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Abstract

Sulfur dioxide (SO2) emissions have been a great challenge in China over the last few decades due to their serious impact on the environment and human health. In this paper, a random effect eigenvector spatial filtering (RE-ESF) approach without and with non-spatially varying coefficients (SNVC) is identified to examine spatial heterogeneity and economic driving factors of SO2 emissions in China from 2011 to 2017. Using the Moran eigenvectors to extract information on spatial dependence, the main findings of the RE-ESF approach are as follows: First, after comparing different approaches for dealing with spatial dependence, it is found that the RE-ESF approach demonstrates the best fit to the dataset. Second, the global investigation shows that SO2 emissions are negatively determined by economic growth and government expenditure for environment protection, but are positively determined by road freight transport, coal consumption and oil consumption. Third, the local investigation indicates that the spatially varying coefficients of economic growth and coal consumption range from 0.1401 to 0.2732 with the median value of 0.2478 and from 0.2406 to 0.3611 with the median value of 0.3210, respectively, revealing significant spatial heterogeneity of SO2 emissions driven by economic growth and coal consumption. These findings provide meaningful insights into centralized and province-specific policies for reducing SO2 emissions.

Item Type:	Article
Authors/Creators:	Shi, W and Du, Y and Chang, C-H and Nguyen, S and Wu, J
Keywords:	environmental sustainability, spatial dependence, economic determinants, non-spatially varying coefficients, province-specific policies
Journal or Publication Title:	Ecological Indicators
Publisher:	Elsevier Science Bv
ISSN:	1470-160X
DOI / ID Number:	10.1016/j.ecolind.2021.108001
Copyright Information:	© 2021 Published by Elsevier Ltd. This is an open access article under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license, (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Related URLs:	Google Scholar: Shi, W UTAS Profile: Shi, W Google Scholar: Du, Y UTAS Profile: Du, Y
Item Statistics:	View statistics for this item

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