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Disaggregating Electricity Generation Technologies in CGE Models

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  • Vipin Arora
  • Yiyong Cai

Abstract

We illustrate the importance of disaggregating electricity generation when considering responses to environmental policies. We begin by reviewing various approaches to electric sector modelling in Computable General Equilibrium (CGE) models, and then clarify and expand upon the structure and calibration of the “technology bundle” approach. We also simulate the proposed U.S. Clear Power Plan and show how a disaggregate electricity sector can change results. Our simulations indicate that both the ability to switch between generation technologies and the manner of aggregation in electricity production are important for quantifying the economic costs of the plan.

Suggested Citation

  • Vipin Arora & Yiyong Cai, 2014. "Disaggregating Electricity Generation Technologies in CGE Models," CAMA Working Papers 2014-54, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2014-54
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2014-07/54_2014_arora_cai.pdf
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    References listed on IDEAS

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    Cited by:

    1. Cai, Yiyong & Newth, David & Finnigan, John & Gunasekera, Don, 2015. "A hybrid energy-economy model for global integrated assessment of climate change, carbon mitigation and energy transformation," Applied Energy, Elsevier, vol. 148(C), pages 381-395.
    2. Dai, Hancheng & Xie, Xuxuan & Xie, Yang & Liu, Jian & Masui, Toshihiko, 2016. "Green growth: The economic impacts of large-scale renewable energy development in China," Applied Energy, Elsevier, vol. 162(C), pages 435-449.
    3. repec:eee:eneeco:v:63:y:2017:i:c:p:116-128 is not listed on IDEAS
    4. repec:aen:journl:ej38-5-peters is not listed on IDEAS
    5. repec:eee:rensus:v:74:y:2017:i:c:p:788-799 is not listed on IDEAS
    6. repec:eee:appene:v:205:y:2017:i:c:p:57-68 is not listed on IDEAS
    7. Peters, Jeffrey C. & Hertel, Thomas W., 2016. "The database–modeling nexus in integrated assessment modeling of electric power generation," Energy Economics, Elsevier, vol. 56(C), pages 107-116.
    8. Gabriele Standardi & Yiyong Cai & Sonia Yeh, 2016. "Sensitivity of Modeling Results to Technological and Regional Details: The Case of Italy’s Carbon Mitigation Policy," EcoMod2016 9557, EcoMod.
    9. Stefan Nabernegg & Birgit Bednar-Friedl & Fabian Wagner & Thomas Schinko & Janusz Cofala & Yadira Mori Clement, 2017. "The Deployment of Low Carbon Technologies in Energy Intensive Industries: A Macroeconomic Analysis for Europe, China and India," Energies, MDPI, Open Access Journal, vol. 10(3), pages 1-26, March.
    10. Galik, Christopher S. & Abt, Robert C. & Latta, Gregory & Méley, Andréanne & Henderson, Jesse D., 2016. "Meeting renewable energy and land use objectives through public–private biomass supply partnerships," Applied Energy, Elsevier, vol. 172(C), pages 264-274.
    11. Peng Ou & Ruting Huang & Xin Yao, 2016. "Economic Impacts of Power Shortage," Sustainability, MDPI, Open Access Journal, vol. 8(7), pages 1-21, July.
    12. Yeh, Sonia & Cai, Yiyong & Huppman, Daniel & Bernstein, Paul & Tuladhar, Sugandha & Huntington, Hillard G., 2016. "North American natural gas and energy markets in transition: insights from global models," Energy Economics, Elsevier, vol. 60(C), pages 405-415.
    13. Yan, Junna & Zhao, Tao & Kang, Jidong, 2016. "Sensitivity analysis of technology and supply change for CO2 emission intensity of energy-intensive industries based on input–output model," Applied Energy, Elsevier, vol. 171(C), pages 456-467.
    14. Pothen, Frank & Hübler, Michael, 2017. "A Regional Trade Model with Ricardian Productivity Gains and Multi-technology Electricity Supply," Hannover Economic Papers (HEP) dp-585, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    15. repec:eee:appene:v:195:y:2017:i:c:p:1-12 is not listed on IDEAS

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