Research on the coupling coordination and spatial influence effect of the digital economy and carbon emission

The coupling coordination between the digital economy and industrial green high-quality development has become an inevitable choice to promote economic high-quality development in China. This paper conducts an empirical analysis of the coupling coordination degree between the digital economy and industrial green high-quality development via the entropy evaluation method, coupling coordination model, Dagum Gini coefficient and its decomposition, and β spatial convergence model, based on the spatial data of 31 provinces in China for a time period ranging from 2008 to 2020. The major research findings of this study are as follows: (1) the coupling coordination between the digital economy and industrial green high-quality development represents a whole steady-upward trend in China, with large regional differences. (2) There is a gradual decrease in the overall spatial difference of the coupling coordination, with the largest intra-regional difference in the eastern region, and significant inter-regional differences in the east-west, east-northeast, and east-central regions. Moreover, the hypervariable density serves as the major source of the regional differences. (3) There exists β Convergence for coupling coordination degrees of the whole country and the four regions. However, the spatial effects are different in different regions due to different influencing factors. Therefore, sufficient attention should be paid to the dynamic trend, the difference, and the imbalance of the coupling coordination degree between the digital economy and industrial green high-quality development. The research is of great significance for accurately implementing policies according to different levels of local resource endowments and economic development, and narrowing the regional differences of the coupling coordination between the digital economy and industrial green high-quality development.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China's DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China's ICEE has consistent σ-convergence and β-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition β-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.

When studying the coupling coordination relationship between agricultural eco-efficiency and urbanization, it is crucial to consider food security, especially in a populous country like China. This paper focuses on 31 provinces in China as the research units, covering the time period from 2000 to 2020. Based on the concept of agricultural eco-efficiency, an evaluation index system was developed to include undesirable outputs (carbon emissions), and agricultural eco-efficiency scores were calculated using the SBM–DEA model. An urbanization evaluation index system, covering six dimensions and twelve indexes, was constructed. A comprehensive index of urbanization is measured using the entropy method. On this basis, a coupling coordination model was applied to quantify the relationship between agricultural eco-efficiency and urbanization at the provincial scale in China. The results showed that the agricultural eco-efficiency of all provincial units in China exhibited an overall trend of improvement. Average efficiency followed a spatial pattern of majority grain-consuming areas > grain production–consumption balance areas > majority grain-producing areas. The level of coupling between agricultural eco-efficiency and urbanization is generally low. Currently, no regions have reached the stage of synergy or high-level coupling. Most regions are currently in an antagonistic stage with a coupling degree of 0.3 < C ≤ 0.5. The classification of coupling coordination levels changed from four levels of “severe imbalance”, “moderate imbalance”, “mild imbalance”, and “primary coordination” to “moderate imbalance”, “mild imbalance”, “primary coordination”, and “intermediate coordination”. The level of “severe imbalance” disappeared, the level of “intermediate coordination” appeared, and the level of “mild imbalance” became the largest scale level. From the perspective of food security, the proportion of grain production in the categories of “primary coordination” and “intermediate coordination” was less than 10%, and these provinces never achieved self-sufficiency in food production. The proportion of grain production at the “mild imbalance” level reached 62.4%, while the per capita grain production at the “moderate imbalance” level reached 846.7 kg. Provinces with lower levels of coupling coordination have stronger food security capabilities. It can be observed that the weaker the coupling coordination between agricultural eco-efficiency and urbanization, the higher the food self-sufficiency. Based on the research results above, we discussed strategies to enhance agricultural eco-efficiency in majority grain-producing regions by focusing on technological progress and technical efficiency. Additionally, we analyzed approaches to achieve grain self-sufficiency in regions characterized by a high level of coordination between agricultural eco-efficiency and urbanization, considering both production and trade dimensions.

The carbon emission reduction effect of the digital economy from the perspective of biased technological progress.

Can the digital economy boost rural residents’ income? Evidence from China based on the spatial Durbin model

The digital economy, as a sustainable and innovative mechanism for economic change, has emerged as a crucial national strategic driver for China’s attainment of high-quality development and low-carbon transition. Assessing the digital economy’s contribution to both high-quality development and carbon reduction, as well as evaluating the extent of its effects, can offer accurate decision-making assistance for prioritizing development sectors and optimizing resource distribution. This research utilizes the Entropy Weight TOPSIS approach and spatial autocorrelation analysis to investigate the spatiotemporal characteristics of the digital economy and high-quality development in Guangdong Province. A coupling coordination model evaluates the extent of coordination between the two, while a Spatial Durbin Model (SDM) is utilized to estimate the coefficients regarding the digital economy’s influence on both high-quality development and carbon emission reduction at the prefecture-level city size. The primary findings of the study are as follows: (1) Both the digital economy and high-quality development in Guangdong Province have progressively advanced, demonstrating notable spatial autocorrelation (Global Moran’s I ranging from 0.21 to 0.36, p < 0.01). Nonetheless, development r is uneven, with the Pearl River Delta region significantly outpacing other areas. (2) The coupling coordination level between the digital economy and high-quality development exhibits fluctuating stability and significant regional disparities. The Pearl River Delta, particularly Shenzhen and Guangzhou, consistently achieves a coordination quality level exceeding 0.90, whereas Meizhou and Maoming frequently fall below 0.20, indicating severe discoordination. (3) The digital economy has a substantial beneficial impact and spatial spillover effect on both high-quality development and carbon reduction. The SDM findings reveal that the digital economy exerts a substantial direct influence on high-quality development (coefficient = 0.4837, p < 0.01), while demonstrating a slight negative impact on carbon emissions (coefficient = −0.2012, p < 0.05), thereby affirming that its impact on high-quality development is predominantly more pronounced than its influence on low-carbon development. This report recommends focused ways to enhance the digital economy’s fundamental role in fostering high-quality development while also realizing its promise for low-carbon synergy.

Digital economy has become an important engine for promoting green development.The existing literature mainly analyzed the impact of digital economy on green development, but rarely examines the coordinated development relationship between the two. This study contributes to the literature by constructing a research framework for the coupling coordination of digital economy and urban green development, using entropy method, coupling coordination model, and spatial auto-correlation to examine the coordinated development relationship between the two.The results show that:(1) During the research period, the comprehensive level of digital economy and urban green development in the Yangtze River Delta (YRD) both showed an upward and consistent trend. The level of digital economy was significantly lower than that of green development, but the gap between the two continued to narrow. (2) The overall coupling coordination level between digital economy and green development in the 41 cities of YRD was relatively low but showing a continuous upward trend. It has undergone a transition from near imbalance to primary coordination, still with a long way to realize high-quality coordination. (3) The coupling coordination level of digital economy and urban green development in different regions of YRD showed spatial differences, the order from high to low was Shanghai, Zhejiang, Jiangsu and Anhui province respectively. (4) The coupling coordination of digital economy and urban green development in the YRD exhibited significant spatial positive correlation and spatial dependence characteristics. The research results can provide a new perspective to promote the positive interaction between green development and digital economy.

Enhancing the level of coupling coordination between the digital economy and low-carbon development is not only an inevitable choice for implementing the strategy of ecological protection and high-quality development in the Yellow River Basin, but also a key path to achieve China’s “Double Carbon” goal. The level of coupling coordination between the digital economy and low-carbon development in 78 cities in the Yellow River Basin from 2011 to 2020 is measured by a coupling coordination model, and the spatial-temporal characteristics and driving factors are analysed using the Dagum Gini coefficient, spatial autocorrelation model and geographic detector. This study found the following: (1) Rapid growth of the digital economy, with the slow growth of low-carbon development. The degree of coupling coordination of the two systems steadily improved and moved from a stage of near-disorder to primary coordination. (2) The degree of coupling coordination is spatially characterised by lower reaches > middle reaches > upper reaches, and provincial capitals and some coastal cities have a higher level of coupling coordination. Spatial differences in coupling coordination tend to widen, with inter-regional differences being the main source of overall differences. (3) There was a significant positive spatial correlation in the degree of coupling coordination. Local spatial clustering characteristics were dominated by High-High (H-H) clustering areas in Shandong and Low-Low (L-L) clustering areas in south-eastern Gansu. (4) The degree of coupling coordination was driven by both internal and external factors of the two systems, with internet penetration and the size of the telecommunications industry within the digital economy system as the most important factors driving the coupling coordination, and the interactions between the different drivers were all enhanced.

Exploring the interaction and coupling effects within the digital economy and eco-economic system resilience in urban agglomeration areas is conducive to promoting high-quality sustainable urban development. Based on the coupling effect perspective, we construct a coupling coordination and development system with multiple elements, information, and interaction flow. The JJJ urban agglomeration from 2010 to 2019 was used as the study sample. The spatiotemporal differences and spatial effects of the coupled coordination were evaluated by combining the tools of combined weight model, coupled coordination model, nuclear density estimation, and exploratory spatial data analysis. The main results can be summarized as follows. (1) From 2010 to 2019, the digital economic index and eco-economic system resilience index of JJJ urban agglomeration maintained an upward trend, and the time series characteristics of the two sides showed a significant positive correlation. Additionally, the overall digital economic development index is better than the resilience development index of the urban eco-economic system. (2) In terms of the type of coupling coordination, the JJJ region has experienced a dynamic evolution process from the imbalance in 2010 to the primary coordination in 2019. The coupling and coordinated development levels of Beijing and Tianjin are obviously better than those of Hebei Province as a whole. (3) The coupling coordination of the system shows certain characteristics of spatial agglomeration and distribution. The overall spatial pattern presents a development pattern with Beijing and Tianjin as the core, and the gap between the north and the south is gradually narrowing. (4) Spatial spillovers and diffusion effects are evident. However, the influential factors have significant differences in the coupling and coordinated development between this region and neighboring regions. The results may provide theoretical support for the continuous improvement of ecological environment quality and green sustainable economic efficiency in urban agglomeration. It provides decision-making reference for promoting regional synergistic development strategy and optimizing spatial pattern of regional integration.

The coupling coordinated development between digital economy and green logistics is of great significance in promoting the integration of Digital China and Beautiful China and realizing the goal of "Double Carbon." For the purpose of clarifying the mechanism of coupling coordinated development between the digital economy and green logistics， this study applied the vertical and horizontal scatter degree method， modified coupling coordination degree model， cold-hot spot analysis， and dynamic aggregated fuzzy set qualitative comparative analysis （fsQCA） method to reveal the spatiotemporal characteristics and configuration paths of the coupling coordinated development between China's digital economy and green logistics during the period from 2013 to 2021. The findings showed that： ① In terms of temporal change， the development level of China's digital economy and green logistics fluctuated in an upward trend. The coupling coordination degree between the two also had a growth trend， experienced from the verge of dysfunction to barely coordination by stage spanning， but the overall coupling coordination degree was not high. ② In terms of spatial patterns， the coupling coordination degree between digital economy and green logistics showed a distribution pattern of "high in the east and low in the central and western regions." The eastern region was already in a state of primary coordination， while most provinces in the central and western regions were still in a state of dysfunction characterized by a type of lagging digital economy. ③ In terms of spatial correlation， the spatial positive correlation of the coupling coordination degree between digital economy and green logistics was significant. Jiangsu， Shanghai， Zhejiang， and Fujian formed a hotspot area， and the change was relatively stable， while the coldspot area expanded from Inner Mongolia to the northeast towards Heilongjiang and Jilin， gradually covering Shanxi to the south. ④ Three types of configuration models drove the high coupling coordinated development between digital economy and green logistics， which were technology-led， technology-talent synergy， and technology-structure optimization. A comparative analysis of the study period revealed that technological conditions had become more important over time， whereas human capital could form a substitute for technological conditions in regions with a higher degree of advanced industrial structure.

<p>Digital economy is becoming a new engine of China's economic transformation, leading a new path of green and low-carbon development. However, the positive and negative effects of the digital economy on the environment have also been widely debated. The energy consumption of China's digital economy industry is still increasing, but it has received little attention. This paper studies the emerging links between digital economy and low-carbon sustainable development. Understanding the impact of the digital economy on carbon emissions is critical to addressing the challenges of climate change in the digital age.</p><p>By integrating input-output methods, this paper establishes a comprehensive framework to evaluate China's digital economy and environmental sustainable development. It can not only evaluate the carbon emissions in various sub-industries of the digital economy, but also reveal its formation and change mechanism by determining its source industries, transfer paths and economic drivers. Using STIRPAT model and provincial panel data from 2001 to 2016, this paper investigates the impact of the digital economy industry on carbon emissions at the national and regional levels. In addition, assess the carbon footprint of the entire digital industry, including the relative contribution of major infrastructure, core and integration components of the digital economy to carbon emissions. The results show that the digital economy helps reduce China's carbon emissions. The digital economy in the central region has a greater impact on carbon emissions than the eastern region, while the western region has unconspicuous impact. With the emergence of the digital economy in the energy system, energy consumption can be reduced and energy efficiency can be improved, which can help reduce carbon emissions in the energy sector, and contribute to the sector's carbon emission reduction goal of about 3%. The positive and negative impacts of the digital economy on the environment have resulted in an inverted U-shaped relationship between the digital economy and carbon emissions. The inflection point of the digital economy is slightly higher than the medium level, which means that carbon emissions may increase further with the development of the digital economy at this stage. Without control, the relative contribution of the digital economy to carbon emissions may exceed 10% by 2030. These findings not only help to advance the existing literature, but also deserve special attention from policy makers.</p>

<p _msthash="770" _msttexthash="18511649104"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Under the background of China's “double carbon” goal, digital economy has become an important way to reduce carbon emissions in China. This paper utilizes the provincial panel data of China from 2012 to 2022, introduces the perspective of agricultural science and technology innovation, empirically examines the impact mechanism of regional digital economy development on agricultural carbon emission through regression analysis model, and portrays the dynamic effect and spillover effect of digital economy development on agricultural carbon emission from both time and space dimensions. The empirical results show that: digital economic development will have a significant inhibitory effect on the intensity of agricultural carbon emissions, and the inhibitory effect will be indirectly affected through the path of agricultural scientific and technological innovation; the impact of digital economic development on the intensity of agricultural carbon emissions there is a time lag effect, the current stage of the digital economic development will still have a strong inhibitory effect on the intensity of agricultural carbon emissions in the future; Digital economic development has a spatial spillover effect, i.e., the development of the regional digital economy will have an inhibitory effect on the intensity of agricultural carbon emissions in neighboring provinces. Based on this, it is proposed to strengthen the construction of digital infrastructure, promote the coordinated development of the digital economy in the region, and formulate policies to reduce carbon emissions in agriculture.</span></span></p>

Coupling coordination analysis on digital economy-tourism development-ecological environment

This paper employs two-way fixed effects models, mediation models and spatial panel models to examine the impact of digital economy development on carbon emission intensity in 72 countries from 2013 to 2020 using TIMG index as the proxy variable of digital economic development. The results of this paper show that (1) there is an inverted U-shaped relationship between the development of the digital economy and the carbon emissions in various countries. The development of digital economy first has a positive effect on carbon emissions, and then, its impact turns negative after it reaches a designated inflection point. (2) The results of the mechanism analysis show that the development of the digital economy can reduce carbon emissions by promoting industrial upgrading, while it could have an inverted U-shaped nonlinear effect on carbon emissions through energy efficiency. (3) The development of a country’s digital economy may have a significantly inverted U-shaped spatial spillover effect on neighboring countries. This paper provides reference for countries to formulate carbon emission reduction policies and promote the coordinated development of digital economy and environmental protection.

The deep integration of the health industry and the digital economy represents a crucial pathway toward a sustainable and resilient future, as it enhances the competitiveness and promotes the orderly expansion of the health sector. Utilizing provincial panel data of 30 provinces in China from 2011 to 2022, this study employs the entropy method and a coupling coordination model to quantify the coupling coordination degree between these sectors. Kernel density estimation and center of gravity–standard deviational ellipse analysis reveal spatiotemporal evolutionary patterns. Key findings include: ① Significant regional disparities exist in the development levels of both the health industry and the digital economy, with notable intra-regional variations among provinces. ② The coupling and coordination level of the health industry and digital economy development across China and within each region have shown a continuous growth trend. The regional levels are in the order of East > West > Central > Northeast, while the regional growth rates are East > Central > West > Northeast. Moreover, a polarization trend has emerged in the central and western regions. ③ The center of gravity of the spatial coupling coordination degree across the entire territory of China shows a clustering trend of moving towards the southeast. The spatial distribution pattern of the coupling coordination degree is in an east-northwest to west-southeast direction. The eastern and northeastern regions, respectively, show a dispersed and clustered trend of moving towards the southwest, while the central and western regions all show a clustered trend of moving towards the southeast. Based on this, policy suggestions are put forward for the deep integration and coordinated development of the health industry and the digital economy, with the aim of leveraging digital innovation to build a health sector that is socially inclusive, economically viable, and environmentally sustainable in the long term.