In the digital age, digital resources are naturally sustainable, and they have become the basic technologies and platforms by which to maintain the sustainable development of the social economy. At a time when global epidemic prevention and control are becoming increasingly dynamic, digital empowerment has become a basic tool by which to promote sustainable social and economic development. In terms of digital empowerment, grassroots digital empowerment has become the most critical and urgent link to strengthen. In this study, a literature analysis using CiteSpace and NVivo software shows that the research prospects of grassroots digital governance are broad, but there is a lack of communication and cooperation between the research subjects, and a cooperative network of close and benign interaction has not been formed. There are many hot topics in the research, mainly focusing on five aspects: digitalization, grassroots governance, digital governance, digital technology, and digital countryside. The theme of these papers is changing to digital empowerment, technology empowerment, and “digital intelligence governance”. These characteristics and problems correspond to the practice of digital governance at the grassroots level in China. Therefore, in the theory and practice of Chinese grassroots digital governance, we must adhere to the problem-oriented principle and take “problem-driven” as the basic logic of grassroots digital governance. At the same time, it is necessary to consider the complexity of China’s grassroots communities and the stages of governance technology, adhere to the principles of integrated development and collaborative innovation, and take “mixed governance” as the main logic of grassroots digital governance. Generally speaking, we should realize optimization in the innovation of grassroots digital governance from three aspects, namely, strengthening the integration of grassroots digital systems, improving the ability to solve the problems of digital governance, and advancing the transformation of grassroots digital governance with incremental governance logic.

Recent innovations are increasingly recognizing applications in precision agricultural systems that use data science techniques as well as so-called machine learning techniques. Big data analytics have created various data-intensive decision-making opportunities. This study reviews the big data analysis practices in the agriculture industry to resolve various problems to provide prospects and exciting fields of application in China. In the successful implementation of precise farming, the high-volume and complicated data generated present challenges for the economic growth of China. Emerging deep learning techniques seem promising and must be reinvented to meet current challenges. Thus, this paper suggests a big data analytics agriculture monitoring system (BDA-AMS) to ensure the highly accurate prediction of crop yield in precision agriculture and economic management using a deep learning algorithm. The convolution neural network gathers the raw images from UAVs and performs early predictions of crop yield. The simulation analysis using an open-source agricultural dataset resulted in a high parameter–precision ratio (98.8%), high accuracy (98.9%), a better performance ratio (95.5%), an improved data transmission rate (97.8%), a reduced power consumption ratio (18.8%), and an enhanced weather forecasting ratio (94.8%), production density ratio (98.8%), and reliability ratio (98.6%) compared to the baseline models.

The recycling of solid waste resources can effectively alleviate resource shortages and environmental pollution and can promote the sustainable development of an ecological economy and green economy. Therefore, China has set up a series of recycling policies. In order to promote the development of China’s solid waste resource recycling industry, and to be able to set up and implement correct policy documents according to real-time dynamics, this study uses NVivo software to analyze the content of 10 Core Journals’ documents screened from the China HowNet database and sets up three node classifications and four partition dimensions to build a three-dimensional model for qualitative analysis and emotional analysis. The analysis determines the existing weaknesses of China’s solid waste resource recycling policy under four dimensions and puts forward prospects for its future from these four aspects of system, capacity, measures and environment.

CiteSpace was used to visualize the knowledge ecology of the green finance research literature in CNKI and WOS, and NVivo software was used to root the code analysis of the current green finance policies in China. From the analysis of the research hotspots, both in China and internationally, great importance is attached to the research on green finance, and the research on green financialization has broad prospects. The core group of authors on green finance research in China has taken shape, whereas the core group of authors of green finance research in the rest of the world has not yet taken shape. There is a lack of close cooperation and a relatively low level of communication among important domestic green finance research institutions, and a certain scale of cooperation network has been formed among influential international institutions. The major countries for influential international green finance research are Singapore, France, Switzerland, Canada and Saudi Arabia, and the international influence of China’s green finance needs to be improved. Both domestic and foreign countries attach great importance to the balance between economic growth and the low-carbon green transition. China attaches more importance to macroeconomic development and strategic transition, but internationally, the trend is toward microcorporate green performance, policy optimization and market innovation. The research focus of green finance has achieved in three stages of evolution, namely, green industry in the early stage, green services in the middle stage and green strategy in the near future. International green finance research focuses on climate change, market players, government performance, social responsibility sharing, etc. In particular, reducing the cost of green development is the focus of international green finance. The domestic focus is on climate risk, carbon neutrality, carbon peak, low-carbon transition, carbon reduction, and green transition themes. Internationally, the focus is on financial performance, decisions, green finance, credit, drivers, quality, socially responsible investment and other topics. Considering the practical implementation of green finance in China, the governance logic of China’s green finance policy consists of five main categories: policy belief, policy objective, policy tool, policy feedback and policy cycle. In the future, the development and improvement of China’s green finance policy should achieve breakthroughs in the following aspects: first, guiding the main body of green finance policy to firmly establish policy beliefs; second, improving the clarity of green finance policy objectives; third, enhancing the overall effectiveness of the governance of green financial policy instruments; fourth, strengthening the green finance incentive policy feedback system construction; and fifth, improving the quality of green finance policy cycles.

Overeducation means that the rapid growth in the numbers of secondary and higher education graduates begins to exceed the actual demand of the labor market due to this excessive expansion of education. Consequently, educated workers are faced with knowledge unemployment, or are engaged in jobs that do not match their academic qualifications, resulting in a decline in income and a waste of educational resources. In order to explore the effect of overeducation on workers’ job satisfaction, we selected data from China Household Tracking Survey (CFPS) and conducted a fixed-effect ordered logit model regression analysis. It was found that overeducation has a negative impact on employees’ job satisfaction and an impact on wage penalty. Wage income has a mediating effect on the relationship between overeducation and job satisfaction. We present three policy suggestions: for the Government’s administration department, it is necessary to actively create an environment for matching education and occupation; to improve the possibility of matching education and occupation; and to reduce the negative effect of labor contracts on the improvement of human capital and the job satisfaction of overeducators by adjusting the flexibility and stability of the labor contract. For institutions of higher learning: it is necessary to make forward-looking adjustments to the educational structure, according to the actual needs of economic and social development to adapt to the social demand for talent and development trends; to train highly skilled and high-quality workers needed for social development; and to reduce the unreasonable distribution of resources caused by overeducation. For enterprises: employees should be guided to correctly understand the unpredictable relationship between education and work and reasonably reduce the job expectations of new employees, according to their own work experience and technical level.

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country’s trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown.