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Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

Author

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  • Ke Liu

    (School of Economics and Management & Center for Industry and Innovation, Zhengzhou University of Light Industry, Zhengzhou 450000, China)

  • Yurong Qiao

    (School of Economics and Management & Center for Industry and Innovation, Zhengzhou University of Light Industry, Zhengzhou 450000, China)

  • Qian Zhou

    (Economics School, Zhongnan University of Economics and Law, Wuhan 430073, China)

Abstract

With increasingly severe constraints on resources and the environment, it is the mainstream trend of economic development to reduce industrial pollution emissions and promote green industrial development. In this paper, a super-efficiency slacks-based measure (SBM) model is adopted to measure the industrial green development efficiency (IGDE) of 289 cities in China from 2008 to 2018. Moreover, we analyze their spatiotemporal differentiation pattern. On this basis, the multiscale geographical weighted regression (MGWR) model is used to analyze the scale differences and spatial differences of the driving factors. The results show that the IGDE is still at a low level in China. From 2008 to 2018, the overall polarization of IGDE was relatively serious. The number of high- and low-efficiency cities increased, while that of medium-efficiency cities greatly decreased. Secondly, the IGDE presented an obvious spatial positive correlation. MGWR regression results show that the technological innovation, government regulation, and consumption level belonged to the global scale, and there was almost no spatial heterogeneity. Other driving factors were urbanization, industrial structure, economic development, and population density according to their spatial scale. Lastly, the influence of economic development and technological innovation had a certain circular structure in space; the influence of population size mainly occurred in the cities of the southeast coast and northeast provinces; the influence of urbanization was more obvious in the most northern provinces of the Yangtze River, while that of industrial structure was mainly concentrated in the most southern cities of the Yangtze River Economic Belt (YREB). Spatially, the influence of consumption was manifested as a distribution trend of decreasing from north to south, and the government regulation was manifested as increasing from west to east and then to northeast.

Suggested Citation

  • Ke Liu & Yurong Qiao & Qian Zhou, 2021. "Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR," IJERPH, MDPI, vol. 18(8), pages 1-22, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:3960-:d:533032
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    1. Tenente, Marcos & Henriques, Carla & da Silva, Patrícia Pereira, 2020. "Eco-efficiency assessment of the electricity sector: Evidence from 28 European Union countries," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 293-314.
    2. Rongyan Liu & Deqing Wang & Li Zhang & Lihong Zhang, 2019. "Can green financial development promote regional ecological efficiency? A case study of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 325-341, January.
    3. Jinpeng Fu & Guirong Xiao & Lingling Guo & Chunyou Wu, 2018. "Measuring the Dynamic Efficiency of Regional Industrial Green Transformation in China," Sustainability, MDPI, vol. 10(3), pages 1-19, February.
    4. Zhu, Bangzhu & Zhang, Mengfan & Zhou, Yanhua & Wang, Ping & Sheng, Jichuan & He, Kaijian & Wei, Yi-Ming & Xie, Rui, 2019. "Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach," Energy Policy, Elsevier, vol. 134(C).
    5. Yang Gao & Sang-Bing Tsai & Xingqun Xue & Tingzhen Ren & Xiaomin Du & Quan Chen & Jiangtao Wang, 2018. "An Empirical Study on Green Innovation Efficiency in the Green Institutional Environment," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    6. Ingrid Ott & Susanne Soretz, 2018. "Green Attitude and Economic Growth," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(4), pages 757-779, August.
    7. Jooyoung Park & Jun‐Mo Park & Hung‐Suck Park, 2019. "Scaling‐Up of Industrial Symbiosis in the Korean National Eco‐Industrial Park Program: Examining Its Evolution over the 10 Years between 2005–2014," Journal of Industrial Ecology, Yale University, vol. 23(1), pages 197-207, February.
    8. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    9. Liu, Zhao & Zhang, Huan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2020. "How does industrial policy affect the eco-efficiency of industrial sector? Evidence from China," Applied Energy, Elsevier, vol. 272(C).
    10. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    11. Lee, Jung Wan, 2013. "The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth," Energy Policy, Elsevier, vol. 55(C), pages 483-489.
    12. Xiaoqing Wang & Qiuming Wu & Salman Majeed & Donghao Sun, 2018. "Fujian’s Industrial Eco-Efficiency: Evaluation Based on SBM and the Empirical Analysis of lnfluencing Factors," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    13. Cenjie Liu & Chunbo Ma & Rui Xie, 2020. "Structural, Innovation and Efficiency Effects of Environmental Regulation: Evidence from China’s Carbon Emissions Trading Pilot," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(4), pages 741-768, April.
    14. Alam, Md. Samsul & Atif, Muhammad & Chien-Chi, Chu & Soytaş, Uğur, 2019. "Does corporate R&D investment affect firm environmental performance? Evidence from G-6 countries," Energy Economics, Elsevier, vol. 78(C), pages 401-411.
    15. Yingyu Lu & Bo Cao & Yidi Hua & Lei Ding, 2020. "Efficiency Measurement of Green Regional Development and Its Influencing Factors: An Improved Data Envelopment Analysis Framework," Sustainability, MDPI, vol. 12(11), pages 1-23, May.
    16. World Bank, 2012. "Inclusive Green Growth : The Pathway to Sustainable Development," World Bank Publications - Books, The World Bank Group, number 6058.
    17. 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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    2. Li, Yinan & Huang, Yuxin, 2023. "Enhancing resources efficiency: Studying economic development in resource-rich regions for long-term sustainability of China," Resources Policy, Elsevier, vol. 86(PA).

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