Analysis of the spatiotemporal distribution pattern and driving factors of renewable energy power generation in China

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China's renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China's renewable energy industry.& COPY; 2023 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.