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Environmental regulation efficiency analysis by considering regional heterogeneity

Author

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  • Liu, Xiaohong
  • Yang, Jiangjiang
  • Xu, Chengzhen
  • Li, Xingchen
  • Zhu, Qingyuan

Abstract

Facing the increasingly severe environmental problems, Chinese government takes environmental regulation as an important way to reduce carbon dioxide emissions and control pollution. For the role of environmental regulation, many scholars have done a lot of research. However, there are few articles on the efficiency of environmental regulation at the regional level. Regional heterogeneity also brings obstacles to the analysis of environmental regulation efficiency (ERE). We propose the meta-frontier Malmquist-Luenberger (MML) index to analyze China's regional ERE by considering the regional heterogeneity. Empirical results show that the ERE of the whole country and each region increase year by year from 2014 to 2018. From the viewpoint of regional heterogeneity, the eastern region contributes the most to ERE gains, while the midwestern region have a relatively low contribution. The decomposition results of MML index indicate that national ERE gains come mainly from the contribution of BPC. However, the main contributors to the change in the MML index across the period are not unique for the three regions.

Suggested Citation

  • Liu, Xiaohong & Yang, Jiangjiang & Xu, Chengzhen & Li, Xingchen & Zhu, Qingyuan, 2023. "Environmental regulation efficiency analysis by considering regional heterogeneity," Resources Policy, Elsevier, vol. 83(C).
  • Handle: RePEc:eee:jrpoli:v:83:y:2023:i:c:s0301420723004464
    DOI: 10.1016/j.resourpol.2023.103735
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    References listed on IDEAS

    as
    1. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    2. Yujiro Hayami, 1969. "Sources of Agricultural Productivity Gap Among Selected Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 51(3), pages 564-575.
    3. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    4. Yuanying Chi & Wenbing Zhou & Songlin Tang & Yu Hu, 2022. "Driving Factors of CO 2 Emissions in China’s Power Industry: Relative Importance Analysis Based on Spatial Durbin Model," Energies, MDPI, vol. 15(7), pages 1-15, April.
    5. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
    6. Henry Tulkens & Philippe Eeckaut, 2006. "Nonparametric Efficiency, Progress and Regress Measures For Panel Data: Methodological Aspects," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 395-429, Springer.
    7. Neves, Sónia Almeida & Marques, António Cardoso & Patrício, Margarida, 2020. "Determinants of CO2 emissions in European Union countries: Does environmental regulation reduce environmental pollution?," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 114-125.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali, 2015. "A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs," Omega, Elsevier, vol. 51(C), pages 29-37.
    10. Eirini Stergiou and Kostas Kounetas, 2021. "European Industries' Energy Efficiency under Different Technological Regimes: The Role of CO2 Emissions, Climate, Path Dependence and Energy Mix," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 93-128.
    11. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    12. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    13. Chiu, Ching-Ren & Liou, Je-Liang & Wu, Pei-Ing & Fang, Chen-Ling, 2012. "Decomposition of the environmental inefficiency of the meta-frontier with undesirable output," Energy Economics, Elsevier, vol. 34(5), pages 1392-1399.
    14. Emrouznejad, Ali & Yang, Guo-liang, 2016. "A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries," Energy, Elsevier, vol. 115(P1), pages 840-856.
    15. Wang, Yun & Sun, Xiaohua & Guo, Xu, 2019. "Environmental regulation and green productivity growth: Empirical evidence on the Porter Hypothesis from OECD industrial sectors," Energy Policy, Elsevier, vol. 132(C), pages 611-619.
    16. Li, Zheng & Pan, Lingying & Fu, Feng & Liu, Pei & Ma, Linwei & Amorelli, Angelo, 2014. "China's regional disparities in energy consumption: An input–output analysis," Energy, Elsevier, vol. 78(C), pages 426-438.
    17. M. Portela & A. Camanho & A. Keshvari, 2013. "Assessing the evolution of school performance and value-added: trends over four years," Journal of Productivity Analysis, Springer, vol. 39(1), pages 1-14, February.
    18. Oh, Dong-hyun, 2010. "A metafrontier approach for measuring an environmentally sensitive productivity growth index," Energy Economics, Elsevier, vol. 32(1), pages 146-157, January.
    19. Wu, Haitao & Hao, Yu & Ren, Siyu, 2020. "How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China," Energy Economics, Elsevier, vol. 91(C).
    20. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    21. Tran, Trung Hieu & Mao, Yong & Nathanail, Paul & Siebers, Peer-Olaf & Robinson, Darren, 2019. "Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis," Omega, Elsevier, vol. 85(C), pages 156-165.
    22. Zeng, Juying & Pagàn-Castaño, Esther & Ribeiro-Navarrete, Samuel, 2022. "Merits of Intercity Innovation Cooperation of Environment-friendly Patents for Environmental Regulation Efficiency," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    23. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    24. Song, Malin & An, Qingxian & Zhang, Wei & Wang, Zeya & Wu, Jie, 2012. "Environmental efficiency evaluation based on data envelopment analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4465-4469.
    25. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    26. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    27. R. Ramanathan, 2002. "Combining indicators of energy consumption and CO 2 emissions: a cross-country comparison," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 214-227.
    28. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    29. Feng-Cheng Fu & Chu-Ping Vijverberg & Yong-Sheng Chen, 2008. "Productivity and efficiency of state-owned enterprises in China," Journal of Productivity Analysis, Springer, vol. 29(3), pages 249-259, June.
    30. Yongrok Choi & Dong-hyun Oh & Ning Zhang, 2015. "Environmentally sensitive productivity growth and its decompositions in China: a metafrontier Malmquist–Luenberger productivity index approach," Empirical Economics, Springer, vol. 49(3), pages 1017-1043, November.
    31. Yu, Shasha & Yuan, Xuanyu & Yao, Xinyan & Lei, Ming, 2022. "Carbon leakage and low-carbon performance: Heterogeneity of responsibility perspectives," Energy Policy, Elsevier, vol. 165(C).
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