A multi-dimensional analysis on potential drivers of China's city-level low-carbon economy from the perspective of spatial spillover effects.

Under the background of "carbon peak" and "carbon neutrality", China's cities are at the heart of climate change mitigation. To fulfill the commitments, the potential drivers of China's low-carbon economy at the city level should be explored. Thus, this study estimated the quality of low-carbon economy from the perspectives of carbon emissions and sequestration. Subsequently, spatial econometric approach was adopted to analyze the direct and spatial spillover effects of drivers on low-carbon economy. Moreover, to explore more potential variables, this study combined machine learning algorithms with different weight matrices to assess the importance scores of neighbouring cities' potential variables. The results show that (1) GDP per capita, emissions reduction technological progress, terrestrial vegetation's net primary productivity and the ratio of forest land cover improved cities' low-carbon economy; (2) land urbanization reduced nearby cities' low-carbon economy (especially those with high human footprint index at the inter-city border), which was caused by its negative spatial spillover effects on forest net primary production; (3) based on importance analysis, this study found that the characteristics of different sectors (e.g., the ratio of labor to fixed capital stocks, employment and fixed asset investment expenditure) also significantly influenced nearby cities' low-carbon economy. • China's city-level quality of low-carbon economy (reo) was estimated based on carbon intensity and sequestration. • The importance of 72 variables' spatial spillover effects were ranked based on machine learning algorithms. • Terrestrial vegetation's net primary productivity and the ratio of forest land cover greatly improved the reo. • The spatial spillover effect of land urbanization was negative in nearby cities. [ABSTRACT FROM AUTHOR]