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Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM 2.5 in the Yangtze River Economic Belt

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  • Weiguang Wang

    (School of Economics, Liaoning University, Shenyang 110036, China)

  • Yangyang Wang

    (School of Economics, Liaoning University, Shenyang 110036, China)

Abstract

The proposal of a “dual-carbon” goal puts forward higher requirements for air pollution control. Identifying the spatial-temporal characteristics, regional differences, dynamic evolution, and driving factors of PM 2.5 are the keys to formulating targeted haze reduction measures and ameliorating air quality. Therefore, adopting the Dagum Gini Coefficient and its decomposition method, the Kernel Density Estimation model, and spatial quantile regression model, this study analyzes the regional differences, dynamic evolution, and driving factors of PM 2.5 concentrations (PM 2.5 ) in the Yangtze River Economic Belt (YREB) and the upstream, midstream, and downstream (the three regions) from 2003 to 2018. The study shows that: (1) PM 2.5 in the YREB was characterized by increasing first and then decreasing, with evident heterogeneity and spatial agglomeration characteristics. (2) Inter-regional differences and intensity of trans-variation were the primary sources of PM 2.5 differences. (3) The density curve of PM 2.5 shifted to the left in the YREB and the upstream, midstream, and midstream, suggesting that PM 2.5 has declined. (4) Industrial service level (IS) and financial expenditure scale (FES) exerted a significant and negative effect on PM 2.5 across the quantiles. On the contrary, population density (PD) showed a significant and positive influence. Except for the 75th quantile, the technology level (TEC) significantly inhibited PM 2.5 . The remaining variables had a heterogeneous impact on PM 2.5 at different quantiles. The above results suggest that regional joint prevention and control mechanisms, collaborative governance mechanisms, and comprehensive policy mix mechanisms should be established to cope with PM 2.5 pollution and achieve green, sustainable economic development of the YREB.

Suggested Citation

  • Weiguang Wang & Yangyang Wang, 2023. "Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM 2.5 in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3381-:d:1066417
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    References listed on IDEAS

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    1. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    2. 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).
    3. Quah, D., 1990. "Galton'S Fallacy And The Tests Of The Convergence Hypothesis," Working papers 552, Massachusetts Institute of Technology (MIT), Department of Economics.
    4. Jin-Wei Yan & Fei Tao & Shuai-Qian Zhang & Shuang Lin & Tong Zhou, 2021. "Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt," IJERPH, MDPI, vol. 18(5), pages 1-25, February.
    5. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    6. Senlin Hu & Gang Zeng & Xianzhong Cao & Huaxi Yuan & Bing Chen, 2021. "Does Technological Innovation Promote Green Development? A Case Study of the Yangtze River Economic Belt in China," IJERPH, MDPI, vol. 18(11), pages 1-18, June.
    7. Subodh Kumar & R. Robert Russell, 2002. "Technological Change, Technological Catch-up, and Capital Deepening: Relative Contributions to Growth and Convergence," American Economic Review, American Economic Association, vol. 92(3), pages 527-548, June.
    8. Zhenlin Yang & Liangjun Su, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Working Papers 05-2007, Singapore Management University, School of Economics.
    9. Cai-Rong Lou & Hong-Yu Liu & Yu-Feng Li & Yu-Ling Li, 2016. "Socioeconomic Drivers of PM 2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China," IJERPH, MDPI, vol. 13(10), pages 1-19, September.
    10. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    11. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    12. Luo, Zhi & Wan, Guanghua & Wang, Chen & Zhang, Xun, 2018. "Urban pollution and road infrastructure: A case study of China," China Economic Review, Elsevier, vol. 49(C), pages 171-183.
    13. Tang, Liwei & He, Gang, 2021. "How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China," Energy, Elsevier, vol. 235(C).
    14. Qing Zhao & Chih-Hung Yuan, 2020. "Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    15. Mao Mao & Haofei Sun & Xiaolin Zhang, 2020. "Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter," IJERPH, MDPI, vol. 17(24), pages 1-17, December.
    16. Yang Li & Hua Shao & Nan Jiang & Ge Shi & Xin Cheng, 2018. "The Evolution of the Urban Spatial Pattern in the Yangtze River Economic Belt: Based on Multi-Source Remote Sensing Data," Sustainability, MDPI, vol. 10(8), pages 1-21, August.
    17. Tan, Shukui & Hu, Bixia & Kuang, Bing & Zhou, Min, 2021. "Regional differences and dynamic evolution of urban land green use efficiency within the Yangtze River Delta, China," Land Use Policy, Elsevier, vol. 106(C).
    18. Yang, Yuan & Cai, Wenjia & Wang, Can, 2014. "Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces," Applied Energy, Elsevier, vol. 131(C), pages 117-127.
    19. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    20. Wang, Mingwei & Che, Yue & Yang, Kai & Wang, Min & Xiong, Lijun & Huang, Yuchi, 2011. "A local-scale low-carbon plan based on the STIRPAT model and the scenario method: The case of Minhang District, Shanghai, China," Energy Policy, Elsevier, vol. 39(11), pages 6981-6990.
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