Wenjie Zou’s research while affiliated with Fujian Normal University and other places

Employment significantly impacts the national economy and people’s livelihoods and affects millions of households. How to effectively and sustainably improve the quality of employment for the population has thus become a key issue facing China at present. In recent years, under the strategic background of “Digital China” and “Employment Priority”, the digital economy has brought about profound changes in the efficiency, dynamics, and distribution of social production. It affects the quality of employment by creating jobs, reshaping employment patterns, and improving labor quality, but its impact and transmission path are still unclear. This research employs a multi-dimensional evaluation approach to score the digital economy and employment quality at the provincial level in China, utilizing data from the country’s provincial panels between 2012 and 2022, and examines the feasibility and effect pathways of the digital economy in promoting sustainable improvement in the quality of residential employment. The empirical results provide ample evidence of the significant role played by the digital economy in the sustainable improvement of the employment quality of the inhabitants. Regions with more robust innovation environments tend to benefit more from this impact, and there are discernible regional variations in the impact. The upgrading of industrial structure mediates this process, and the influencing mechanism is regulated by the innovation environment—that is, the innovation environment exerts a facilitating influence on the process of industrial restructuring. The findings herein offer novel insights into the intrinsic mechanism of the digital economy in promoting sustainable improvement in the quality of residential employment.

This research adopts a two-stage network DEA model to measure marine green economy efficiency from 2006 to 2018 and employs the panel Tobit model to analyze the influencing factors. The results indicate that total efficiency and production efficiency of China’s marine green economy generally show a fluctuating downward trend. Further investigation of influencing factors shows that foreign direct investment and opening up have a significantly positive effect on total efficiency of the marine green economy, while industrial development level and marine economy development level have a negative effect on it. Additionally, these variables have varying impacts on different stages of the marine green economy. Our findings help identify the operational characteristics of the marine green economy at different stages and can assist policymakers in optimizing the development pattern of the marine economy.

To measure and analyze the evolution characteristics and influencing factors of China’s industrial green development level is of great significance in achieving carbon neutrality goal. Based on the panel data from 2000 to 2018 of 30 provinces in China, this research uses the super slack-based measuring model and the Malmquist-Luenberger index to calculates China’s industrial green total factor productivity and to describe its evolution characteristics using the kernel density function and moreover uses the Spatial Durbin model and the partial differential method to explores its main influencing factors. This study finds that China’s overall industrial green development level is not high but shows an upward trend year by year; carbon emissions, fiscal decentralization, and urbanization are not conducive to improving the national industrial green development level, whereas economic development, foreign direct investment, industrial structure, and technological progress are positive contributors. At the same time, the level of economic development and technological progress have significant direct and spatial spillover effect. Our findings also provide some policy implications for improving China’s industrial green development.

The effectiveness of a health care system is an important factor for improving people's health and quality of life. The purpose of this research is to analyze the efficiency and spatial spillover effects of provincial health systems in China using panel data from 2009 to 2020. We employ the two-stage network DEA model to evaluate their efficiencies and use a spatial econometric model for empirical estimation. The results suggest that the overall efficiency, resource allocation efficiency, and service operation efficiency of health systems in different regions of China generally have fluctuating upward trends, with large differences in efficiency among the various regions. Further analysis reveals that the efficiency of China's health system has a significant spatial spillover effect. The level of economic development, fiscal decentralization and old-age dependency ratio are important factors affecting the health system efficiency. Our findings help to identify the efficiency and internal operating mechanisms of China's health system at different stages, and are expected to contribute to policymakers' efforts to build a high-quality health service system.

This study analyzed the nexus of natural resources volatility, carbon emissions, renewable energy resources, and economic performance in Brazil, Russia, India, China and South Africa over the period 1990–2020. For empirical investigation, the panel estimations are used which reveals that all these variables are stationary with mixed order of integration. While the Westerlund test confirms that these variables are cointegrated. Due to non-normality behavior of the data, panel quantile regression is employed that demonstrate that renewable energy resources and carbon emissions are positively associated with the region's economic performance, whereas carbon emission is found a prominent factor of economic performance than renewable energy resources. Moreover, natural resources volatility is also a crucial factor of economic performance. The results asserted that natural gas rents and coal rents are positively associated with economic performance, while oil rents are insignificant. Moreover, the study found unidirectional causal nexus running from renewable energy to economic performance, and bidirectional causality between carbon emissions and economic performance, oil rents and economic performance, natural gas rents and economic performance. However, no causal association is evident in the coal rents and economic performance. Based on the empirical results, policy suggestions are also provided for sustainable economic performance.

The study aims to analyze the impacts of capital misallocation and technological innovation on green development efficiency in China by using the panel data from 2000 to 2018. We employ the Super-SBM model to evaluate green development efficiency and use the system generalized method of moments (GMM) for empirical estimation. The results suggest that capital misallocation has a significant inhibitory effect on green development efficiency. Moreover, capital misallocation restrains technological innovation, which plays an important role in improving green development efficiency. Further research on the effect of regional heterogeneity indicates that the negative impact of capital misallocation on green development efficiency is more significant in the central and western regions, while the effect is not significant in the eastern region. Our findings provide useful policy implications for improving green development efficiency in China.

The green transformation and development of industry form the foundation of sustainable development for a country’s society, economy, and environment. Industrial pollution control is one inevitable choice for all industries following the path of sustainable development. Improving industrial pollution control efficiency is also a natural requirement for reducing pollution emissions and achieving carbon peak and carbon neutrality. Based on panel data of 30 provinces in China from 2012–2018, this research applies DEA window analysis to measure the efficiency of industrial pollution control inputs and outputs, and empirically evaluates those factors influencing such efficiency. The findings demonstrate that overall industrial pollution control efficiency in China exhibits a decreasing trend from 2012 to 2018, but there are clear differences among provinces. Industrial pollution control efficiencies in the east and central regions are consistent with the national average, while said efficiencies in the west and northeast regions fluctuate in waves, with the effect of influencing factors in different regions varying significantly. Lastly, based on the results of empirical analysis, this research puts forward the optimization path to further improve industrial pollution control efficiency in China, and to provide new suggestions for its advancement.

The outbreak of the COVID-19 pandemic has brought several challenges to China's national health services, causing great risks and uncertainties to people's lives. Considering China's huge population and relatively small medical investment and its good performance in the COVID-19 pandemic, this research utilizes the hybrid meta-frontier model to analyze health expenditure efficiencies of 30 provinces in China from 1999 to 2018 and compares spatial and temporal differences of the efficiencies in regards to regional forward position and national common frontier. The results show an obvious difference in health expenditure efficiency in different provinces along the regional frontier, in which the efficiency gap in the eastern region is the largest. Moreover, the room for improvement in health expenditure efficiency varies from region to region. For the national common frontier, Beijing is the most efficient, while Guizhou is the least. The eastern region owns the most efficient technical level of health expenditure efficiency, and there is a large efficiency distance between it and the western region. The findings offer effective guidance for elevating the expenditure structure and spatial resource allocation of public health and for promoting the equalization of high quality basic medical services.

The high-tech industry plays a crucial role in reducing carbon emission and achieving green economic development. This research uses Meta-Frontier data envelopment analysis to measure the innovation efficiency level of the high-tech industry in China’s provinces from 1999 to 2018, compares the difference in this industry’s innovation efficiency under the regional Frontier and common Frontier, and inspects the convergence condition of its innovation efficiency in the three major areas of eastern, central, and western China. The results show under the regional Frontier that the difference in innovation efficiency of the western region’s high-tech industry is the biggest, while the difference in the central region is the smallest, and under the national common Frontier the innovation efficiency level of the eastern region’s high-tech industry is the highest, while that of the western region is the lowest. The regional pattern of innovation efficiency in the high-tech industry is consistent with the development trend of the regional economy. Moreover, by using the ratio of the technology gap ratio, we find that the eastern region has the potential optimal technology in China, whereas the central and western regions have large room for improvement. Lastly, the stochastic convergence test shows that the innovation efficiency of the central region’s high-tech industry presents a convergence trend, but the same trend does not occur in the western and eastern regions as well as for the whole country.

The rapid development of China’s economy has largely relied on energy consumption, which has caused serious air pollution and affected public health, and economic development, energy consumption, air pollution, and public health have nowadays become the focus of academic attention. However, the previous literature failed to consider undesirable output when constructing the Dynamic Network DEA model to study the efficiencies of energy consumption, air pollution, and public health. As a result, past studies did not employ those three issues in a structure to effectively reflect and solve the problems. Therefore, this paper constructs the Two-Stage Dynamic Undesirable DEA model and puts energy consumption, air pollution, and public health into the same framework in order to fill the gap in the literature. Findings show that the production consumption efficiency stage is better than the health protection stage, and that the efficiency values of variables vary significantly in different regions. The efficiency of tumor and tuberculosis is the lowest, with oil consumption and birthrate efficiencies are the best, followed by coal, nitrogen oxide (NOx), and dust efficiencies. Coal efficiency exhibits a fluctuating downward trend, whereas the efficiencies of electricity, air pollutants, tuberculosis, and tumor tend to fluctuate upwards during the research period. In consideration of the varying performances of different regions in the two stages, we put forward suggestions based on these findings to improve the efficiencies of energy, environment, and public health in China.