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The Spatial Spillover Effects of Environmental Regulation on China’s Industrial Green Growth Performance

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  • Xiping Wang

    (Department of Economics and Management, North China Electric Power University, No. 619, Yonghua North Street, Baoding 071003, China)

  • Moyang Li

    (Department of Economics and Management, North China Electric Power University, No. 619, Yonghua North Street, Baoding 071003, China)

Abstract

This study investigated the spatial spillover effects of environmental regulation (ER) on industrial green growth performance ( IGGP ) in China. Firstly, a parametric stochastic frontier analysis (SFA) was estimated to measure IGGP using the data of China’s 30 provincial industry sectors during 2000–2014. Then, considering the space–time characteristics in IGGP , the spatial spillover effects of three types of ER, namely, administrative environmental regulation (AER), market-based environmental regulation (MER), and voluntary environmental regulation (VER), on IGGP was examined by employing spatial Durbin model (SDM). The main findings are: (1) the IGGP is low but shows a trend of continuous improvement and there is a significant disparity and spatial autocorrelations amongst regions; (2) the spillover effects of the three types of ER are different, specifically, the spillover effects of AER are significant negative, while the effects of MER and VER are both significant positive. The difference between the latter two is that the positive spillover effect of MER on IGGP is so large to outperform the negative direct effect, while the effect of VER is very minor. Based on these findings, relevant policy suggestions are presented to balance industrial economic and environmental protection in order to promote IGGP .

Suggested Citation

  • Xiping Wang & Moyang Li, 2019. "The Spatial Spillover Effects of Environmental Regulation on China’s Industrial Green Growth Performance," Energies, MDPI, vol. 12(2), pages 1-13, January.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:267-:d:198165
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    1. Subal Kumbhakar & M. Denny & M. Fuss, 2000. "Estimation and decomposition of productivity change when production is not efficient: a paneldata approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 312-320.
    2. Jie Wu & Qingyuan Zhu & Junfei Chu & Liang Liang, 2015. "Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 455-477, October.
    3. Ouyang, Xiaoling & Sun, Chuanwang, 2015. "Energy savings potential in China's industrial sector: From the perspectives of factor price distortion and allocative inefficiency," Energy Economics, Elsevier, vol. 48(C), pages 117-126.
    4. Zhao, Xiaoli & Yin, Haitao & Zhao, Yue, 2015. "Impact of environmental regulations on the efficiency and CO2 emissions of power plants in China," Applied Energy, Elsevier, vol. 149(C), pages 238-247.
    5. Yang, Zhenbing & Shao, Shuai & Yang, Lili & Miao, Zhuang, 2018. "Improvement pathway of energy consumption structure in China's industrial sector: From the perspective of directed technical change," Energy Economics, Elsevier, vol. 72(C), pages 166-176.
    6. Shao, Benjamin B.M. & Lin, Winston T., 2016. "Assessing output performance of information technology service industries: Productivity, innovation and catch-up," International Journal of Production Economics, Elsevier, vol. 172(C), pages 43-53.
    7. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    8. Gray, Wayne B, 1987. "The Cost of Regulation: OSHA, EPA and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 77(5), pages 998-1006, December.
    9. Lin, Boqiang & Yang, Lisha, 2013. "The potential estimation and factor analysis of China′s energy conservation on thermal power industry," Energy Policy, Elsevier, vol. 62(C), pages 354-362.
    10. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Yanbing Mao & Kui Liu & Jizhi Zhou, 2019. "Evolution of Green Industrial Growth between Europe and China based on the Energy Consumption Model," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    2. Tinghui Wang & Qi Fu & Yue Wang & Mengfan Gao & Jinhua Chen, 2022. "The Interaction Mechanism of Fiscal Pressure, Local Government Behavioral Preferences and Environmental Governance Efficiency: Evidence from the Yangtze River Delta Region of China," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    3. Shoujun Lyu & Xingchi Shen & Yujie Bi, 2020. "The Dually Negative Effect of Industrial Polluting Enterprises on China’s Air Pollution: A Provincial Panel Data Analysis Based on Environmental Regulation Theory," IJERPH, MDPI, vol. 17(21), pages 1-16, October.
    4. Pan, Xiongfeng & Xu, Haitao & Li, Mengna & Zong, Tianjiao & Lee, Chew Tin & Lu, Yuduo, 2020. "Environmental expenditure spillovers: Evidence from an estimated multi-area DSGE model," Energy Economics, Elsevier, vol. 86(C).

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