Energy Efficiency in Fossil-Fuel Electricity Generation: A

Energy Efficiency in Fossil-Fuel Electricity Generation: A Panel Data Empirical Analysis

IEFE Milan, 22 January 2010

Elena Verdolini Catholic University Milan and FEEM


0. Outline

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A. Motivation1. Forecasts of CO2 emissions: the Power sector2. Forecasts of Electricity production3. Curbing CO2 emissions in the electricity sector

B. Energy Efficiency in Fossil Fuel Electricity Generation1. Definition2. Data for OECD

C. Review of the literatureD. Simple framework to look at energy efficiencyE. Knowledge stocks as proxies of technological

development and availabilityF. ResultsG. Conclusions and Next Steps

Energy Efficiency in Fossil-Fuel Electricity Generation: A Panel Data Empirical Analysis2

A.1 CO2 Emission Forecasts

Widespread agreement that unless we take significant actions in a Business-As-Usual (BAU) scenario global anthropogenic CO2 emissions grow rapidly, oil and gas prices are high and energy security concerns increase.

ETP (2008) revises previous estimated of CO2 emissions upward by 7%, pointing once more to the role of developing economies. CO2 emissions in 2050 are 130% above the level of 2005


A.1 CO2 Emissions Forecasts: the Power Sector

Higher global emissions reflect rapid economic growth and increasing carbon intensity of energy use, which overwhelm

decoupling of economic activity and energy use


A.2 Electricity Production Forecasts

BAU: electricity demand increases by 2.2%/yr

(DCs: 3.8%)

Factors: rapid population and income growth in DCs, increase in the number of electricity consuming devices (homes and commercial

buildings), growth in electrically driven industrial processes. Fossil Fuels will remain the main input


The power sector is the sector in which higher emission reductions can be achieved

A.3 Curbing CO2 Emissions in the Electricity Sector



(1) Improving efficiency in energy-intensive sector/end-useWill global energy demand really decrease?

(2) Reduce the share of FFs power generation through A. Decarbonization of the electricity sector --

substitution with non-fossil energy sources (nuclear and renewable) or some “break-through”

Technical limitations (grids) and social acceptance ?B. Carbon Capture and Storage (CCS)

General concerns for storage? Location? Existing power plants? Moreover, capturing CO2 from low efficiency is not economically viable

(3) Increase energy efficiency of FF electricity productionAttractive option as it would combine reduce impact on environment and energy security




A.3 Curbing CO2 Emissions in the Electricity Sector: EE

• All the projections of future power mix, as well as of future efficiency levels, are based on the optimal behaviour of the economic agents. In many cases, such as widespread deployment of renewables or nuclear, as well as other frontier technologies, such assumptions are necessary because we can’t observe past performance.

• On the other hand, the dynamics of fossil-fuel electricity generation and its efficiency over time can be studied, since the technology has been used for a long time, data is available for a number of countries.

• A study of how the efficiency of power plants has developed over time can allow for a cross-country comparison as well as shed light on the determinants of technical efficiency


Energy efficiency for the power sector is often referred to as “technical efficiency”, as it relates to the ability to extract the energy content of FFs and transform it into electricity. In its definition, it is necessary to take into account that electricity can be produced in a traditional plants or in CHP plants, where heat is a by product of the electricity generation process plant (Phylipsen et al., 1998 and Graus et al., 2007).

EL = Electricity produced with FF (GWh*3.6)H = Heat produced with FF (TJ)s = Correction factor between electricity and heat (s=1.75)I = FF inputs (coal, gas, oil) (TJ)

i

ii

IsHEL

iEE )*(+=

B.1 Energy-efficiency in fossil-fuel power generation


B.1 Energy-efficiency in FF power generation: OECD


C. Review of the Literature

Aspects of power sector have been researched: • 1980s: rate of return regulation, environmental controls• 1990s: natural monopoly (distribution), ownership restructuring of

the industry, break in constituent parts (transmission, distribution, retail) and competition introduced in wholesale and retail market

• Soderholm (1999): Interfuel substitution in FF power sector

Recently, interest in energy efficiency of FF power plants: • Some case studies at the country level • Grauss et al (2007) and (IEA 2008): descriptive analysis• Grauss et al (2008): links energy efficiency to age of power plant

and to carbon intensity


C. Review of the Literature

This paper provides a quantitative analysis of the factors influencing energy efficiency in fossil-fuel power generation at

the national level, with the novelty of taking into account the impact of technological availability in the market

The aim is to substantiate known facts with an empirical analysis that allows to go past simple “case study” approaches

to the explanation of improvements in energy efficiency

In particular, previous descriptive studies point to the fact that energy efficiency in the power sector is negatively correlated with the age of the capital stock and positively correlated with the load factor, the type of technology used in the plant and

the prices of input.


D. Framework 1/3

A representative firm produces electricity using FF

Assuming that the firm is a price taker in the market for inputs and that fossil- fuel inputs and the non-energy inputs are weakly separable, in the short-run the production choices of the firm can be characterized by a cost minimization problem

where SRC is the short-run cost function of the firm and wFF is a function that aggregates the fossil fuel input prices, namely coal (wC ), gas (wG ), and oil (wO ).

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),,,),,,(( AKZLOGCQhEL FF=

A),K O), G, (C,H(Q EL s.t.A) EL,,K ),w,w,(w g(w SRCmin

FF

GOCFF

==


Applying Sheppard’s lemma, the conditional demand of FF input can be derived

Log-linearizing we can write the following equation:

and, subtracting ln(EL) from both sides, we obtain:

Note: 14

),,,( AELKwfQwSRC

FFFF

FF==

∂∂

AELKwQ FFFF lnlnlnlnln 54321 βββββ ++++=

AELKwELQ

FFFF lnln)1(lnlnln 54321 βββββ +−+++=

D. Framework 2/3

EEELQFF 1

=


Input weighted EE of FFs

Input weighted index of fossil fuel prices (oil, coal, gas),

Defined as (stock of capital * utilization ratio)Stock of capitalLoad factor

Electricity production (proxied by GDP)Index of technological availability built using patent data for fossil fuel electricity technologies

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D. Framework 3/3: Definition of Variables

∑∑

=i

ii

IIEEEE *

FFw

K

A

AELKwEE FF lnlnlnlnln 54321 ααααα ++++=

1)1( −−+= ttt KIK δPELELL /=

EL

EE

02 >α

03 >α

05 >α

04 >α


E. Index of Technological development/availability 1/2

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Patents are costly. They are: ‐

indicator of the outcome of the innovation process,

‐

Indicator technological availability in the market

Limitations of patent data are well-known (Griliches 1990)

Identified classes that include energy-efficient technologies for fossil-fuel power generation

Coal Gasification, Fluidised-bed combustion, Integrated gasification combined cycle (IGCC), Process heaters and super-heaters, Compressed ignition engines, Efficiency improving gas turbines, Co-generation, Combined cycle combustion

Built a dataset that includes all singular patents, claimed priorities and duplicate patents


E. Innovation: Environmental/Energy-Efficient Technologies


E. Index of Technological development/availability 2/2

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Following Popp 2002, Bottazzi and Peri 2005, Verdolini and Galeotti 2009, build a measure of knowledge stock using patent data

Measure built using claimed priorities only, cps and duplicates and cps, dup and singulars to account for different value of patentsEffect lagged (10 years) to account for time differences between innovation and deployment


E. Innovation: Environmental/Energy-Efficient Technologies


Sample of 20 OECD over the period 1979-2006 USA, Japan, Germany, France, UK, France, Canada, Italy, Netherlands, Austria, Korea, Finland, Belgium, Hungary, Spain, Czech Republic, Mexico, Portugal, Turkey, Slovak Republic

Input, output and capacity installed data from IEA – Electricity Information Database 2008

Patents from EPO/OECD PATSTAT Database (2008)

Pooled OLS estimation with robust standard errors

Country fixed effects are included

F. The sample


F. Results 1/2

(1) (2) (3) (4)Input Weighted 0.0277*** 0.0273*** 0.0212*** 0.0217***Price Index (0.00433) (0.00435) (0.00425) (0.00426)Capital Stock 0.0190*** 0.0111**

(0.00333) (0.00431)Technological 0.0224*** 0.0148***Availability (CP) (0.00353) (0.00474)Technological 0.0325*** 0.0323*** 0.0245*** 0.0256***Availability (CP+SIN) (0.00447) (0.00442) (0.00467) (0.00459)GDP Per Capita 0.108*** 0.0903***

(0.0182) (0.0194)GDP 0.119*** 0.105***

(0.0192) (0.0210)Country Fixed Effects yes yes yes yesNr of Cases 500 500 500 500R‐Square 0.878 0.879 0.881 0.882


F. Results 2/2

(5) (6) (7)Input Weighted 0.0199*** 0.0205*** 0.0205***Price Index (0.00426) (0.00442) (0.00418)Capital Stock 0.0120*** 0.0104*** 0.0146***

(0.00423) (0.00402) (0.00440)Technological 0.0162*** 0.0169*** 0.0173***Availability (CP) (0.00594) (0.00577) (0.00620)GDP 0.134*** 0.147*** 0.119***

(0.0212) (0.0201) (0.0220)Average age of Plant ‐0.0412** ‐0.0383** ‐0.0467**

(0.0178) (0.0168) (0.0181)Share of Electricity Imports ‐0.105***

(0.0346)Share of Electricity Exports 0.185***

(0.0617)Nr of Cases 500 500 500R‐Square 0.883 0.886 0.885


Good fit of the modelConsistent results with expectations, robust to different specifications of the modelNeed to improve

Sample size: unreliable data for a few countriesMeasures of environmental regulationRelax assumption of global knowledge stock and construct national knowledge stocks to study spillovers

Further examine how technical change and innovation contribute to CO2 emissions reductions

G. Conclusions

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