Prediction and evaluation of the energy structure under the green finance development in Chongqing municipality, China.

Chongqing, as the last ecological barrier of the Upper Yangtze River, is constrained to achieve "dual carbon" goals due to imbalanced energy structure. Based on selecting the energy structure influencing factors through Copula function and Granger causality, a multi-dimensional dynamic support vector machine model (SSA-MFD-SVR-ARIMA) by adopting sparrow algorithm was constructed to predict the proportion of Chongqing's energy structure from 2021 to 2030 under the drive of green finance development, and an optimization path was obtained. The novel findings confirm that (1) the correlated contribution rate of Green Finance to optimizing Chongqing's Energy Structure is 10.8 %; (2) under the sustained growth rate of Green Finance at 4.5 %, the proportion of coal consumption will reach 40.03 % by 2030, and non-fossil energy consumption will account for 27 %. It confirms that Chongqing can achieve the Energy Development Plan assigned by the Central Government in 2025. The research proposes a four-dimensional optimized pathway from a financial perspective that includes green equity investments, digital finance for energy, financing environmental rights and interests, and developing an industry fund. Furthermore, our put forward the safeguard strategies for financing, innovation, linkage, and protection mechanisms of this pathway optimization.