[Spatiotemporal Differences in Green Total Factor Productivity and Its Influencing Factors in China's Power Industry]

Improving the green total factor productivity of the electric power industry is crucial to realize China's clean and low-carbon transition and sustainable economic growth. The super-efficient SBM model and ML index method were used to measure the green total factor productivity and its growth rate in the power industry in 30 provinces of China from 2005 to 2021, and the dynamic green total factor productivity is split to analyze the impact of technological progress and changes in technological efficiency. The spatial transfer paths and spatiotemporal differences of dynamic green total factor productivity in the electric power industry are further investigated through a spatial Markov chain model and panel Tobit model, and their influencing factors are empirically examined. The results show that: ① The static green total factor productivity of the electric power industry in each province during the study period was low and showed large differences, with considerable room for improvement. ② The green total factor productivity of China's electric power industry increased at an average annual rate of 3.9%, of which the average annual growth rate of technological progress was 3%. Among the six power grid regions, the ML index of green total factor productivity of the power industry of East China, Central China, and South China Power Grid was among the top three in China, and technological progress was the main driving force. ③ The transfer probability of the green total factor productivity level in the power industry between regions was affected by the spatial lag effect. The transfer trend of regions with different levels varied, showing a low state maintenance probability and high transfer probability. ④ Economic level, power industry capital intensity, industrial structure, and green technology innovation were the key factors for the growth of green total factor productivity in the power industry, and there was heterogeneity in the mechanisms of influencing factors in the six power grid regions.