The utilization of renewable energy (RE) is a meaningful way to realize the low-carbon transformation of energy systems. However, due to the imbalance of resources, economy, technology, society, and environment among regions, the coordinated development of regional RE may be restricted by different factors, which brings challenges to the formulation of relevant development policies. This paper focuses on the development of RE in 30 provinces in China from 2011 to 2019. It uses the AHP-EM integrated evaluation model to evaluate the constructed multilayer indicator system for the comprehensive development of RE. The characteristics of the coupling and coordination relationship between indicators are explored, and the critical driving factors affecting the coordinated development and change in RE in different regions are quantitatively identified through the logarithmic mean Divisia index method. The results show that the comprehensive development level of RE in each province is relatively low, and the relatively high-level areas gradually move eastward in terms of spatial distribution. The degree of coupling and coordination between indicators is still in a low-level coupling stage, and RE in each region has not achieved coordinated development. In addition, the comprehensive development of regional RE is consistent with the spatial evolution characteristics of the degree of coordination among indicators, emphasizing the importance of coordinated development among indicators for RE. These findings will provide broader insights for improving the comprehensive development level of regional RE and formulating differentiated policies.

As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of wind power generation will contribute substantially to China’s ability to achieve its energy-saving and emission reduction goals. This paper measured the wind power efficiency of 30 provinces in China from 2012 to 2017 using the data envelopment analysis (DEA) method. Moran’s I index and the spatial Durbin model were applied to analyse the spatial distribution of the wind power efficiency and the spatial effects of influencing factors. The results show obvious differences in the spatial distribution of wind power efficiency in China; specifically, the wind power efficiency in the eastern and western regions is higher than that in the central areas. Moreover, wind power efficiency has a significant positive spatial correlation between regions: the eastern and western regions show certain high-high clustering characteristics, and the central area shows certain low-low clustering characteristics. Among the influencing factors, the fixed asset investment and carbon emission intensity of the wind power property have a negative impact on the efficiency of regional wind power production, while the urbanization process and carbon emission intensity have significant spatial spillover effects. The optimization of the economic structure, technological innovation and the construction of energy infrastructure are expected to improve the regional wind power efficiency. The results present a new approach for accurately identifying the spatial characteristics of wind power efficiency and the spatial effects of the influencing factors, thus providing a reference for policymakers.