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Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases

  • Hampf, Benjamin
  • Krüger, Jens J.
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    This study explores the reduction potential of greenhouse gases for major pollution emitting countries of the world using nonparametric productivity measurement methods and directional distance functions. In contrast to the existing literature we apply optimization methods to endogenously determine optimal directions for the e ciency analysis. These directions represent the compromise of output enhancement and emissions reduction. The results show that for reasonable directions the adoption of best-practices would lead to sizable emission reductions in a range of about 20 percent compared to current levels.

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    File URL: http://econstor.eu/bitstream/10419/79699/1/VfS_2013_pid_313.pdf
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    Paper provided by Verein für Socialpolitik / German Economic Association in its series Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order with number 79699.

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    Date of creation: 2013
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    Handle: RePEc:zbw:vfsc13:79699
    Contact details of provider: Web page: http://www.socialpolitik.org/
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    1. 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.
    2. Joseph E. Aldy & Alan J. Krupnick & Richard G. Newell & Ian W. H. Parry & William A. Pizer, 2010. "Designing Climate Mitigation Policy," Journal of Economic Literature, American Economic Association, vol. 48(4), pages 903-34, December.
    3. Lozano, Sebastián & Gutiérrez, Ester, 2008. "Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions," Ecological Economics, Elsevier, vol. 66(4), pages 687-699, July.
    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. Dale W. Jorgenson & Marcel P. Timmer, 2011. "Structural Change in Advanced Nations: A New Set of Stylised Facts," Scandinavian Journal of Economics, Wiley Blackwell, vol. 113(1), pages 1-29, 03.
    6. Rolf F�re & 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.
    7. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
    8. Rolf Färe & Shawna Grosskopf & Carl A Pasurka, Jr., 2001. "Accounting for Air Pollution Emissions in Measures of State Manufacturing Productivity Growth," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 381-409.
    9. Byung M. Jeon & Robin C. Sickles, 2004. "The role of environmental factors in growth accounting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 567-591.
    10. Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
    11. Taskin, Fatma & Zaim, Osman, 2000. "Searching for a Kuznets curve in environmental efficiency using kernel estimation," Economics Letters, Elsevier, vol. 68(2), pages 217-223, August.
    12. Picazo-Tadeo, Andres J. & Reig-Martinez, Ernest & Hernandez-Sancho, Francesc, 2005. "Directional distance functions and environmental regulation," Resource and Energy Economics, Elsevier, vol. 27(2), pages 131-142, June.
    13. Rolf Färe & Shawna Grosskopf & Dimitri Margaritis & William Weber, 2012. "Technological change and timing reductions in greenhouse gas emissions," Journal of Productivity Analysis, Springer, vol. 37(3), pages 205-216, June.
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