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Analysis of China’s Regional Economic Environmental Performance: A Non-radial Multi-objective DEA Approach

Author

Listed:
  • Tao Ding

    (Hefei University of Technology)

  • Zhixiang Zhou

    (Hefei University of Technology)

  • Qianzhi Dai

    (Hefei University of Technology)

  • Liang Liang

    (Hefei University of Technology)

Abstract

One of the hot topics is how to achieve more accurate results of economic and environmental efficiency evaluation in China. Previous data envelopment analysis (DEA) literature on environmental performance measurement often follow the concept of non-radial efficiency measure for calculating the performance on resources and economic-environmental factors respectively. This paper proposes a non-radial and multi-objective generalized DEA model for economic-environmental efficiency evaluation. The results illustrate that this model can not only analyze the relationship between DEA efficiency and Pareto optimality of the multi-objective programming problem defined on the production possibility set, but also obtain the performance improvement direction by using the projection of decision making units. Finally, a case on measuring the economic-environmental performance of Chinese provincial regions is employed to indicate that the proposed model can be helpful to promote the accuracy of economic-environmental efficiency evaluation.

Suggested Citation

  • Tao Ding & Zhixiang Zhou & Qianzhi Dai & Liang Liang, 2020. "Analysis of China’s Regional Economic Environmental Performance: A Non-radial Multi-objective DEA Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1209-1231, April.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:4:d:10.1007_s10614-019-09884-0
    DOI: 10.1007/s10614-019-09884-0
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    2. Bowen Sun & Haibo Wang & Jaime Ortiz & Jun Huang & Can Zhao & Zelang Wang, 2022. "A Decomposed Data Analysis Approach to Assessing City Sustainable Development Performance: A Network DEA Model with a Slack-Based Measure," Sustainability, MDPI, vol. 14(17), pages 1-23, September.

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