Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain

The development of a green finance system can be important for the control of energy consumption. This research focuses on the effects of the spatial externality of institutional support from the green finance systems on the energy efficiency of Chinese cities. The Malmquist data envelopment analysis (DEA) model is used to analyze the efficiency of energy consumption. The spatial Durbin model is used to analyze the effects of the spatial externality of green finance on energy efficiency. The results show that green finance has an inhibitory impact on energy consumption. The interaction of the policy of green finance and the development of the digital economy also has a negative spatial externality effect on energy consumption in cities. In addition, the control of energy consumption can be further enhanced due to negative spatial autoregressive relationships with cities. The control of energy consumption in cities with large population can be further strengthened through the positive interaction between gross domestic product (GDP) and green finance. Cluster analysis is performed to analyze the characteristics of cities, and the stationary probability in the switch of clusters is forecasted by the Markov chain. Hidden Markov Chain Modeling (HMM) is applied to analyze the probability of city clusters based on hidden connection between institutional support for digital economy and development of green finance with energy consumption. This study can provide policy makers with a systematic comprehension of the impact of spatial externalities on energy efficiency by green finance system in the cross-functional digital economy.