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Exploring the convergence patterns of PM2.5 in Chinese cities

Yan Wang (), Yuan Gong (), Caiquan Bai (), Hong Yan () and Xing Yi ()
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Yan Wang: Shandong University
Yuan Gong: Renmin University of China
Caiquan Bai: Shandong University
Hong Yan: Fudan University
Xing Yi: Shandong University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2023, vol. 25, issue 1, No 31, 708-733

Abstract: Abstract Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771–1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Keywords: PM2.5 concentration; Spatial–temporal evolution; Log t test; Club convergence; Chinese cities (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10668-021-02077-6

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