Optimizing the distribution of renewable energy production requires knowledge of spatiotemporal distribution characteristics and an understanding of influencing factors of renewable energy production. Therefore, a spatial analysis of specific mechanisms of China’s renewable energy production is required. Using panel data of 30 Chinese provinces from 2001 to 2020, we employed the spatial Gini coefficient and standard deviation ellipse method to investigate the degree of spatial agglomeration and characteristics of distribution of location of China’s renewable energy production. Then, we explored spatiotemporal regularity, spatial correlation, and spillover effects of renewable energy production by applying Moran’s I index and the spatial Durbin model (SDM). Results show that China’s renewable energy production presents prominent spatial agglomeration characteristics, and the spatial agglomeration of solar power is significantly higher than those of other renewable energies. Renewable energy production is more evenly distributed in the geographical space. There is a significant positive spatial autocorrelation in China’s renewable energy production, and areas are mainly in high–high and low–low clusters. Results of SDM indicate that China’s renewable energy production has an apparent spillover effect. Gross domestic product (GDP) per capita, research and development (R&D) investment, transmission infrastructure, environmental regulation, and urbanization rate have significant positive total effects on renewable energy production. Unemployment rate, SO₂ emissions, and population, have significant negative total effects, while effects of energy intensity and CO₂ emissions are insignificant. The results of this study will be of great significance for decision-makers to accurately identify the spatial spillover effects of renewable energy production.
Optimizing the distribution of renewable energy production requires knowledge of spatiotemporal distribution characteristics and an understanding of influencing factors of renewable energy production. Therefore, a spatial analysis of specific mechanisms of China’s renewable energy production is required. Using panel data of 30 Chinese provinces from 2001 to 2020, we employed the spatial Gini coefficient and standard deviation ellipse method to investigate the degree of spatial agglomeration and characteristics of distribution of location of China’s renewable energy production. Then, we explored spatiotemporal regularity, spatial correlation, and spillover effects of renewable energy production by applying Moran’s I index and the spatial Durbin model (SDM). Results show that China’s renewable energy production presents prominent spatial agglomeration characteristics, and the spatial agglomeration of solar power is significantly higher than those of other renewable energies. Renewable energy production is more evenly distributed in the geographical space. There is a significant positive spatial autocorrelation in China’s renewable energy production, and areas are mainly in high–high and low–low clusters. Results of SDM indicate that China’s renewable energy production has an apparent spillover effect. Gross domestic product (GDP) per capita, research and development (R&D) investment, transmission infrastructure, environmental regulation, and urbanization rate have significant positive total effects on renewable energy production. Unemployment rate, SO₂ emissions, and population, have significant negative total effects, while effects of energy intensity and CO₂ emissions are insignificant. The results of this study will be of great significance for decision-makers to accurately identify the spatial spillover effects of renewable energy production.

Renewable energy production; Spatiotemporal distribution characteristics; Spatial spillover effect; Spatial Durbin model; Influencing factors