Finance contributes to poverty alleviation through economic growth, and the development of green finance is related to the sustainable development of the world economy and environment. Green finance not only helps promote sustainable economic development but also helps reduce poverty. Based on the analysis of related theories about green finance and poverty alleviation, this paper selects 18 indicators from three dimensions of economic development, financial development, and social environmental development and uses the improved entropy method to measure the green finance development index of China’s 25 provinces and municipalities from 2004 to 2017. The results show that the development level of green finance in China’s 25 provinces and municipalities is quite different. On the basis of the above analysis, make an empirical analysis of the impact of the green finance development index on poverty alleviation using multiple regression analysis and static panel and dynamic panel estimation methods. The research results show that there is a significant positive correlation between green finance and poverty alleviation; the higher the level of green finance development, the more conducive the poverty alleviation. So, this paper suggests that poverty can be better alleviated by improving the level of green finance development, financial asset level, and economic development level.

This paper studies the impact of the development of green finance on China’s energy consumption structure. 17 basic indexes and the improved entropy weight method are used to construct the green finance index (GFI). Multiple regression, panel regression, and spatial regression are used to study the impact of green finance on China’s traditional energy and renewable energy consumption. The results show that there is a positive spatial spillover effect in the development of green finance among provinces in China. The development of green finance contributes to the conversion of traditional to renewable energy consumption. The effect of green finance on the transformation of energy consumption structure is mainly reflected in the direct effect. The green finance in each province not only helps the local development of green energy but also plays a good role in the production and utilization of clean energy consumption in surrounding provinces. Therefore, the government should support the green finance, reduce traditional energy consumption, and increase renewable energy consumption.

Agricultural green finance is an important means to boost the development of agricultural green. Based on the panel data of 30 provinces in China from 2011 to 2021, this paper theoretically discusses the impact of agricultural green finance on the level of agricultural green development and conducts an empirical study by using the two-way fixed effect model and panel threshold model. The empirical findings show that agricultural green finance can significantly improve the level of agricultural green development mainly through facilitating the level of agricultural green technology innovation. The promoting effect of agricultural green finance on the level of agricultural green development is remarkable in the eastern region and non-major grain-producing areas, but not obvious in the central and western regions and major grain-producing areas. Indeed, the impact has a single threshold effect with environmental regulation as the threshold. Therefore, efforts should be made to improve the level of agricultural green development from the following aspects: vigorously developing agricultural green finance; promoting agricultural green technology innovation; strictly implementing environmental protection policies; and designing appropriate environmental regulation zones.

This paper uses the 5‐five‐minute high‐frequency data of energy‐listed companies in China's A‐share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time‐varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time‐varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.

As the greenhouse effect and the environmental problems thus arising become increasingly prominent, great concern has been aroused for agricultural carbon emissions reduction and sustainable development. Aiming at exploring how to reduce agricultural carbon emissions from agricultural green finance perspective, this study theoretically analyzed the impact and mechanism of agricultural green finance on agricultural carbon emission intensity, and empirically examined the specific impact and the corresponding impact mechanism of agricultural green finance on agricultural carbon emission intensity. Specifically, a two-way fixed effect model was employed to conduct the study based on the panel data of 30 provinces in China from 2011 to 2022. The results show that agricultural green finance can significantly reduce agricultural carbon emission intensity, which has been verified to be valid after a series of robustness tests; and the reduction displays a heterogenous pattern. As it is, the impact of agricultural green finance is more prominent in the eastern region than in the central and western regions, and greater in non-major-grain-producing areas than in major grain-producing areas. On this basis, some policy suggestions are put forward, mainly including developing agricultural green finance, promoting agricultural and green technological innovation, and implementing differentiated agricultural green finance policies so as to reduce agricultural carbon emission intensity.

Risk preference has constantly been one of the vital issues in economics and finance. In this study, the time series and term structure of Chinese investors’ implicit risk aversion are investigated using the implicit risk aversion extraction method, and the dynamic term structure of Chinese investors’ implicit risk aversion is modeled using the Vasicek model. In the empirical process, the SSE 50 ETF option data from March 2015 to July 2018 are adopted to extract model‐free risk‐neutral skewness. The standard deviation, skewness, and kurtosis of reality measure are extracted using the Shanghai 50 ETF data, and then the monthly implied risk aversion time series and term structure of Chinese investors are obtained. As indicated by the results of this study, the risk preference of Chinese investors exhibits significant time series characteristics, and it will show risk‐loving and risk‐averse phenomena at certain times. Moreover, for different periods in the future, differences are generated in investors’ risk preferences, suggesting an aversion to short‐term risk and a certain tolerance for long‐term risk, i.e., there are significant characteristics of the term structure. Besides, the term structure of implied risk aversion of Chinese investors is dynamically modeled using the Vasicek model. To be specific, its first principal component can account for 90% of the change in the term structure. The “level factor” refers to the critical factor load, and the level of long‐term implied risk aversion reaches 0.658. Furthermore, the term structure of implied risk aversion exhibits the characteristics of mean regression. Next, more effective research results on investors’ risk preference are achieved, and the time‐varying characteristics and term structure characteristics of investors’ risk preference are investigated. The result suggests that the long‐term risk preference of Chinese investors approaches 1, and there is a significant feature of “mean regression,” i.e., a vital finding of this study.