Xiuwu Zhang’s research while affiliated with Huaqiao University and other places

In this paper, the industry classification of digital economy is determined, the comprehensive index framework of digital economy development level in China is constructed, and the input-output analysis method and index method are used to measure the digital investment status of manufacturing industry in China. At the same time, based on the realistic demand that China’s manufacturing industry needs to climb the regional value chain, this paper constructs the forward participation index, backward participation index, regional value chain position index, and regional value chain Balassa, and analyzes the current situation of China’s manufacturing industry in RCEP region. Construct a semi-parametric additive model to explore the impact of digital investment on China’s manufacturing industry’s participation in the RCEP regional value chain. In addition, this paper uses the community analysis method to build the RCEP regional manufacturing digital input trade network, and comprehensively evaluates the trade network pattern of digital input from multiple dimensions. The results show that: (1) the digital investment in China’s manufacturing industry is increasing year by year. (2) The indicators of manufacturing industry’s participation in RCEP regional value chain show a fluctuating situation, which is generally consistent with China’s economic development. (3) Within a certain range, digital investment will promote the participation of China’s manufacturing industry in the RCEP regional value chain. China is gradually becoming an important participant in RCEP regional trade. According to the corresponding measurement results, it provides policy suggestions for the deep integration of China’s manufacturing industry and digital economy.

This study evaluates the impact of green finance (GF) on agricultural nonpoint source pollution (ANPSP) control and emission reduction in 30 Chinese provinces from 2005 to 2021. Utilizing the entropy value method and the unit survey inventory method, the research measures the levels of GF development and ANPSP. It employs a mediation effect model to empirically assess the pollution control efficacy of GF and to elucidate the mechanisms underlying its influence. The findings indicate that GF development significantly curtails ANPSP emissions. It achieves this through government environmental regulation (ER) and land transfer mechanisms. Heterogeneity test results show that GF has a stronger impact on ANPSP in regions with lower economic development level and GF reform policies. Therefore, the study suggests strengthening the GF infrastructure in rural areas, aligning GF policies with ER, promoting large-scale land operations, and implementing tailored strategies for regions with different levels of economic development and GF reform policies.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China’s total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/μg/m³ for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology–economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

This paper uses relevant mathematical statistical models to verify the interactive effect between population mobility and the high-quality development of the manufacturing industry based on the panel data of 43 counties in the Fujian province from 2007 to 2021. The results show that (1) the inflow areas of population flow in the Fujian province are mainly concentrated in the southeast region; (2) there is mutual promotion between population mobility and the high-quality development of the manufacturing industry in the Fujian province, and the population mobility in adjacent areas has a significant promotion effect on the high-quality development of the local manufacturing industry; and (3) heterogeneity results show that after 2011, the interaction effect between population mobility and the high-quality development of the manufacturing industry has declined, and population inflow has a stronger role in promoting the high-quality development of the manufacturing industry than population outflow. The conclusions of this study are of great significance to the Fujian province for implementing a reasonable population policy and promoting the coordinated development of manufacturing enterprises.

As the main goal of economic development, inclusive growth (IG) is an important strategic measure to achieve common prosperity. Whether digital inclusive finance can make use of the advantages of scientific and technological innovation to promote IG is of great significance to promote the fairness, effectiveness, and inclusiveness of global development. Based on the panel data of 30 provinces in China from 2011 to 2021 (excluding Tibet, Hong Kong, Macao and Taiwan), this article first measures the IG index of China from three dimensions: sustainable economic growth, income distribution, and social equity. Subsequently, the article uses a series of mathematical statistical models to verify the transmission path and mechanism of the influence of digital inclusive finance on IG. The findings are as follows: (1) The level of IG in China shows a decreasing trend from east to middle to west, while the average annual growth rate of IG in the eastern region is obviously lower than that in the central and western regions; (2) digital inclusive finance has a significant promotion effect on IG, and digital inclusive finance in the central and western regions has a more obvious promotion effect on IG; (3) digital inclusive finance can achieve IG by increasing innovation activity and improving the level of human capital. Finally, based on the research conclusions, the article puts forward relevant policy suggestions, which provide reference value for formulating high-quality national development strategies and promoting high-quality economic development.

Introduction: Achieving peak carbon dioxide emissions and carbon neutrality is an extensive and profound systematic economic and social change. Through market-oriented financial means, green finance has moved forward the effective governance port, curbed polluting investment and promoted technological progress such as green low-carbon, energy conservation and environmental protection, which has become a powerful starting point to support the practice of low-carbon development. Methods: Based on the panel data of 30 provinces in China (except Tibet, Hongkong, Macau and Taiwan Province) from 2004 to 2021, this paper calculates the development level of green finance in China provinces by using entropy weight method, and on this basis, uses mathematical statistical model to verify the impact of green finance and its sub-dimensions on carbon emissions and the regulatory effect of heterogeneous environmental regulation tools. Results: The results show that the development of green finance has a significant inhibitory effect on carbon emissions during the investigation period, and there is a time lag effect. After a series of robustness tests and considering endogenous problems, this conclusion still holds. From the results of heterogeneity analysis, the carbon emission reduction effect of green credit is the most obvious, and the impact of green finance on carbon emission is slightly different in different regions. Besides, Command-controlled environmental regulation tools and public participation environmental regulation tools play a positive regulatory role in the transmission path of green finance’s impact on carbon emissions, but market-driven environmental regulation tools cannot effectively enhance the carbon emission reduction effect of green finance development. Discussion: The research results of this paper provide a basis for the government to formulate flexible, accurate, reasonable and appropriate green financial policies, help to strengthen the exchange and cooperation between regions in reducing carbon and fixing carbon, and actively and steadily promote China’s goal of “peak carbon dioxide emissions, carbon neutrality”.

Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact of digital technology on air pollution by using the two-way fixed effect model. The results show that digital technology will significantly reduce the concentration of fine particles in the air and help protect the atmospheric environment. The results are still valid after using the interactive fixed effect model and the two-stage least square method after the robustness test and causality identification. Digital technology can also reduce the air pollution by promoting green innovation, improving energy efficiency, and easing market segmentation. The effect of digital technology on reducing the concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial role in reducing pollution in resource-based cities and areas with a high degree of modernization of the commodity supply chain. The positive effect of digital technology in reducing air pollution is affected by the amount of air pollutants emitted. When the concentration of PM2.5 in the air is high, the role of digital technology in protecting the atmosphere will be strongly highlighted. This research is a beneficial exploration of protecting the atmospheric environment by using digital technology while building an ecological civilization society. The conclusion will help urban managers, the public, and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet of Things in their respective fields to protect the atmospheric environment.

Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”. Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm. Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak. Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.