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.

Utilizing provincial panel data from China spanning 2000 to 2020, this study constructed an indicator system for high-quality economic development by incorporating 35 indicators. These indicators stem from five domains: innovative, coordinated, green, open, and shared development. To assess carbon finance development, an evaluation system was created using twelve basic indicators derived from three categories: financial environment, energy efficiency, and scientific and technological progression. The entropy weight method facilitated the computation of indices such as the economic quality development, innovative development, coordinated development, green development, open development, shared development, and carbon finance development indices. Both static and dynamic panel models served to empirically ascertain the specific influence of carbon finance on high-quality economic development. Additionally, regional and temporal variances in carbon finance's impact on high-quality economic development were scrutinized. Findings indicate that provinces like Beijing, Shanghai, Jiangsu, and Guangdong, known for advanced economic development, exhibit elevated levels of high-quality economic and carbon finance development. Nonetheless, disparities among provinces are evident. While carbon finance significantly bolsters China's high-quality economic growth, its influence is not uniformly observed across all five development domains. It primarily augments coordinated, green, and shared development. Furthermore, the role of carbon finance in boosting China's high-quality economic development exhibits regional and temporal variations. The promotion of high-quality economic growth can be achieved by fostering an amenable carbon finance environment, addressing aging, maximizing governmental influence, enhancing transportation infrastructure, propelling new urbanization, and refining education standards.

Based on an analysis of the function of technology finance for green high-quality development, this paper selects 35 basic indicators from the five dimensions of innovation, coordination, greenness, openness, and sharing to construct an Green High-quality Development Index. Moreover, 14 basic indicators are selected from the three dimensions of technology finance resources level, technology finance input level, and technology finance output level to construct a technology finance development evaluation system. Then, using the entropy method, panel data from the period 2000–2020 from China’s 30 provinces was employed to assess the Economic Quality Development Index and the Technology Finance Development Index. The results show that provinces and cities with a good economic development level have a relatively high level of strong economic and technology finance development, including Beijing, Shanghai, and Jiangsu. Moreover, the results reveal a certain gap between different provinces. Multiple regression, static panel regression, dynamic panel regression, the static space Dubin model, the dynamic space Dubin model, and the semi-parametric space lag model were used to test the effect of technology finance on China’s green high-quality development. This study found that technology finance has significantly enhanced green high-quality development of China’s as a whole through a non-linear promotion effect. To better promote green high-quality development, the following measures are recommended: 1) improving the level of technological and financial development, 2) increasing the scale of government expenditures, 3) strengthening infrastructure construction, 4) accelerating the construction of new urbanization, and 5) improving the level of human capital.

In this paper, 22 indexes are selected at three levels, including the informatization development level, the Internet development level, and the digital transaction development level, based on China’s provincial panel data from 2011 to 2020, so as to build a digital economy development index system. Moreover, 28 basic indexes are selected from three aspects, including energy construction, energy production and energy consumption, so as to develop an energy economy development evaluation index system. The development index of China’s digital economy and energy economy are measured by using the entropy weight method. The effect of the digital economy on the energy economy and its mechanism are tested by the static panel, the dynamic panel, and the mediating effect and regulating effect models. The results indicate that the digital economy has pronouncedly promoted the development of China’s energy economy, and the development of the digital economy can have an effect on the rationalization of the industrial structure and then affect the development of the energy economy, and there is an intermediary effect. Moreover, the upgrading of the industrial structure is conducive to regulating the digital economy and facilitates the development of the energy economy. The development of the energy economy can be better promoted by focusing on the coordinated regional layout of the digital economy development, building a reliable energy commodity trading platform, and expediting the optimization and upgrading of the industrial structure.

This article uses the entropy weight method to calculate China’s economic high-quality development index and carbon finance development index from 2000 to 2020. Additionally, it conducts empirical examinations to scrutinize impacts of carbon finance regarding quality-focused development of China’s economy through dynamic spatial models. The findings reveal that the advancement of carbon finance substantively promotes the quality-focused economy advancement through enhancing the overall factor productivity, thereby establishing a mediating effect. Concurrently, the allocation of resources in green fiscal expenditure assumes a moderating effect, subsequently amplifying the promoting effect of carbon finance regarding quality-focused economy advancement. Consequently, it is recommended that a robust financial milieu be established, energy efficiency be enhanced, scientific and technological progress be bolstered, total factor productivity be further augmented, and the expenditure on green conservation be escalated. These measures will effectively stimulate the quality-focused development of China’s economy.

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.