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Analyzing of the relative roles of mitigation measures in tackling global climate change
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Analyzing of the relative roles of mitigation measures in tackling global climate change
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Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 3
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Analyzing of the relative roles of mitigation measures in tackling global climate change
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Analyzing of the relative roles of mitigation measures in tackling global climate change
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Analyzing of the relative roles of mitigation measures in tackling global climate change
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Analyzing of the relative roles of mitigation measures in tackling global climate change
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Analysis of the relative roles of supply-side and demand-side measures in tackling global climate change

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Analysis of the relative roles of supply-side and demand-side measures in tackling global climate change

  1. 1. Analyzing of the relative roles of mitigation measures in tackling global climate change Formatvorlage des Untertitelmasters durch Klicken bearbeiten Institut für Energiewirtschaft und Rationelle Energieanwendung (IER) 17.11.2016 IER Analysis of the relative roles of supply-side and demand-side measures in tackling global climate change: TIAM-MACRO with Variable Elasticity of Substitution Babak Mousavi Markus Blesl 70th semi-annual IEA-ETSAP workshop – Madrid (Ciemat)
  2. 2. Analyzing of the relative roles of mitigation measures in tackling global climate change Outline IER Universität Stuttgart 2 Conclusions and outlook Scenario Analysis Normalization of production function Variable elasticity of substitution TIAM-MACRO Objective Motivation
  3. 3. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 3 Motivation Challenge of meeting long term decarbonisation targets cost-effectively, not only requires the large scale uptake of low carbon technologies and fuels but also reductions in energy-services (Webler and Tuler, 2010; Sorrell, 2015). As time passes firms and individuals respond more strongly to a unit increase of price of energy-services. Mitigation Higherrenewables Highernuclear HigherCCS Fossilfuelswitching Demand-side Efficiency improvement Service-demand reduction GeoengineeringAdaptation Attempts to tackle climate change Priceindependent Supply-side Efficiency improvement Pricedependent Projected surface temperature changes for the late 21st century (2090-2099). Temperatures are relative to the period 1980-1999 – Source: IPCC (2008)
  4. 4. Analyzing of the relative roles of mitigation measures in tackling global climate change Including: 1) 2) Macroeconomic impacts of climate mitigation policies. IER Universität Stuttgart 4 TIAM MACRO Implementation of variable (time-dependent) elasticity of substitution Normalization of the production function Extensions: Methodology Analysis of the relative roles of the decarbonisation measures in tackling global climate change: Research question Objective 3) The responsiveness of energy-services to their prices becomes stronger over time. Coupling the energy system model (TIAM) with a macroeconomic model (MACRO): TIAM-MACRO model with normalized production function and variable elasticity of substitution: CO2 reduction CO2 reduction Policy Higher price of energy services Lower energy service demand Lower energy consumption
  5. 5. Analyzing of the relative roles of mitigation measures in tackling global climate change Objective Function: Maximization of Negishi Weighted sum of regional Consumptions (C): 𝑀𝑎𝑥 𝑈 = 𝑡=1 𝑇 𝑟 𝑛𝑤𝑡 𝑟 . 𝑑𝑓𝑎𝑐𝑡 𝑟,𝑡 . ln (𝐶𝑟,𝑡) 𝐶𝑟,𝑡 = 𝑌𝑟,𝑡 − 𝐼𝑁𝑉𝑟,𝑡 − 𝐸𝐶𝑟,𝑡 − 𝑁𝑇𝑋𝑟,𝑡 𝐴𝐺𝐷𝑃𝑟,𝑡 = 𝐶𝑟,𝑡 + 𝐼𝑁𝑉𝑟,𝑡 + 𝑁𝑇𝑋 𝑟,𝑡 Production (𝑌𝑟,𝑡) = Constant elasticity of substitution function of labour (𝐿 𝑟,𝑡), capital (𝐾𝑟,𝑡) and service demands (𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚) : 𝑌𝑟,𝑡 = 𝑎𝑘𝑙 𝑟. 𝐾𝑟,𝑡 𝑘𝑝𝑣𝑠 𝑟.𝜌 𝑟 . 𝐿 𝑟,𝑡 1−𝑘𝑝𝑣𝑠 𝑟 .𝜌 𝑟 + 𝑑𝑚 𝑏 𝑟,𝑑𝑚. 𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚 𝜌 𝑟 1 𝜌 𝑟 𝜌𝑟= 1 − 1/𝐸𝑠𝑢𝑏 𝑟 IER Universität Stuttgart 5 Macroeconomic model (MACRO stand alone) Quadratic supply-cost function 𝐸𝐶𝑟,𝑡 = 𝑞𝑎 𝑟,𝑡 + 𝑑𝑚 𝑞𝑏 𝑟,𝑡,𝑑𝑚 . (𝐷𝐸𝑇𝑟,𝑡,𝑑𝑚)2 Energy model (TIAM) 𝐷𝐸𝑇𝑟,𝑡,𝑑𝑚 = 𝐴𝐸𝐸𝐼 𝑟,𝑡,𝑑𝑚 × 𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚 𝑃𝐺𝐷𝑃𝑟,𝑡 𝐴𝐸𝑆𝐶𝑟,𝑡 𝐷𝐸𝑇𝑟,𝑡 𝑃𝑟,𝑡 𝐺𝐷𝑃 𝐿𝑜𝑠𝑠 𝑟,𝑡 = (𝑃𝐺𝐷𝑃𝑟,𝑡 − 𝐴𝐺𝐷𝑃𝑟,𝑡) 𝑃𝐺𝐷𝑃𝑟,𝑡 × 100 For region (r), time period (t) and service-demand type (dm): TIAM-MACRO 𝑷𝑮𝑫𝑷: Projected GDP 𝑵𝑻𝑿: Trade in the numeraire good 𝑨𝑬𝑺𝑪: Energy system cost of TIAM 𝑷 : Marginal price 𝑫𝑬𝑻: Energy service demand of TIAM 𝑬𝑪: Energy system cost [MACRO] 𝑫𝑬𝑴: Energy service demand [MACRO] 𝑨𝑬𝑬𝑰: Autonomous energy efficiency improvement 𝒅𝒇𝒂𝒄𝒕: Discount factor 𝒂𝒌𝒍, 𝒃: Production function constants 𝝆: Substituion constant (time independent) 𝒒𝒂: constant term of the QSF 𝒒𝒃: Coefficient of demands in QSF 𝑰𝑵𝑽: Investment 𝒗𝒔: Capital value share 𝑨𝑮𝑫𝑷: Actual GDP Source: (Kypreos and Lehtila, 2013)
  6. 6. Analyzing of the relative roles of mitigation measures in tackling global climate change Objective Function: Maximization of Negishi Weighted sum of regional Consumptions (C): 𝑀𝑎𝑥 𝑈 = 𝑡=1 𝑇 𝑟 𝑛𝑤𝑡 𝑟 . 𝑑𝑓𝑎𝑐𝑡 𝑟,𝑡 . ln (𝐶𝑟,𝑡) 𝐶𝑟,𝑡 = 𝑌𝑟,𝑡 − 𝐼𝑁𝑉𝑟,𝑡 − 𝐸𝐶𝑟,𝑡 − 𝑁𝑇𝑋𝑟,𝑡 𝐴𝐺𝐷𝑃𝑟,𝑡 = 𝐶𝑟,𝑡 + 𝐼𝑁𝑉𝑟,𝑡 + 𝑁𝑇𝑋 𝑟,𝑡 Production (𝑌𝑟,𝑡) = Constant elasticity of substitution function of labour ( 𝐿 𝑟,𝑡) , capital ( 𝐾𝑟,𝑡) and service demands (𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚) : 𝑌𝑟,𝑡 = 𝑎𝑘𝑙 𝑟. 𝐾𝑟,𝑡 𝑘𝑝𝑣𝑠 𝑟.𝜌 𝑟 . 𝐿 𝑟,𝑡 1−𝑘𝑝𝑣𝑠 𝑟 .𝜌 𝑟 + 𝑑𝑚 𝑏 𝑟,𝑑𝑚. 𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚 𝜌 𝑟 1 𝜌 𝑟 𝜌𝑟= 1 − 1/𝐸𝑠𝑢𝑏 𝑟 IER Universität Stuttgart 6 Macroeconomic model (MACRO stand alone) Quadratic supply-cost function 𝐸𝐶𝑟,𝑡 = 𝑞𝑎 𝑟,𝑡 + 𝑑𝑚 𝑞𝑏 𝑟,𝑡,𝑑𝑚 . (𝐷𝐸𝑇𝑟,𝑡,𝑑𝑚)2 Energy model (TIAM) 𝐷𝐸𝑇𝑟,𝑡,𝑑𝑚 = 𝐴𝐸𝐸𝐼 𝑟,𝑡,𝑑𝑚 × 𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚 𝑃𝐺𝐷𝑃𝑟,𝑡 𝐴𝐸𝑆𝐶𝑟,𝑡 𝐷𝐸𝑇𝑟,𝑡 𝑃𝑟,𝑡 𝐺𝐷𝑃 𝐿𝑜𝑠𝑠 𝑟,𝑡 = (𝑃𝐺𝐷𝑃𝑟,𝑡 − 𝐴𝐺𝐷𝑃𝑟,𝑡) 𝑃𝐺𝐷𝑃𝑟,𝑡 × 100 For region (r), time period (t) and service-demand type (dm): TIAM-MACRO 𝑷𝑮𝑫𝑷: Projected GDP 𝑵𝑻𝑿: Trade in the numeraire good 𝑨𝑬𝑺𝑪: Energy system cost of TIAM 𝑷 : Marginal price 𝑫𝑬𝑻: Energy service demand of TIAM 𝑬𝑪: Energy system cost [MACRO] 𝑫𝑬𝑴: Energy service demand [MACRO] 𝑨𝑬𝑬𝑰: Autonomous energy efficiency improvement 𝒅𝒇𝒂𝒄𝒕: Discount factor 𝒂𝒌𝒍, 𝒃: Production function constants 𝝆: Substituion constant (time independent) 𝒒𝒂: constant term of the QSF 𝒒𝒃: Coefficient of demands in QSF 𝑰𝑵𝑽: Investment 𝒗𝒔: Capital value share 𝑨𝑮𝑫𝑷: Actual GDP Source: (Kypreos and Lehtila, 2013)
  7. 7. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 7 Variability of elasticity of substitution • In the MACRO model, elasticity of substitution represents the ease or difficulty of price-induced substitution between energy- service demands and the value-added pair capital and labor. • The assumption of constant elasticity of substitution, which limits the reaction of economy to energy price changes, represents a unique function form of the production function over time. • However, the constancy of this parameter may contain specification bias in the sense that as time passes firms and individuals may react differently to price changes. • To address this issue, Revankar (1966) introduced the concept of Variable Elasticity of Substitution (VES) production function, in which the assumption of constancy is dropped. • Several studies (e.g., Diwan, 1970; Lovell 1973; Zellner and Ryu, 1998; Karagiannis et al., 2005) analyzed the validity of VES production function using empirical data and found it a better function compared to CES and Cobb- Douglas. Capital Labor Energy service demands
  8. 8. Analyzing of the relative roles of mitigation measures in tackling global climate change Applying first-order optimality condition for the base year (t = 0): • Impossible to implement variable elasticity of substitution. IER Universität Stuttgart 8 Normalization of production function • The production function constants are dependent to elasticity of substitution value(s): 𝑌𝑟,𝑡 = 𝑎𝑘𝑙 𝑟. 𝐾𝑟,𝑡 𝑘𝑝𝑣𝑠 𝑟.𝜌 𝑟 . 𝐿 𝑟,𝑡 1−𝑘𝑝𝑣𝑠 𝑟 .𝜌 𝑟 + 𝑑𝑚 𝑏 𝑟,𝑑𝑚. 𝐷𝐸𝑀𝑟,𝑡,𝑑𝑚 𝜌 𝑟 1 𝜌 𝑟 𝑏 𝑟,𝑑𝑚 = 𝑃𝑟,0,𝑑𝑚 . ( 𝐷𝐸𝑀𝑟,0,𝑑𝑚 𝑌𝑟,0 )1−𝜌 𝑟 𝑎𝑘𝑙 𝑟 = 𝑌𝑟,0 𝜌 𝑟 − 𝑑𝑚 br,dm. DEMr,0,dm ρr 𝐾𝑟,0 𝑘𝑝𝑣𝑠 𝑟.𝜌 𝑟 • To tackle this issue, the production function is normalized (introduced by Klump and Grandville, 2000) 𝑌′ 𝑟,𝑡 = 𝑎𝑘𝑙 𝑟 ∗ . 𝐾′ 𝑟,𝑡 𝑘𝑝𝑣𝑠 𝑟.𝜌 𝑟 . 𝐿 𝑟,𝑡 1−𝑘𝑝𝑣𝑠 𝑟 .𝜌 𝑟 + 𝑑𝑚 𝑏 𝑟,𝑑𝑚 ∗ . 𝐷𝐸𝑀′ 𝑟,𝑡,𝑑𝑚 𝜌 𝑟 1 𝜌 𝑟 Where: 𝑌′ 𝑟,𝑡 = 𝑌𝑟,𝑡 𝑌𝑟,0 𝐾′ 𝑟,𝑡 = 𝐾 𝑟,𝑡 𝐾 𝑟,0 𝐷𝐸𝑀′ 𝑟,𝑡,𝑑𝑚 = 𝐷𝐸𝑀 𝑟,𝑡,𝑑𝑚 𝐷𝐸𝑀 𝑟,0,𝑑𝑚 𝑏 𝑟,𝑑𝑚 ∗ = 𝑃𝑟,0,𝑑𝑚 . ( 𝐷𝐸𝑀𝑟,0,𝑑𝑚 𝑌𝑟,0 ) 𝑎𝑘𝑙 𝑟 ∗ = 1 − 𝑑𝑚 𝑏 𝑟,𝑑𝑚 ∗ • Normalization creates specific ‘families’ of functions whose members share the same baseline points.
  9. 9. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 9 VES versus CES: scenario description • In order to compare VES production function with CES production function, following two scenarios are defined: Scenario Description Elasticity of substitution 1000 GtCO2 budget (2020-2100) 2D (0.25) 0.25 (constant)  2D (0.2-0.3) 0.2 - 0.3 (variable)  0.1 0.15 0.2 0.25 0.3 0.35 2000 2020 2040 2060 2080 2100 Elasticityofsubstitution
  10. 10. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 10 VES versus CES: results World GDP-Loss • The level of net effect of decarbonisation on GDP is related to the demand reduction possibility. Thus, higher/lower elasticity of substitution leads to lower/higher GDP-loss. • Due to the assumed function of variable elasticity, service demand reduction of 2D (0.25) case is lower than that of 2D (0.2-0.3) before 2060 and higher after this year. • In 2100, service demand reduction and GDP losses of 2D (0.2-0.3) are 23% higher and 12% lower than those of the other case, respectively. • changes of total global energy-service demand: % • Total global GDP-Loss: 0 1 2 3 4 5 6 2020 2030 2040 2050 2060 2070 2080 2090 2100 % 2D (0.2-0.3) 2D (0.25) -30 -25 -20 -15 -10 -5 0 2020 2030 2040 2050 2060 2070 2080 2090 2100 2D (0.2-0.3) 2D (0.2)
  11. 11. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 11 Scenario definition 1. To limit 2 degree temperature increase by the probability of more than 50%. 2. The initial potentials are mainly based on the high estimate of nuclear electrical generation capacity in 2013 report of International Atomic Energy Agency (IAEA). 3. The initial potentials of carbon storages are given based on the “best estimation” of econfys (2004). 4. The initial potentials of renewables are based on the assumed possible expansion pathways of different renewables which are provided by different studies (e.g. IEA PV roadmap 2014, IEA CSP roadmap 2014, GWEC 2014, …). Scenario 1000Gt carbon budget 2020- 2100 1 25% higher potential of nuclear 2 25% higher potential of carbon storages 3 25% higher potential of biomass 4 25% higher potential of other renewables 4 Elasticity of substitution Base      2D-DEM      ~0 2D      0.2 – 0.3 2D+NUC      0.2 – 0.3 2D+CCS      0.2 – 0.3 2D+REN      0.2 – 0.3 2D+REN+BIO      0.2 – 0.3 2D+All      0.2 – 0.3
  12. 12. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 12 What if energy-services do not repsond to price changes 0 500 1000 1500 2000 2500 3000 3500 4000 4500 2000 2020 2040 2060 2080 2100 US$/t-CO2 Marginal CO2 abatement cost 2D 2D-DEM • In 2D-DEM scenario where the possibility of reducing energy-services is limited to almost zero, the global GDP-loss and marginal CO2 abatement costs will be much higher than those of 2D case. • In 2100, for example, GDP-loss is almost 93% and marginal abatement costs in 2D-DEM is about 95% higher that those of 2D. 0 1 2 3 4 5 6 7 8 9 10 2020 2030 2040 2050 2060 2070 2080 2090 2100 % 2D 2D-DEM Global GDP-Loss
  13. 13. Analyzing of the relative roles of mitigation measures in tackling global climate change 0 200 400 600 800 1000 1200 1400 Base 2D 2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All Base 2D 2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All Base 2D 2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All 2020 2060 2100 EJ IER Universität Stuttgart 13 World-Primary Energy Consumption Coal Oil Natural gas Nuclear Biomass Other renewables • The total amount of the energy supply increases in all scenarios. Therefore: • The speed of the energy efficiency improvement is slower than of demand drivers (e.g. GDP growth). • Fossil fuels (especially coal) remain the main sources in the Base scenario. • Decarbonisation scenarios have lower primary energy consumption compared to the Base case which is mainly a consequence of reduction in the demand of energy-services. • In decarbonisation scenarios, the level of primary energy consumption varies (mainly) according to the demand of energy-services which is set by the price of energy-services. 6.6% 19.8% 29.7 %
  14. 14. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 14 Differences in decarbonisation scenarios • Relative changes compared to the 2D scenario (cumulated over 2020-2100): -20% -10% 0% 10% 20% 30% Biomass (with CCS) Biomass (excl. CCS) Other renewables Nuclear Fossil fuels (excl. CCS) Fossil fuel (with CCS) 2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All • Similar fossil fuel consumption in all scenarios is due to the inflexibility of some sectors in being completely decarbonized (e.g. some industrial processes). • Biomass with CCS found to be a vital and relatively cost-effective measure. However, its contribution is restricted to the capacity of carbon storages and the potential of Biomass.
  15. 15. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 15 Electricifaction of final consumption and CCS in power generation • Share of decarbonized electricity in total generation in all mitigation scenarios (especially after 2030) is almost the same. This is mainly due to the high flexibility of this sector in being decarbonized. • Policy recommendation: 0 20 40 60 80 100 120 2000 2020 2040 2060 2080 2100 Share of decarbonized electricity in total generation Base 2D 2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All % 0 10 20 30 40 50 60 2000 2020 2040 2060 2080 2100 2120 Share of electricity in final energy consumption Base 2D 2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All % Decarbonizing power generation and allowing electricity to substitute fossil fuels in inflexible energy uses (e.g. mobility) is a cost-effective decarbonisation strategy.
  16. 16. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 16 Decomposition of CO2 emissions • In all the scenarios, renewables is found to be the main mitigation option. • Higher contribution of service-demand reduction in the 2D compared to the others, denotes higher energy prices in this scenario caused by the climate target. • The presence of fossil-fuel switching after the year 2090 (negative emissions) can be traced back to the inflexibility of some sectors in being completely decarbonized. • Negligible share of efficiency improvement is due to the fact that the TIAM is an optimization model which selects cost-efficient technologies in all scenarios (including the base scenario). • Without Bio-CCS, achieving the 2°C target seems to be barely possible. The relative role of different measures in reducing total (cumulated) CO2 emissions over the time horizon: 2D 2D+REN 2D+NUC 2D+REN+Bio 2D+All 2D+CCS Service-demand 23 22 22 20 18 21 Efficiency 4 5 3 4 3 4 Renewables 29 32 27 34 32 27 Nuclear 18 16 21 15 17 17 CCS 20 19 20 20 24 25 Fossil fuel switching 6 6 6 6 6 6 -10 0 10 20 30 40 50 60 70 80 2020 2030 2040 2050 2060 2070 2080 2090 2100 Gt CO2 reduction in 2D compared to Base case Efficiency Service-demand Renewables Nuclear Fossil fuel switching Fossil fuel CCS Biomass CCS Base
  17. 17. Analyzing of the relative roles of mitigation measures in tackling global climate change 17IER Universität Stuttgart Macroeconomic impacts of the climate policy • Total global GDP-loss in 2D+All case is 23% less than that in 2D. • From the global perspective, next to renewables (specially biomass), CCS is found to be relatively cost- effective mitigation measure. This can be traced back to the vital role of biomass with CCS. 0 0.5 1 1.5 2 2.5 3 3.5 % GDP-loss differences between 2D and 2D+All 0 1 2 3 4 5 2020 2030 2040 2050 2060 2070 2080 2090 2100 % Total global GDP-loss over 2020-2100
  18. 18. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 18 Conclusions and Outlook Normalization of production function - Better economic interpretation - Necessary for modelling VES production function VES production function Possibility to consider dynamic reaction of economies to higher prices over the whole century. A cost effective decarbonisation strategy Decarbonizing power generation and allowing electricity to substitute fossil fuels in inflexible energy sectors (e.g. mobility) Feasibility of the 2D target Without deployment of a mixture of different mitigation options, it seems to be barely technically feasible and comes with huge macroeconomic impacts. Role of energy-service demand reduction In all the mitigation scenarios, the cumulative contribution of service demand is more than 18%. To address the uncertainty in elasticity of substitution Performing a sensitivity analysis on the elasticity of substitution as a epistemic uncertain factor. Without the possibility of reducing demands, the GDP-Loss and CO2 marginal price can increase to more than 90%.
  19. 19. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 19 References Diwan, R.K., 1970. About the Growth Path of Firms. Am. Econ. Rev. 60, 30–43. ETP, 2014a. Technology Roadmap Solar Thermal Electricity. Int. Energy Agency. ETP, 2014b. Technology Roadmap Solar Photovoltaic Energy. Int. Energy Agency. doi:10.1007/SpringerReference_7300 Global Wind Energy Council, 2014. Global Wind Energy Outlook 2014. Glob. Wind Energy Counc. 1–60. Hendriks, C., Graus, W., van Bergen, F., 2004. Global carbon dioxide storage potential and costs. Ecofys 64. IPCC, 2008. Climate Change 2007 Synthesis Report, Intergovernmental Panel on Climate Change [Core Writing Team IPCC. Karagiannis, G., Palivos, T., Papageorgiou, C., 2005. Variable elasticity of substitution and economic growth: Theory and evidence. New Trends Macroecon. 21–37. Klump, R., De La Grandville, O., 2000. Economic growth and the elasticity of substitution: Two theorems and some suggestions. Am. Econ. Rev. 90, 282–291. Kypreos, S., Lehtila, A., 2013. TIMES-Macro : Decomposition into Hard-Linked LP and NLP Problems. Lovell, C.A.K., 1973. CES and VES Production Functions in a Cross-Section Context. Chicago Journals 81, 705–720. Revankar, N.S., 1966. Revankar, Nagesh S. The constant and variable elasticity of substitution production functions : a comparative study in U.S. manufacturing industries. Unpubl. Mimeogr. Pap. Present. Econom. Soc. Meet. San Fr. California, 1966. Madison, Wisconsin, Soc. Syst. Res. Institute, Univ. Wisconsin. Sorrell, S., 2015. Reducing energy demand: A review of issues, challenges and approaches. Renew. Sustain. Energy Rev. 47, 74–82. Webler, T., Tuler, S.P., 2010. Getting the engineering right is not always enough: Researching the human dimensions of the new energy technologies. Energy Policy 38, 2690–2691. Zellner, A., Ryu, H., 1998. Alternative Functional Forms for Production, Cost and Returns to Scale Functions. J. Appl. Econom. 13, 101–127.
  20. 20. Analyzing of the relative roles of mitigation measures in tackling global climate change E-Mail Telefon +49 (0) 711 685- Universität Stuttgart Heßbrühlstraße 49a 70565 Stuttgart Babak Mousavi 878-66 Institut für Energiewirtschaft und Rationelle Energieanwendung (IER) am@ier.uni-stuttgart.de Institut für Energiewirtschaft und Rationelle Energieanwendung (IER) IER Thank you!
  21. 21. Analyzing of the relative roles of mitigation measures in tackling global climate changeIER Universität Stuttgart 21 Appendix - TIAM (TIMES Integrated Assessment Model) Intermediate Summer Winter Day Night 2005 2100 Global-15 regions Time dimension Purpose Analytical approach Methodology Programming technique Mathematical logic Geographical coverage General sectoral coverage Energy sectoral coverage Time resolution Time horizon Static Myopic dynamic Perfect foresight Fore- casting Back- casting Exploring Bottom-up Top-down Input/ Output Spreadsheet Simulation Optimization Economic equilibrium Econo- metric Other LinearNon- linearDynamic Mixed- integer Other Determ- inistic Stochastic Fuzzy Interval Local National Regional Global Energy sector Overall economy Specific sector All sectors 6 Short term Medium term Long term
  22. 22. Analyzing of the relative roles of mitigation measures in tackling global climate change 03.06.201 6 IER Universität Stuttgart 22 Appendix - TIAM (TIMES Integrated Assessment Model)

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