The construction and examination of social vulnerability and its effects on PM2.5 globally: combining spatial econometric modeling and geographically weighted regression

Abstract

Fine particulate matter (PM2.5) is of widespread concern, as it poses a serious impact on economic development and human health. Although the influence of socioeconomic factors on PM2.5 has been studied, the constitution and the effect analysis of social vulnerability to PM2.5 remain unclear. In this study, a comprehensive theoretical framework with appropriate indicators for social vulnerability to PM2.5 was constructed. Using spatial autocorrelation analysis, a positive global spatial autocorrelation and notable local spatial cluster relationships were identified. Spatial econometric modeling and geographically weighted regression modeling were performed to explore the cause-effect relationship of social vulnerability to PM2.5. The spatial error model indicated that population and education inequality in the sensitivity dimension caused a significant positive impact on PM2.5, and biocapacity and social governance in the capacity dimension strongly contributed to the decrease of PM2.5 globally. The geographically weighted regression model revealed spatial heterogeneity in the effects of the social vulnerability variables on PM2.5 among countries. These empirical results can provide policymakers with a new perspective on social vulnerability as it relates to PM2.5 governance and targeted environmental pollution management.

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Availability of data and materials

The datasets generated and analyzed during the current study are available in the OECD statistics database (https://stats.oecd.org/); UNDP database (http://hdr.undp.org/en/data/); Global Footprint Network database (http://www.footprintnetwork.org); World Bank database (https://data.worldbank.org/region/world); and GWI database (http://info.worldbank.org/governance/wgi/).

Funding

This study was found by the National Social Science Foundation of China (Grant number 18BSH122), Major Project (Key grant) of National Social Science Fund of China (Grant number 19ZDA149) and Fundamental Research Funds for the Central Universities (Grant number 010914370122). The founders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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All authors contributed to the study conception and design. Methodology, data collection, and analysis were performed by Xinya Yang. The first draft of the manuscript was written by Xinya Yang, Liuna Geng, and Kexin Zhou, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Liuna Geng.

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Yang, X., Geng, L. & Zhou, K. The construction and examination of social vulnerability and its effects on PM2.5 globally: combining spatial econometric modeling and geographically weighted regression. Environ Sci Pollut Res (2021). https://doi.org/10.1007/s11356-021-12508-6

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Keywords

  • PM2.5
  • Social vulnerability
  • Spatial econometric model
  • Geographically weighted regression