Literature DB >> 33383516

The spatial spillover effect and nonlinear relationship analysis between environmental decentralization, government corruption and air pollution: Evidence from China.

Yu Hao1, Zhiqiang Gai2, Guanpeng Yan3, Haitao Wu4, Muhammad Irfan5.   

Abstract

The massive development of the Chinese economy is being hindered by the deteriorating air pollution problem. Many methods have been used by the Chinese government to solve this environmental dilemma, out of which environmental decentralization is one of the important measures. The transparency of environmental decentralization may be weakened by the existence of the corruption problem, resulting in further deterioration of the air pollution problem. To examine this problem, the provincial panel data of 30 provinces in China from 2005 to 2016 is selected and the spatial measurement method is used to study the relationship between environmental decentralization, government corruption, and air pollution. The results indicate that air pollution in different provinces of China is spatially dependent. Local environmental decentralization has a significant inhibitory effect on air pollution, while local air pollution is not inhibited by neighbor's environmental decentralization. However, air quality is significantly deteriorated by local or neighbor's corruption problem. After adding the interaction item of environmental decentralization and government corruption as the adjusting variable, it is found that the inhibitory effect of local environmental decentralization on air pollution is weakened by the problem of local government corruption, while the government corruption of neighbor does not have this effect. In addition, both local and neighbor's environmental decentralization have heterogeneous effects on air pollution from the spatial dynamic threshold regression results with regional corruption as the threshold variable.
Copyright © 2020 Elsevier B.V. All rights reserved.

Keywords:  Air pollution; Corruption; Environmental decentralization; Spatial dynamic threshold regression; Spatial econometric model

Year:  2020        PMID: 33383516     DOI: 10.1016/j.scitotenv.2020.144183

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


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