The coupling coordination degree (CCD) of the production-living-ecological space (PLES) functional index is an indicator of regional sustainable development potential. However, previous studies have failed to reveal the driving mechanisms of the CCD of PLES functional index on a global scale. Therefore, this study employed the CCD model to evaluate the CCD of the PLES functional index and spatial regression models to measure the heterogeneous drivers using multi-source data in 2000, 2010, and 2020. The results demonstrated that ecological spaces dominated (85%) globally, while living spaces comprised the smallest share (3%). The ecological functional index was higher than the production and living functional indices. Further analyses revealed that topographic factors were the main restricting factors for PLES; the proportion of production and living spaces decreased with increasing altitude and slope, whereas ecological spaces showed the opposite trend. The global CCD values of the PLES functional index in 2000, 2010, and 2020 were 0.186, 0.189, and 0.198, respectively, showing an increasing trend. High CCD was generally observed in areas with dense population and industry where human activity systems interact with natural ecosystems. The formation of the CCD of the global PLES functional pattern results from the joint action of natural and socioeconomic factors, with pronounced spatial heterogeneity. Our findings can help optimize global territorial space utilization, improve territorial space utilization efficiency, and realize global sustainable development goals.

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China’s PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a ‘club convergence’ phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the “dual carbon” goal.