Modern digital life has produced big data in modern businesses and organizations. To derive information for decision-making from these enormous data sets, a lot of work is required at several levels. The storage, transmission, processing, mining, and serving of big data create problems for digital domains. Despite several efforts to implement big data in businesses, basic issues with big data remain (particularly big-data management (BDM)). Cloud computing, for example, provides companies with well-suited, cost-effective, and consistent on-demand services for big data and analytics. This paper introduces the modern systems for organizational BDM. This article analyzes the latest research to manage organization-generated data using cloud computing. The findings revealed several benefits in integrating big data and cloud computing, the most notable of which is increased company efficiency and improved international trade. This study also highlighted some hazards in the sophisticated computing environment. Cloud computing has the potential to improve corporate management and accountants' jobs significantly. This article's major contribution is to discuss the demands, advantages, and problems of using big data and cloud computing in contemporary businesses and institutions.

The significance of environmental protection activities is well known, but little literature has focused on the well-being effects of environmental protection behavior among farmer groups. This study provides new literature support for farmers and rural development issues. Using data from the 2013 China Integrated Social Survey, a systematic and robust examination of the happiness effects of environmental protection behavior among Chinese farmers and their transmission mechanisms was conducted with the help of multiple regression techniques and mediated impact analysis. The study found that Chinese farmers' environmental protection behavior can directly trigger the experience of well-being and also indirectly enhance subjective well-being by improving the quality of life in other areas, thanks to their characteristics in avoiding environmental risks and enhancing social interactions. Increased education may contribute to farmers being more motivated by benefits such as material rewards, experience, and skills, and thus experiencing less well-being from environmental protection behavior. The fact that farmers of all household incomes experience equal well-being from environmental protection behavior is consistent with the view of non-differential well-being experiences in the volunteering literature. The research in this paper adds new evidence to the existing literature and provides an essential reference for policymakers and participants in rural development in China. In addition, studying individual issues in environmental governance in rural China provides a Chinese case study and practical lessons for farmer development in other countries worldwide.

With the strategic background of accelerating the transformation of the low-carbon economy in China, how to better help the new energy automobile industry realize green and high-quality development under the goal of “dual-carbon” with the strengthening of science and technology has become one of the most important issues nowadays, and it is of great significance to explore the relationship between financial technology (fintech) and the environmental, social, and governance (ESG) performance of the new energy automobile (NEV) industry. Using panel data from NEV companies listed on the Shanghai and Shenzhen A-share markets between 2011 and 2022, this study applies text mining techniques to construct a fintech index and analyze the transmission mechanisms through which fintech influences ESG performance. The findings show that fintech directly improves ESG outcomes for NEV companies, a result that remains robust across a series of validation tests. The analysis reveals that fintech reduces financing constraints and enhances corporate environmental information disclosure, which in turn drives better ESG performance. Furthermore, the impact of fintech is particularly pronounced in state-owned enterprises, large-scale firms, and technologically advanced NEV companies, as evidenced by heterogeneity analysis. This study provides empirical insights into fintech’s role in advancing sustainable development in the NEV sector, offering guidance for policymakers and industry stakeholders aiming to align technological progress with environmental and social governance objectives.

China is about to enter a moderate aging society. In the process of social and economic development, the family socioeconomic status and health status of the elderly have also changed significantly. Learning the impact of family socioeconomic status on elderly health can help them improve family socioeconomic status and better achieve healthy and active aging. Using the data of the Chinese Longitudinal Healthy Longevity Survey in 2018, this study firstly analyzed the impact of family socioeconomic status on elderly health using the multivariate linear regression model and quantile regression model, the heterogeneity of different elderly groups using subsample regression, and the mediation effects of three conditions associated with the family socioeconomic status of the elderly. The results show that family socioeconomic status has a negative effect on the frailty index, that is, it has a positive impact on elderly health. Family socioeconomic status has a higher positive impact on the health status of the middle and lower age elderly and rural elderly. Overall living status and leisure and recreation status both have mediation effects, while health-care status has no mediation effect.

In global climate change, improving carbon productivity holds great importance for China’s sustainable growth. Based on panel data of 30 Chinese provinces and cities from 1997–2017, the drivers, spatial effects, and convergence characteristics of carbon productivity in China are explored by combining a factor decomposition framework and a spatial panel model. The findings show that (1) China’s carbon productivity shows continuous positive growth, and the substitution effect of capital for energy dominates this changing pattern; (2) There is a β-convergence trend and club convergence in China’s carbon productivity, and the spatial technology spillover accelerates the convergence rate; (3) With its accelerated industrial transformation and technological upgrading, China’s current carbon productivity converges faster than its earlier stage, and the role of physical capital investment has gradually shifted to suppression. In contrast, the positive push of human capital investment has been strengthened; (4) From the perspective of the realization mechanism, the convergence of carbon productivity in China mainly comes from the convergence of energy restructuring and capital-energy substitution. These findings can help China narrow the inter-provincial carbon productivity gap in terms of improving factor structure, upgrading technology, etc., and provide references for sustainable growth decision making in China and around the world.

Based on the neoclassical framework, we propose the convergence hypothesis of carbon productivity under sustainable growth and prove the different effects of knowledge spillover and technology diffusion on convergence. The convergence hypothesis is tested using China’s provincial spatial dynamic panel data from 1995 to 2019. The results show that China’s provincial carbon productivity has conditional convergence and club convergence characteristics. The convergence speed of dynamic panel regression estimation is greater than that of cross-sectional regression. The convergence rate of dynamic spatial panel regression estimation is faster depending on the spatial spillover difference between the two technologies. In the early stage, the provincial spatial dependence of China’s carbon productivity is mainly knowledge spillover, and the convergence rate is lower than that of the closed economy. Over the past decade, the spatial spillover, dominated by low-carbon technology diffusion, has become the dominant force. The convergence rate is significantly faster than that of a non-spatial-dependent economy. In addition, the mechanism test found that the development of energy efficiency dominates the spatial transfer of technology, so the overall convergence of carbon productivity in China mainly comes from the apparent convergence of energy efficiency in provinces and cities. Our conclusion provides a new reference for the emission reduction actions of countries worldwide because the spatial knowledge spillover carried by capital flows is not conducive to the pursuit of carbon productivity in less developed regions. On the contrary, the dissemination and diffusion of low-carbon technologies can significantly reduce carbon equivalent input in the production process, accelerating the pursuit of developing countries or regions.