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A New Endogenous Direction Selection Mechanism for the Direction Distance Function Method Applied to Different Economic–Environmental Development Modes

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  • Junchao Wang

    (The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province, Xiamen Institute of Technology, Xiamen 361021, China)

  • Jun-Hong Ye

    (Digital Intelligence Management Research Institute, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321015, China)

  • Lei Chen

    (School of Economics & Management, Fuzhou University, Fuzhou 350108, China)

Abstract

As a direction selection in the direction distance function (DDF), endogenous DDF can accurately reflect the numerical characteristics of inputs/outputs, but it is difficult to effectively popularize. And it is also difficult to effectively combine with reality. To solve those problems, this paper introduces slack variables to construct a new endogenous direction-setting mechanism, which makes the endogenous model have the conditions to be popularized. Based on the original endogenous DDF, we consider environmental concern, economic concern, coordinated development, and priority development, and then construct six new extended DDF models with slack variables. Based on priority development, we further propose six new extended DDF models. These new extended models can not only realize the complete internalization of direction determination but also overcome the limitations of traditional endogenous models. Combined with the actual case, the emission reduction potential of different areas is revealed, and the improved path is proposed. The results show that the new extended DDF models effectively reflect the different development modes of carbon emissions, and different development modes have a significant impact on emission reduction potential. In addition, compared with economic concern and priority development, coordinated development and environmental concern are most beneficial to carbon emission reduction, but the development mode of environmental concern can better reveal the improved path of environmental development.

Suggested Citation

  • Junchao Wang & Jun-Hong Ye & Lei Chen, 2025. "A New Endogenous Direction Selection Mechanism for the Direction Distance Function Method Applied to Different Economic–Environmental Development Modes," Sustainability, MDPI, vol. 17(7), pages 1-26, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3151-:d:1626723
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    1. Barbero, Javier & Zofío, José L., 2023. "The measurement of profit, profitability, cost and revenue efficiency through data envelopment analysis: A comparison of models using BenchmarkingEconomicEfficiency.jl," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    2. Krüger, Jens & Hampf, Benjamin, 2015. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77007, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Gulati, Rachita, 2022. "Global and local banking crises and risk-adjusted efficiency of Indian banks: Are the impacts really perspective-dependent?," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 23-39.
    4. Kaneko, Shinji & Fujii, Hidemichi & Sawazu, Naoya & Fujikura, Ryo, 2010. "Financial allocation strategy for the regional pollution abatement cost of reducing sulfur dioxide emissions in the thermal power sector in China," Energy Policy, Elsevier, vol. 38(5), pages 2131-2141, May.
    5. 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.
    6. Liang, Liang & Wu, Jie & Cook, Wade D. & Zhu, Joe, 2008. "Alternative secondary goals in DEA cross-efficiency evaluation," International Journal of Production Economics, Elsevier, vol. 113(2), pages 1025-1030, June.
    7. Xu, Guangcheng & Wu, Jie & Zhu, Qingyuan & Pan, Yinghao, 2024. "Fixed cost allocation based on data envelopment analysis from inequality aversion perspectives," European Journal of Operational Research, Elsevier, vol. 313(1), pages 281-295.
    8. Fare, Rolf, 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.
    9. Nepomuceno, Thyago C.C. & Agasisti, Tommaso & Bertoletti, Alice & Daraio, Cinzia, 2024. "Multicriteria panel-data directional distances and the efficiency measurement of multidimensional higher education systems," Omega, Elsevier, vol. 125(C).
    10. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-891, December.
    11. M.D. Troutt, 1997. "Derivation of the Maximin Efficiency Ratio model from the maximum decisional efficiency principle," Annals of Operations Research, Springer, vol. 73(0), pages 323-338, October.
    12. Wang, Ailun & Hu, Shuo & Li, Jianglong, 2022. "Using machine learning to model technological heterogeneity in carbon emission efficiency evaluation: The case of China's cities," Energy Economics, Elsevier, vol. 114(C).
    13. Ma, Chunbo & Hailu, Atakelty & You, Chaoying, 2019. "A critical review of distance function based economic research on China’s marginal abatement cost of carbon dioxide emissions," Energy Economics, Elsevier, vol. 84(C).
    14. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
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