Taran Fæhn, Research Dep., Statistics Norway The best of two traditions: Integrating bottom-up information in CGE models, including TIMES input Linking CGE and TIMES Models - Workshop Technology and Innovation Centre, Strathclyde Univ., 1 09 Nov. 2016
Background Traditional approaches overestimate the costs of climate policies: potential abatement options are omitted TOP-DOWN approach (e.g. CGE-models) • Disregard most opportunities for future technologies • Assume technologies of today (when calibrating) and of yesterday (when estimating) BOTTOM-UP approach (e.g. energy system models): • Disregard most reallocations to cleaner activities taking place when emitting is costly • Exogenous consumption and production patterns The approaches complement each other and should be combined 2
Analyses of climate and energy policies with linking-approaches • Soft-linking CGE and MARKAL ( Bjertnæs, Martinsen&Tsygankova,2013; Martinsen, 2011) • Integrating bottom-up info in CGE 1: Inserting marginal abatement cost functions ( Fæhn&Isaksen, 2016) 2: Abatement as a CES composite (Bye, Fæhn&Rosnes, 2015) • Further plans 3
Soft-linking CGE and MARKAL a) Bjertnæs, Martinsen&Tsygankova (2013, Energy Economics 39) b) Martinsen (2011, Energy Policy 39) Research questions: Effects of unilateral vs. global carbon pricing on a) public budgets, welfare and emissions b) learning and costs of technologies Approach: Three models in cooperation 1) Global MARKAL IN: carbon pricing OUT: Global energy prices, learning effects – no feedback 2) National MARKAL IN: energy prices + technology costs from 1); demand from 2) OUT: system costs and emissions 3) National CGE IN: sector-dispersed system costs (acc. to sectorial pattern, as productivity loss) and total emission cuts (added to those from CGE), OUT: public budgets, welfare 4
Soft-linking CGE and MARKAL Some lessons learned: We gained in terms of: • more response possibilities and related costs • interaction between national and global responses The costs were large: • the models have different aggregation – matching sectors and instruments • much overlapping and inconsistent endogeneity • iteration was costly ex ante (communicating across disciplines and trial and error with technical solutions) • iterating model solutions was also costly (many simulations) The result: • not fully iterated solutions • two different publications with focus on each model/discipline 5
Integrating bottom-up info in CGE Three different approaches: 1) Inserting estimated sector-wise abatement equations in CGE (Fæhn and Isaksen, Energy Journal, 2015) ( 1 ) D f ( c ) U / U . 0 ( 2 ) ( ) / . 0 C cD f c dc U U ~ ( 3 ) U U D ( 4 ) U / X ~ ( 5 ) V V C ( 6 ) X / V 2) Introducing abatement as a CES composite of emissions and capital (Kiuila and Rutherford, Energy efficiency: Abatement Ecological Economics, 2013; Bye et al., SSB-DP 817/2016) Emissions Capital 3) Introduce each abatement option as Leontief functions - activate the profitable step by step (Böhringer, Energy Economics ,1998) Costs Abatement by technology 6
1) Inserting estimated sector-wise abatement equations in CGE Fæhn and Isaksen (2016, Energy Journal 37/2) Research question: What if commitment problems hamper investments in clean technologies? Carbon pricing and subsidies to reach national targets Approach: 1) Bottom-up data on sector-wise technology options included as MACs 2) Technological abatement added to the sector’s abatement through CGE reallocations and factor substitution Results : Without confidence in persistent policy, no investments and a trippling of abatement costs. Up-front subsidies is a second-best policy option. 7
1) Inserting estimated sector-wise abatement equations in CGE Bottom-up input information (Process industry): Abatement measure Annuity (EUR/tCO 2 e) Abatement (Mt CO 2 e) Accumulated abatement (Mt CO 2 e) a Process optimisation (metals) 6 0.50 0.50 b Energy efficiency and substitution (metals) 6 0.30 0.80 C Energy efficiency and substitution (pulp and paper) 6 0.29 1.09 d Substitution of bio (cement and other minerals) 6 0.16 1.25 e Energy efficiency and substitution (chemicals) 6 0.04 1.30 f <40% charcoal for coke (ferrosilicon) 52 0.45 1.75 g <20% charcoal for coke (ferromanganese) 76 0.19 1.94 h <80% charcoal for coke (ferrosilicon) 79 0.50 2.44 i Substitution of bio (cement) 81 0.10 2.54 j Process optimisation (petrochemicals) 83 0.02 2.56 k Charcoal substitute for coke (silicon carbide) 109 0.02 2.58 l Substitution of bio (anodes) 137 0.07 2.65 m CCS (fertilisers) 163 0.69 3.34 n CCS (cement) 163 0.79 4.13 o Substitution of bio (pulp and paper) 241 0.09 4.21 500 €/t CO 2 e 400 300 200 100 0 0 1 2 3 4 5 6 7 Mt CO 2 e 8
1) Inserting estimated sector-wise abatement equations in CGE Bottom-up input information (+ Petroleum extraction): Abatement measure Annuity Abatement (million Accumulated abatement (EUR/tonne CO 2 e) tonnes CO 2 e) (million tonnes CO 2 e) a Energy efficiency offshore 50 0.20 0.20 Electrification Melkøya -1 50 0.17 0.37 b c Electrification Melkøya 2 150 0.30 0.67 d Electrification Melkøya 3 156 0.13 0.80 e Mongstad processing CCS 163 0.62 1.42 f Electrification North Sea south 169 0.42 1.84 Electrification new site 175 0.15 1.99 G h Electrification North Sea north 250 1.13 3.12 €/t CO 2 e 500 i Kårstø processing CCS 281 0.77 3.89 400 300 petroleum 200 process 100 0 0 1 2 3 4 5 6 7 Mt CO 2 e 9
1) Inserting estimated sector-wise abatement equations in CGE Bottom-up input information (+ Road transport): Annuity Abatement Accumulated Abatement measure (EUR/ (million tonnes CO 2 e) abatement (million tonne CO 2 e) tonnes CO 2 e) Efficiency improvements private cars – level 1 a 44 0.72 0.72 Efficiency improvements private cars – level 2 60 0.62 1.34 b Zero emissions vehicles – private and public c 109 0.27 1.61 d Intermixture of ethanol E85 128 0.19 1.80 e Intermixture of 1.generation biodiesel 166 0.69 2.49 f Intermixture of ethanol E5, E10, E20 219 0.13 2.62 g Intermixture of 2. generation biodiesel 341 0.59 3.21 500 €/t CO 2 e 400 300 Fig 2.1 process Fig 2.2 petroleum 200 Fig 2.3 transport 100 0 Mt CO 2 e 0 1 2 3 4 5 6 7 10
1) Inserting estimated sector-wise abatement equations in CGE Estimated marginal abatement cost (MAC) curves: y = 13,31x 3 - 35,547x 2 + 82,021x y = 15,076x 3 - 99,501x 2 + 234,12x 500 R² = 0,9829 R² = 0,9324 400 300 y = 7,843x 2 + 16,851x €/t CO 2 e R² = 0,8539 200 100 0 0 1 2 3 4 5 6 7 11
1) Inserting estimated sector-wise abatement equations in CGE The equations: The CGE adjustments: ~ ~ 1) Technological abatement curve= ( 1 ) D f ( c ) U / U . 0 Relationship between accumulated abatement and ~ marginal costs (x scale factor) ( 2 ) U U D 2) Total abatement = abatement in the traditional model+resulting ( 3 ) U / X from climate technology investm. 3) Endogenous em.coefficients ( 4 ) C cD f ( c ) dc U / U . 0 4) Total abatement costs= integral above curve = added input costs ~ in the industry (less efficient inputs) ( 5 ) V V C 5) Total production costs include the abatement costs ( 6 ) X / V 6) Endogenous productivity 12
1) Inserting estimated sector-wise abatement equations in CGE Some lessons learned We gained in terms of: • Sector-wise technology information could be exploited • While soft-linking procedures need to be repeated for every project and simulation, integration is made once and for all Still potential for improvements: • Abatement technologies assumed to have same factor intensities as the production technology -> Wrong factor-market responses • Potential double counting, as estimated substitution elasticities may embody abatement potentials (e.g. between energy goods or between energy and capital ) 13
2) Introducing abatement as a CES composite of emissions and capital Bye, Fæhn&Rosnes (2015, SSB-DP 817) Research question: Introducing energy efficiency targets in households – effects on household behaviour, rebound, emissions and welfare. Approach: 1) Limited to energy efficiency measures in households. Bottom-up data from the TIMES model. 2) Used the data to estimate substitutiton elasticity between energy and capital (3) Analogue procedure can be used between emissions and capital) Results: Energy efficiency gains come at a substantial cost and also with 40% energy rebound to the economy at large. 14
2) Introducing energy efficiency improvements as a CES composite of energy and capital The CES structure of household consumption: Consumption Housing Transport Other goods and services … Dwelling Energy Transport Fuel 1 n Electricity Fossil Gas Paraffin and heating oil Fuel wood, coal, etc. District
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