Impact and mechanism of digital economy on China's carbon emissions: from the perspective of spatial heterogeneity

Digital economy and green transformation of regional industries: New insights from sustainability

Studying the impact of the digital economy on carbon emissions in the distribution industry is of great significance for realizing sustainable development goals and coping with climate change. This study finds that increasing the level of digital economy development can reduce the carbon emission intensity of the circulation industry through fixed-effects modeling. Moreover, the effect is different in different geographic regions, and the improvement of the digital economy development level in the east and central regions can significantly reduce the carbon emission intensity of the distribution industry. The digital economy can simultaneously reduce the carbon emission intensity of the circulation industry by reducing the degree of labor factor mismatch in the circulation industry and improving the regional green innovation capacity. Therefore, in order to promote the green development of the distribution industry, it is also necessary to make efforts to improve the construction of network infrastructure, accelerate the process of research and development and cultivation of green technology, and break down the barriers of cross-regional mobility of talents.

The impact of digital infrastructure on provincial green innovation efficiency-empirical evidence from China

In the epoch of the digital economy, technological innovation and energy conservation are significantly facilitated by digital infrastructure, leading to substantial improvements in green innovation efficiency at the provincial level. This study employed the feasible generalized least square (FGLS) method to examine the effects of digital infrastructure on the green innovation efficiency across 30 provinces in the Chinese mainland, utilizing panel data from 2011 to 2020. Additionally, this investigation delves into the intervening role of industrial structure upgrading and the amplifying effects of environmental regulation and human capital on the process. Findings indicate that, to begin with, digital infrastructure contributes to the meaningful enhancement of green innovation efficiency within provinces. Subsequently, the industrial structure upgrading partially mediates the impact of digital infrastructure on the efficiency of provincial green innovation. Lastly, both human capital and environmental regulations amplify the beneficial influence of digital infrastructure on the effectiveness of green innovation at the provincial level. This study provides valuable insights into the mechanisms through which digital infrastructure boosts green innovation efficiency, aiding policymakers in formulating appropriate policies to augment digital infrastructure, thereby promoting provincial green innovation efficiency.

Can digital economy development contribute to carbon emission reduction? Evidence from China

With the rapid growth of the digital economy, it is essential to understand its impact on carbon emissions reduction. This study uses provincial panel data from China during 2011-2019 to construct a moderating mediating effect model and a spatial panel Durbin model to examine the relationship between the digital economy and carbon emissions reduction. This study analyzes the mediating effect of the energy structure on the digital economy's impact on carbon emission reduction, and the spatial effect and regional heterogeneity of the digital economy's impact on carbon emission reduction. The findings indicate that the development of the digital economy can effectively promote regional carbon emission reductions, both directly and indirectly, with a significant spatial spillover effect. Second, the energy structure plays a significant mediating role in promoting carbon emission reduction in the digital economy, and the industrial structure has a positive moderating effect. Third, the impact of the digital economy on carbon emissions reduction has significant regional heterogeneity, and the inhibitory effect of the digital economy is more effective in the central and western provinces. This study provides a theoretical reference for achieving high-quality development of the digital economy while promoting carbon emissions reduction.

Nonlinear impact of digital economy on carbon intensity: the moderating role of low-carbon regulation

The development of the digital economy is an effective way to mitigate the carbon emission problem in the broader setting of the significant data era and green development. Based on the panel data of 271 cities in China from 2011 to 2019, this paper constructs a bidirectional fixed model to analyze the nonlinear effect of the digital economy (DE) on carbon intensity (CI) and the moderating role of low-carbon regulation from theoretical and empirical perspectives. The results show that (1) DE has an enormous inverted U-shaped impact on CI. The findings remain after introducing instrumental variables to mitigate endogeneity and robustness tests. (2) Low-carbon regulation (CP) can strengthen the inverted U-shaped impact between the two and shift the inflection point to the left. (3) Heterogeneity analysis shows that the inverted U-shaped effect of DE on CI is more significant in the central and western regions, high human capital (HC) regions, and high urbanization regions. (4) The mediating effect of energy mix (EM) and green technology innovation (GTI) still hold after introducing instrumental variables to alleviate the endogenous effect of the intermediary effect. This study suggests that the adoption of carbon emission reduction strategies, which will more effectively lower carbon intensity CI, should go hand in hand with the development of DE.

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

Spatio-temporal heterogeneity of the coupling between digital economy and green total factor productivity and its influencing factors in China

The synergy of the digital economy and green total factor productivity (TFP) is the foundation for achieving beneficial outcomes for both the economy and environment. This synergy is also the catalyst for high-quality development and sustainable economic growth in China. The study applied a modified Ellison-Glaeser (EG) index, super-efficiency slacks-based measure (SBM) with a Malmquist-Luenberger (ML) index, coupling coordination degree, and other models to explore the spatiotemporal heterogeneity of the coupling between the digital economy and green TFP from 2011 to 2020 and also analyzed the influencing factors of the coupling. The results show that the coupling between the digital economy and green TFP showed an overall upward trend from imbalance to synergy during the study period. The distribution of the synergistic coupling expanded from point-like to band-like, and there was a significant spreading pattern from the east to the center or west China. The number of cities in a transition state decreased significantly. Spatial jumps, a coupling linkage effect, and evolution in time were prominent. Additionally, the absolute difference among cities expanded. Although coupling in the west experienced the fastest growth rate, the coupling in the east and resource-based cities showed significant benefits. Coupling did not reach an ideal coordinated state, and a neutral interaction pattern remains to be formed. Industrial collaboration, industrial upgrading, government support, economic foundation, and spatial quality all positively impacted the coupling; technological innovation had a lagged effect; and environmental regulation has not reached its full potential. Further, the positive effects of government support and spatial quality performed better in the east and in non-resource-based cities. Due to the optimization of industrial structure, the coupling of the west and resource-based cities achieved better dividends; however, spatial quality needs further improvement. Therefore, the efficient coordination of China's digital economy and green TFP requires a scientific, reasonable, localized, and distinctive approach.

Spatial Pattern Evolution and Influencing Factors on Agricultural Non-Point Source Pollution in Small Town Areas under the Background of Rapid Industrialization

To promote sustainable agricultural development in small town areas during rapid industrialization, it is important to study the evolution of agricultural non-point source pollution (ANSP) and its influencing factors in small town areas in the context of rapid industrialization. The non-point source inventory method was used to study the characteristics of ANSP evolution in 14 small town areas in Gongyi City from 2002 to 2019. Using the spatial Durbin model and geographical detectors, the factors influencing ANSP in small town areas were analyzed in terms of spatial spillover effects and the spatial stratified heterogeneity. The results showed a zigzagging downward trend of ANSP equivalent emissions over time. Spatially, the equivalent emissions of ANSP showed a distribution pattern of being high in the west and low in the east. There was a significant positive global spatial autocorrelation feature and there was an inverted "U-shaped" Environmental Kuznets Curve relationship between industrialization and ANSP. Affluence, population size, and cropping structure positively contributed to the reduction of ANSP. Population size, land size, and industrialization were highly influential factors affecting the spatial variation of ANSP and the interaction of these factors was bivariate or nonlinearly enhanced. This study provides a feasible reference for policymakers and managers to develop reasonable management measures to mitigate ANSP in small town areas during rapid industrialization.

Impact of digital economy development on carbon emission intensity in the Beijing-Tianjin-Hebei region: a mechanism analysis based on industrial structure optimization and green innovation

Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H-H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable.

Research on Theoretical Mechanism and Promotion Path of Digital Economy Driving China's Green Development under "Double Carbon" Background

(1) Background: Under the background of building a new pattern of "double cycle" development, the green meaning of the digital economy is highly valued. The innovative feature of the digital economy is forming a new economic growth pole, and gradually becomes the driving force for China's economic restructuring and green development; (2) methods: this paper empirically tests whether the digital economy can promote green development by using various econometric models based on panel dataset with 30 provinces from 2011 to 2019 in mainland China and measuring the development level of the digital economy and the greening index; (3) results: it is found that the digital economy can directly boost green development in greening degree of economic growth, resources and environment-carrying potential, and government policy support. The digital economy's influence on green development has an inverted U-shaped trend; environmental control is an effective regulatory variable with a substitution effect on green development. With an obvious regional heterogeneity, the digital economy promotes green development; the digital economy can greatly affect green growth through technical innovation through mechanism analysis. The robustness test supports the above conclusion; (4) conclusions: the findings provide a foundation for multi-party policymakers to effectively formulate and implement policies for the digital economy that encourage green growth.

Coupling coordination development of energy-economy-carbon emissions in China under the background of "double carbon"

Based on the panel data of 30 provinces in China from 2010 to 2019, this paper measured the coupling coordination development of energy-economy-carbon emissions and investigated its regional differences and spatial convergence. The research methods in this paper include entropy weight technique method for order preference by similarity to an ideal solution, coupling coordination degree model, Dagum Gini coefficient and decomposition method, Moran's I index, σ convergence model and β convergence model. The study found that the coupling coordination degree of energy-economy-carbon emissions in China has been continuously improved and has obvious regional and stage characteristics, but it is still on the verge of imminent disorder; the overall difference in the coupling coordination degree of energy-economy-carbon emissions shows a decreasing and then increasing trend, the main source of which is inter-regional differences; the coupling coordination degree of energy-economy-carbon emissions has a positive spatial correlation; except for the Southern Coastal Economic Zone and the Middle Yangtze River Economic Zone, there is no significant σ-convergence and β-convergence in the coupling coordination degree of energy-economy-carbon emissions system in other economic zones; the coupling coordination degree of energy-economy-carbon emissions changes fastest in the Middle Yangtze River Economic Zone. The innovation of this paper is to measure the coupling coordination degree of energy-economy-carbon emissions and to analyse its regional differences and spatial effects. It is of great practical significance to promote the coupling coordination development and regional balanced development of energy-economy-carbon emissions in China under the background of "dual carbon".

Digital Economy and Environmental Quality: Insights from the Spatial Durbin Model

Recent developments in attaining carbon peaks and achieving carbon neutrality have had enormous effects on the world economy. Digitalization has been considered a viable way to curtail carbon emissions (CE) and promote sustainable economic development, but scant empirical studies investigate the link between digitalization and CE. In this context, this study constructs the digitalization index using the entropy value method and spatial Markov chain, and the spatial Durbin model is employed to analyze its impact mechanism and influence on urban CE in 265 prefecture-level cities and municipalities in China from 2011 to 2017. The results indicate that: (1) The overall development level of the digital economy (DE) posed a significant spatial effect on urban environmental pollution. However, the effect varies according to the different neighborhood backgrounds. (2) The DE impedes urban environmental deterioration directly and indirectly through the channels of industrial structure, inclusive finance, and urbanization. (3) The development of the DE significantly reduces pollution in cities belonging to urban agglomerations, while the development of the DE escalates emissions in nonurban agglomeration cities. Finally, based on the results, important policy implications are put forward to improve the environmental quality of cities.

Impact of Digital Economy Development on Carbon Emission Efficiency: A Spatial Econometric Analysis Based on Chinese Provinces and Cities

In the realistic context of the development of China's digital economy and carbon peaking and carbon neutrality goals, to efficiently achieve high-quality economic and green and low-carbon transformation, this paper investigates the impact of digital economy development on the carbon emission efficiency of 30 Chinese provinces and cities from 2011-2019. In this paper, firstly, the digital economy development index and carbon emission efficiency are calculated by the entropy method and the Super-SBM-Undesirable Model. Secondly, the Spatial Lag Model (SAR) and the Spatial Durbin Model (SDM) are respectively constructed under the adjacency matrix and the geographic distance matrix to empirically test the spatial impact of the digital economy on carbon emission efficiency. The results show that: the digital economy development and carbon emission efficiency of Chinese provinces and cities both show the spatial distribution characteristics of stronger in the East and weaker in the Middle and West; the digital economy development in Chinese provinces and cities has a significantly positive direct and spatial spillover effect on carbon emission efficiency; there are differences in the direct and spatial spillover effects of various dimensions of the digital economy development on the carbon emission efficiency in Chinese provinces and cities; the direct effect of the digital economy development on the carbon emission efficiency in Chinese provinces and cities has significant regional heterogeneity among eastern, central, and western regions. This paper provides new empirical evidence for developing countries such as China to proactively develop a digital economy to promote energy conservation and emission reduction to realize green and low-carbon transformation.

An Empirical Analysis of the Impact of Digital Economy on Manufacturing Green and Low-Carbon Transformation under the Dual-Carbon Background in China

The deep integration of digital economy and green development has become an inevitable requirement and an important aid in achieving the goal of carbon peaking and carbon neutrality and promoting high-quality economic development. At the same time, the manufacturing industry is the main sector of energy consumption and carbon emissions in China and the main force for achieving the carbon peaking and carbon neutrality goals. This paper constructs a mathematical model to measure the scale of the digital economy development and the efficiency of the green, low-carbon transformation of the manufacturing industry. It builds a panel data model to study the effect of the development of the digital economy on the green, low-carbon transformation of the manufacturing industry based on data of 30 Chinese provinces from 2016 to 2020. The results find that (1) there is a significant positive effect of the digital economy on the green, low-carbon transformation of the manufacturing industry, with an impact coefficient of 0.477, and this finding remains significant in the robustness test. (2) A further test of the mediating effect finds that the digital economy can drive the green, low-carbon transformation of the manufacturing industry by enhancing technological innovation, and it shows a partial mediating effect that accounts for 28% of the total effect. (3) In the regional heterogeneity analysis, it is found that the effect of the digital economy in promoting manufacturing transformation is more prominent in the central region, and the impact coefficients are 0.684, 0.806, 0.340, and 0.392 for the east, central, west, and northeast regions, respectively. This study can provide a theoretical basis and policy support for governments and enterprises.