The best of two traditions: Integrating bottom-up information in - - PowerPoint PPT Presentation

Taran Fæhn, Research Dep., Statistics Norway

Linking CGE and TIMES Models - Workshop Technology and Innovation Centre, Strathclyde Univ., 09 Nov. 2016 1

The best of two traditions:

Integrating bottom-up information in CGE models, including TIMES input

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Background

TOP-DOWN approach (e.g. CGE-models)

• Disregard most opportunities for future technologies
• Assume technologies of today (when calibrating) and
• f 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

Traditional approaches overestimate the costs of climate policies: potential abatement options are omitted The approaches complement each other and should be combined

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

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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

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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

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Soft-linking CGE and MARKAL

Three different approaches:

1)

Inserting estimated sector-wise abatement equations in CGE

(Fæhn and Isaksen, Energy Journal, 2015)

2)

Introducing abatement as a CES composite of emissions and capital

(Kiuila and Rutherford, Energy efficiency: Ecological Economics, 2013; Bye et al., SSB-DP 817/2016)

3)

Introduce each abatement option as Leontief functions - activate the profitable step by step

(Böhringer, Energy Economics,1998)

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Integrating bottom-up info in CGE

V X C V V X U D U U U U dc c f cD C U U c f D / ) 6 ( ~ ) 5 ( / ) 4 ( ~ ) 3 ( . / ) ( ) 2 ( . / ) ( ) 1 (           



 

Abatement Emissions Capital Costs Abatement by technology

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.

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1) Inserting estimated sector-wise abatement equations in CGE

Abatement measure Annuity (EUR/tCO2e) Abatement (Mt CO2e) Accumulated abatement (Mt CO2e)

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

• Substitution of bio (pulp and paper)

241 0.09 4.21

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100 200 300 400 500 1 2 3 4 5 6 7

Bottom-up input information (Process industry):

Mt CO2e €/t CO2e

1) Inserting estimated sector-wise abatement equations in CGE

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Abatement measure Annuity (EUR/tonne CO2e) Abatement (million tonnes CO2e) Accumulated abatement (million tonnes CO2e) a Energy efficiency offshore 50 0.20 0.20 b Electrification Melkøya -1 50 0.17 0.37 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 G Electrification new site 175 0.15 1.99 h Electrification North Sea north 250 1.13 3.12 i Kårstø processing CCS 281 0.77 3.89

100 200 300 400 500 1 2 3 4 5 6 7

petroleum

process

€/t CO2e Mt CO2e

Bottom-up input information (+ Petroleum extraction):

1) Inserting estimated sector-wise abatement equations in CGE

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Abatement measure Annuity (EUR/ tonne CO2e) Abatement (million tonnes CO2e) Accumulated abatement (million tonnes CO2e) a Efficiency improvements private cars– level 1 44 0.72 0.72 b Efficiency improvements private cars– level 2 60 0.62 1.34 c Zero emissions vehicles– private and public 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

100 200 300 400 500 1 2 3 4 5 6 7 Fig 2.1 Fig 2.2 Fig 2.3

process

transport petroleum

Mt CO2e €/t CO2e

Bottom-up input information (+ Road transport):

1) Inserting estimated sector-wise abatement equations in CGE

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€/t CO2e

Estimated marginal abatement cost (MAC) curves:

y = 7,843x2 + 16,851x R² = 0,8539 y = 15,076x3 - 99,501x2 + 234,12x R² = 0,9324 y = 13,31x3 - 35,547x2 + 82,021x R² = 0,9829 100 200 300 400 500 1 2 3 4 5 6 7

1) Inserting estimated sector-wise abatement equations in CGE

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The equations:

V X C V V U U dc c f cD C X U D U U U U c f D / ) 6 ( ~ ) 5 ( . / ) ( ) 4 ( / ) 3 ( ~ ) 2 ( . ~ / ~ ) ( ) 1 (           



 

The CGE adjustments:

1) Technological abatement curve= Relationship between accumulated abatement and marginal costs (x scale factor) 2) Total abatement = abatement in the traditional model+resulting from climate technology investm. 3) Endogenous em.coefficients 4) Total abatement costs= integral above curve = added input costs in the industry (less efficient inputs) 5) Total production costs include the abatement costs  6) Endogenous productivity

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 )

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1) Inserting estimated sector-wise abatement equations in CGE

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.

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2)Introducing abatement as a CES composite of emissions and capital

The CES structure of household consumption:

Fuel Consumption Housing Transport Other goods and services Dwelling Energy

Fossil Electricity

Transport n 1

Paraffin and heating oil Gas District Fuel wood, coal, etc.

…

2)Introducing energy efficiency improvements

as a CES composite of energy and capital

Available energy efficiency measure information from TIMES-Norway

Household Measure Investment cost Lifetime Saving potential (Gwh/yr) Annuities Excess cost Type code type €/kWh yrs 2010 €/kWh % Existing single unit RSIOH031 Post-insulation roof 2,21 30 1 495 0,14 +15 % New blocks RMUNH045 Solar collector 2,50 20

• 0,20

+60 % New single unit RSINH045 Solar collectors, new 2,50 20

• 0,20

+60 % Existing blocks RMUOH033 Post-insulation wall 3,58 30 168 0,23 +86 % Existing blocks RMUOH032 Post-insulation floor 3,61 30 30 0,23 +88 % Existing single unit RSIOH032 Post-insulation floor 3,65 30 1 906 0,24 +90 % Existing blocks RMUOH045 Solar collectors, rehab 3,00 20 192 0,24 +93 % Existing single unit RSIOH045 Solar collectors, rehab 3,00 20 1 206 0,24 +93 % Existing single unit RSIOH033 Post-insulation wall 4,52 30 4 069 0,29 +135 % Existing blocks RMUOH034 Replacement entrance door 4,56 30 7 0,30 +137 % Existing single unit RSIOH034 Replacement entrance door 4,56 30 424 0,30 +137 % Existing blocks RMUOH035 Replacement windows 10,57 30 81 0,69 +450 % Existing single unit RSIOH035 Replacement windows 10,57 30 1 814 0,69 +450 %

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2)Introducing energy efficiency improvements as a CES composite of

energy and capital

Investment costs of energy savings data and calibrated substitution

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1 2 3 4 5 6 2 4 6 8 10 12

Relative investment costs Energy savings base year, TWh

DATA ESTIMATED CES (0.3)

2)Introducing energy efficiency improvements

as a CES composite of energy and capital

Some lessons learned

We gained in terms of:

• The new elasticity is based on future expectations, not old, outdated? information.
• Much technology information abstracted to one parameter

Still potential for improvements:

• Not all technology information is relevantly captured by the substitution elasticity –

more parameters could be calibrated anaogously to capture more

• Technology data typically include measures with negative costs – need to identify the

reason for such data to treat them correctly

• The measures cannot be precisely identified in the CGE results – extrapolations
• Smoothed curves – costs typically have discrete jumps, not incremental steps
• New information requires new estimations/calibrations

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2)Introducing energy efficiency improvements

as a CES composite of energy and capital

Further plans

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Costs Abatement by technology

• Combine the two approaches:
• Energy efficiency and energy mix changes better represented

by substitution elasticities,

• Process emissions and end-of-the-pipe-like abatement better

represented by MACs

• We avoid double-counting
• Use stepwise MAC functions
• Measures can be precisely identified
• Don’t need any estimations – easier to update
• Input-output information of the measures are represented and

included in the input-output system of the CGE – right factor-market responses

Summing up the experiences in SSB

• Soft-linking top-down and bottom-up models

(Bjertnæs,Martinsen&Tsygankova 2013, Martinsen 2011)

Doable, but inconsistencies remain Time-consuming and must be repeated for every new policy case

• Integrating technological information into CGE

(Fæhn&Isaksen, 2016; Bye, Fæhn&Rosnes, 2015)

By far the most promising

• use bottom-up model inputs instead of outputs
• integrate in CGE model once and for all

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Thank you for the attention taran.faehn@ssb.no

REFERENCES:

Bjertnæs, G, T. Martinsen, M. Rybalka (2013): Norwegian climate policy reforms in the presence of an international quota market, Energy Economics 39, 147-158 Martinsen, T (2011): Introducing technology learning for energy technologies in a national CGE model through softlinks to global and national energy models, Energy Policy 39 , 3327–3336 Fæhn, T. and E.T. Isaksen (2016): Diffusion of climate technologies in the presence of commitment problems, Energy Journal 37 (2), 155-180 Bye, B., T. Fæhn, O. Rosnes (2015): Residential energy efficiency and European carbon policies: A CGE-analysis with bottom-up information on energy efficiency technologies, Discussion Papers No. 817, Statistics Norway.

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