Analysis of the generation efficiency of disaggregated renewable energy and its spatial heterogeneity influencing factors: A case study of China

The world has witnessed a surge in renewable power installed capacity in recent years, and there is an emerging trend of renewable penetration in electricity production. However, there is a lack of quantitative comparative study on disaggregated renewable power sources concerning their generation efficiency performance, regional heterogeneity, development potential, and influencing mechanism in the existing literature. In the case of China's 30 provinces, this paper evaluates the hydropower, solar power, and wind power generation efficiency by stochastic frontier analysis method, and then reveals the distribution characteristics and deployment potential of different renewable sources. Furthermore, from the perspective of spatial heterogeneity, geographical detector model is utilized to study the influence mechanism of the generation efficiency of different renewable sources. The main results are as follows. Firstly, production inefficiency prevails in hydropower, solar power, and wind power generation industries. The installed capacity, utilized hours, and auxiliary power consumption have positive impacts on the three renewable energy sources. Every 1% increase in auxiliary power consumption leads to 0.16% increase in solar power generation, which is quite larger than the increase in hydropower and wind power. Secondly, on average, hydropower has the highest level of generation efficiency, followed by wind power and solar power. Kernel density curves indicate the generation efficiency of hydropower, solar power, and wind power displays distinct aggregation characteristics. Different energy types show significant regional differences in deployment potential. Thirdly, annual precipitation accounts for 76% of the spatial heterogeneity in hydropower generation efficiency, followed by hydropower technology innovation and power structure. As for solar power generation efficiency, the most important influencing factors are electricity investment and economic development. By contrast, wind power generation efficiency is primarily affected by power structure, electricity investment, and urbanization. Additionally, there exist distinct synergistic effects among different variables. These results provide insightful policy support for the improvement of renewable power generation efficiency. The study can be extended to the global scale using country-level data. (c) 2021 Elsevier Ltd. All rights reserved.