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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
2
http://www.medeas.eu/MEDEAS H2020 project (2016-2020)
3
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:
4
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
5
6
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
8
MEDEAS-World
Source: Hardt et al. (2017)
3. Main assumptions - methodology
Emerging field of macro-ecological economics modelling:
1) Representation of economic processes
9
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
10
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
11
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
12
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
13
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
14
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.
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