MEDEAS-World: a new IAM framework integrating biophysical and socioeconomic constraintsIñigo Capellán-Pérez, Ignacio de Blas, Jaime Nieto, Carlos De Castro, Luis Javier Miguel, Margarita Mediavilla, Óscar Carpintero, Luis Fernando Lobejón, Noelia Ferreras-Alonso, Paula Rodrigo, Fernando Frechoso, David Álvarez Antelo, Pedro L. Lomas, Gonzalo Parrado Hernando
University of Valladolid (GEEDS)
Sevilla, IAMC (14/11/2018)
[email protected]://www.geeds.eu/
INDEX
1. Motivation: why a new IAM?
2. Overview of MEDEAS-World model
3. Main assumptions – methodology
4. Summary
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http://www.medeas.eu/MEDEAS H2020 project (2016-2020)
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Objective: provide strategic recommendations for the transition towards a sustainable energy system in the EU.
Model development: Group of Energy, Economy and System Dynamics of the University of Valladolid (Spain): http://www.geeds.eu/
1. Motivation: why a new IAM?
1. Lack of plurality/simplified representation of economic processes: optimization, equilibrium dynamics, aggregated production functions, etc. (Scrieciuet al., 2013)
2. Despite uncertainties about availability of energy resources, assumption of future high availability of both renewable and non-renewables (Capellán-Pérez et al., 2016)
3. Omission/partial integration (i.e. undervaluation) of climate change damages (Dietz&Stern 2015; Diaz&Moore 2017)
4. No consideration of mineral availability (Valero et al., 2018)5. Analyses in terms of gross energy vs. net energy (e.g. EROI) (Carbajales-Dale et
al., 2014)
(of course + other limitations exist which are not tackled!)
MEDEAS addresses some common weaknesses in the field of IAMs focusingon the energy transition:
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2. Overview of MEDEAS-World model
• Policy-simulation• 3 geographical levels (one-way integration): World → EU → country• Complex system approach: focus on the interrelation between different
dimensions rather than on the detail of each one individually• Complex&large model of thousands of variables and equations• Developed in Vensim DSS 6.4c (System Dynamics proprietary software); also
available Python (open source): https://www.medeas.eu/
• Modularity:• Modules can be expanded or simplified• A module can be replaced by another version• A new module can be added• …while keeping consistency and coherence
MEDEAS framework
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3. Main assumptions - Methodology1) Representation of economic processes2) Availability of non-renewable energies3) Climate change damages4) Mineral availability5) Net energy approach
3. Main assumptions - methodology
• Policy-simulation (no optimization/equilibrium)• Sectoral demand-driven production• Leontief production function (input-output analysis): 35 sectors (complementarity vs
perfect substitution).
Selected IOT: WIOD database (www.wiod.org):• Multi-Regional IOT• Time series (1995-2009)• Includes social and environmental accounts (energy, emissions, land, etc.)• Open source
1) Representation of economic processes
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�𝑋𝑋 = (𝑰𝑰 − 𝑨𝑨)−1� 𝐹𝐹𝐹𝐹X: sectoral productionA: Leontief matrixFD: Sectoral final demand
3. Main assumptions - methodology
• Policy-simulation (no optimization)• Sectoral demand-driven production• Leontief production function (input-output analysis): 35 sectors: complementarity vs
perfect substitution.• Energy demand by fuel and sector estimated through the sectoral final energy
intensities by sector (180=5*(35+1))• Production of sectors (i.e. GDP) is endogenous:
• Dependence on final energy availability• Affected by climate change damages
1) Representation of economic processes
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MEDEAS-World
Source: Hardt et al. (2017)
3. Main assumptions - methodology
Emerging field of macro-ecological economics modelling:
1) Representation of economic processes
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3. Main assumptions - methodology
Most models consider supply cost curves (cumulated extraction vs cost).Consideration of stock and flow constraints (geological “peak-oil” phenomena):
2) Availability of non-renewable energies
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00-6
ener
gy
ener
gy/t
ime
Time
Flow(extraction rate)
Cumulative extractionStock
Source: Kerschner & Capellán-Pérez (2017)
3. Main assumptions - methodology
2) Availability of non-renewable energies
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0
50
100
150
200
1990 2010 2030 2050
Ann
ual T
rilo
n Cu
bir
feet
(TCF
/yea
r)
Natural gas
ASPO (2009)
Laherrère (2010)
Maggio&C (2012) URR=9500 Tcf
Maggio&C (2012) URR=12500 Tcf
Maggio&C (2012) URR=15400 Tcf
Mohr (2012) Best Guess
Mohr et al (2015) Low
Mohr et al (2015) BG
Mohr et al (2015) High
WEO (2012) Current Policies
WEO (2012) 450 ScenarioHistorical extraction
0
2,000
4,000
6,000
1990 2010 2030 2050
Mto
e
Coal
Mohr2012 High
Mohr2009 High Case
Mohr2009 Best Guess
Mohr2009 Hub. Lin.
Höök2010
EWG2007
EWG2013
Maggio2012 URR=750 Gtoe
Maggio2012 URR=650 Gtoe
Maggio2012 URR=550 Gtoe
Patzek2010
WEO 2012 Current policies
WEO 2012 450 Scenario
Historical extraction
0
50
100
150
1990 2010 2030 2050
Kt (p
rimar
y en
ergy
) Ura
nium
Uranium
EWG 2006
Zittel 2012
EWG 2013
Historical extraction
0
30
60
90
1990 2010 2030 2050
Mb
/ da
yOil
Laherrère (2006)
Skrebowski (2008)
Maggio&C (2012) URR=2250 Gb
Maggio&C (2012) URR=2600 Gb
Maggio&C (2012) URR=3000 Gb
Aleklett et al (2010)
ASPO (2009)
EWG (2013)
EWG (2008)
WEO (2012) Current Policies
WEO (2012) 450 Scen.
Historical extraction
Source: MEDEAS D4.1
3. Main assumptions - methodology
Large uncertainties.Large gap between natural scientists’ understanding of climate impacts and theirrepresentation in IAMs (e.g. Stern 2013).
3) Climate change damages
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Natural scientists IAMs
- “The modeling, paleoclimate evidence, and ongoing observations together imply that 2⁰C global warming above the preindustrial level could bedangerous.” (Hansen, Sato et al., 2016)- “This target [2°C] was a political decision informed by science, but no scientific assessment ever defended or recommended a particular target”. (Knutti, Rogelj et al., 2016)- Updated Reasons for Concern (IPCC SR1.5 2018): high and very risks & vulnerability > 2°C.
Cost-benefit models: GDP loss by2100 roughly equates a year of economic growth (Tol 2018).
IPCC-AR5 ensemble of baselinescenarios: by 2100 interquartilerange: 3.7-4.8°C (also virtually unaffected ΔGDP).
Paris Agreement: “Recognizing that climate change represents an urgent and potentially irreversible threat to human societies and the planet”.
3. Main assumptions - methodology
4) Mineral availability
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Generation & transportation of renewable energies will require large amounts of mineral materials.
Tracking of kg/MW of 19 mineral requirements for 6 transition technologies (lit. review).
AluminiumCadmiumChromium
CopperGalliumIndium
IronLead
LithiumMagnesiumManganese
MolybdenumNickelSilver
TelluriumTin
TitaniumVanadium
Zinc
PVCSP
Wind onshoreWind offshore
Li batteriesOvergrids
3. Main assumptions - methodology
5) Net energy approach
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Dynamic and endogenous computation of:- EROI of individual technologies- EROI at system level- Overdemand to maintain the same level of
net energy consumption as the initiallyintended
Source: Dale et al., (2014)
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 =𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑒𝑒𝑒𝑒𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑖𝑖𝑒𝑒𝑖𝑖𝑒𝑒𝑖𝑖𝑟𝑟𝑒𝑒𝑟𝑟
𝐶𝐶𝐸𝐸𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁 𝑐𝑐𝑐𝑐𝑐𝑐,𝑖𝑖 𝑟𝑟 = 𝑀𝑀𝑀𝑀𝑟𝑟𝑒𝑒𝑒𝑒𝑖𝑖𝑀𝑀𝑀𝑀 𝑖𝑖𝑒𝑒𝑟𝑟𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑟𝑟𝑒𝑒𝑖𝑖𝑗𝑗 𝑘𝑘𝑒𝑒𝑀𝑀𝑀𝑀 � 𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑒𝑒𝑖𝑖𝑟𝑟𝑐𝑐𝑐𝑐𝑟𝑟𝑖𝑖𝑐𝑐𝑒𝑒 𝑐𝑐𝑒𝑒𝑒𝑒 𝑟𝑟𝑒𝑒𝑖𝑖𝑟𝑟 𝑐𝑐𝑜𝑜 𝑐𝑐𝑀𝑀𝑟𝑟𝑒𝑒𝑒𝑒𝑖𝑖𝑀𝑀𝑀𝑀 𝑐𝑐𝑐𝑐𝑒𝑒𝑖𝑖𝑟𝑟𝑐𝑐𝑐𝑐𝑟𝑟𝑖𝑖𝑐𝑐𝑒𝑒𝑗𝑗[
𝑀𝑀𝑀𝑀𝑘𝑘𝑒𝑒](𝑟𝑟)
3. Main assumptions - methodology
5) Net energy approach. Example: 100% renewables in electricity mix (Green Growth)
15
0%
20%
40%
60%
80%
100%
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
Share RES in electricity mix
GG-100%
Technologies more affected by mineral scarcity
Solar PV Tellurium, indium, silver, manganese
Solar CSP Silver, manganese
Li batteries Lithium, manganese0
2
4
6
8
10
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
%
Total final energy intensity in GG-100% scenario
TFES intensity
TFES intensitywithout EROIfeedback
Substantial rematerialization of theeconomy during the transition
0
2
4
6
8
10
12
14
16
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
Dmnl
EROIst system
GG-100%
4. Summary
Original contributions of MEDEAS framework:
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1) Representation of economic processes: integration of input-output analysis with system dynamics technological disaggregated model. Postkeynesian approach (demand driven, not assumption of equilibrium, etc.)
2) Consideration of geological constraints for the extraction of non-renewable energy sources,
3) Simple & transparent methodology to integrate climate change damages4) Assessment of mineral demand associated to the energy transition5) Endogenous and dynamical implementation of net energy approach
considering energy investments for the transition
+ Planned further work.
BibliographyCapellán-Pérez, Iñigo, Ignacio de Blas, Jaime Nieto, Carlos De Castro, Luis Javier Miguel, Margarita Mediavilla, Óscar Carpintero, Paula Rodrigo, Fernando Frechoso, and Santiago Cáceres. “MEDEAS Model and IOA Implementation at Global Geographical Level.” Deliverable MEDEAS project, http://www.medeas.eu/deliverables. GEEDS, University of Valladolid, June 30, 2017.Capellán-Pérez, Iñigo, Iñaki Arto, Josué M. Polanco-Martínez, Mikel González-Eguino, and Marc B. Neumann. “Likelihood of Climate Change Pathways under Uncertainty on Fossil Fuel Resource Availability.” Energy & Environmental Science 9, no. 8 (August 2, 2016): 2482–96. https://doi.org/10.1039/C6EE01008C.Carbajales-Dale, Michael, Charles J. Barnhart, Adam R. Brandt, and Sally M. Benson. “A Better Currency for Investing in a Sustainable Future.” Nature Climate Change 4, no. 7 (July 2014): 524–27. https://doi.org/10.1038/nclimate2285.Diaz, Delavane, and Frances Moore. “Quantifying the Economic Risks of Climate Change | Nature Climate Change,” 2017. https://www.nature.com/articles/nclimate3411.Dietz, Simon, and Nicholas Stern. “Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus’ Framework Supports Deep Cuts in Carbon Emissions.” The Economic Journal 125, no. 583 (2015): 574–620.Hansen, James, M. Sato, P. Hearty, R. Ruedy, M. Kelley, V. Masson-Delmotte, G. Russell, et al. “Ice Melt, Sea Level Rise and Superstorms: Evidence from Paleoclimate Data, Climate Modeling, and Modern Observations That 2 °C Global Warming Could Be Dangerous.” Atmos. Chem. Phys. 16, no. 6 (Mach 2016): 3761–3812. https://doi.org/10.5194/acp-16-3761-2016.Hardt, Lukas, and Daniel W. O’Neill. “Ecological Macroeconomic Models: Assessing Current Developments.” Ecological Economics134 (Abril 2017): 198–211. https://doi.org/10.1016/j.ecolecon.2016.12.027.Kerschner, Christian, and Iñigo Capellán-Pérez. “Peak-Oil and Ecological Economics.” In Routdlege Handbook of Ecological Economics: Nature and Society, edited by Clive L. Spash, Routledge., 425–35. Abingdon, 2017.“Knutti et Al. - 2016 - A Scientific Critique of the Two-Degree Climate Ch.Pdf,” n.d.Scrieciu, S., A. Rezai, and R. Mechler. “On the Economic Foundations of Green Growth Discourses: The Case of Climate Change Mitigation and Macroeconomic Dynamics in Economic Modeling.” Wiley Interdisciplinary Reviews: Energy and Environment 2, no. 3 (May 1, 2013): 251–68. https://doi.org/10.1002/wene.57.Stern, Nicholas. “The Structure of Economic Modeling of the Potential Impacts of Climate Change: Grafting Gross Underestimation of Risk onto Already Narrow Science Models.” Journal of Economic Literature 51, no. 3 (September 1, 2013): 838–59. https://doi.org/10.1257/jel.51.3.838.Tol, Richard S. J. “The Economic Impacts of Climate Change.” Review of Environmental Economics and Policy 12, no. 1 (February 1, 2018): 4–25. https://doi.org/10.1093/reep/rex027.Valero, Alicia, Antonio Valero, Guiomar Calvo, and Abel Ortego. “Material Bottlenecks in the Future Development of Green Technologies.” Renewable and Sustainable Energy Reviews 93 (October 2018): 178–200. https://doi.org/10.1016/j.rser.2018.05.041.Weitzman, Martin L. “GHG Targets as Insurance Against Catastrophic Climate Damages.” Journal of Public Economic Theory 14, no. 2 (2012): 221–244. https://doi.org/10.1111/j.1467-9779.2011.01539.x.WIOD Background document available at http://www.wiod.org, 2012.