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The structure of renewable energy (RE) development varies substantially across China's provinces, and the spatio-temporal pattern evolution is too pronounced to allow direct comparisons. Therefore, a reasonable method for RE efficiency estimation is important. Markov and spatial Markov chains can be used to analyze the evolution of the temporal and spatial patterns of provincial RE. Simultaneously, data envelopment analysis game cross-efficiency evaluation can quantify the RE generation efficiency of each province from the perspective of competition. Nine factors were selected to identify the key influencing factors the RE generation efficiency. From the perspective of spatio-temporal heterogeneity, a geographically and temporally weighted regression model was combined for spatial modeling. Results show a significant change in the categories of wind and solar power installations, a medium change in biomass installations, and a small change in hydropower installations. Two-thirds of the provincial RE generation efficiency increased from 2015 to 2020. There is spatio-temporal heterogeneity regarding the impact of various factors on the RE generation efficiency: The impact of technology market transactions exhibits the greatest inter-provincial variation and the impact of electricity industry investment exhibits the smallest inter-provincial variation. [ABSTRACT FROM AUTHOR]

Facing the problem of energy shortage and the responsibility of carbon reduction, to achieve sustainable regional economic development, renewable energy must be vigorously developed. Technology can not only boost the development of renewable energy but be a new driving force for the economy. However, the development of the economy, technology, and renewable energy are interrelated, and the coupling and coordination among them lack sufficient evaluation and analysis. Therefore, an evaluation index system is constructed for the economy–technology–renewable energy ternary group. The evaluation model is applied to measure the coupling coordination degree, and a geographically weighted regression model is used to analyze the influence of key factors and their spatial differences. 30 provinces in China are used as examples for the study, and the results show that the coupling coordination degree is low in most regions, except for Guangdong, which has been maintained at an extremely coordinated level, and most provinces are in a fluctuating upward trend during the decade. Additionally, the results of the geographically weighted regression report that the influence of factors has spatial heterogeneity. The study provides a basis for relevant policy formulation in China, and the evaluation and analysis ideas can provide a reference for other countries or regions. [ABSTRACT FROM AUTHOR]