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In recent years, digital finance has become a crucial part of the financial system and reshaped the mode of green finance in China. Digital finance has brought certain impact on economic growth, industrial structure, and resident income, which may affect pollution. The nexus of digital finance and environment in China is thus worth exploring. By revising the traditional Environmental Kuznets Curve model with income inequality variable, this paper decomposes the environmental effects of economic activities into income growth effect, industrial structure effect and income inequality effect, and use panel data of China’s provinces to conduct an empirical analysis. The results reveal the following: (1) the Environmental Kuznets Curve is still valid in sample, and digital finance can reduce air and water pollution (as measured through SO 2 and COD emission) directly; (2) in the influence mechanism, digital finance can alleviate income inequality and promote green industrial structure, thus reducing pollution indirectly, but the scale effect of income growth outweighs the technological effect, which increases pollution indirectly; and (3) digital finance has a threshold effect on improving the environment, then an acceleration effect appears after a certain threshold value. From the regional perspective, digital finance development in eastern regions is generally ahead of central and western regions, and the effects of environmental improvement in the eastern regions are greater. According to the study, this paper suggest that digital finance can be an effective way to promote social sustainability by alleviating income inequality and environmental sustainability by reducing pollution.

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Green technology innovation is an important means to help reduce carbon emissions. Most of the current researches focus on the evaluation of green technology innovation and its relationship with economic factors, while ignoring its relationship with financial factors. In fact, financial development is an important driving force for further improving the efficiency of technological innovation, especially in developing countries. China, which has put forward the “double carbon” strategy (achieve peak carbon dioxide emission and carbon neutrality) in recent years, has a lot of interaction between green technology innovation and financial development, which is a good research sample. Based on the provincial panel data of 30 provinces in China from 2003 to 2020, this paper follows the research steps: 1 ) From the perspective of carbon emissions, we use the non-radial super-efficiency SBM-ML model to measure the total factor productivity of green technology innovation. 2 ) We analyze the temporal and spatial changes of green technology innovation and financial agglomeration in each province. 3 ) We establish a spatial simultaneous model of generalized three-stage least squares to study the intrinsic relationship between green technology innovation and financial agglomeration. The results show that: 1 ) The financial agglomeration level of each province is basically stable due to the fixedness of the financial core area, but the development speed of green technology innovation in the southeast coastal area is significantly higher than that in the inland area. 2 ) The interaction between green technology innovation and financial agglomeration has a nonlinear mechanism of “low-level inhibition and high-level promotion,” that is, low-level financial agglomeration has an inhibitory effect on green technology innovation, and as the level of financial agglomeration increases, its impact on green technology innovation gradually develops positively, and vice versa. 3 ) The green technology innovation and financial agglomeration in the surrounding provinces have positive and significant promoting effects on the green technology innovation and financial agglomeration in the province, but the impact of green technology innovation is significantly stronger than that of financial agglomeration. Finally, suggestions are put forward for further reducing carbon emissions, realizing the sustainable economic growth and approaching goals of “carbon peaking and carbon neutrality.”

Ground source heat pumps (GSHPs), a high-efficiency and energy-saving air-conditioning technology that utilizes shallow geothermal resources for both heating and cooling, are a vital green energy system for residential and commercial buildings. Improving the performance of such a system was the focus of the current research. As soil temperature and thermal radius are two important aspects that affect the performance of ground source heat pump systems, we conducted a new numerical simulation to capture the changes in sensitive factors and propose the optimized paths. The numerical simulation analyzed the thermal characteristics of a borefield under different pre-cooling times and soil types. The results indicated the following: (1) The rate of the ground temperature change with pre-cooling during the discharging period had a faster rise than in the case without pre-cooling. The longer the precooling time was, the smaller the thermal radius became. In particular, when the precooling time was longer than 14 days, the decrease in the thermal radius rate percentages was less than 4%. (2) Among the three kinds of soils compared, the soils with lower thermal conductivity and thermal diffusivity best suppressed the thermal interference effects. (3) Using a multivariate nonlinear function regression model, a simulation formula was proposed to predict- the thermal radius, which considered the factors of thermal diffusivity, precooling time, and discharging time. The prediction deviation was within 14.8%.

The evaluation of corporation operation efficiency (especially innovation efficiency) has been always a hot topic. The currently popular evaluation methods are data envelopment analysis (DEA) and its improved methods. However, these methods have the following problems: the production process is regarded as a black box, and the actual production relationship between input and output is not analyzed. To solve these problems: (1) the black box theory and production function theory are introduced to uncover the black box of input and output; (2) regression models are used to alleviate the multicollinearity problem of inputs, and the most appropriate model of production relationship is selected; and (3) the results of the production function are compared with the results of the efficiency evaluation from multiple perspectives. Taking rural commercial banks in China as examples to evaluate their innovation efficiency, this article shows the following: (1) with the black box theory and production function theory, the staff, equipment, and intermediate business cost are suitable as innovation input variables, and intermediate business income is suitable as an innovation output variable; (2) the main challenges faced by rural commercial banks are reducing the reliance on human capital investment, strengthening technological innovation, and improving the efficiency of intermediate business cost management, which is hard to reveal with traditional DEA. The method proposed in this article provides an applicable reference for improving DEA method analysis.

Based on the microdata of 11,547 farmers from the China Labor Dynamics Survey (CLDS) database in 2017, an ordered multi-classification logistic model was constructed to empirically test the impact of social capital (i.e., social networks, social participation, and social trust) on farmers’ willingness to adopt agricultural technology. The moderating effect of demographic changes (i.e., the number of instances of hukou migration) on social capital and farmers’ willingness to adopt new agricultural technology was further investigated. The results show that the following: (1) Social trust has a significant positive impact on farmers’ willingness to adopt new agricultural technologies, while social participation has no significant impact on farmers’ willingness to adopt new technologies. (2) Social networks influence farmers’ technology adoption behavior differently, e.g., the scope of relatives’ wedding gifts has a significant and positive influence on farmers’ technology adoption behavior, while the scope of non-relatives’ wedding gifts has no significant influence on farmers’ technology adoption behavior. (3) Demographic change plays a moderating role in the impact of social capital on farmers’ willingness to adopt new agricultural technologies. In other words, the greater the number of instances of hukou migration, the less the promoting effect of social capital on farmers’ willingness to adopt agricultural technology. (4) In the eastern and central regions of China, social capital has a significant positive impact on farmers’ adoption of new agricultural technologies. In the western region of China, social capital has a significant negative impact on farmers’ adoption of new agricultural technology. In the northeast region of China, social capital has no significant impact on farmers’ adoption of new agricultural technologies. Social capital and population changes are important factors that affect farmers’ willingness to adopt new agricultural technologies. Therefore, attention should be paid to cultivating and promoting farmers’ social capital to improve farmers’ willingness to adopt new agricultural technologies.

The spatial spillover effect of regional green innovation efficiency (GIE) is a heated issue of academic research; however, it has rarely been discussed from a network perspective. It is pretty meaningful to clarify its spatial association network’s evolutionary rules and driving factors. To fill the lack of research, this study measures the regional GIE in China from 2010 to 2019 using an epsilon-based metric (EBM) model that considers undesirable outputs. A modified gravity model and social network analysis (SNA) method are used to analyze the evolutionary rules and spatial spillover effects of the network structure of GIE, and a quadratic allocation process (QAP) was employed to identify its driving factors. The findings reveal that: 1) China’s regional GIE has a geographic correlation network structure with a low network density (peaking at 0.210 in 2018) and an annually increasing slow trend. 2) The network structure is relatively loose and has a certain hierarchical gradient, with “dense in the eastern” and “sparse in the western” characteristics. 3) The eastern provinces are at the relative center position and play a leading role in the network; the central, western, and northeastern regions are relatively inferior and play a fulcrum and conduction role. 4) Spatial adjacency, the differences in infrastructure, urbanization, and economic development level positively affect the spatially correlated regional GIE. In contrast, differences in environmental regulations and differences in science and technology innovation (STI) have negative effects. Finally, from the perspectives of national, regional, block, and driving factors, several recommendations are made to enhance the overall improvement and balanced development of regional GIE in China.

The benefit of cultivated land use is an essential indicator for measuring the optimal allocation of cultivated land resources and the high-quality development of agriculture. Taking Shiyan City, Xiangyang City, and Suizhou City in Northern Hubei as the research objects, this paper presents an evaluation index system for cultivated land use efficiency from the perspectives of ecology, economy, and society. The entropy TOPSIS method and the obstacle degree model were applied to estimate the cultivated land use efficiency and identify obstacle factors in the three study areas from 2010 to 2020, and the results were as follows. (1) The comprehensive benefit level of cultivated land utilization in Northern Hubei showed an upward trend, and the individual benefit levels of cultivated land utilization in different cities were significantly different. Xiangyang City had outstanding economic performance, Shiyan City had the fastest growth rate of ecological benefits, and various benefits of Suizhou City were “steady”. (2) The fluctuation ranges of the obstacle factors for cultivated land use were relatively large in the Northern Hubei region. From 2010 to 2016, the effective irrigation index, land-averaged fertilizer input level, agricultural input–output ratio, and per capita income of farmers were the main factors restricting the improvement of cultivated land utilization efficiency in Northern Hubei. During 2017–2020, the per capita pesticide input level, per capita grain output, forest coverage rate, land output rate, and agricultural mechanization efficiency became the main obstacles restricting the improvement of cultivated land-use efficiency. (3) All cities of Northern Hubei should take measures according to local conditions, implement specific policies to address the restrictive factors of cultivated land use, improve the level of cultivated land-use benefit in the region, and promote the coordination and unity of the economic, ecological, and social benefits of cultivated land use.

In the era of big data, investor sentiment will have an impact on personal decision making and asset pricing in the securities market. This paper uses the Easteconomy stock forum and Sina stock forum as the carrier of investor sentiment to measure the positive sentiment index based on stockholders’ comments and to construct an evaluation index system for the public opinion dimension. In addition, the evaluation index system is constructed from four dimensions, which include operation, innovation, finance and financing, to evaluate the overall condition of listed companies from multiple perspectives. In this paper, the SBM model in the data envelopment analysis method is used to measure the efficiency values of each dimension of the multidimensional efficiency evaluation index system, and the efficiency values of each dimension are the multidimensional efficiency indicators. Subsequently, two sets of input feature indicators of the SVM model were established: one set contains traditional financial indicators and multidimensional efficiency indicators, and another set has only traditional financial indicators. The early warning accuracy of the two sets of input feature indicators was empirically analyzed based on the support vector machine early warning model. The results show that the early warning model incorporating multidimensional efficiency indicators has improved the accuracy compared with the early warning model based on traditional financial indicators. Then, the model was optimized by the particle swarm intelligent optimization algorithm, and the robustness of the results was tested. Moreover, six mainstream machine learning methods, including Logistic Regression, GBDT, CatBoost, AdaBoost, Random Forest and Bagging, were used to compare with the early warning effect of the DEA–SVM model, and the empirical results show that DEA–SVM has high early warning accuracy, which proves the superiority of the proposed model. The findings of this study have a positive effect on further preventing and controlling the financial crisis risk of Chinese-listed companies and promoting as well as facilitating the healthy growth of Chinese-listed companies.

The world is facing the problem of resource scarcity and environmental degradation. Improving energy efficiency is an effective way to reduce energy consumption and reduce pollutant emissions. Based on relevant data from 30 Chinese provinces from 2011 to 2019, this paper constructs energy efficiency indicators by establishing a super-efficient three-stage SBM-DEA model. It explores the impact of digital finance on energy efficiency using a systematic generalized moment estimation method and constructs an analytical framework for the impact of digital inclusive finance on energy efficiency from the breadth of coverage, depth of use, and degree of digitization of digital inclusive finance. In addition, this paper examines the differences in the impact of digital inclusive finance on energy efficiency from a sub-regional perspective. Research indicates the following: (1) At the national level, the relationship between digital inclusive finance development and energy efficiency in China shows an inverted “U”-shape; the breadth of digital financial coverage, the use of digital insurance services and digital credit services, and the degree of digitalization of digital finance all have significant effects on energy efficiency. (2) From a regional perspective, the impact of digital inclusive finance on energy efficiency has regional heterogeneity. Based on this finding, first, the government should speed up the construction of digital financial infrastructure to promote the further development of digital finance. Second, the government should take appropriate measures to regulate industry giants. Third, the government should adjust measures to local conditions when formulating policies. The above research has certain implications for improving the targeting of digital finance–related policies and promoting the high-quality development of China’s economy.