The effect of the digital economy on carbon emissions in China's construction industry: evidence from spatial econometric analysis.

Carbon emission reduction in the construction industry is vital for realizing sustainable development, and the development of the digital economy plays an important role in this process. The impact of the digital economy on reducing carbon emissions in the construction industry is empirically explored through econometric analyses on a sample of panel data from 30 provinces in China from 2012 to 2021. The empirical results show that developing a digital economy can significantly reduce the construction industry's carbon emission intensity. Additionally, this impact has a significant spatial spillover effect and can benefit the neighboring regions. The mechanism test shows that the digital economy can reduce carbon emissions by improving the technological level of the construction industry. Moreover, the inhibiting effects of the digital economy on carbon emissions in the construction industry vary across different regions. They are more pronounced in the eastern and western regions of low coal-consuming regions. These findings offer valuable insights for policymakers to help drive the deeper integration of the digital economy with the construction industry and facilitate its transition to low-carbon development.

The impact of public fiscal expenditure on industrial transformation and upgrading: An inverted U-shape evidence from China.

High-quality economic development relies on industrial transformation and upgrading. To promote industrial transformation and upgrading, efficient fiscal expenditures are undoubtedly important as pillars of national governance. However, in the context of the market economy, the government's excessive intervention in industrial development will lead to the "promotion tournament" of officials and the "beggar-thy-neighbor" local protectionism, resulting in the convergence of regional industrial structure, which will bring uncertain impact on the upgrading of regional industrial structure. Thus, this study empirically assesses how public fiscal expenditure impacts industrial transformation and upgrading as well as the mechanism by developing a spatial econometric model using the panel data of 250 Chinese cities from 2007 to 2020 and further discusses the differential impact from the perspective of urban scale. The findings disclose that public fiscal expenditure serves a crucial role in facilitating industrial transformation and upgrading, but their relationship resembles an inverted U. Therefore, an optimal scale of public fiscal expenditure exists. Heterogeneity findings reveal that the promoting effect of public fiscal expenditure on industrial transformation and upgrading decreases with the expansion of the city scale. The role mechanism implies that public fiscal expenditure indirectly leverages industrial transformation and upgrading through promoting technological innovation, reducing resource dependence, and expanding scale economies. The conclusion provides a theoretical and practical framework for the government to optimize public fiscal expenditure, promote the transformation and upgrading of China's industrial structure, and ultimately attain high-quality development.

Exploring the impacts of high-speed rail on technology-intensive manufacturing: the case of the Yangtze River Delta region, 2007-2016.

High-speed rail (HSR) may influence economic activities that rely heavily on innovation by facilitating skilled labour, face-to-face interactions, and knowledge spillovers. This study explores how HSR development affects the spatial distribution of technology-intensive manufacturing (TIM) in the Yangtze River Delta (YRD), China. Using a panel dataset including 24 cities for the period 2007-2016 and employing the output of communications equipment, computers, and other electronic equipment (CCOE) as a proxy for TIM's economic productivity at the city level, we apply the staggered difference-in-differences (DID) and spatial Durbin model (SDM) to measure the impacts of HSR's initial opening and connectivity on CCOE development and capture the spatial spillover effects of HSR connectivity. Our findings indicate that the initial opening of HSR and HSR connectivity are negatively associated with CCOE productivity in both DID and SDM. Additionally, the reduction of CCOE is more pronounced in cities with larger populations and higher levels of economy. Moreover, HSR has a more significant effect on CCOE than other manufacturing sectors. However, the spillover effects remain insignificant, indicating HSR's limited impact on CCOE development in adjacent cities within the YRD.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province.

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

The impact and spatial spillover effect of traditional culture on urban green innovation: Empirical evidence from China.

The issues of resource scarcity and environmental pollution has become huge challenges that all of humanity faces together. To resolve the issue, promoting urban green innovation capacity is important. This study uses 189 prefecture-level cities and above in China from 2011 to 2020 as samples, and empirically test the impact and spatial spillover effect of traditional culture on urban green innovation. This study finds that: (1) Traditional culture can improve the local green innovation and positively impact the green innovation in neighboring cities by radiation effect, and the conclusion remains valid after robustness tests; (2) Talent flow and data flow negatively moderate the positive impact of traditional culture on green innovation in local cities. In addition, from a dynamic perspective, traditional culture has a negative impact on green innovation in neighboring cities because of the siphon effect; (3) The spatial spillover effect of traditional culture on urban green innovation shows distinct heterogeneity due to the different regions and whether it is a low-carbon pilot city. This study verifies the important value of Chinese traditional culture in urban sustainable development, which is of great significance for the protection and inheritance of traditional culture and the improvement of urban green innovation.

[Spatial Correlation Structure and Influencing Mechanism of Development Level of Zero-waste Cities in China].

Based on the entropy weight TOPSIS method to measure the development level of "zero-waste cities" in China from 2004 to 2021, the social network analysis method and spatial Durbin model were used to explore the spatial correlation network structure and impact mechanism of the development level of "zero-waste cities." The results showed that： ① The development level of "zero-waste cities" was generally on the decline in the whole country and the eastern and central regions. However, it was on the rise in the western regions. ② The spatial correlation of the development level of "zero-waste cities" presented a core-edge structure, with an overall upward trend in network density and a stable state in the overall network. ③ Beijing, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong were at the center and dominant position of the network. ④ Beijing, Tianjin, Shanghai, and Jiangsu belonged to the "net benefit" sector； Zhejiang, Fujian, and Guangdong belonged to the "broker" sector； and the other provinces belonged to the "net overflow" sectors. ⑤ The level of urbanization, economic development, technological innovation, foreign investment, environmental regulations, government intervention, and population size had a significant impact on the development level of "zero-waste cities" in local or neighboring provinces, respectively. The research results can provide a reference for the proposal of policies for constructing and coordinating the development of "zero-waste cities" in various regions.

Which works better? Comparing the multiple effects of heterogeneous environmental regulations on urban green economic transformation in China.

The coordination of heterogeneous environmental regulations (HER) is crucial for promoting regional green synergistic development. The primary objective of this study is to evaluate the impact of various heterogeneous environmental regulations (HER) on the green economic transformation (GET) of cities in China. We developed a comprehensive index system to measure the GET across three dimensions: the level of economic green development, the capacity for resource and environmental support, and the level of support for green transformation. This study examines 284 Chinese cities during the period from 2011 to 2020. Applying a dynamic panel model, a dynamic Durbin model, and a synergy model, we explore direct effects, spatial effects, and asymmetry of synergistic effects of HER on the GET of Chinese cities. We find that, in terms of direct effects, all environmental regulations can promote urban GET, though the magnitude of effects is heterogeneous. In terms of spatial spillover effects, market-incentive and public-participation environmental policies in a given region inhibit green transformation in neighboring regions, while overall environmental regulation and command-and-control regulation have significant positive effects on neighboring regions' green transformation. Furthermore, the total short-term effect of HER is significantly higher than the total long-term effect. The synergistic effect of HER is positive for the Beijing-Tianjin-Hebei city cluster and the Yangtze River Delta city cluster. This study can provide valuable policy implications for regional coordinated development with a low-carbon focus.

Can transportation infrastructure improve resource misallocation? Evidence from China.

Transportation infrastructure plays a crucial role in the free movement and rational allocation of resources. Utilizing provincial panel data from China spanning from 2000 to 2017, we employ a dynamic spatial Durbin model (DSDM) and a panel threshold model to analyze the impact of transportation infrastructure on resource misallocation. The findings are as follows: (1) Overall, transportation infrastructure serves to correct misallocations of capital and labor, exhibiting spatial effects; (2) Under the influence of spatial spillover effects, transportation infrastructure improves capital misallocation in neighboring regions, yet exacerbates labor misallocation; (3) The impact of transportation infrastructure on capital and labor misallocations shows regional heterogeneity; (4) Transportation infrastructure indirectly ameliorates resource misallocation through three pathways: opening up to external markets, urbanization, and industrial upgrading; (5) There is a threshold effect of transportation infrastructure on labor misallocation - it only improves labor misallocation when it reaches a certain scale.

Does carbon reduction and sequestration conflict with food security in rural China?-What, why and how?

Based on panel data of 31 provinces in rural China from 1997 to 2020, this manuscript first applies a carbon reduction and sequestration (CRS) model from the perspective of agricultural carbon emissions and agricultural carbon sinks. We then construct a food security evaluation system to examine the four dimensions of quantity, quality, ecological and economic security. Finally, the study uses a spatial Durbin model to empirically analyze the impact of CRS on food security and the moderating effect of fiscal decentralization. The relevant results: First, from 1997 to 2020, carbon emissions rose from 221.9794 million tons (1997) to 251.1368 million tons (2020), representing an increase of 13.14 %. The total amount of carbon sinks increased from 518.259 million tons (1997) to 758.887 million tons (2020); an increase of 46.43 %. CRS exhibited a fluctuating downward trend, falling from 0.98 (1997) to 0.90 (2020). However, food security showed an increasing trend, rising 0.12 (1997) to 0.32 (2020), with an average annual growth rate of 6.94 %. Second, in the short term, national CRS has had a significantly negative impact on food security, whereas the long term the result is exactly the opposite. In terms of control variables, planting structure, openness to the world, and economic development have significantly positive impact on food security, and urbanization, technological progress, and environmental regulation have significantly negative impact on food security. Regional heterogeneity is evident in the three functional attribute areas. Third, fiscal decentralization can enhance the negative impact of CRS on food security in the short term and weaken the positive impact of CRS on food security in the long term. Similarly, some regional heterogeneity is found among different regions.

Will structural adjustment and financial support affect low-carbon agricultural production in the Yellow River Basin?

Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this manuscript measures the agricultural low-carbon production efficiency scientifically through the Super-SBM model. In addition, the deviation degree of agricultural industry is used as the index of structural adjustment. Finally, the spatial Durbin model is used to analyze the effect direction and degree of structural adjustment, financial support, and their synergistic effect on agricultural low-carbon production efficiency. The results show that ① the agricultural low-carbon production efficiency in the Yellow River Basin shows a trend of fluctuating downward and a spatial distribution pattern of "high in the east and low in the west". ② Structural adjustment in local region and adjacent areas has a significantly negative impact on agricultural low-carbon production, and the inhibitory effect in adjacent areas is more obvious, and the negative spatial spillover effect is strong. Financial support has a significantly positive impact on agricultural low-carbon production, but the spatial spillover effect of adjacent areas is not obvious. ③ By region, structural adjustment has a significantly negative impact on low-carbon agricultural production in the midstream and downstream regions, while financial support has a significantly positive impact on low-carbon agricultural production in the upstream region. The impact of control variables on agricultural low-carbon production varies from region to region. ④ The synergistic effect of structural adjustment and financial support in the whole and midstream region shows a significantly positive impact on agricultural low-carbon production, indicating that financial support has a certain correction effect on structural adjustment. The coefficient between the upstream and downstream regions is positive but not significant. The conclusions have important reference significance for promoting the ecological protection and high-quality development and agricultural low-carbon development in the Yellow River Basin.

The impact of environmental regulations on the green development of the mariculture industry: empirical analysis based on provincial panel data in coastal areas of China.

With the continuous increase in global mariculture production and aquaculture areas, the environmental pollution caused by the mariculture industry is becoming increasingly serious. Faced with environmental issues, countries worldwide have formulated environmental regulations to scientifically intervene in marine environmental pollution issues and promote the green development of the mariculture industry. However, we must determine if strict environmental regulations can effectively promote the green development of the mariculture industry. This article uses the inter-provincial panel data of China's coastal areas from 2003 to 2019 as a sample. We use the entropy-technique for order of preference by similarity to ideal solution method to measure the intensity of environmental regulation in China's coastal areas and the level of green development of the mariculture industry. On this basis, an economic geography nested matrix was selected to construct a spatial panel econometric model to empirically explore the impact of environmental regulations on the green development of the mariculture industry. This approach also allows us to examine the heterogeneity of the impact of different types of environmental regulations on the green development of the mariculture industry. The research results indicate that environmental regulations have a "U-shaped" impact on the green development of the local mariculture industry, while they have an inverted "U-shaped" impact on the green development of the nearby mariculture industry. Furthermore, heterogeneity exists in the impact of different types of environmental regulations on the green development of the mariculture industry. Based on the research results, this article proposes policy recommendations from the perspectives of flexibly adjusting the intensity of environmental regulations, accelerating the development of voluntary-based environmental regulations, and regulating competition among local governments, which can provide decision-making references for the government to adjust environmental regulation policies and improve the level of green development in the mariculture industry.

Spatio-temporal differentiation characteristics and the influencing factors of PM2.5 emissions from coal consumption in Central Plains Urban Agglomeration.

Central Plains urban agglomeration (CPUA) had developed rapidly, but its air pollution was also serious. Despite advances in study on China's PM2.5 emissions from coal consumption (CC), the differentiation characteristics and the affecting variables of PM2.5 in CPUA required further investigation. This paper computed the PM2.5 emissions of each city from 2000 to 2020 using CC data from CPUA, evaluated its spatio-temporal fluctuation characteristics using the spatial autocorrelation and analyzed its influencing factors by combining various indicators through the spatial Durbin model (SDM). The results verified that: (1) There was a trend of rapid increase of PM2.5 emissions from CC; (2) The Moran's I of the PM2.5 emissions from CC showed a significant agglomeration effect; (3) PM2.5 emissions from CC had a strong spillover effect. The recommendations were in this following: (1) The urban pollution regulation and the pace of industrial green transformation should be Strengthened; (2) Close linkages between cities should be established and attention should be paid to pollution management; (3) The spillover of PM2.5 emissions from CC should be lessened and development of environmental governance technology should be enhanced.

Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China.

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model's ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Spatio-temporal pattern evolution of green development efficiency in Northeastern China and its driving factors.

Scientific analysis of green development efficiency is crucial for promoting healthy green development at home. The subjects of this study were 181 counties in three provinces in Northeast China. As a first step, the Super-SBM model is utilized to estimate the efficiency of 181 counties from 2006 to 2020; in addition, spatial autocorrelation analysis is applied to assess green development efficiency, spatially and temporally, of 181counties; and to examine the driving factors and spillover effects associated with the efficiency of 181 counties, the Spatial Durbin model (SDM) is utilized. The conclusions show that 181 counties have low levels of green development efficiency and are all on a downward trajectory. Liaoning Province has the highest level, Jilin Province has the second highest level, and Heilongjiang Province has the lowest level. According to the geographical distribution, the locations with high and very high green development efficiency are roughly located in Mohe City, Huma County, Xunke County, Daqing Municipal District, Harbin Municipal District, Changchun Municipal District, Wafangdian City, Dalian Municipal District, and Zhuanghe City. There is a favorable spatial connection of efficiency across regions, but the positive spatial agglomeration trend first diminishes and then gradually increases. Economic development, industrial structure, policy regulations, and environmental protection play significant roles in economic development, industrial structure, policy regulations, and environmental protection. The contribution of this essay is of paramount importance for understanding the status quo and potential for green development in different counties in Northeast China and for realizing coordinated regional green development.

Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China.

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China's air quality level has improved in general, but there are large differences in air quality between regions; (2) China's AQI has significant spatial positive autocorrelation, and the Moran's scatter plot shows a high-high and low-low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the "Three-Year Strategy for Conquering the Blue Sky War" policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations.

The Impact of Renewable Energy Development on Regional Carbon Emission Reduction: Based on the Spatio-Temporal Analysis of 30 Provinces in China.

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies.

Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China.

As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction.

How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China.

Reducing soil erosion (SE) is crucial for achieving harmony between human society and the ecological environment. The cultivated land fragmentation (CLF), directly or indirectly, alters soil structure, diminishes its water-holding capacity, and escalates the risk of SE. Scientific assessment of the effect of CLF on SE can provide new insights into controlling of SE across watersheds in China. However, few studies have quantified the effect of CLF on SE. Therefore, we utilized land use change data in the Yangtze River basin from 2000 to 2020, measuring the levels of CLF and SE using Fragstats and InVEST models. The bivariate spatial autocorrelation model was employed to reveal the spatial relationship between CLF and SE. Additionally, we constructed a spatial Durbin model and introduced the geographically and temporally weighted regression model to analyze the role of CLF on SE. The south bank of the upper and middle reaches of the Yangtze River basin exhibited high CLF and SE. The bivariate spatial autocorrelation results showed a significant positive spatial correlation between CLF and SE. The spatial Durbin model results showed that CLF had a spatial spillover effect and time lag on SE, and the effect of CLF on SE had an inverted "N" curve. The study also confirmed that last SE and neighboring SE areas influenced local SE. Currently, CLF had a negative effect on SE in the Sichuan Basin, Yunnan-Guizhou Plateau, and the middle and lower Yangtze River Plain, and positively in Qinghai, Hunan, and Jiangxi provinces. These findings suggest that the government should enhance cross-regional and cross-sectoral cooperation and monitoring of cultivated land changes to prevent and control SE effectively.

Slowing the expansion of impervious surfaces: the key to promoting high-quality and sustainable regional development.

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4-5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City.

How does the development of the digital economy influence carbon productivity? The moderating effect of environmental regulation.

Improving carbon productivity is of great significance to China's "30 · 60" carbon target, while the development of the digital economy is a driving force for green transformation. However, few studies discuss the relationship between the digital economy and carbon productivity. We investigate the influence of digital economic development on carbon productivity using panel data from 30 Chinese provinces from 2011 to 2020. Spatial econometric and moderating effects are considered. The results show that (i) digital economy has a positive direct and negative spatial spillover effect on carbon productivity, and this conclusion is still valid after the robustness test and endogeneity test; (ii) digital infrastructure has a greater impact on carbon productivity than digital industrialization and industrial digitalization; (iii) the mechanism analysis shows that environmental regulation negatively moderates the relationship between the digital economy and carbon productivity; (iv) heterogeneity analysis shows that the effect of the digital economy on carbon productivity is more obvious in the central region compared to the western region, while it is not significant in the eastern region. Overall, this paper not only provides a new analytical perspective for understanding the improvement of carbon productivity in the digital economy but also provides policy inspiration for promoting carbon peak and carbon neutrality goals.