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Carbon dioxide emission standards for US power plants: An efficiency analysis perspective

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  • Hampf, Benjamin
  • Rødseth, Kenneth Løvold

Abstract

On June 25, 2013, President Obama announced his plan to introduce carbon dioxide emission standards for electricity generation. This paper proposes an efficiency analysis approach that addresses which mission rates (and standards) would be feasible if the existing generating units adopt best practices. A new efficiency measure is introduced and further decomposed to identify different sources' contributions to emission rate improvements. Estimating two Data Envelopment Analysis (DEA) models - the well-known joint production model and the new materials balance model - on a dataset consisting of 160 bituminous-fired generating units, we find that the average generating unit's electricity-to-carbon dioxide ratio is 15.3 percent below the corresponding best-practice ratio. Further examinations reveal that this discrepancy can largely be attributed to non-discretionary factors and not to managerial inefficiency. Moreover, even if the best practice ratios could be implemented, the generating units would not be able to comply with the EPA's recently proposed carbon dioxide standard.

Suggested Citation

  • Hampf, Benjamin & Rødseth, Kenneth Løvold, 2014. "Carbon dioxide emission standards for US power plants: An efficiency analysis perspective," Darmstadt Discussion Papers in Economics 219, Darmstadt University of Technology, Department of Law and Economics.
  • Handle: RePEc:zbw:darddp:219
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    1. Kenneth Rødseth & Eirik Romstad, 2014. "Environmental Regulations, Producer Responses, and Secondary Benefits: Carbon Dioxide Reductions Under the Acid Rain Program," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 111-135, September.
    2. Hoang, Viet-Ngu & Coelli, Tim, 2011. "Measurement of agricultural total factor productivity growth incorporating environmental factors: A nutrients balance approach," Journal of Environmental Economics and Management, Elsevier, vol. 62(3), pages 462-474.
    3. Murty, Sushama & Russell, R. Robert, 2010. "On modeling pollution-generating technologies," Economic Research Papers 271176, University of Warwick - Department of Economics.
    4. Simar, Léopold & Vanhems, Anne & Wilson, Paul W., 2012. "Statistical inference for DEA estimators of directional distances," European Journal of Operational Research, Elsevier, vol. 220(3), pages 853-864.
    5. Coggins, Jay S. & Swinton, John R., 1996. "The Price of Pollution: A Dual Approach to Valuing SO2Allowances," Journal of Environmental Economics and Management, Elsevier, vol. 30(1), pages 58-72, January.
    6. Fare, Rolf & Grosskopf, Shawna & Tyteca, Daniel, 1996. "An activity analysis model of the environmental performance of firms--application to fossil-fuel-fired electric utilities," Ecological Economics, Elsevier, vol. 18(2), pages 161-175, August.
    7. Stephen P. Holland, 2010. "Spillovers from Climate Policy," NBER Working Papers 16158, National Bureau of Economic Research, Inc.
    8. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    9. Rolf Färe & Shawna Grosskopf, 2003. "Nonparametric Productivity Analysis with Undesirable Outputs: Comment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 1070-1074.
    10. Ellerman, A.D., 2003. "Lessons form Phase 2 Compliance with the US Acid Rain Program," Cambridge Working Papers in Economics 0325, Faculty of Economics, University of Cambridge.
    11. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    12. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    13. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
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    Cited by:

    1. Dakpo, K & Jeanneaux, Philippe & Latruffee, Laure, 2015. "Empirical comparison of pollution generating technologies in nonparametric modelling: The case of greenhouse gas emissions in French sheep meat farming," 2015 Conference, August 9-14, 2015, Milan, Italy 211557, International Association of Agricultural Economists.
    2. Jeanneaux, Philippe & Latruffe, Laure, 2016. "Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric frameworkAuthor-Name: Dakpo, K. Hervé," European Journal of Operational Research, Elsevier, vol. 250(2), pages 347-359.
    3. K Hervé Dakpo & Philippe Jeanneaux & Laure Latruffe, 2014. "Inclusion of undesirable outputs in production technology modeling:The case of greenhouse gas emissions in French meat sheep farming," Working Papers SMART 14-08, INRAE UMR SMART.
    4. Roshdi, Israfil & Hasannasab, Maryam & Margaritis, Dimitris & Rouse, Paul, 2018. "Generalised weak disposability and efficiency measurement in environmental technologies," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1000-1012.
    5. Duan, Na & Guo, Jun-Peng & Xie, Bai-Chen, 2016. "Is there a difference between the energy and CO2 emission performance for China’s thermal power industry? A bootstrapped directional distance function approach," Applied Energy, Elsevier, vol. 162(C), pages 1552-1563.
    6. Lee, Chia-Yen, 2018. "Mixed-strategy Nash equilibrium in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1013-1024.

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    More about this item

    Keywords

    Emission standards; Carbon dioxide emissions; Materials balance condition; Electricity generation; Weak G-disposability; Data Envelopment Analysis;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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