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A dynamic approach to environmental compliance decisions in U.S. Electricity Market: The Acid Rain Program revisited

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  • Hancevic, Pedro Ignacio

Abstract

The Acid Rain Program (ARP) was implemented in 1995. Since then, coal-fired boilers have had to choose among three main compliance alternatives: purchase pollution permits; switch to an alternative lower-sulfur coal; or adopt a scrubber. This decision problem is driven by the evolution of several economic variables and is revised when significant changes (to prices, quality of inputs, output level, technology, transport costs, regulations, among others) occur. Using a structural dynamic discrete choice model, I recover cost parameters and use them to evaluate two different counterfactual policies. The results confirm there is a trade-off between fuel switching and scrubbing costs (with the latter having a higher investment cost and a lower variable cost), and also the existence of regional heterogeneity. Finally, the ARP implied cost savings of approximately $4.7 billions if compared to a uniform emission rate standard and $14.8 billions if compared to compulsory scrubbing for the 1995–2005 period.

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  • Hancevic, Pedro Ignacio, 2017. "A dynamic approach to environmental compliance decisions in U.S. Electricity Market: The Acid Rain Program revisited," Energy Policy, Elsevier, vol. 106(C), pages 129-137.
    • Handle: RePEc:eee:enepol:v:106:y:2017:i:c:p:129-137
    DOI: 10.1016/j.enpol.2017.03.050
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    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Mortensen, Dale & Pissarides, Christopher, 2011. "Job Creation and Job Destruction in the Theory of Unemployment," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 1-19.
    3. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    4. Curtis Carlson & Dallas Burtraw & Maureen Cropper & Karen L. Palmer, 2000. "Sulfur Dioxide Control by Electric Utilities: What Are the Gains from Trade?," Journal of Political Economy, University of Chicago Press, vol. 108(6), pages 1292-1326, December.
    5. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
    6. John R. Swinton, 2002. "The Potential for Cost Savings in the Sulfur Dioxide Allowance Market: Empirical Evidence from Florida," Land Economics, University of Wisconsin Press, vol. 78(3), pages 390-404.
    7. Canova, Fabio & Ciccarelli, Matteo, 2013. "Panel Vector Autoregressive Models: A Survey," CEPR Discussion Papers 9380, C.E.P.R. Discussion Papers.
    8. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    9. Richard Schmalensee & Robert N. Stavins, 2013. "The SO 2 Allowance Trading System: The Ironic History of a Grand Policy Experiment," Journal of Economic Perspectives, American Economic Association, vol. 27(1), pages 103-122, Winter.
    10. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    11. Bellas, Allen S., 1998. "Empirical evidence of advances in scrubber technology," Resource and Energy Economics, Elsevier, vol. 20(4), pages 327-343, December.
    12. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    13. Richard Ericson & Ariel Pakes, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(1), pages 53-82.
    14. Michael P. Keane & Kenneth I. Wolpin, 2009. "Empirical Applications of Discrete Choice Dynamic Programming Models," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(1), pages 1-22, January.
    15. Rothwell, Geoffrey & Rust, John, 1997. "On the Optimal Lifetime of Nuclear Power Plants," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 195-208, April.
    16. Elaine F. Frey, 2013. "Technology Diffusion and Environmental Regulation: The Adoption of Scrubbers by Coal-Fired Power Plants," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    17. Zvi Eckstein & Kenneth I. Wolpin, 1989. "The Specification and Estimation of Dynamic Stochastic Discrete Choice Models: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 24(4), pages 562-598.
    18. Peter Arcidiacono & Robert A. Miller, 2011. "Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 79(6), pages 1823-1867, November.
    19. Joskow, Paul L & Schmalensee, Richard & Bailey, Elizabeth M, 1998. "The Market for Sulfur Dioxide Emissions," American Economic Review, American Economic Association, vol. 88(4), pages 669-685, September.
    20. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729, Elsevier.
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    More about this item

    Keywords

    Scrubbers; Fuel switching; Coal-fired boilers; Sulfur dioxide emissions; Dynamic discrete choice;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects

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