departed from es-

tablished conventions.

There has been recent progress towards the adoption of consequential analysis. However, since the

systems-engineering of electricity supply lies outside the domain of life-cycle research, it has tended

to be insufficiently described.

The research method to addressing these issues is to approach the methodological problem from

two ‘directions’, based on the conceptual idea of hybrid-LCA analysis. Firstly, using a top-down

approach for systematic completeness; and a bottom-up approach for a more detailed technology-

specific analysis.

Chapter 4 comprises the top-down approach. It comprises an environmentally extended input-

output analysis (EEIOA) of the Australian electricity supply industry. This is the first study to

calculate the EROI of electricity at a national level, with a calculated EROI of 40:1. The result confirms

the common assumption that EROI is not a major factor with respect to the current configuration

of the Australian electricity supply industry. Yet the study also validates the biophysical economists’

intuition that a shift away from the incumbent generation suite risks significantly increasing the

energetic costs of the system, and consequently lowering the system EROI below an energetically

viable threshold.

The bottom-up approach includes two technology-focused studies. Chapter 5 introduces a frame-

work for implementing a consequential EROI analysis of variable renewable energy (VRE) and storage.

Since the underlying function of electricity grids is to ensure supply meets demand at a prescribed

reliability level, a power based functional unit of 1 kW was adopted to supplement the conventional 1

kWh of energy delivered to the grid. This is the first EROI study to undertake a power-based analysis,

and provides a framework for comparing the energetic costs of VRE and electrical storage, with the

system value that storage provides.

Chapter 6 is an evaluation of the factors that contribute to a divergence in the renewables EROI

literature. This is the first comprehensive evaluation of these factors, and establishes a framework for

incorporating process-based LCA into a consequential EROI assessment.

The most significant factors are ‘primary energy equivalence’; the role of storage; the choice of LCA

methodology; and differences between assumed values for key performance parameters and real-world

performance. The first factor has been inadequately settled in the electricity-based EROI literature.

It is related to energy reporting conventions, sometimes known as the ‘IEA’ and ‘EIA’ reporting

conventions. Since combustion fuels have historically dominated commercial energy supply, by con-

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vention, primary energy has been expressed in thermal equivalence. Since electricity is a secondary

energy carrier, statistical accounting of primary fuels requires standard reporting conventions. Ther-

mal equivalence provides a consistent metric for comparing combustion fuels, but is less meaningful

for non-thermal fuels, such as hydro, solar PV and wind. Since nuclear power and concentrated solar

thermal power include a thermodynamic cycle, with a thermal efficiency of 30 to 40%, but use pri-

mary energy without combustion, different approaches to reporting convention can result in around

a three-fold difference in primary energy consumption.

Furthermore, the LCA literature adopts a further reporting convention: the cumulative energy

demand (CED). CED accounts for all forms of energy extracted from nature, and is more diverse

than the thermal equivalence adopted by energy reporting agencies. The LCA literature differentiates

between the ‘energy harvested’ and the ‘energy harvestable’ concepts. For example, in the case of solar

PV, the former evaluates the CED as equivalent to the electricity generated, while the latter evaluates

the CED as the total solar radiation that is incident on a panel. With a panel solar conversion efficiency

of 14%, the latter evaluates CED at around seven times the former. The choice and application of

conventions, between both the energy investments of manufacturing and constructing the generation

assets, and the electricity generated during the operational phase, can significant alter the results of

an EROI study. In the case of the ‘IEA versus EIA’ conventions, the choice can alter the result by a

factor of three. The choice of convention, and the contexts in which each applies, differs depending

on study goal. The common approach to accounting for the electricity generated has been to assume

that a unit of electricity, from any source, is considered equivalent to the primary energy required to

produce a unit of electricity from the overall mix of sources for the grid in question. The so-called

‘replacement logic’ makes sense from an energy payback perspective, but is less meaningful where the

study goal is transition feasibility. In the future, a relative decline in combustion-based generation will

reduce the appropriateness of thermal equivalence. In such cases, a more comprehensive and dynamic

approach is required.

The second factor relates to the appropriate magnitude of ‘buffering’ for VRE. There is no single

best answer to the appropriate quantity of storage, if any. The contribution of storage to EROI

should be assessed at a system level in order to capture system-level effects, such as geographic and

technology diversity, curtailment, demand management, and additional transmission. A weakness

in the literature has been the adoption of ad hoc renewable overbuild and storage requirements. A

contribution of this thesis is to establish a framework for evaluating the role of storage in a consistent

manner, described in chapter 5.

The choice of LCA methodology and boundaries significantly alters the result. The LCA litera-

ture emphases the importance of standardization, and complete and verifiable documentation. The

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consistent treatment of system boundaries is important for comparing findings of studies with similar

goals. However, where the context for NEA is feasibility assessment for large scale energy transition,

providing a comprehensive account from a net-energy perspective may be a higher priority than data

precision, especially if precision necessarily comes at the cost of narrowing the focus for data collection.

A further issue is the necessary adoption of key performance indicators. Meta-analyses typically

adopt standard values for performance indicators, such as assumed lifetime, capacity factor, and

performance degradation. These ensure a standard set of conditions for inter-study comparison.

In the case of rapidly evolving technologies, there is uncertainty of long-term performance due to

insufficient operational time to establish empirical data. Furthermore, there is often a divergence

between assumed values and the average real-world operating conditions. Studies tend to assume that

renewable technologies will be constructed in locations with favourable natural resources. However,

the availability of natural resources is only one of many factors that contribute to the geographic

location of renewable installations. Important factors include population and per-capita income,

the availability of transmission infrastructure, alternative investment options, and cultural-political

support and subsidies.

The key conclusion of this thesis is that a technology-specific EROI analysis needs to establish

what the impact on the overall system EROI will be, in order to establish the energetic viability of a

transition pathway.

This thesis provides a foundation for incorporating net-energy analysis into integrated and scenario

modelling. In order to place the research within a policy-relevant context, chapter 7 is an analysis of

the IPCC Fifth Assessment Report (AR5), specifically in relation to Integrated Assessment Models

(IAMs). The chapter concludes with a modelling proposal to supplement and support IAMs.

In summary, this thesis has shown that EROI is critical for determining technology choices in the

future, and establishing the energetic viability of low-emission electricity transition pathways.

8.2 Electrical storage

One of the important contributions of this thesis is a re-appraisal of the role of electrical storage in

the context of energy transitions. Chapter 5 introduces the surplus energy-storage synergy hypothesis

as a general principle for exploring the role of electrical storage. The value of storage is nearly always

framed in terms of a financial analysis within the particularly market or tariff regime that the storage

operates in. On the other hand, the NEA approach is less about the value to particular economic

agents, but the contribution of storage to the system as a whole, with an emphasis on an energetic

valuation.

Since electrical storage time-shifts power rather than converting primary energy, by definition, it

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must lower the system EROI. Furthermore, some forms of electrical storage, especially electrochemical

batteries, possess a high embodied energy. At modest scale, there may be worthwhile energetic trade-

offs with the adoption of storage. For example, with sufficiently high-EROI renewable power, it may

be preferable to store and re-use a portion of surplus power rather than curtail. However, there is

a marked diminishing return to VRE and storage, such that beyond a given penetration, scenarios

with a sufficiently high storage are energetically infeasible. ‘Infeasible’ refers to a system-EROI that

is significantly less than 10:1. A low EROI does not imply technical infeasibility – there are many

successful examples of remote and small island grids that operate reliably. However, these examples

are inevitably small scale and subsidised, both economically and energetically.

8.3 What would a low-emission scenario of Australian elec-

tricity look like if it was optimised for EROI?

A pending question of this research is – what would a low-emission scenario of Australian electricity

look like if it was optimised for EROI? A full analysis is beyond the scope of the present study.

However, the highest EROI of the scalable renewable options, and the most biophysically feasible VRE

is wind power. Furthermore, since long distance transmission has a low embodied energy relative to

the transmitted energy, an EROI-optimal solution would likely include geographically diversified wind

power to supply a large proportion of system energy.

In a high-VRE scenario, load balancing would be provided by some combination of fossil fuel gen-

eration, hydro power and nuclear power, subject to other constraints, especially emissions target, cost,

social licence, resource availability, and practical utilisation of dispatchable generators. Synchronous

generators would also provide other essential system functions, such as inertia and reactive power.

Since electrical storage lowers the system EROI, an optimal system would likely possess modest or

no storage for bulk generation1. The three technologies above essentially provide storage services

in the form of: ‘stored sunlight’; potential energy behind a dam wall; and stored nucleosynthesis,

respectively. Some Australian low-emission scenarios include biofuels to provide an energy storage

role for load balancing. Although the modest use of biofuels may provide a useful role measured by

market value, the EROI of biofuels is too low to supply a large fraction of generation. The addition of

carbon capture to fossil fuel generation impairs the EROI, however the EROI of coal-fired generation

is sufficiently high that coal with CCS may still be energetically viable.

Wind and solar PV continue to show an upward trend of EROI. Ongoing technological advances in

manufacturing and construction are likely to continue this trend, but may be offset by an expansion

1 Storage can also be effectively used for network support as a substitute for network augmentation.

143

into regions with less favourable wind or solar resources. In general, global oil and gas show a long-run

downward trend. The global average EROI for coal remains high, but is likely to decline through the

twenty first century.

Since Australia has a surplus of natural resources, both renewable and non-renewable, there is likely

a multitude of near optimal solutions that are biophysically feasible. On the other hand, in many

parts of the world, such as high latitude regions, and without geographically diverse and favourable

wind resources, high-penetration renewable options would be far more limited.

8.4 Future work

1. Nearly all low-emission policy prescriptions focus on, or include price as a key driver. The main

IPCC integrated models are driven by optimising costs. Carbon pricing is broadly considered an

essential element of climate mitigation. Since the period of competition reform and liberalization

of electricity systems, markets and prices have become the principle mechanism for public policy.

However, if a central hypothesis of this thesis is accepted – that the price system alone is

insufficient for understanding energy transitions – then other questions are opened up. What

is the role of energy and carbon markets? Can markets drive the optimal trajectory? What if

markets push towards energetic bottlenecks? Should there be a greater role for command-and-

control of scenario pathways?

2. The adoption of EROI constraints to energy system optimisation modelling (ESOM) would

improve their biophysical rigour. This could take the form of an additional technology-based

constraint, subject to geographic location or other parameters.

3. Alternatively, ESOM models could be run as an EROI-maxima optimisation to contrast with

cost-minima model runs.

4. A challenge of NEA is improving the linkages between energy and economic metrics. The

question of whether EROI contributes more as a metric that stands separately to conventional

economic metrics, or whether EROI can be better incorporated into economic metrics, is an

open question. The role of energy generally, and electricity specifically, in national economic

productivity is an important question.

5. This thesis focused on electricity, for which there are multiple technology substitutes. A greater

substitution challenge is transport fuels. There are currently no scalable substitutes for liquid

fuels for air travel, oceanic shipping, and heavy freight. Electricity-based light transport util-

ising battery and hydrogen-based options are generally more embodied energy intensive than

144

petroleum fuelled vehicles, and therefore require greater attention to NEA. Expanding electricity-

based EROI boundaries to a ‘well-to-wheels’ vehicles analysis would reveal potential constraints

in electrifying transport.

6. A proposal for future work is detailed in chapter 7. It addresses a significant weakness of

integrated assessment models (IAMs), which is that GDP and productivity growth are given

as exogenous. It is based on deriving energy-industry-own-use (EIOU) with environmentally

extended input-output analysis (EEIOA). The EIOU provides a basis for estimating economic

growth constraints and provides feedback mechanism for IAMs.

145

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