This study conducts an empirical investigation into the decarbonization effects of urban polycentric structures, exploring their mechanisms and varied influences. Our findings reveal key insights. First, urban polycentric structures effectively reduce carbon emission intensity, a conclusion supported by robust tests and endogeneity analysis. Second, our analysis shows that polycentric structures significantly alleviate downtown overcrowding, reducing carbon emission intensity. These structures also impact carbon emissions through factors like labor allocation across industries and the entry of high-productivity enterprises. Third, our analysis shows that the impact of the polycentric structure on carbon emission intensity follows a ‘U-shaped’ pattern. In this ‘U-shaped’ pattern, the growth of an urban population scale can offset the potential negative impacts of this structure. Finally, government support, urban transportation networks, and communication technologies are crucial in enhancing the effectiveness of polycentric decarbonization. Our research contributes to the literature on polycentric structures and carbon emissions and offers policy implications for addressing climate change, applicable not only to China but also to other economies.

Cities in China do not constitute a few global metropolises, but are characterized by heterogeneity. Studying counties can give us a comprehensive picture of urban sprawl in China. This study measured the sprawl index of 1880 counties in China from 2005 to 2020 for the first time and then revealed the evolution of their spatiotemporal characteristics and driving mechanisms. The results revealed the following. (1) China’s counties had a noticeable sprawling trend from 2005 to 2020, and their evolutionary process was characterized by spatiotemporal heterogeneity. (2) From 2005 to 2020, the counties’ sprawl gradually evolved into a spatial distribution pattern of high in the east and low in the west. The spatial distribution of sprawl in county and municipal districts had the characteristics of an interlocking distribution. (3) High–high cluster areas of CSI are mainly distributed in plains, and hilly, basin, and plateau areas tend to be low–low cluster areas. High–low outliers were distributed in a “point–line” pattern along the railroad lines and a cluster pattern near railroad intersections and central cities. Low–high outliers had the trend of encircling the high–high cluster areas. (4) The coefficient of the natural drivers was higher but tended to decrease, while the coefficient of economic and spatial drivers was lower but gradually increased. This study is the first to refine the study of urban sprawl to the county scale, which provides a reference for decision making to optimize the spatial structure of counties and thus promote high-quality development.