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Spatial–Temporal Evolution Characteristics and Influencing Factors of Industrial Pollution Control Efficiency in China

Author

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  • Wenjie Zou

    (School of Economics, Fujian Normal University, Fuzhou 350117, China)

  • Liqin Zhang

    (School of Economics, Fujian Normal University, Fuzhou 350117, China)

  • Jieying Xu

    (School of Economics, Fujian Normal University, Fuzhou 350117, China)

  • Yufeng Xie

    (School of Economics, Fujian Normal University, Fuzhou 350117, China)

  • Huangxin Chen

    (School of Economics, Fujian Normal University, Fuzhou 350117, China)

Abstract

The green transformation and development of industry form the foundation of sustainable development for a country’s society, economy, and environment. Industrial pollution control is one inevitable choice for all industries following the path of sustainable development. Improving industrial pollution control efficiency is also a natural requirement for reducing pollution emissions and achieving carbon peak and carbon neutrality. Based on panel data of 30 provinces in China from 2012–2018, this research applies DEA window analysis to measure the efficiency of industrial pollution control inputs and outputs, and empirically evaluates those factors influencing such efficiency. The findings demonstrate that overall industrial pollution control efficiency in China exhibits a decreasing trend from 2012 to 2018, but there are clear differences among provinces. Industrial pollution control efficiencies in the east and central regions are consistent with the national average, while said efficiencies in the west and northeast regions fluctuate in waves, with the effect of influencing factors in different regions varying significantly. Lastly, based on the results of empirical analysis, this research puts forward the optimization path to further improve industrial pollution control efficiency in China, and to provide new suggestions for its advancement.

Suggested Citation

  • Wenjie Zou & Liqin Zhang & Jieying Xu & Yufeng Xie & Huangxin Chen, 2022. "Spatial–Temporal Evolution Characteristics and Influencing Factors of Industrial Pollution Control Efficiency in China," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5152-:d:801350
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    1. Chen, Huangxin & Shi, Yi & Zhao, Xin, 2022. "Investment in renewable energy resources, sustainable financial inclusion and energy efficiency: A case of US economy," Resources Policy, Elsevier, vol. 77(C).
    2. Ying-yu Lu & Yue He & Bo Wang & Shuang-shuang Ye & Yidi Hua & Lei Ding, 2019. "Efficiency Evaluation of Atmospheric Pollutants Emission in Zhejiang Province China: A DEA-Malmquist Based Approach," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    3. Jyri Seppäläa & Matti Melanen & Ilmo Mäenpää & Sirkka Koskela & Jyrki Tenhunen & Marja‐Riitta Hiltunen, 2005. "How Can the Eco‐efficiency of a Region be Measured and Monitored?," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 117-130, October.
    4. Shen, Neng & Peng, Hui, 2021. "Can industrial agglomeration achieve the emission-reduction effect?," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    5. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    6. 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.
    7. Li, Xuehui & Xu, Yangyang & Yao, Xin, 2021. "Effects of industrial agglomeration on haze pollution: A Chinese city-level study," Energy Policy, Elsevier, vol. 148(PA).
    8. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    9. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    10. Li, Hai-ling & Zhu, Xue-hong & Chen, Jin-yu & Jiang, Fei-tao, 2019. "Environmental regulations, environmental governance efficiency and the green transformation of China's iron and steel enterprises," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    11. Bampatsou, Christina & Halkos, George, 2019. "Economic growth, efficiency and environmental elasticity for the G7 countries," Energy Policy, Elsevier, vol. 130(C), pages 355-360.
    12. Miao, Zhuang & Baležentis, Tomas & Shao, Shuai & Chang, Dongfeng, 2019. "Energy use, industrial soot and vehicle exhaust pollution—China's regional air pollution recognition, performance decomposition and governance," Energy Economics, Elsevier, vol. 83(C), pages 501-514.
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    Cited by:

    1. Zhaoxian Su & Yang Yang & Yun Wang & Pan Zhang & Xin Luo, 2023. "Study on Spatiotemporal Evolution Features and Affecting Factors of Collaborative Governance of Pollution Reduction and Carbon Abatement in Urban Agglomerations of the Yellow River Basin," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    2. Han Zou & Yang Liu & Baihao Li & Wenjing Luo, 2022. "Sustainable Development Efficiency of Cultural Landscape Heritage in Urban Fringe Based on GIS-DEA-MI, a Case Study of Wuhan, China," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    3. Quan Guo & Zijing Liang & Xiang Bai & Mengnan Lv & Anying Zhang, 2022. "The Analysis of Carbon Emission’s Characteristics and Dynamic Evolution Based on the Strategy of Unbalanced Regional Economic Development in China," Sustainability, MDPI, vol. 14(14), pages 1-31, July.
    4. Aneta Masternak-Janus, 2025. "Coal Consumption Efficiency in the European Union—Trends and Challenges," Energies, MDPI, vol. 18(16), pages 1-19, August.
    5. Ricardo Casonatto & Tales Souza & Gustavo Silva & Victor Oliveira & Simone Monteiro, 2025. "Assessing Resource Management in Higher Education Sustainability Projects: A Bootstrap Dea Case Study," Sustainability, MDPI, vol. 17(19), pages 1-16, September.

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