How does digital economy affect green total factor productivity at the industry level in China: from a perspective of global value chain

Does the digital economy really help reduce industrial carbon intensity? Empirical evidence from intermediate inputs of digital products in China

The booming digital economy is promoting industrial upgrading and transformation, providing an opportunity for the industrial low-carbon development. Based on input-output analysis and panel data regression, the impact of the input of intermediate digital products on Chinese industrial carbon intensity from 1997 to 2017 has been evaluated. The results reveal that the input of China's intermediate digital products can significantly reduce industrial carbon intensity, and significantly reduce the carbon intensity of non-energy-intensive industries. It has also been discovered that the input of intermediate digital product manufacturing can significantly reduce the carbon intensity of different types of industries, while the input of the intermediate digital products services industry has no significant effect on the reduction of industrial carbon intensity. Channel analysis shows that the input of the intermediate digital products can reduce industrial carbon intensity by improving productive efficiency and promoting innovative technology. In the current climate, it is especially necessary to increase the input of the intermediate digital product manufacturing industry in industrial development.

Digital economy and fiscal decentralization: Drivers of green innovation in China

The impact of government behavior under a fiscal decentralization system on the interplay between the digital economy and both the quality and efficiency of green innovation poses an intriguing question. To address this, the present study employs two-way fixed-effects models, instrumental variables, and spatial econometric techniques, using data from 30 provinces and cities in China spanning 2004 to 2019. The findings reveal that the advancement of the digital economy significantly enhances the quality and efficiency of green innovation. In the context of China's fiscal decentralization, local governments frequently employ a "race to the top" strategy, amplifying the digital economy's beneficial impact on green innovation. This effect is particularly pronounced in economically prosperous regions that prioritize environmental assessments. Additionally, the study identifies a spatial demonstration effect, indicating that fiscal decentralization bolsters the digital economy's influence in adjacent regions. Consequently, policy recommendations include deepening the digital economy, advocating for increased fiscal autonomy for local governments, refining the performance appraisal systems for local officials, and establishing a well-calibrated environmental transfer mechanism. Further, leveraging the positive spatial correlations among local governments can foster a competitive yet collaborative landscape for green innovation.

Digital economy structuring for sustainable development: the role of blockchain and artificial intelligence in improving supply chain and reducing negative environmental impacts

In the current global context of environmental degradation and resource constraints, the pursuit of sustainable development has become an imperative. One avenue that holds promise for achieving this objective is the application of digital technologies, which have the potential to decouple economic growth from its carbon footprint. However, it is crucial to ensure that these technologies are designed and governed in a prudent manner, with a strong alignment to environmental priorities. This study focuses on exploring the potential roles of blockchain and artificial intelligence (AI) in supply chain coordination and impact mitigation. Furthermore, they have the capacity to incentivize recycling and circular business models, as well as facilitate carbon accounting and offsetting. To fully realize these benefits, it is essential to deploy these technologies within inclusive collaborative frameworks that take into consideration social and ecological considerations. The study also offers policy recommendations that highlight key leverage points for digital innovation, enabling countries to embark on smart and green industrial transformation pathways. By harnessing the potential of blockchain and AI in supply chains, governments can promote transparency, traceability, and accountability, thereby fostering sustainable practices and reducing environmental impacts. Incorporating blockchain and AI technologies into supply chain approaches leads to a substantial improvement in efficiency, as demonstrated by a numerical analysis. In conclusion, the integration of innovative digital technologies offers significant opportunities to optimize production systems and economic activity while prioritizing sustainability objectives for the betterment of society and the environment. These technologies have the potential to mitigate environmental externalities by addressing information imbalances within global supply chains. However, it is essential to prioritize inclusive governance that emphasizes democratic participation to mitigate any unintended negative consequences, especially for vulnerable communities. By ensuring inclusive decision-making processes, we can maximize the positive impact of these technologies while minimizing potential harm.

Research on quantitative evaluation of digital economy policy in China based on the PMC index model

The development of digital economy is a strategic choice to grasp the revolution of new science and technology and the new opportunities of industrial reform. The development of digital economy depends on the good support of policy and theoretical system. Therefore, the quantitative evaluation of policy texts provides the basis of decision-making and the suggestions of path optimization for the formulation and improvement of digital economy policy of China. By selecting the text of digital economy policy issued by China government, the paper constructs a quantitative evaluation model of digital economy policy using the methods of content analysis and text mining. The empirical research results show that the overall design evaluation of the selected policy is relatively reasonable. Six policies were evaluated as excellent and two as acceptable. In view of the problems such as lack of predictive policy in the policy type, lack of specific policy in the policy timeliness, imbalance in the use of policy guarantee, and lack of comprehensive coverage in the policy objectives, the paper puts forward corresponding countermeasures and suggestions.

Research on the coupling coordination relationship between the digital economy and high-quality energy development: Evidence from China

The deep integration of the digital economy and high-quality energy development is a vital breakthrough in promoting the digital transformation and upgrading of energy, and it is also a critical path to achieving green and low-carbon development. However, the degree of integration of the two has yet to be discovered. This article measures the coupling coordination degree of the digital economy and high-quality energy development using panel data from 30 provinces in China from 2013 to 2020, explores the spatiotemporal evolution characteristics of the coupling coordination degree, and further analyzes the driving factors of the coupling coordination degree. The results show that:(1) The coupling coordination degree shows an upward trend, but there are apparent gradient differences and spatial non-equilibrium features in the coupling coordination degree among provinces. (2) The coupling coordination degree shows a "parabolic" spatial trend of "high east and low west" in the east-west direction and an "inverted U-shaped" spatial trend in the north-south direction. (3) The center of gravity of the coupling coordination degree moves to the southwest, clustering in the northeast-southwest direction and showing a spreading trend in the southeast-northwest direction. (4) The coupling coordination degree has a significant positive spatial correlation, and the cold-hot spot gradually develops into a distribution pattern with the Yangtze River Delta in China as the agglomeration center. (5) Economic development, industrial structure, government behavior, environmental regulation, urbanization, technological innovation, and external openness significantly impact the coupling coordination degree. In addition, economic development and human capital have a positive spatial spillover effect on the coupling coordination degree. Urbanization level and technological innovation have a negative spatial spillover effect on the coupling coordination degree. Accordingly, to promote the coupling and interaction between the digital economy and high-quality energy development, the government should take effective measures in optimizing the industrial structure, scientifically promoting the urbanization process, and enhancing the scientific and technological innovation capacity.

The impact of digital economy on green total factor productivity considering the labor-technology-pollution factors

The digital economy provides new impetus for the high-quality development of manufacturing industry. Through the DEA-Malmquist model and panel regression model, this paper confirmed that there is a positive and significant relationship between the development of digital economy and the green total factor productivity (GTFP) of manufacturing industry. The research result is as follows: (1) the development of digital economy can enhance the overall GTFP of manufacturing industry. (2) The green technology progress brought by the development of digital economy is the main path to promote the GTFP of manufacturing industry. (3) The heterogeneity analysis shows that the impact of digital economy on GTFP of high pollution manufacturing industry is significantly positive, the impact of labor-intensive manufacturing industry is significantly negative, and the impact of technology intensive manufacturing industry is not obvious. The contributions of this study are as follow. In terms of theory, this study theoretically continues Solow's classical theory, demonstrating the scientific nature of digital technology progress in promoting GTFP growth. In empirical analysis, this study build a new digital economy development level evaluation index system based on the perspective of manufacturing industry. In addition, this study also add a labor-technology-pollution perspective for the development of relevant policies.

How does the digital economy affect the development of the green economy? Evidence from Chinese cities

The digital economy may accelerate the upgrading of industrial structures and boost regional innovation output, effectively contributing to China's green economic transformation. The impact of the digital economy on developing the urban green economy is analyzed using data from 280 cities across China from 2010-2019. Using a fixed-effects model and the Spatial Durbin model, the digital economy is found to have a significant impact on urban green economy development. This result is shown to be robust to various factors. There is significant regional variability in the impact of the digital economy on green economic growth, with the strongest impact in the northeast, followed by the central and western regions. Meanwhile, non-resource-based cities and policy pilot cities have a more pronounced role in promoting the digital economy. The intermediate transmission chain of industrial structural upgrading and regional innovation output fosters the growth of the urban green economy via the digital economy. Regional innovation production is responsible for 30.848% of this growth, with the intermediate effect of industrial structural upgrading contributing to 38.155%.

Explaining and modeling the impact of industrial co-agglomeration on regional economic growth in China: integrated a quality concern of night-time light perspective

The double-wheel drive of manufacturing industries and producer services industries is one of the key pathways to promote high-quality development relying on a modern industrial system. This paper explores the impacts of industrial co-agglomeration on regional economic growth in China with systematic consideration of static, dynamic, and spatio-temporal perspectives based on panel data for 285 prefecture-level cities from 2003 to 2020, employing the consolidated night-time light data. The empirical results show that industrial co-agglomeration significantly accelerates regional economic growth, especially high-tech intensity producer services industries, information industries, and finance industries. In addition, its spatial spillover effects are evidently established, which are characterized by cyclic accumulative, feedback features, and distance attenuation. Carrying out robustness tests, the preliminary regression results are verified. The heterogeneous influences are established across cities with different geographical locations, innovation capacities, and resource endowments. The further mechanism analysis indicates that industrial upgrading, technological progress, and efficiency enhancements account for the main channels for a sharp promotion in regional economic growth over the sample period. Furthermore, the government moderates this process more significantly than market forces do, especially when it comes to macroexogenous shocks that are regulated by the government. The findings of this study recommend policymakers to give full play to the positive externalities of industrial co-agglomeration and accelerate the industrial co-agglomeration process in a reasonable manner, so as to promote high-quality economic growth in China.

Impact of digital economic development and environmental pollution on residents' health: an empirical analysis based on 279 prefecture-level cities in China

BACKGROUND

The digital economy based on the internet and IT is developing rapidly in China, which makes a profound impact on urban environmental quality and residents' health activities. Thus, this study introduces environmental pollution as a mediating variable based on Grossman's health production function to explore the impact of digital economic development on the health of the population and its influence path.

METHODS

Based on the panel data of 279 prefecture-level cities in China from 2011 to 2017, this paper investigates the acting mechanism of digital economic development on residents' health by employing a combination of mediating effects model and spatial Durbin model.

RESULTS

The development of digital economy makes direct improvement on residents' health condition, which is also obtained indirectly by means of environmental pollution mitigation. Besides, from the perspective of spatial spillover effect, the development of digital economy also has a significant promoting effect on the health of adjacent urban residents, and further analysis reveals that the promoting effect in the central and western regions of China is more pronounced than that in the eastern region.

CONCLUSIONS

Digital economy can have a direct promoting effect on the health of residents, and environmental pollution has an intermediary effect between digital economy and residents' health; At the same time, there is also a regional heterogeneity among the three relationships. Therefore, this paper believes that the government should continue to formulate and implement scientific digital economy development policies at the macro and micro levels to narrow the regional digital divide, improve environmental quality and enhance the health level of residents.

Impact of global value chain and technological innovation on China's industrial greenhouse gas emissions and trend prediction

The global value chain has introduced profound changes in international trade, economic development, and technology progress as well as greenhouse gas emissions worldwide. This paper investigated the impact of the global value chain and technological innovation on greenhouse gas emissions by introducing a partially linear functional-coefficient model based on panel data of 15 industrial sectors in China from 2000 to 2020. Moreover, the greenhouse gas emission trends of China's industrial sectors from 2024 to 2035 were predicted using the autoregressive integrated moving average model. The results showed that (1) Greenhouse gas emissions were affected negatively by global value chain position and independent innovation. Nevertheless, foreign innovation had the opposite effect. (2) The results of the partially linear functional-coefficient model implied that the inhibitory effect of independent innovation on GHG emissions decreased with an improvement in the global value chain position. (3) The positive effect of foreign innovation on greenhouse gas emissions increased and then, decreased as the global value chain position improved. (4) The prediction results indicated that greenhouse gas emissions will continue on an upward trend from 2024 to 2035, while industrial carbon dioxide emissions should peak at 10.21 Gt in 2028. This carbon-peaking goal would be achieved in China's industrial sector by actively improving the global value chain position. Addressing these issues will enable China to take full advantage of the development opportunities of participating in the global value chain.

Towards green economic recovery: how to improve green total factor productivity

Achieving green economic recovery is crucial to improving environmental quality and sustainable development. This study examines the influence of new digital infrastructure on green total factor productivity (GTFP) using panel data from 30 regions in China from 2008 to 2019. The results are as follows: (1) New digital infrastructure has a significant improvement effect on GTFP. After a series of robustness tests, the conclusion is still valid. (2) The improvement effect of new digital infrastructure on GTFP shows significant heterogeneity. In regions with high industrial agglomeration, high environmental regulation and strong government environmental preference, the improvement effect of new digital infrastructure on GTFP is more obvious. (3) New digital infrastructure improves GTFP through green technology innovation and factor allocation optimization. The government should strengthen the fiscal incentives for green technology development while increasing R&D investment in fiscal expenditure, thus promoting green economic recovery.

Digitalization, Electricity Consumption and Carbon Emissions-Evidence from Manufacturing Industries in China

The development of China's manufacturing industry is constrained by factors such as energy and resources, and low-carbon development is arduous. Digitalization is an important method to transform and upgrade traditional industries. Based on the panel data of 13 manufacturing industries in China from 2007 to 2019, a regression model and a threshold model were used to empirically test the impact of digitalization and electricity consumption on carbon emissions. The research results were as follows: (1) The digitalization level of China's manufacturing industry was steadily increasing; (2) The proportion of electricity consumption in China's manufacturing industries in the total electricity consumption hardly changed from 2007 to 2019, basically maintaining at about 6.8%. The total power consumption increased by about 2.1 times. (3) From 2007 to 2019, the total carbon emissions of China's manufacturing industry increased, but the carbon emissions of some manufacturing industries decreased. (4) There was an inverted U-shaped relationship between digitalization and carbon emissions, the higher the level of digitalization input, the greater the carbon emissions of the manufacturing industry. However, when digitalization develops to a certain extent, it will also suppress carbon emissions to a certain extent. (5) There was a significant positive correlation between electricity consumption and carbon emissions in the manufacturing industry. (6) There were double energy thresholds for the impact of labor-intensive and technology-intensive manufacturing digitalization on carbon emissions, but only a single economic threshold and scale threshold. There was a single scale threshold for capital-intensive manufacturing, and the value was -0.5352. This research provides possible countermeasures and policy recommendations for digitalization to empower the low-carbon development of China's manufacturing industry.

Can the digital economy improve green total factor productivity? An empirical study based on Chinese urban data

With the new generation of technological revolution, the digital economy has progressively become a key driver of global economic development. In this context, how to promote green economic growth and improve green total factor productivity (GTFP) with the help of the digital economy is an important issue that urgently needs empirical research. We adopted the panel data of 278 Chinese prefecture-level cities from 2011 to 2020 to test whether the digital economy improves the GTFP through the Gaussian Mixed Model (GMM) dynamic panel model. The moderating effect model has been used to explore the impact mechanism from the perspectives of industrial structure upgrade and environmental regulation. In addition, a grouping regression was applied to the sample cities to test the heterogeneous impact of the digital economy on the GTFP. Based upon the empirical findings, this work has the following conclusions. First, the digital economy plays a significant role in improving the GTFP. Second, an industrial structure upgrade has a positive moderating effect on the ability of the digital economy to enhance the GTFP. The environmental regulation, in contrast, has a negative moderating effect. Third, the digital economy exerts heterogeneous impacts on the GTFP across regions, but not at the city level.

Impact of Digital Transformation on Enterprise Carbon Intensity: The Moderating Role of Digital Information Resources

In the context of China's "digital power" strategy, the realization of a green and low-carbon shift in manufacturing has become a necessary condition to promote the economy, and the digital factor has increasingly become a new driving force. The text mining and IPCC methods were used to measure manufacturing enterprise digitalization and the level of enterprise carbon emission intensity from 2011 to 2021, respectively. This study then explored the impact of digitalization on manufacturing enterprise carbon emission intensity based on the least squares method model and instrumental variable method model. This research comes to three conclusions. (1) Digitalization can significantly reduce the enterprise carbon emission intensity of China's manufacturing industry, and the influence shows a "marginal increase." (2) Notably, a mechanism analysis indicates the intermediary effect sizes of four crucial intermediaries: green technology innovation > financing constraint > information asymmetry > energy use efficiency. Interestingly, digital information resources positively moderate the positive effect of digitalization on carbon emission intensity through three paths: financing constraints, green technology innovation, and information asymmetry. (3) The influence shows evident signs of heterogeneity-as environmental regulation, financial development, executive education, and R&D quality advance, the inhibiting effect of digitalization on enterprise carbon emission intensity becomes more pronounced. Finally, corresponding policy suggestions are proposed.

How does digital village construction influences carbon emission? The case of China

Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an "inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications.

Exploring the Mechanism of the Impact of Green Finance and Digital Economy on China's Green Total Factor Productivity

In the context of the "double cycle," promoting the development of a green economy is an important goal for China's high-quality economic development in the digital age. This paper uses data from 30 provinces (municipalities and autonomous regions) in China during the 2006-2019 period using the Compiled Green Finance Index (GF) and Digital Economy Index (DE). The interrelationship between green finance, digital economy and green total factor productivity (GTFP) is empirically tested by conducting multiple regressions on panel data from 2006-2019 to perform an empirical analysis. Based on this, further analysis was performed with the threshold model. This study found that green finance and digital economy can contribute well to green total factor productivity, but the combination of the two does not have a good effect on green total factor productivity. Further study found that the green finance and digital economy's contribution to green total factor productivity is mainly derived from technological progress. The regression results based on the panel threshold model show that the more underdeveloped the digital economy is in certain regions, the stronger the role of green finance in promoting efficiency improvement. Therefore, policymakers should formulate differentiated green financial policies according to the level of development of the digital economy and give play to the role of green finance and the digital economy in promoting green total factor productivity.

Impact of Artificial Intelligence on Regional Green Development under China's Environmental Decentralization System-Based on Spatial Durbin Model and Threshold Effect

Artificial intelligence (AI) is the core technology of digital economy, which leads the transition to a sustainable economic growth approach under the Chinese-style environmentally decentralized system. In this paper, we first measured the green total factor productivity (GTFP) of 30 Chinese provinces from 2011 to 2020 using the super-efficiency slacks-based measure (SBM) model, analyzed the mechanism of the effect of AI on GTFP under the environmental decentralization regime, and secondly, empirically investigated the spatial evolution characteristics and the constraining effect of the impact of AI on GTFP using the spatial Durbin model (SDM) and the threshold regression model. The findings reveal: a U shape of the correlation of AI with GTFP; environmental decentralization acts as a positive moderator linking AI and GTFP; the Moran index demonstrates the spatial correlation of GTFP; under the constraint of technological innovation and regional absorptive capacity as threshold variables, the effect of AI over GTFP is U-shaped. This paper provides a useful reference for China to accelerate the formation of a digital-driven green economy development model.

Can Global Value Chain Participation Drive Green Upgrade in China's Manufacturing Industry?

Engagement in the global division of labor has greatly influenced China's economy and environment. With the multi-regional input-output (MRIO) framework, we calculate the global value chain (GVC) participation index of China's 16 manufacturing sectors. We also measure the green upgrade index of manufacturing sectors based on the super-efficiency epsilon-based measure (SEBM) and the Malmquist-Luenberger (ML) index. In addition, the effect of GVC participation on the green upgrade of manufacturing sectors is empirically tested with a fixed effects regression model for panel data. Results show that: (1) sectors that rank high in the forward linkage-based GVC participation index also tend to rank high in the backward linkage-based GVC participation index; (2) the ML index is greater than 1 in most years, indicating that the green upgrade of China's manufacturing sectors shows an uptrend; (3) for both forward and backward linkage, the rise of the GVC and complex GVC participation indexes significantly promotes the green upgrade of manufacturing sectors. Finally, GVC participation of China's manufacturing sectors promotes green upgrade mainly through green technology progress. The conclusions have empirical evidence and policy implications for the advancement to medium- and high-end GVC participation and the green transition of China's manufacturing sectors.