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.

At present, many developing countries around the world are experiencing urbanization, and China has the largest scale of urbanization. The current literature mainly focuses on the relationship between economic factors, environmental factors and urbanization, ignoring the human factors. In fact, whether sufficient social security can be provided to solve people’s worries, as well as people’s social attitudes, has an important impact on their migration from rural areas to urban areas. By using the China General Social Survey (CGSS) 2018 data and constructing a binary logistic model, this paper studies the impact of social security on migration from rural areas to urban areas, as well as the mediating effects of people’s social attitudes. The results reveal that: (1) Social security has a significant positive effect on migration from rural areas to urban areas. (2) The improvement of the sense of fairness, happiness and security is conducive to the integration willingness and identity of the rural population and promotes urbanization. Therefore, social attitude plays an important mediating role. According to our study, policymakers need to consider how to build a suitable social security system and make rural residents feel safe and happy, so as to promote the sustainable development of urbanization.

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.

Relying on social capital to promote farmers’ adoption of green control technology is of great significance for the governance of rural environment and the realization of sustainable agricultural development. Based on the survey data of 754 farmers in Shandong Province, this paper uses the Probit model and the instrumental variable method to empirically analyze the impact of social capital on farmers’ green control technology adoption behavior. The results show that: social capital has a promoting influence on farmers’ green control technology adoption behavior; the influence of the three dimensions of social capital on farmers’ green control technology adoption behavior is in turn social norms, social networks, and social trust; social networks play an enhanced moderating role in the process of social trust and social norms promoting farmers’ green control technology adoption behavior; education level, the number of family labor force and annual family income level have a significant positive impact on farmers’ green control technology adoption behavior, while age has a significant negative impact. Therefore, the government should make full use of social capital to promote farmers to adopt green control technology.

The relationship between environmental factors and the indoor physical environment is very close, and external shading is considered an effective way to adjust the interaction between the indoor and outdoor environment. However, determining how to set up an external shading system remains a notable issue. In the early design stage, architects have adopted the process of designing the form and function first and then checking whether those characteristics meet the energy-saving specifications. However, this process involves a great deal of repetitive and inefficient work and cannot meet the requirements of energy savings and emission reductions in a global context. Therefore, it is particularly important to seek a design method that combines energy-saving design with form-based design. This paper takes a construction project in Northwest China as its research object. In this study, typical parametric models for external shading are designed. Furthermore, indoor performance objectives based on light environment analysis are proposed, and Ladybug Tools and the genetic algorithm (GA) are used for optimization and verification. The optimization results show that the adaptive shading system can significantly reduce the total cooling energy consumption per unit area in summer by 20% and 15%, respectively. The comfort level throughout the year improved by 14.8% (air conditioning on) and 4.7% (air conditioning off). This study proposes a fast and effective shading parametric design method for architects in the early stage, improving the efficiency and accuracy of performance-based design.