VIRTUAL London.

Abstract

Cities are considered at the forefront of sustainable development and net-zero emissions. However, in China, the lack of direct emissions data often leads to a gap in research on estimating CO2 emissions in less developed western cities and a thorough explanation for spatial heterogeneity in drivers. To address this issue, this study first estimated the CO2 emissions of cities within the Chengdu-Chongqing Economic Circle (CCEC) in southwestern China from 2013 to 2022, utilizing comprehensive energy consumption data. The estimation revealed a significant spatial auto-correlation of CO2 emissions through the application of Moran’s I model. By analyzing the effects of different socioeconomic factors on CO2 emissions using both global and local regression models, the study identified key drivers of emissions in the CCEC cities. Specifically, the global regression model (OLS) uncovered that population density (correlation coefficient t=3.298), the proportion of secondary industry (t=1.146), and GDP per capita (t=5.199) were the main factors contributing to CO2 emissions in the CCEC cities, while road density had a negative impact. The geographically weighted regression (GWR) model provided more detailed insights, showing significant spatial heterogeneity in the effects of different factors across the region. Population density and the proportion of secondary industry had the most significant positive effects on central and western cities (P<0.01, two-tailed), while GDP per capita had a significant positive effect on CO2 emissions in the southern cities (P<0.01, two-tailed). In northeastern cities, public transportation had an inhibitory effect on emissions, while urban expansion was positively correlated (P<0.01, two-tailed). Additionally, total electricity consumption and foreign direct investment (FDI) played significant roles in emissions levels in Chengdu (P<0.01, two-tailed).