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.

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 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.

At present, there is much literature on economic growth and energy consumption, but there is little literature combined with the industry perspective. This paper aims to clarify whether the development of energy-intensive industries is an indirect way for economic growth to affect energy consumption, which can provide a reference for the coordination of economic growth goals, industry development and reducing energy consumption. Based on China’s provincial panel data from 2000 to 2019, this paper measures the scale of provincial energy-intensive industries by entropy method and uses the panel regression model to test its transmission effect on energy consumption. The results show that 23.96% of the effects of economic growth on energy consumption are indirectly generated through the transmission of energy-intensive industries. Moreover, the transmission effects are only established in the eastern and western regions but are not significant in the central region. Therefore, controlling the rapid development of energy-intensive industries is an effective way to curb the expansion of China’s energy consumption scale. Green technology innovation, new-type urbanization construction and other supportive measures should be taken in accordance with local conditions. This research contributes to the coordinated and sustainable development of the economy, industry, and energy.

Macroeconomic indicators include gross domestic product (GDP), consumer price index (CPI), and retail price index (RPI). These indicators are important for understanding the macroeconomic situation and controlling the macroeconomic trend, as they provide a macroscopic view of a country or regions economic performance. If macroeconomic indicators can be predicted accurately in advance, the government and relevant macroeconomic control departments can propose more forward-looking and targeted macroeconomic control policies and deploy the necessary control measures. In addition, individuals can make more reasonable decisions on their investments and savings if they know the macroeconomic indexes in advance. From these two aspects, prediction of macroeconomic indexes is of great research significance. In this paper, we propose a prediction model based on chaotic vector autoregression and neural networks for macroeconomic forecasting, and we model and test the prediction with GDP and inflation as the main concerns, and we find that the improvement of GDP forecasting shows an increase of expected inflation rate, indicating the usefulness of using expected GDP data.

Summary: 1. Please indicate the year of the data in this paper. 2. At the end of the abstract, one sentence should summarize the significance of this study. Introduction: Add the contribution and significance of this article in this part. Methodolo...