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

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  • Benjamin Hampf
  • Jens J. Krüger

Abstract

This article 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 efficiency 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 approximately 20% compared with current levels.

Suggested Citation

  • Benjamin Hampf & Jens J. Krüger, 2015. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(3), pages 920-938.
    • Handle: RePEc:oup:ajagec:v:97:y:2015:i:3:p:920-938.
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    File URL: http://hdl.handle.net/10.1093/ajae/aau035
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    Citations

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    Cited by:

    1. Dakpo, K Hervé & Lansink, Alfons Oude, 2019. "Dynamic pollution-adjusted inefficiency under the by-production of bad outputs," European Journal of Operational Research, Elsevier, vol. 276(1), pages 202-211.
    2. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2016. "Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings," Omega, Elsevier, vol. 63(C), pages 48-59.
    3. Krüger, Jens, 2021. "Nonparametric Portfolio Efficiency Measurement with Higher Moments," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130825, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Jens J. Krüger, 2017. "Revisiting the world technology frontier: a directional distance function approach," Journal of Economic Growth, Springer, vol. 22(1), pages 67-95, March.
    5. Färe, Rolf & Pasurka, Carl & Vardanyan, Michael, 2017. "On endogenizing direction vectors in parametric directional distance function-based models," European Journal of Operational Research, Elsevier, vol. 262(1), pages 361-369.
    6. Vardanyan, Michael & Valdmanis, Vivian G. & Leleu, Hervé & Ferrier, Gary D., 2022. "Estimating technology characteristics of the U.S. hospital industry using directional distance functions with optimal directions," Omega, Elsevier, vol. 113(C).
    7. Benjamin Hampf, 2018. "Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?," Empirical Economics, Springer, vol. 54(1), pages 101-127, February.
    8. Cherchye, Laurens & De Rock, Bram & Walheer, Barnabé, 2016. "Multi-output profit efficiency and directional distance functions," Omega, Elsevier, vol. 61(C), pages 100-109.
    9. Jens J. Krüger & Moritz Tarach, 2022. "Greenhouse Gas Emission Reduction Potentials in Europe by Sector: A Bootstrap-Based Nonparametric Efficiency Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(4), pages 867-898, April.
    10. Du, Limin & Lu, Yunguo & Ma, Chunbo, 2022. "Carbon efficiency and abatement cost of China's coal-fired power plants," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Sebastián Lozano & Narges Soltani & Akram Dehnokhalaji, 2020. "A compromise programming approach for target setting in DEA," Annals of Operations Research, Springer, vol. 288(1), pages 363-390, May.
    12. Sebastián Lozano & Narges Soltani, 2018. "DEA target setting using lexicographic and endogenous directional distance function approaches," Journal of Productivity Analysis, Springer, vol. 50(1), pages 55-70, October.
    13. Scott E. Atkinson & Mike G. Tsionas, 2018. "Shadow directional distance functions with bads: GMM estimation of optimal directions and efficiencies," Empirical Economics, Springer, vol. 54(1), pages 207-230, February.
    14. Lozano, Sebastián & Khezri, Somayeh, 2021. "Network DEA smallest improvement approach," Omega, Elsevier, vol. 98(C).
    15. Krüger, Jens J., 2018. "Direct targeting of efficient DMUs for benchmarking," International Journal of Production Economics, Elsevier, vol. 199(C), pages 1-6.
    16. Kumbhakar, Subal C. & Tsionas, Mike G., 2021. "Dissections of input and output efficiency: A generalized stochastic frontier model," International Journal of Production Economics, Elsevier, vol. 232(C).
    17. Moriah Bostian & Rolf Färe & Shawna Grosskopf & Tommy Lundgren & William L. Weber, 2018. "Time substitution for environmental performance: The case of Swedish manufacturing," Empirical Economics, Springer, vol. 54(1), pages 129-152, February.
    18. Atkinson, Scott E. & Primont, Daniel & Tsionas, Mike G., 2018. "Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions," Journal of Econometrics, Elsevier, vol. 204(2), pages 131-146.
    19. Moriah Bostian & Rolf Färe & Shawna Grosskopf & Tommy Lundgren, 2022. "Prevention or cure? Optimal abatement mix," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(4), pages 503-531, October.

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