Marginal Abatement Cost Curves: Combining Energy System Modelling and Decomposition Analysis

Fabian Kesicki

doi:10.1007/s10666-012-9330-6

Environmental Modeling & Assessment

February 2013, Volume 18, Issue 1, pp 27–37 | Cite as

Marginal Abatement Cost Curves: Combining Energy System Modelling and Decomposition Analysis

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Fabian KesickiEmail author

Article

First Online: 10 June 2012

Received: 04 February 2011

Accepted: 29 May 2012

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Abstract

Marginal abatement cost (MAC) curves are a useful policy tool to communicate findings on the technological structure and the economics of CO₂ emissions reduction. However, existing ways of generating MAC curves do not display consistent technological detail and do not consider system-wide interactions and uncertainty in a structured manner. This paper details a new approach to overcome the present shortcomings by using an energy system model, UK MARKAL, in combination with index decomposition analysis. In addition, this approach allows different forms of uncertainty analysis to be used in order to test the robustness of the MAC curve. For illustration purposes, a sensitivity analysis concerning fossil fuel prices is applied to the transport sector of the UK. The resulting MAC curves are found to be relatively robust to different fuel costs at higher CO₂ tax levels. The new systems-based approach improves MAC curves through the avoidance of an inconsistent emissions baseline, the incorporation of system-wide interactions and the price responsiveness of demand.

Keywords

Marginal abatement cost curve CO₂ emissions Emissions reduction Interactions

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Notes

Acknowledgments

The author gratefully acknowledges the support of a German Academic Exchange Service (DAAD) scholarship and would like to thank two anonymous reviewers who helped to improve the article.

Appendix

The general decomposition formula of the Divisia index in the logarithmic mean specification is defined as follows:

$$ \Delta \;{\text{C}}{{\text{O}}_{{2,x}}} = \sum\limits_i {\frac{{{\text{CO}}_{{2,i}}^C - {\text{CO}}_{{2,i}}^0}}{{\ln \;{\text{CO}}_{{2,i}}^C - \ln \;{\text{CO}}_{{2,i}}^0}} \times \ln \left( {\frac{{x_i^C}}{{x_i^0}}} \right)} $$

(5)

In this context, x is a factor that drives CO₂ emissions, e.g. fuel intensity, and i is a criterion for structural differentiation. The superscript 0 and C represent a base scenario and a CO₂ reduction scenario.

In the example for the UK transport sector, the activity effect is calculated as follows:

$$ {\text{Activity}}\;{\text{effect}} = \sum\limits_{{i = {\text{vehicle}}\;{\text{type}}}} {\sum\limits_{{j = {\text{technology}}}} {\frac{{{\text{CO}}_{{2i,j}}^C - {\text{CO}}_{{2i,j}}^0}}{{{\text{ln CO}}_{{2i,j}}^C - {\text{ln CO}}_{{2i,j}}^0}} \times { \ln }\left( {\frac{{a_i^C}}{{a_i^0}}} \right)} } $$

(6)

The activity effect describes reductions in CO₂ emissions related to changes in the demand for energy services. As demand is assumed to be price elastic, it will decrease if the price for an energy service increases as a result of carbon policies. For example if the demand for car travel decreases, the emissions saved will fall under this category.

The structure effect is calculated as follows:

$$ {\text{Structure}}\;{\text{effect}} = \sum\limits_{{i = {\text{vehicle}}\;{\text{type}}}} {\sum\limits_{{j = {\text{technology}}}} {\frac{{{\text{CO}}_{{2i,j}}^C - {\text{CO}}_{{2i,j}}^0}}{{{\text{ln CO}}_{{2i,j}}^C - {\text{ln CO}}_{{2i,j}}^0}} \times { \ln }\left( {\frac{{s_{{i,j}}^C}}{{s_{{i,j}}^0}}} \right)} } $$

(7)

The structure effect as specified in Eq. (7) highlights the CO₂ emission reduction due to a shift in technologies satisfying transport demands. Emission savings related to this effect occur due to a reduction of the relative part of carbon intensive measures in the technology mix. However, it is more interesting to see what technologies are chosen instead of the carbon intensive ones. Therefore, the emission reduction associated with the reduced use of a carbon intensive technology is redistributed to less carbon intensive technologies satisfying a higher part of transport demands. In an example where 5 % of all petrol cars are substituted for electric cars, the emission reduction is not attributed to the lower use of petrol cars, but to the higher use of electric cars.

The fuel intensity effect is calculated as follows:

$$ \matrix{ {{\text{Fuel}}\;{\text{intensity}}\;{\text{effect}}} \hfill &{} \hfill \\ {} \hfill &{ = \sum\limits_{{i = {\text{vehicle}}\;{\text{type}}}} {\sum\limits_{{j = {\text{technology}}}} {\frac{{{\text{CO}}_{{2i,j}}^C - {\text{CO}}_{{2i,j}}^0}}{{{\text{ln CO}}_{{2i,j}}^C - {\text{ln CO}}_{{2i,j}}^0}} \times { \ln }\left( {\frac{{f_{{i,j}}^C}}{{f_{{i,j}}^0}}} \right)} } } \hfill \\ }<!end array> $$

(8)

The fuel intensity effect represents the impact of changes in the fuel efficiency of technologies on CO₂ emissions. If a more efficient car type, which still uses the same fuel, but can travel the same distance with less fuel is chosen by the model, the resulting decrease in emissions is recorded as a fuel intensity effect.

The carbon intensity effect is calculated as follows:

$$ \matrix{ {{\text{Carbon}}\;{\text{intensity}}\;{\text{effect}}} \hfill &{} \hfill \\ {} \hfill &{ = \sum\limits_{{i = {\text{vehicle}}\;{\text{type}}}} {\sum\limits_{{j = {\text{technology}}}} {\frac{{{\text{CO}}_{{2i,j}}^C - {\text{CO}}_{{2i,j}}^0}}{{{\text{ln CO}}_{{2i,j}}^C - {\text{ln CO}}_{{2i,j}}^0}} \times { \ln }\left( {\frac{{c_{{i,j}}^C}}{{c_{{i,j}}^0}}} \right)} } } \hfill \\ }<!end array> $$

(9)

The carbon intensity effect refers to changes in the carbon intensity of fuels. The carbon intensity of diesel can be decreased by blending in biodiesel and the carbon intensity of electricity can be reduced by switching towards CCS technologies, renewables or nuclear in the electricity sector.

References

1.
Committee on Climate Change. (2008). Building a low-carbon economy—the UK’s contribution to tackling climate change. London: Committee on Climate Change.Google Scholar
2.
Carmel, A. (2008). Paying for mitigation—the GLOCAF model. Bali: Paper presented at the United Nations Framework Convention on Climate Change COP 13.Google Scholar
3.
HM Government. Department of Trade and Industry. (2007). Meeting the energy challenge: a white paper on energy. London: Stationery Office.Google Scholar
4.
HM Government (2009). Analytical annex—the UK low carbon transition plan. London: HM Government.Google Scholar
5.
Blok, K., Worrell, E., Cuelenaere, R., & Turkenburg, W. (1993). The cost effectiveness of CO₂ emission reduction achieved by energy conservation. Energy Policy, 21(6), 656–667. doi: 10.1016/0301-4215(93)90289-R.CrossRefGoogle Scholar
6.
Kennedy, M. (2010). Ireland’s future: a low carbon economy? The impact of green stimulus investment. Vilnius: IAEE European Conference.Google Scholar
7.
Kiuila, O., & Rutherford, T. F. (2010). Abatement options and climate policy choices. Stockholm: International Energy Workshop.Google Scholar
8.
Poswiata, J., & Bogdan, W. (2009). Assessment of greenhouse gas emissions abatement potential in Poland by 2030. Warsaw: McKinsey.Google Scholar
9.
Sweeney, J., Weyant, J., Chan, T. T., Chowdhary, R., Gillingham, K., Guy, A., et al. (2008). Analysis of Measures to Meet the Requirements of California’s Assembly Bill 32. Stanford: Precourt Institute for Energy Efficiency, Standford University.Google Scholar
10.
Grubb, M., Edmonds, J., ten Brick, P., & Morrison, M. (1993). The costs of limiting fossil–fuel CO₂ emissions: a survey and analysis. Annual Review of Energy and the Environment, 18, 397–478.CrossRefGoogle Scholar
11.
Nauclér, T., & Enkvist, P. A. (2009). Pathways to a low-carbon economy—version 2 of the global greenhouse gas abatement cost curve. In McKinsey & Company (Ed.).Google Scholar
12.
Kesicki, F., & Ekins, P. (2012). Marginal abatement cost curves: a call for caution. Climate Policy, 12, 219–236.CrossRefGoogle Scholar
13.
McKitrick, R. (1999). A derivation of the marginal abatement cost curve. Journal of Environmental Economics and Management, 37(3), 306–314.CrossRefGoogle Scholar
14.
Klepper, G., & Peterson, S. (2006). Marginal abatement cost curves in general equilibrium: the influence of world energy prices. Resource and Energy Economics, 28(1), 1–23.CrossRefGoogle Scholar
15.
Morris, J., Paltsev, S., & Reilly, J. (2012). Marginal abatement costs and marginal welfare costs for greenhouse gas emissions reductions: results from the EPPA model. Environmental Modeling and Assessment. doi: 10.1007/s10666-011-9298-7.
16.
Hourcade, J.-C., Halsnaes, K., Jaccard, M., Montgomery, W. D., Richels, R., Robinson, J., et al. (1995). A review of mitigation cost studies. In J. J. Houghton, L. G. Meiro Filho, B. A. Callander, N. Harris, A. Kattenberg, & K. Maskell (Eds.), Climate change 1995: contribution of Working Group III to the Second Assessment of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Google Scholar
17.
Ellerman, A. D., & Decaux, A. (1998). Analysis of post-Kyoto CO ₂ emissions trading using marginal abatement curves. Cambridge: Massachusetts Institute of Technology.Google Scholar
18.
van Vuuren, D. P., de Vries, B., Eickhout, B., & Kram, T. (2004). Responses to technology and taxes in a simulated world. Energy Economics, 26(4), 579–601.CrossRefGoogle Scholar
19.
Blok, K., de Jager, D., Hendriks, C., Kouvaritakis, N., & Mantzos, L. (2001). Economic Evaluation of Sectoral Emission Reduction Objectives for Climate Change — Comparison of ‘Top-down’ and ‘Bottom-up’. In DG Environment European Commission (Ed.), Analysis of Emission Reduction Opportunities for CO ₂ in European Union. Brussels: Ecofys Energy and Environment, National Technical University of Athens.Google Scholar
20.
Kesicki, F. (2010). Marginal abatement cost curves for policy making—expert-based vs. model-derived curves. Rio de Janeiro: Paper presented at the IAEE’s 2010 International Conference.Google Scholar
21.
Stoft, S. (1995). The economics of conserved-energy ‘supply’ curves. Energy Journal, 16(4), 109–140.Google Scholar
22.
Loulou, R., Goldstein, G., & Noble, K. (2004). Documentation for the MARKAL family of models. Energy Technology Systems Analysis Programme.Google Scholar
23.
Kannan, R. (2009). Uncertainties in key low carbon power generation technologies—implication for UK decarbonisation targets. Applied Energy, 86(10), 1873–1886. doi: 10.1016/j.apenergy.2009.02.014.CrossRefGoogle Scholar
24.
Strachan, N., Pye, S., & Kannan, R. (2009). The iterative contribution and relevance of modelling to UK energy policy. Energy Policy, 37(3), 850–860. doi: 10.1016/j.enpol.2008.09.096.CrossRefGoogle Scholar
25.
Anandarajah, G., & Strachan, N. (2010). Interactions and implications of renewable and climate change policy on UK energy scenarios. Energy Policy, 38(11), 6724–6735. doi: 10.1016/j.enpol.2010.06.042.CrossRefGoogle Scholar
26.
Strachan, N., Kannan, R., & Pye, S. (2008). Scenarios and sensitivities on long-term UK carbon reductions using the UK MARKAL and MARKAL-macro energy system models. London: UKERC.Google Scholar
27.
Kannan, R., Strachan, N., Balta-Ozkan, N., & Pye, S. (2007). UK MARKAL model documentation. www.ukerc.ac.uk. Accessed 18 Nov 2010.
28.
Ang, B. W., & Zhang, F. Q. (2000). A survey of index decomposition analysis in energy and environmental studies. Energy, 25(12), 1149–1176.CrossRefGoogle Scholar
29.
Diakoulaki, D., Mavrotas, G., Orkopoulos, D., & Papayannakis, L. (2006). A bottom-up decomposition analysis of energy-related CO₂ emissions in Greece. Energy, 31(14), 2638–2651.CrossRefGoogle Scholar
30.
Shrestha, R. M., Anandarajah, G., & Liyanage, M. H. (2009). Factors affecting CO₂ emission from the power sector of selected countries in Asia and the Pacific. Energy Policy, 37(6), 2375–2384.CrossRefGoogle Scholar
31.
Ang, B. W. (2004). Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy, 32(9), 1131–1139.CrossRefGoogle Scholar
32.
Divisia, F. (1925). L’indice monétaire et la théorie de la monnaie. Revue d’économie politique, 39(5), 980–1008.Google Scholar
33.
Vartia, Y. (1976). Ideal log-change index numbers. Scandinavian Journal of Statistics, 3(3), 121–126.Google Scholar
34.
Montgomery, J. K. (1937). The mathematical problem of the price index. London: King.Google Scholar
35.
Ang, B. W., Zhang, F. Q., & Choi, K.-H. (1998). Factorizing changes in energy and environmental indicators through decomposition. Energy, 23(6), 489–495.CrossRefGoogle Scholar
36.
Ang, B. W., & Liu, N. (2007). Energy decomposition analysis: IEA model versus other models. Energy Policy, 35(3), 1426–1432.CrossRefGoogle Scholar
37.
Department of Energy and Climate Change (2010). Provisional 2009 results for UK greenhouse gas emissions and progress towards targets. London.Google Scholar
38.
Kesicki, F. (2012). Intertemporal issues and marginal abatement costs in the UK transport sector. Transportation Research Part D: Transport and Environment, 17(5), 418–426.CrossRefGoogle Scholar

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Fabian Kesicki
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1.UCL Energy InstituteUniversity College LondonLondonUK

Marginal Abatement Cost Curves: Combining Energy System Modelling and Decomposition Analysis

Abstract

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Acknowledgments

References

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