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Analyzing the impact of urbanization quality on CO2 emissions: What can geographically weighted regression tell us?

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  • Wang, Yanan
  • Li, Xinbei
  • Kang, Yanqing
  • Chen, Wei
  • Zhao, Minjuan
  • Li, Wei

Abstract

China is facing increasingly severe challenges in its quest to achieve urbanization and mitigate CO2 emissions. The existing studies have usually introduced a single indicator to describe urbanization and have ignored the complexity and multi-dimensionality of urbanization. This study establishes an evaluation system of urbanization quality to estimate the urbanization development level. The geographically weighted regression (GWR) model is employed to examine the impact of the urbanization quality on CO2 emissions and reveals the spatial differences of 30 provinces in 2000, 2005, 2010, and 2015. The results show that there are significant temporal and spatial differences in the effects of the urbanization quality on CO2 emissions between provinces. Improvements in the urbanization quality have contributed to cutting CO2 emissions in most provinces. The impact of the urbanization quality on CO2 emissions in the central region and western region is greater than that in the eastern region. The energy intensity has the largest positive impact on CO2 emissions, which indicates that technical progress can effectively reduce CO2 emissions. The industrial structure has a positive impact on CO2 emissions in 2000 and 2015, whereas it has a negative impact on the CO2 emissions of some provinces in 2005 and 2010. This paper provides valuable findings and conclusions of the relationship between urbanization quality and CO2 emissions. Differentiated policy implications are proposed according to geographical differences.

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  • Wang, Yanan & Li, Xinbei & Kang, Yanqing & Chen, Wei & Zhao, Minjuan & Li, Wei, 2019. "Analyzing the impact of urbanization quality on CO2 emissions: What can geographically weighted regression tell us?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 127-136.
    • Handle: RePEc:eee:rensus:v:104:y:2019:i:c:p:127-136
    DOI: 10.1016/j.rser.2019.01.028
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