To answer to global climate change, promote climate governance and map out a grand blueprint for sustainable development, carbon neutrality has become the target and vision of all countries. Green finance is a means to coordinate economic development and environmental governance. This paper mainly studies the trend of carbon emission reduction in China in the next 40 years under the influence of green finance development and how to develop and improve China’s green finance system to help China achieve the goal of “carbon neutrality by 2060”. The research process and conclusions are as follows: (1) Through correlation test and data analysis, it is concluded that the development of green finance is an important driving force to achieve carbon neutrality. (2) The grey prediction GM (1,1) model is used to forecast the data of carbon dioxide emissions, green credit balance, green bond issuance scale and green project investment in China from 2020 to 2060. The results show that they will all increase year by year in the next 40 years. (3) BP neural network model is used to further predict carbon dioxide emissions from 2020 to 2060. It is expected that China’s CO₂ emissions will show an “inverted V” trend in the next 40 years, and China is expected to achieve a carbon peak in 2032 and be carbon neutral in 2063. Based on the results of the research above, this paper provides a supported path of implementing the realization of the carbon-neutral target of China from the perspective of developing and improving green financial system, aiming to provide references for China to realize the vision of carbon neutrality, providing policy suggestions for relevant departments, and provide ideas for other countries to accelerate the realization of carbon neutrality.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

Aiming at the problems of the influencing factors of coal mine dust wettability not being clear and the identification process being complicated, this study proposed a coal mine dust wettability identification method based on a back propagation (BP) neural network optimized by a genetic algorithm (GA). Firstly, 13 parameters of the physical and chemical properties of coal dust, which affect the wettability of coal dust, were determined, and on this basis, the initial weight and threshold of the BP neural network were optimized by combining the parallelism and robustness of the genetic algorithm, etc., and an adaptive GA–BP model, which could reasonably identify the wettability of coal dust was constructed. The extreme learning machine (ELM) algorithm is a single hidden layer neural network, and the training speed is faster than traditional neural networks. The particle swarm optimization (PSO) algorithm optimizes the weight and threshold of the ELM, so PSO–ELM could also realize the identification of coal dust wettability. The results showed that by comparing the four different models, the accuracy of coal dust wettability identification was ranked as GA–BP > PSO–ELM > ELM > BP. When the maximum iteration times and population size of the PSO algorithm and the GA algorithm were the same, the running time of the different models was also different, and the time consumption was ranked as ELM < BP < PSO–ELM < GA–BP. The GA–BP model had the highest discrimination accuracy for coal mine dust wettability with an accuracy of 96.6%. This study enriched the theory and method of coal mine dust wettability identification and has important significance for the efficient prevention and control of coal mine dust as well as occupational safety and health development.

With the gradual formation of the open pattern of higher education, the issue of the cultural integration of international students has attracted increasing attention. This paper conducts a questionnaire survey of 2000 international students in East China and used factor analysis and structural equation modeling to determine the effects of life adaptation, interpersonal communication, and the training system on cultural integration, with possible moderation by study adaptation to explore the cultural integration of international students. The study found that the cultural integration of international students is jointly affected by multiple realistic factors, among which life adaptation and interpersonal communication are the primary factors. Furthermore, the training system has an important impact on the cultural integration of international students through study adaptation. Accordingly, we argue that universities should strengthen the informal organization and guidance of international students, enhance individual adaptability, promote international exchange, cooperation, and policy regulation of international students, and improve their training system. Moreover, efforts are needed to improve the study adaptability of international students so as to comprehensively address the problem of the cultural integration of international students and improve their education level.

An important topic in ecological environmental protection concerns the determination of a method to guide individual pro-environmental behavior from the dual perspectives of immediate intervention and long-term shaping. This research analyzed the influence of group emotions and moral beliefs on the pro-environmental behavior of residents and introduces the concept of the “psychological cluster” to explore the mediating effect of the “psychological cluster” on group emotions, moral beliefs, the pro-environmental behavior of residents, and its various dimensions. The results of a questionnaire survey, factor analysis, regression analysis, and other methods showed that group emotions and moral beliefs can significantly the predict pro-environmental behaviors of residents, and moral beliefs have a significant impact on basic pro-environmental behaviors. Each dimension of the psychological cluster has a significant effect on pro-environmental behavior. Among them, the psychological cluster has the most significant effect on the citizen-type pro-environmental behavior. That is to say, when individuals are passionate about environmental causes, and feel angry and guilty about damaging the environment, they will be encouraged to actively participate in environmental protection activities in public places and give advice on environmental protection. Group emotion and moral belief can drive the occurrence of a psychological cluster, which leads to the generation of group behavior. In other words, psychological clustering acts as a “middleman” between group emotion, moral belief, and pro-environmental behavior of residents. However, its influence is not completely mediated via the psychological cluster. In summary, the psychological cluster plays a partial mediating role in the relationship between group emotion, moral belief, and the pro-environmental behavior of residents. Finally, corresponding policy suggestions are put forward based on this research.

Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry comprehensively, this study puts forward the evaluation framework of “governance framework-implementation process-governance effectiveness” for ESEP management level. Based on the comprehensive collection and collating of related information reports (e.g., sustainable development reports) of listed energy companies from 2009 to 2018, the ESEP information was extracted, and the portfolio weight cloud model was used to evaluate the ESEP management status of listed energy companies in China. It is of great theoretical innovation and practical significance to promote the evolution of the economy from “green development” to “dark green development”. The results show that: (1) the number of SHEE information released by listed companies in the energy industry shows a steady increasing trend, but the release rate is low, and there are differentiation characteristics in different industries. (2) The ESEP management level of most listed companies in the energy industry is still at the low level, and only 17.19% (S = 65) of the sample companies are at the level of “IV level-acceptable” and “V level-claimable”. (3) In terms of governance framework-implementation process-governance effectiveness, B1-governance framework ($E_x$ = 3.4451) and B2-implementation process ($E_x$ = 2.9480) are relatively high, but B3-governance effectiveness ($E_x$ = 2.0852) and B4-public welfare ($E_x$ = 2.0556) are relatively low. The expectation of most ESEP evaluation indexes fluctuates between “III level-transition level” and “II Level-improvement level”. Finally, some suggestions are put forward to improve ESEP management levels.