The Spatial Effect of Low-Carbon Development of Regional Industries Driven by the Digital Economy: Evidence from Chinese Cities

The Spatial Effect of Low-Carbon Development of Regional Industries Driven by the Digital Economy: Evidence from Chinese Cities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Spatial Effect of Low-Carbon Development of Regional Industries Driven by the Digital Economy: Evidence from Chinese Cities Tian Zhang, TIAN ZHANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3844460/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 May, 2024 Read the published version in Humanities and Social Sciences Communications → Version 1 posted You are reading this latest preprint version Abstract Utilizing data that encompass municipalities and regions within China at the prefectural level and beyond, spanning the period from 2012 to 2021, this study employed the spatial Durbin model to assess the spatial spillover impact of the digital economy in propelling low-carbon advancement within regional physical industries. This investigation elucidates the spatial spillover mechanism that underlies the low-carbon evolution of regional industries catalyzed by the digital economy and offers nuanced insights. The findings delineate the following observations: (1) The digital economy propels the low-carbon progression of indigenous physical industries and stimulates the low-carbon development of proximate regions’ physical industries through discernible spatial spillover effects. (2) The spatial spillover ramifications of the digital economy manifest a substantive correlation with urban attributes, including geographical positioning, population size, and market integration levels. Notably, municipalities situated in the southeast coastal region, those characterized by larger population sizes, and those exhibiting heightened market integration levels show greater spatial spillover effects attributable to the digital economy. (3) The safeguarding of intangible asset equity property, a pivotal institutional underpinning for fostering digital economic development, amplifies the spatial spillover effect of the digital economy in propelling low-carbon development within regional industries. (4) As geographical and spatial distances expand, the spatial spillover effect of the digital economy attenuates, indicating a diminishing influence with increasing spatial separation. digital economy space overflow regional industries low-carbon total factor productivity degree of marketization population size intangible asset equity Figures Figure 1 Figure 2 Figure 3 1. Introduction Physical industries are the main component of a national economy and are also an important source of energy consumption and carbon emissions. Currently, China’s physical industries are mainly responsible for processing, assembling, and other low-end links in the global value chain. They are characterized by high resource investment, energy consumption, and pollution emissions. Under increasingly tight resource and environmental constraints, it is theoretically and practically important to explore the low-carbon development of physical industries to achieve the two strategic goals of “manufacturing power” and “double carbon”. At the same time, digital technology, is a knowledge-intensive “clean” production factor and is widely used in the production and operation of regional industries and enterprises. It has played an important role in reducing costs, improving efficiency, promoting innovation, and improving performance [ 1 ]. Digital technology has strong “permeability” characteristics [ 2 ] that can integrate with local industries to change the economic operation mode [ 3 ] and provide digital empowerment to surrounding areas. However, there are significant regional differences in industrial structure, infrastructure, and other aspects throughout China that affect these regional differences in the development of the digital economy [ 4 ]. Therefore, it is very important to study the spatial impact mechanism and effect of the digital economy on the low-carbon development of regional industries. Currently, research on the digital economy and industrial development is increasing; it mainly focuses on the following three aspects: (1) In the study of the digital economy and total factor productivity, one view holds that digital technology is a strong driving force that improves the production efficiency of physical industrial enterprises [ 5 ], while the other view holds that relying too much on digital technology is not conducive to improving total factor productivity [ 6 ]. (2) The research on digital technology and the global value chain indicates that digital technology enhances the degree of Chinese enterprises’ participation in the division of labor in the global value chain, and also improves the status of the division of labor in the global value chain [ 7 ]. (3) Research on digital technology and technological innovation suggests that digital empowerment promotes the technological innovation of enterprises through three channels: optimizing resource allocation [ 8 ], reducing costs [ 9 ], and improving the level of the labor force [ 10 ]. As China’s resource and environmental constraints continue to strengthen, low-carbon development of physical industries has begun to attract scholars’ attention. This research studies the impacts of environmental and ecological technology standards [ 11 ], global value chain embedding [ 12 ], institutional and technological innovation [ 13 ], financial factor agglomeration [ 14 ], and other factors on the low-carbon development of industry. Improving the level of low-carbon industrial development in a region affects development in surrounding regions through the following mechanisms: (1) Coercion and accountability mechanisms. The accountability system for environmental protection assessment strengthens the assessment of government officials’ performance in energy conservation and emissions reduction, and performance assessments can promote the leading cadres [ 15 ]. When a region strictly implements the concept of low-carbon development, it forms a “target effect” between provinces and cities [ 16 ], forcing surrounding regions to improve their low-carbon development of physical industries. (2) Frequent “free rider” behavior. When a locality undertakes low-carbon restructuring of its physical industrial sector, this induces a “crowding-out effect” on energy-intensive enterprises. These entities may consequently migrate to regions with lax environmental oversight, subsequently amplifying the ecological contamination levels within proximate industries. The extant body of literature fails to delve into the intricate interplay between the digital economy and the low-carbon evolution of the industrial sector. This study analyzes the mechanism and effect of the digital economy that drives low-carbon development of industry from a spatial perspective, aiming to evaluate the impact of the digital economy in enabling industry. Moreover, this research provides a theoretical basis for formulating policies to promote the low-carbon development of physical industries. 2. Theoretical Analysis and Research Hypothesis 2.1. Mechanism of Digital Economy Driving Low-Carbon Development of Industry from a Spatial Perspective Digital technology has strong penetration, wide coverage, substitution, and synergy [ 17 ], which affects the low-carbon development level of local industries, breaks through spatial constraints, and produces spatial spillover effects on the low-carbon development level of physical industries in other regions. 2.1.1. Mechanism of Digital Economy Driving Low-Carbon Development of Local Industry The digital economy exerts a direct spillover effect on the low-carbon development of local industries through the following channels: (1) Optimizing the element structure. The application of digital technology replaces the physical elements and improves the cohesion and coordination between the elements [ 18 ]. (2) Improving the efficiency of resource allocation. Through the deep integration of digital technology with R&D, production, marketing, branding, and other links, it helps to improve the allocation efficiency of resources in all links of the industrial chain. (3) Reducing costs. Big data analysis reduces the search and matching cost of transactions, the Internet of things greatly shortens the switching time between processes, and digital trade reduces logistics and marketing costs by breaking through the constraints of time and space. (4) Technological innovation. Through simulation experiments, digital technology helps to improve the probability of success in research and development, customizes personalized innovation schemes according to consumer needs, and reduces resource consumption [ 19 ]. (5) Scale expansion. China’s physical industries mainly require high investment, high consumption, and high emissions. The increase in capacity brought about by digitalization further aggravates resource consumption. The expansion of the digital economy and the continuous improvement in industry for low-carbon development demonstrate that the digital economy has improved low-carbon development of local industry. Therefore, this paper proposes research hypothesis 1: the digital economy promotes low-carbon development of local physical industries. 2.1.2. Spatial Spillover Mechanism of the Digital Economy Driving Low-Carbon Development of Local Industry The path and direction of the spatial spillover mechanism for low-carbon development of physical industries driven by the digital economy is refined from the local area, adjacent area, and interactions between regions. (1) Technology spillover from the digital economy. In the process of promoting deep integration of industry and digital technology, the region voluntarily or involuntarily spills over the digital economy, enabling its effect to adjacent areas, thus affecting the resource input and energy consumption of industry in the adjacent areas. (2) “Imitation effect” of adjacent areas. Promotion of the digital economy to the low-carbon development of industry in this region stimulates neighboring regions to learn and imitate, actively promote the integration of digital technology into industry, and promote the low-carbon development of industry. (3) Inter-regional spatial “interaction effect”. Development of the digital economy and the low-carbon development of physical industries in one region inevitably causes strategic interactions in other regions. This competitive situation helps each region to enhance the spatial spillover effect of the digital economy. Therefore, this paper proposes research hypothesis 2: the digital economy promotes the low-carbon development of physical industries in adjacent areas through spatial spillover. 2.2. The Impact of Urban Characteristics on the Spatial Spillover Effect of Low-Carbon Development of Industries Driven by the Digital Economy Using three indicators of geographical location, population size, and the degree of marketization to describe urban characteristics, this research sorted out the role of urban characteristics in the low-carbon development of industries driven by the digital economy. (1) Geographical location. In areas with relatively perfect information technology infrastructure, the digital economy has stronger penetration and synergy effects on physical industries in the surrounding areas, and the spatial spillover effect is positively correlated with the development level of the digital economy. (2) Urban population size. The larger the population size, the larger the number of customers for the development of the digital economy; on the other hand, it also increases the demand for digital technology in surrounding areas, improving the spatial spillover effect of the digital economy [ 20 ]. (3) The degree of market integration. The higher the degree of market integration, the smoother factors circulate, including digital technology [ 21 ], which is conducive to the spillover effect of digital economy technology and the imitation effect in adjacent areas. Therefore, this paper proposes research hypothesis 3: the spatial spillover effect of the digital economy that drives the low-carbon development of physical industries is affected by the characteristics of cities; there are differences in the spatial spillover effect of the digital economy in cities with different geographical locations, population sizes, and market integration degrees. 2.3. The Moderating Effect of Intangible Asset Equity Protection on the Spatial Spillover Effect of the Digital Economy The regulatory role of intangible asset equity protection includes the following: (1) Intangible asset equity protection plays a crucial role in enhancing the spillover effects of the digital technology. A good intangible asset equity protection system can optimize the environment for contract performance, encourage R&D and innovation of digital technology in the region [ 22 ], and enhance the spillover effect of digital technology. (2) Intangible asset equity protection strengthens the “demonstration effect” of adjacent areas. In the era of the digital economy, enterprises’ innovation achievements are more likely to be occupied [ 23 ]. The intangible asset equity protection system affords digital technology enterprises a certain technological monopoly, which forces enterprises in adjacent regions to accelerate their progress in digital technology and the development of the digital economy. (3) Intangible asset equity protection promotes “spatial interaction” between regions. Improvements in intangible asset equity protection helps to dispel the concerns of enterprises about digital technology innovation, promotes cross regional digital technology R&D cooperation, protects the interests of cross-regional digital element flow, and forms a competitive mechanism for digital technology innovation [ 24 ]. Therefore, this paper proposes research hypothesis 4: intangible asset equity protection has a regulatory effect on the spatial spillover effect of the digital economy, and improvements in intangible asset equity protection strengthen the role of spatial spillover effects of the digital economy in promoting the low-carbon development of physical industries. 2.4. Attenuation of Spatial Spillover Effects of the Digital Economy The spatial diffusion impact of the digital economy is anticipated to diminish progressively with escalating geographical separation and the presence of administrative delineations, indicative of the prevalence of spatial diffusion effects within regional confines. (1) Geographical distance weakens the spatial spillover effect. The transmission of invisible knowledge and technology to industry tends to decline with increases in geographical distance [ 25 ]; thus, the spillover of digital technology and the “imitation effect” of adjacent areas will decrease with geographical distance. (2) Administrative boundaries weaken the spatial spillover effect of the digital economy. Regional barriers formed by local protection increase the cost of factor circulation, which is not conducive to cross-regional circulation of factors including digital technology, and increases the difficulty of cooperation between regional enterprises in the field of digital industrialization and industrial digitalization, thus weakening the spatial spillover effect of the digital economy. Therefore, this paper proposes research hypothesis 5: the spatial spillover effect of the digital economy that drives the low-carbon development of physical industries in surrounding areas attenuates over distance, and there is a certain regional boundary to spatial spillover. 3. Research Design 3.1. Spatial Econometric Model Setting The spatial Durbin model (SDM) reflects the spatial interdependence of explained variables among regions, and also reflects the spatial influence of explained variables in other regions; moreover, the estimation results are unbiased. Therefore, this research constructed the following SDM model to estimate the spatial impact of digital economic development on the low-carbon total factor development of industry: $$\text{I}\text{n}LTF{P}_{it} = \delta {\sum }_{j=1}^{N}{w}_{ij}\text{I}\text{n}LCTF{P}_{jt}+\theta \text{I}\text{n}DID{E}_{it}+\rho {\sum }_{j=1}^{N}{w}_{ij}\text{I}\text{n}DID{E}_{jt}+{\gamma }_{\text{I}}\text{I}\text{n}Contro{l}_{jt}+\varphi {\sum }_{j=1}^{N}{w}_{ij}\text{I}\text{n}Contro{l}_{jt}+{\mu }_{i}+{\lambda }_{i}+{\epsilon }_{it}$$ 1 where \(i,\) \(j\) mean city; \(t\) is the year; N is the number of cities; \(LCTF{P}_{jt}\) means low-carbon total factor productivity; the coefficient to be estimated, \(\delta\) , measures the spatial spillover effect of low-carbon of urban industrial development; \(DID{E}_{jt}\) represents the development level of the digital economy; the coefficient to be estimated, \(\theta\) , measures the impact of the digital economy on the low-carbon development of local industries. A coefficient to be estimated, \(\rho\) , represents the spatial spillover effect of the digital economy; \({w}_{ij}\) represents a spatial weight matrix that including three types: the geographical distance spatial weight matrix \({w}_{{d}_{ij}}\) , the economic distance spatial weight matrix \({w}_{{eco}_{ij}}\) , and the economic and geographical distance-nested matrix \({w}_{d{eco}_{ij}}\) ; \(Control\) is the set of control variables; \({\mu }_{i}\) is the urban effect; \({\lambda }_{i}\) is the time effect; and \({\epsilon }_{it}\) stands for a random error term. 3.2. Direct and Indirect Effects The spatial Durbin model (SDM) encompasses the spatial spillover impact of the explained variables among regions, and also incorporates the spatial spillover effect of the explained variables among regions into the dependent variables. The estimated coefficient contains interactive information between regions, and cannot directly explain the relationship between dependent variables and independent variables. This research used partial differential matrix analysis to decompose the total effect of the independent variables on the dependent variables into direct effects (intra-regional spillover effects) and indirect effects (spatial spillover effects) [ 26 ]. This can accurately reflect the impact of spatial spillover effects under the SDM, and then correctly interpret the estimated coefficient of the SDM. The partial derivative matrix of the expected \(lnLCTFP\) of low-carbon development of urban industry to the \(lnDIDE\) of the digital economy level can be written as follows: $$\frac{\partial E\left(lnLCTFP\right)}{\partial lnDID{E}_{1}}\frac{\partial E\left(lnLCTFP\right)}{\partial lnDID{E}_{2}}\dots \frac{\partial E\left(lnLCTFP\right)}{\partial lnDID{E}_{N}}= {\left(1-\delta \sum _{j=1}^{N}{w}_{ij}\right)}^{-1}\left[\begin{array}{cccc}\theta & {w}_{12}\rho & \cdots & {w}_{1N}\rho \\ {w}_{21}\rho & \theta & \cdots & {w}_{2N}\rho \\ ⋮& ⋮& \ddots & ⋮\\ {w}_{N1}\rho & {w}_{N2}\rho & \cdots & \theta \end{array}\right]$$ 2 The direct effect is the mean value of all elements on the main diagonal of the matrix in Formula (2), which is the impact of the digital economy on the low-carbon development of local industries. The spatial spillover effect of the digital economy on the low-carbon development of industries in other regions is expressed in terms of the mean of the column sum of the non-diagonal elements. The total effect of the digital economy on the low-carbon development of industry is obtained by summing up the direct effect and the indirect effect. 3.3. Variables and Data Selection of GML Index Model 3.3.1. Explained Variable: Low-Carbon Development Level of Industry Low-carbon total factor productivity is used to measure the low-carbon development level of urban industry, and the GML index model based on the SBM distance function is used to measure it. First of all, the real industrial sectors of the city are taken as the decision-making unit m, along with a combination of production factors of real industry input x = ( \({x}_{1} {\cdots x}_{n}\) ), producing an “expected” output y = ( \({y}_{1 }\cdots { y}_{n}\) ) and an “unexpected” output b = ( \({b}_{1}\cdots {b}_{n}\) ); data envelopment analysis (DEA) is used to construct a production possibility set, including both the “expected” output and “unexpected” output. Secondly, the directional distance function of the SBM that considers the “unexpected” output is defined as \({D}_{V}^{G}({x}^{t{m}^{{\prime }}}, {y}^{t{m}^{{\prime }}},{b}^{t{m}^{{\prime }}};{g}^{x},{g}^{y},{g}^{b})\) , with \(\left({g}^{x},{g}^{y},{g}^{b}\right)\) denoting the direction vector. Then, the GML index is constructed to measure the dynamic change in low-carbon total factor productivity of industries from period t to t + 1, specifically as follows: $${GML}_{t}^{t+1}=\frac{1 +{ D}_{v}^{G}({x}^{t},{y}^{t},{b}^{t};g)}{{1 + D}_{v}^{G}({x}^{t+1},{y}^{t+1},{b}^{t+1};g)}$$ 3 where x is the input factor of industry, including capital, labor, and energy; y is the “expected” output, expressed in real GDP; b is the “unexpected” output, including industrial wastewater discharge, industrial \({\text{S}\text{O}}_{2}\) discharge, and industrial dust discharge. 3.3.2. Core Explanatory Variable, Urban Digital Economy Development Level The digital economy development index is used to measure the digital economic development level of cities [ 27 ]. Considering the availability of data, the digital economy development index includes the inclusive development of digital finance and the development of the Internet [ 28 ]. The entropy method and principal component analysis are used to measure the digital economy development index at the city level, which are used for benchmark regression and robustness testing, respectively. 3.3.3. Control Variables There are seven control variables: (1) Economic development level ( \({E}_{co}\) ), measured via urban per capita GDP; (2) factor endowment structure ( FE ), expressed as the ratio of fixed assets of industrial enterprises above a designated size to the number of employed persons at the end of the year; (3) industrial structure ( IS ), expressed as the proportion of the added value of the secondary industry in GDP; (4) ( Inno ) is expressed by the proportion of the sum of science and technology expenditure and education expenditure in the general public budget for expenditures; (5) the level of foreign investment ( FI ), measured by the proportion of actually used foreign capital in urban GDP; (6) environmental regulation ( ER ), expressed by the comprehensive utilization rate of general industrial solid waste; and (7) the degree of marketization ( MK ), expressed as the proportion of employment in non-public enterprises. 3.3.4. Data Sources In view of the availability of data, the sample constructed in this study is the panel data of 241 cities from 2012 to 2021. The original data come from the China Urban Statistics Yearbook, China Energy Statistics Yearbook, China Environmental Statistics Yearbook, and the China Digital inclusive finance index. In order to eliminate data volatility and heteroscedasticity, natural logarithms were taken for the above variables. See Table 1 for the statistical description of the variables. Table 1 Variable descriptive statistics. Variable MeanTitle Standard deviation Minimum value Maximum value Observations Variable low-carbon total factor productivity ( In LCTFP) 0.0083 0.0028 −0.0407 0.0647 2169 Digital economy development( In DIDA)(Entropy Method) −2.4528 0.4815 −3.9431 −0.1985 2169 Digital economy development( In LCTFP )(Principal Component Analysis) −1.1552 1.3029 −7.7769 2.2826 2169 Economic development level ( In Eco) 10.7745 0.5671 9.2193 15.6752 2169 Factor endowment structure( In FE) 3.2266 0.5389 −0.0034 6.1849 2169 Industrial structure ( In IS) −0.7829 0.2579 −3.0477 −0.1978 2169 Innovation capability( In Inno) −4.4386 1.3018 −12.9371 −0.2551 2169 Foreign investment level ( In FI) −4.1381 0.6851 −4.8841 −0.7713 2169 Environmental regulation( In ER) 4.4551 0.1771 3.0919 4.8032 2169 Degree of marketization( In MK) −2.4592 0.6706 −4.8061 0.3295 2169 4. Change Characteristics of Digital Economy Development Level and Low-Carbon Development Level of Industry 4.1. Change Characteristics of Digital Economy Development Level The spatiotemporal evolution trends in the urban digital economy development index are as follows (Fig. 1 ): (1) from the perspective of time, the development level of the digital economy in each city continuously improved. In 2012, only a few cities such as Beijing and Shenzhen had a digital economy development index above 0.15. In 2014, the digital economy development index of Hangzhou, Nanjing, and Guangzhou exceeded 0.2. In 2016, the digital economy development indexes of Suzhou, Xiamen, Dongguan, and other cities exceeded 0.2. In 2019, the digital economy development indexes of Shanghai and Shenzhen exceeded 0.5, and the digital economy development indexes of major cities in the central and western regions also increased significantly. (2) In terms of spatial dimensions, there are spatial heterogeneity and disequilibrium in the development level of the digital economy. The top 20% of cities in the digital economy development index are mainly concentrated in coastal areas. Driven by these cities, the digital economic development level of surrounding cities is also high. In 2019, although the digital economy development index of Wuhan, Nanchang, Chengdu, Xi’an, and other central and western cities also entered a high level, these cities limited the use of the digital economy development belt in their provinces, and did not develop digital economy agglomeration. 4.2. Temporal and Spatial Evolution of Low-Carbon Development Level of Industry Temporal and spatial evolution trends of the low-carbon total factor productivity of industries in Chinese cities are shown in Fig. 2 . Generally, the low-carbon total factor productivity of urban industries from 2012 to 2021 showed an initial trend of decline and then increase, and the spatial distribution also showed obvious heterogeneity and imbalance. In 2012, the number of cities with a low-carbon total factor productivity from industries greater than 1.02 was large and widely distributed. In 2016, the low-carbon total factor productivity of industry in Beijing, Suzhou, and other strong manufacturing cities declined, and Shijiazhuang, Zhuzhou, Liuzhou, Lhasa, and other cities in the central and western regions also began to decline, indicating that the decline in total factor productivity from industry during this period shows a trend of diffusion. By 2019, the low-carbon total factor productivity of industries in cities turns upward, which is related to the implementation of measures to promote the low-carbon development of industries in the “made in China 2025” strategy. 5. Empirical Results and Analysis 5.1. Spatial Correlation Analysis In this research, the Moran index is used to describe spatial autocorrelation. It can be seen from the estimation results in Table 2 that the Moran index of low-carbon total factor productivity of industry is positive and significant, indicating that the low-carbon development of industry among cities shows a strong positive spatial correlation; that is, cities with similar low-carbon development levels for industry are geographically close to each other. The development of the digital economy also shows a positive spatial correlation, indicating that development of the digital economy among cities shows mutual influence. Table 2 Moran’s I test results of manufacturing green development and digital economic development. Year In LCTFP In DIDE Year In LCTFP In DIDE 2011 0.318*** 0.171*** 2017 0.389*** 0.207*** 2012 0.349*** 0.197*** 2018 0.392*** 0.181*** 2013 0.358*** 0.155*** 2019 0.370*** 0.212*** 2014 0.384*** 0.183*** 2020 0.354*** 0.201*** 2015 0.372*** 0.160*** 2021 0.372*** 0.209*** 2016 0.361*** 0.199*** Note: ***, *, and * represent significance at the 1%, 5%, and 10% significance levels, respectively. 5.2. Selection of Spatial Econometric Model The results of the spatial model selection test show the following: (1) For the LM_ spatial error and robust LM_, the spatial error statistics were 23.147 and 9.725, respectively, which were significantly positive, indicating that the selected spatial econometric model should include the spatial error term; simultaneously, the LM_ spatial lag and robust LM_ spatial lag statistics are 58.726 and 16.284, respectively, which are also significantly positive. The spatial econometric model should also include the spatial lag term of the dependent variable. (2) After checking the more general SDM model, it was found that the LR_ spatial error (15.379), Wald_ spatial error (22.153), LR_ spatial lag (16.262), and the Wald_ spatial lag (23.174) were significantly positive, indicating that the SDM is the preferred model. (3) The Hausman test showed that the fixed effect model of the SDM should be selected. (4) The LR test shows that the time and space double fixed effect model should be selected. Therefore, the optimal model is the spatiotemporal double fixed effect model of the SDM. 5.3. Spatial Econometric Estimation Results and Analysis 5.3.1. Benchmark Estimation Results of the Spatial Durbin Model Under the three different spatial weight matrices, the estimated coefficients of urban digital economy development ( \(lnDIDE\) ) are significantly positive, indicating that the development of the local digital economy promoted low-carbon total factor productivity in local industries. The estimation coefficient of the spatial lag variable for urban digital economy development ( \(w·lnDIDE\) ) is also positive, which indicates that the development of the digital economy affects the low-carbon development of physical industries in surrounding areas through a spatial spillover effect. The spatial lag coefficient of low-carbon development of industry is significantly positive, which means that there is a positive spatial interaction effect of low-carbon development on industry in cities. Therefore, in addition to the impact of local digital economy development on the low-carbon development of local industry, the spatial spillover effect of the low-carbon development of industry and digital economic development in the surrounding areas cannot be ignored. Table 3 Estimation results of SDM. \({w}_{{d}_{ij}}\) \({w}_{{eco}_{ij}}\) \({w}_{d{eco}_{ij}}\) \({w}_{{d}_{ij}}\) \({w}_{{eco}_{ij}}\) \({w}_{d{eco}_{ij}}\) In DIDE 0.815***(3.15) 0.195***(2.93) 0.236***(3.64) W \(·\) In DIDE 0.083**(1.99) 0.076**(2.11) 0.068*(1.69) In Eco 0.262***(2.97) 0.297***(3.16) 0.345***(3.87) W \(·\) In Eco 0.129*(1.67) 0.156*(1.78) 0.190**(1.98) In FE 0.137*(1.69) 0.145*(1.74) 0.164*(1.85) W \(·\) In FE 0.045(0.89) 0.089(0.74) 0.109(1.21) In IS −0.135***(-2.80) −0.176***(-2.98) −0.182***(-3.80) W \(·\) In IS 0.045(0.94) 0.095(0.67) 0.082(0.71) In Inno 0.364***(3.74) 0.376***(3.90) 0.332***(3.63) W \(·\) In Inno 0.256**(2.57) 0.279***(2.85) 0.293***(3.14) In FI −0.234**(-2.54) −0.248***(-2.85) −0.224**(-2.65) W \(·\) In FI 0.157**(1.99) 0.198***(2.78) 0.185**(2.15) In ER 0.557***(4.56) 0.589***(4.91) 0.576***(3.91) W \(·\) In ER −0.125**(-1.98) −0.112**(-1.97) −0.156**(-2.23) In MK 0.225***(3.13) 0.263***(3.45) 0.471***(3.32) W \(·\) In MK 0.123*(1.69) 0.135**(2.01) 0.187***(3.54) 0.463***(4.67) 0.478***(4.82) 0.492***(4.93) Log-L 345.52 457.21 521.27 \({ R}^{2}\) 0.8546 0.8712 0.8316 N 2169 2169 2169 Note: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters. 5.3.2. Direct Effect, Indirect Effect, and Total Effect of Digital Economy Next, the total effect of the digital economy on the low-carbon development of industries was divided into direct effects (intra-regional spillover effects) and indirect effects (spatial spillover effects), in order to correctly interpret the impact of digital economy development. It can be seen from Table 4 that under different spatial weight matrices, the impact of the same explanatory variable on the low-carbon development of industries is basically the same. The direct effect of the digital economy on the low-carbon development of industry is significantly positive, indicating that the development of a local digital economy is an important driving force for the low-carbon development of industry. The spatial spillover effect of digital economic development on low-carbon industrial development is less than the direct effect, but it is also positive, indicating that developing the digital economy in surrounding areas also promotes the low-carbon development of local industries through spatial interaction. Therefore, hypothesis 1 and hypothesis 2 are verified. Table 4 Estimation results of direct effects, indirect effects, and the total effect of the digital economy. Weight Matrix Type In DIDE In Eco In FE In IS In Inno In FI In ER In MK Direct 0.196***(3.48) 0.163**(2.04) 0.161*(1.73) −0.188***(− 2.90) 0.388***(3.94) −0.345**(− 3.12) 0.436***(4.33) 0.311***(3.22) \({w}_{{d}_{ij}}\) Indirect 0.114**(2.43) 0.118*(1.88) 0.073(0.90) 0.078(1.55) 0.293***(3.18) 0.123*(1.75) −0.102*(− 1.91) 0.145*(1.88) Total 0.130**(2.66) 0.281*(1.93) 0.234*(1.91) −0.110**(2.45) 0.681***(3.43) −0.222**(− 2.38) 0.334**(2.55) 0.456**(2.64) Direct 0.164***(2.88) 0.185***(3.01) 0.183*(1.88) −0.162***(− 2.83) 0.392***(4.12) −0.211**(− 2.69) 0.478***(3.88) 0.256***(3.02) \({w}_{{eco}_{ij}}\) Indirect 0.109*(1.73) 0.122*(1.83) 0.099(1.18) 0.045(0.74) 0.256**(2.65) 0.168**(2.34) −0.123**(− 2.23) 0.155**(2.66) Total 0.273**(2.59) 0.307**(2.55) 0.282*(1.69) −0.117**(− 2.24) 0.648***(3.14) −0.043(1.01) 0.355**(2.05) 0.411***(3.87) Direct 0.211***(3.08) 0.231***(3.18) 0.155*(1.68) −0.049***(− 2.85) 0.312***(3.09) −0.288***(− 2.99) 0.485***(4.12) 0.401***(4.15) \({w}_{d{eco}_{ij}}\) Indirect 0.127***(3.12) 0.217**(2.02) 0.086(1.41) 0.094(0.93) 0.189***(2.88) 0.199***(3.13) −0.147**(− 2.55) 0.183***(3.07) Total 0.338***(3.24) 0.448***(4.58) 0.241*(1.81) −0.055**(− 2.11) 0.501***(4.94) −0.089*(− 1.66) 0.338**(2.13) 0.584***(4.88) Note: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters. 5.4. Robustness Test To assess the resilience of the benchmark regression outcomes, principal component analysis was employed to gauge the city-level digital economic development index; subsequently, the independent variable index was substituted for robustness testing. Across three distinct spatial weight matrices, the influence of digital economic development observed for both low-carbon advancement within local industry and its equivalent in neighboring regions demonstrates a notably positive correlation. Furthermore, the estimated coefficient for the temporal lag associated with low-carbon development in industry exhibits a statistically significant positive trend. A decomposition of the total effect reveals both a direct spillover effect and a spatial spillover effect, which stem from digital economic development. Thus, the original findings from the benchmark regression remain largely unaffected by the alteration in the measurement approach used for the independent variables, thereby affirming the robustness of the spatial measurements. 5.5. Heterogeneity Test 5.5.1. Geographical Location Heterogeneity Test To investigate the heterogeneity of geographical location, Chinese cities were divided into eight groups for regression. The results are shown in Table 5 . The direct and indirect effects of digital economic development in the eastern and southern coastal areas are greater. The reason is that the digital economy of Shanghai, Hangzhou, and other cities in the Yangtze River Delta urban agglomeration is developed, and there are a large number of industrial enterprises in Suzhou, Ningbo, Wuxi, and other cities in the region. The developed digital economy and perfect manufacturing network produce spatial interaction, which enhances the spillover and spatial spillover effects in the region. Similarly, the digital economy and industry are the two wheels driving the economic development in Shenzhen and Guangzhou. Foshan and Dongguan also have developed production and manufacturing systems, so the intra-regional and spatial spillover effects of the digital economy highly promote the low-carbon development of industries. The direct promotional effect of the digital economy on the low-carbon development of industry in the northern coastal area is strong, but the spatial spillover effect is less than that in the eastern and southern coastal areas. The spatial spillover effect of urban digital economic development in southwest China is stronger than that in the middle reaches of the Yangtze River and the Yellow River. The reason is that the digital economies in Chengdu, Chongqing, and Guiyang are developing rapidly, and development of the big data industry has natural advantages. Northeast China and northwest China are two regions with low spatial spillover effects from the digital economy. As a result of the low level of digital economic development in these two regions, the proportion of modern industries is small, and the degree of regional integration is low. Table 5 Estimation results of SDM in different regions. \({w}_{{d}_{ij}}\) \({w}_{{eco}_{ij}}\) \({w}_{d{eco}_{ij}}\) Direct effect Indirect effect Direct effect Indirect effect Direct effect Indirect effect Northeast region 0.135**(2.24) 0.088*(1.78) 0.138**(2.58) 0.093*(1.69) 0.196**(2.67) 0.101*(1.87) Northern coastal areas 0.226***(3.21) 0.201**(2.52) 0.271***(3.45) 0.174**(2.70) 0.254***(3.34) 0.145**(2.26) Eastern coastal areas 0.371***(4.56) 0.312***(3.87) 0.322***(4.56) 0.245***(3.11) 0.423***(4.97) 0.371***(3.82) Southern coastal areas 0.321***(4.55) 0.289***(3.83) 0.345***(4.12) 0.289***(4.21) 0.371***(3.03) 0.321***(3.43) Middle Yellow River region 0.155**(2.48) 0.107**(2.23) 0.138**(2.12) 0.111*(1.86) 0.189**(2.27) 0.116**(2.01) Middle Yangtze River region 0.189***(3.45) 0.123**(2.56) 0.174***(2.99) 0.118**(2.04) 0.222***(3.24) 0.134***(3.39) Southwest region 0.202***(3.83) 0.145**(2.56) 0.198***(3.14) 0.156**(2.05) 0.234***(3.45) 0.145***(3.65) Northwest region 0.116**(2.53) 0.095*(1.76) 0.123***(2.56) 0.092*(1.88) 0.118**(2.21) 0.096*(1.69) Note: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters. 5.5.2. Population Size Heterogeneity Test China’s cities are divided into four categories: small cities (with a permanent resident population of less than 500,000 in urban areas), medium-sized cities (with a permanent resident population of 500,000–1 million in urban areas), large cities (with a permanent resident population of 1 million–5 million in urban areas), and cities above mega cities (with a permanent resident population of more than 5 million in urban areas). The estimated results are shown in Table 6 . As far as the direct effect is concerned, the digital economy’s role in promoting the low-carbon development of local industries is relatively significant, and there is no obvious change with changing urban population size. However, the spatial spillover effect is quite different. With the gradual increase in urban population size, the spatial spillover effect of digital economic development increases, which shows that the spatial spillover effect is in direct proportion to the urban population size. The larger the urban population, the more diversified are the figures required for low-carbon development of industrial enterprises, which increases the input demand for digital elements in surrounding cities, thus improving the spatial spillover effect of the digital economy. Table 6 Estimation results of SDM model for cities with different population sizes. \({w}_{{d}_{ij}}\) \({w}_{{eco}_{ij}}\) \({w}_{d{eco}_{ij}}\) Direct effect Indirect effect Direct effect Indirect effect Direct effect Indirect effect Small cities 0.398***(4.38) 0.177*(1.88) 0.336***(4.28) 0.158*(1.77) 0.369***(4.28) 0.143*(1.79) Medium-sized cities 0.293***(3.31) 0.274**(2.62) 0.242***(3.09) 0.207**(2.28) 0.232***(2.89) 0.205**(2.08) Large cities 0.332***(3.69) 0.335***(3.49) 0.311***(3.19) 0.296***(3.03) 0.309***(3.36) 0.275***(3.23) Mega cities 0.438***(4.19) 0.364***(4.65) 0.412***(4.65) 0.322***(3.01) 0.456***(4.92) 0.333***(3.45) Note: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters. 5.5.3. Heterogeneity Test of Market Integration Level The price index method is used to measure the degree of market integration of each city. The degree of market integration is divided into three levels: low, medium, and high. The estimated results are shown in Table 7 . Under different degrees of market integration, the direct and spatial spillover effects of the digital economy that drives the low-carbon development of industry are heterogeneous. With improvements in the degree of market integration, the direct and spatial spillover effects increase. Enhancing market integration serves as a catalyst for the seamless dissemination of digital components. This, in turn, diminishes the operational costs incurred by enterprises that leverage sophisticated digital technologies beyond their immediate locales. Furthermore, such heightened integration optimally facilitates harnessing of the spillover effects intrinsic to the digital economy, thereby elevating the low-carbon development quotient within both local and international industries. The heterogeneity test shows that the spatial spillover effect of the digital economy is related to the geographical location, population size, market integration level, and other urban characteristics; thus, research hypothesis 3 is verified. Table 7 Estimation results of SDM model for cities with different degrees of market integration. \({w}_{{d}_{ij}}\) \({w}_{{eco}_{ij}}\) \({w}_{d{eco}_{ij}}\) Direct effect Indirect effect Direct effect Indirect effect Direct effect Indirect effect Low level of market integration 0.129**(2.31) 0.115*(1.77) 0.134**(2.23) 0.104*(1.69) 0.155**(2.45) 0.119*(1.78) Medium level of market integration 0.243***(3.19) 0.118***(2.99) 0.255***(3.13) 0.175***(2.86) 0.281***(3.42) 0.184***(2.96) High level of market integration 0.483***(4.94) 0.412***(3.83) 0.464***(4.17) 0.408***(3.99) 0.483***(4.28) 0.433***(3.79) Note: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters. 5.6. Extended Analysis 5.6.1. Regulatory Role of Intangible Asset Equity Protection Digital technology has the characteristics of virtuality, replication and sharing, and is prone to infringement in the process of market transaction and use. Therefore, intangible asset equity protection has an important impact on the development of the digital economy and its spatial spillover effect. In this section, the cross term \(lnDIDE·lnIP\) and its spatial lag variable of the digital economy and intangible asset equity protection are included in the spatial econometric model (1) to examine the regulatory role of intangible asset equity protection in the low-carbon development of industries driven by the digital economy, specified as follows: 4 where \(lnIP\) represents the level of urban intangible asset equity protection; \({\sum }_{j=1}^{N}{w}_{ij}(lnDIG{I}_{jt}·lnIP)\) is a spatial lag variable of the interaction between the digital economy and intangible asset equity protection, which is used to capture the impact of intangible asset equity protection on the spatial spillover effect of digital economic development. Table 8 shows the moderating effect of intangible asset equity protection on the low-carbon development of physical industries driven by the digital economy. The direct and indirect effects of are still significantly positive. The direct effect of focusing on the variable \(\text{I}\text{n}DIDE\) · \(\text{I}\text{n}IP\) is positive, indicating that intangible asset equity protection strengthens the intra-regional spillover effect of the digital economy driving low-carbon development of physical industries. The indirect effect of \(\text{I}\text{n}DIDE\) · \(\text{I}\text{n}IP\) is significantly positive, indi \(\text{I}\text{n}DIDE\) cating that in regions with higher levels of intangible asset equity protection, the level of digital economic development has a greater spatial spillover effect on the low-carbon development of physical industries in that region. Therefore, improving the level of intangible asset equity protection strengthens the spatial spillover effect of the digital economy on the low-carbon development of physical industries. Therefore, research hypothesis 4 is validated. Table 8 The moderating role of intangible asset equity protection. \({w}_{{d}_{ij}}\) \({w}_{{eco}_{ij}}\) \({w}_{d{eco}_{ij}}\) Direct effect Indirect effect Direct effect Indirect effect Direct effect Indirect effect \(InDIDE\) 0.185***(3.56) 0.155**(2.53) 0.194***(3.98) 0.174**(2.42) 0.158***(2.80) 0.132**(2.31) \(InDIDE·InIP\) 0.093***(3.14) 0.087***(2.88) 0.078***(3.04) 0.068***(2.83) 0.085***(3.55) 0.062***(3.18) Note: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters. 5.6.2. Relationship between Spatial Spillover Effect and Geographical Distance In order to test the relationship between the spatial spillover effect of the digital economy and geographical distance, the spatial econometric model was estimated for every 20 km increase in the geographical distance between cities, and the estimated spatial spillover effect coefficients under different geographical distance thresholds were recorded. It can be seen from Fig. 3 that within a distance of 300 km, the spatial spillover effect of the digital economy is strong and the downward trend is slow, mainly because it is generally within 300 km of provincial administrative boundaries. Compared with outside the province, the mobility of digital elements among cities within the province is better, which better promotes the low-carbon development of physical industries in cities within the province through the spatial spillover effect. However, when the geographical distance exceeds 300 km, the spatial spillover effect of the digital economy suddenly decreases, and the decline rate is faster, indicating that beyond the provincial boundary, the barrier effect is stronger than the spatial spillover effect of the digital economy. When the geographical distance exceeds 520 km, the rate that the spatial spillover coefficient of the digital economy slows down declines, which may be due to a reduction in spatial units with spatial relationships among regions. Therefore, the spatial spillover effect of the digital economy attenuates with the increase in geographical distance, and there is a certain regional boundary to spatial spillover. Thus, research hypothesis 5 is verified. 6. Conclusions and Policy Recommendations This study yields several key findings: (1) The digital economy propels the low-carbon evolution of local physical industries and stimulates low-carbon advancements in proximate industries through the spatial spillover effect. (2) The spatial spillover effect of the digital economy is contingent upon geographic location, population size, and the degree of market integration, among other urban attributes. Notably, municipalities in the southeast coastal regions, those with larger populations, and those boasting higher market integration levels exhibit more pronounced spatial spillover effects. (3) Intangible asset equity protection, as a crucial institutional underpinning for digital technology innovation, amplifies the spatial spillover effect of the digital economy, thereby steering the low-carbon development trajectory of physical industries. (4) The spatial spillover effect of the digital economy adheres to a discernible attenuation pattern. Specifically, the impact diminishes abruptly beyond a geographical distance of 300 km, which is attributed to provincial administrative boundaries. Notably, the rate of decline in the spatial spillover coefficient decelerates once the geographical distance surpasses 520 km. This study has the following policy implications: (1) Accelerate the establishment of a unified factor market, promote the efficient, reasonable, and safe flow of data elements in a wider range, and reduce the cost of using digital elements in physical industries. Maximize the expansion, superposition, and multiplication of digital technology, and improve the spatial spillover effect of the digital economy to enable the low-carbon development of physical industries. (2) Taking into account the geographical location, population size, market integration level, and other urban characteristics, we should take measures to improve the spatial spillover effect of the digital economy. Cities with relatively lagging digital economic development in northwest and northeast China should strengthen their cooperation with cities that have developed digital economies. Cities with small populations should increase the number of employed people in the digital economy, improve their level of market integration, and expand the spatial spillover effect dividend of the digital economy. (3) We should improve the data intangible asset equity protection system, encourage digital technology innovation, and strengthen the protection of the whole process of data production, circulation, and consumption. Safeguard the rights and interests of owners and users of digital elements, and strengthen the spatial spillover effect of the digital economy. (4) Promote the development of regional economic integration, delay the decay rate of the spatial spillover effect, and expand the spatial spillover radius of the digital economy. Declarations (1) Ethical approval: This article does not contain any studies with human participants performed by any of the authors. (2) Informed consent: This article does not contain any studies with human participants performed by any of the authors. (3) Anonymization of article files ： Completely anonymize all my article files. (4)DATA AVAILABILITY STATEMENT ：Data sharing is not applicable to this research as no data were generated or analysed. (5) COMPETING INTERESTS/CONFLICTS STATEMENT ：The authors declare no competing interests. References Chen, M.G.; Zhou, Y.R. The impact of digitalization on labor costs in enterprises. Popul. Sci. 2021 , 4 , 45–60, 127. Cai, Y.Z.; Chen,N. Under the new technological revolution, artificial intelligence is associated with high quality growth and employment Research on Quantitative Economy. Technol. Econ. 2019 , 36 , 3–22. Bresnahan, T.F.; Trajtenberg, M. General purpose technologies ‘Engines of growth’? J. Econom. 1995 , 65 , 83–108. 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Introduction to Spatial Econometrics ; CRC Press: Boca Raton, FL, USA; Taylor and Francis Group: Chicago, IL, USA, 2009. Zhao, T.; t, Z.; Liang, S.K. Digital Economy, Entrepreneurial Activity, and High Quality Development: Empirical Evidence from Chinese Cities. Manag. World 2020 , 36 , 65–76. Huang, Q.H.; Yu, Y.Z.; Zhang, S.L. The Development of the Internet and the Productivity Enhancement of the Physical Industry: Intrinsic Mechanisms and China’s Experience. China Ind. Econ. 2019 , 8 , 5–23. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 May, 2024 Read the published version in Humanities and Social Sciences Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3844460","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271525911,"identity":"0d8b497c-61cb-4f2f-8fb8-e3788e41b192","order_by":0,"name":"Tian Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+/uPj39IqPhfz9jeQKyeA2lpDA/OMCcw9xwgWkuOGePDFuYE9hkJROpgbDhj9iCxgS2Pd+bjjTcYamyiCWphZmwrN0jcwVMsOTut2ILhWFpuAyEtbMzMGyQSz0gwbpydYybB2HCYsBYeNgYDicQ2A8b9N88QqUWCh8UMqCUhsXEGD5FaDCTYkg0SzhwwZuwB+iWBGL8YSDAffPij4oAcY/vhjTc+1NgQ1oKqPYEU5RAtpOoYBaNgFIyCkQEASJhCwvfvhjMAAAAASUVORK5CYII=","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Tian","middleName":"","lastName":"Zhang","suffix":""},{"id":271525912,"identity":"4b66bf98-f41e-4856-9850-e77ddbceec46","order_by":1,"name":"TIAN ZHANG","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"TIAN","middleName":"","lastName":"ZHANG","suffix":""}],"badges":[],"createdAt":"2024-01-08 03:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3844460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3844460/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1057/s41599-024-03215-x","type":"published","date":"2024-05-30T00:28:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50862678,"identity":"abaf77cf-41c1-480b-9e82-8a898270f3c0","added_by":"auto","created_at":"2024-02-08 14:13:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":597448,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal evolution of digital economy development index of prefecture level cities in China.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3844460/v1/9563ebaeb3345dbcf2c0d3ee.png"},{"id":50862677,"identity":"9cac0c9c-e017-43db-a718-762298a00195","added_by":"auto","created_at":"2024-02-08 14:13:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":588827,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal evolution of green total factor productivity of the manufacturing industry in prefecture-level cities of China.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3844460/v1/85513caceceb4c8575074f8c.png"},{"id":50862679,"identity":"e163884f-53e0-4b99-b316-a51d0c120398","added_by":"auto","created_at":"2024-02-08 14:13:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27842,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between spatial spillover effect coefficient of the digital economy and geographical distance threshold.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3844460/v1/4ffde96c850f457818141232.png"},{"id":57455989,"identity":"7d608559-2b11-41d2-91d8-e5e7d847de4c","added_by":"auto","created_at":"2024-05-31 00:28:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2584914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3844460/v1/fadd7fd4-15a8-4862-850a-6568bd7bdfaf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Spatial Effect of Low-Carbon Development of Regional Industries Driven by the Digital Economy: Evidence from Chinese Cities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePhysical industries are the main component of a national economy and are also an important source of energy consumption and carbon emissions. Currently, China\u0026rsquo;s physical industries are mainly responsible for processing, assembling, and other low-end links in the global value chain. They are characterized by high resource investment, energy consumption, and pollution emissions. Under increasingly tight resource and environmental constraints, it is theoretically and practically important to explore the low-carbon development of physical industries to achieve the two strategic goals of \u0026ldquo;manufacturing power\u0026rdquo; and \u0026ldquo;double carbon\u0026rdquo;.\u003c/p\u003e \u003cp\u003eAt the same time, digital technology, is a knowledge-intensive \u0026ldquo;clean\u0026rdquo; production factor and is widely used in the production and operation of regional industries and enterprises. It has played an important role in reducing costs, improving efficiency, promoting innovation, and improving performance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Digital technology has strong \u0026ldquo;permeability\u0026rdquo; characteristics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] that can integrate with local industries to change the economic operation mode [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and provide digital empowerment to surrounding areas. However, there are significant regional differences in industrial structure, infrastructure, and other aspects throughout China that affect these regional differences in the development of the digital economy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is very important to study the spatial impact mechanism and effect of the digital economy on the low-carbon development of regional industries.\u003c/p\u003e \u003cp\u003eCurrently, research on the digital economy and industrial development is increasing; it mainly focuses on the following three aspects: (1) In the study of the digital economy and total factor productivity, one view holds that digital technology is a strong driving force that improves the production efficiency of physical industrial enterprises [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], while the other view holds that relying too much on digital technology is not conducive to improving total factor productivity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. (2) The research on digital technology and the global value chain indicates that digital technology enhances the degree of Chinese enterprises\u0026rsquo; participation in the division of labor in the global value chain, and also improves the status of the division of labor in the global value chain [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. (3) Research on digital technology and technological innovation suggests that digital empowerment promotes the technological innovation of enterprises through three channels: optimizing resource allocation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], reducing costs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and improving the level of the labor force [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs China\u0026rsquo;s resource and environmental constraints continue to strengthen, low-carbon development of physical industries has begun to attract scholars\u0026rsquo; attention. This research studies the impacts of environmental and ecological technology standards [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], global value chain embedding [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], institutional and technological innovation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], financial factor agglomeration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and other factors on the low-carbon development of industry.\u003c/p\u003e \u003cp\u003eImproving the level of low-carbon industrial development in a region affects development in surrounding regions through the following mechanisms: (1) Coercion and accountability mechanisms. The accountability system for environmental protection assessment strengthens the assessment of government officials\u0026rsquo; performance in energy conservation and emissions reduction, and performance assessments can promote the leading cadres [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. When a region strictly implements the concept of low-carbon development, it forms a \u0026ldquo;target effect\u0026rdquo; between provinces and cities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], forcing surrounding regions to improve their low-carbon development of physical industries. (2) Frequent \u0026ldquo;free rider\u0026rdquo; behavior. When a locality undertakes low-carbon restructuring of its physical industrial sector, this induces a \u0026ldquo;crowding-out effect\u0026rdquo; on energy-intensive enterprises. These entities may consequently migrate to regions with lax environmental oversight, subsequently amplifying the ecological contamination levels within proximate industries. The extant body of literature fails to delve into the intricate interplay between the digital economy and the low-carbon evolution of the industrial sector.\u003c/p\u003e \u003cp\u003eThis study analyzes the mechanism and effect of the digital economy that drives low-carbon development of industry from a spatial perspective, aiming to evaluate the impact of the digital economy in enabling industry. Moreover, this research provides a theoretical basis for formulating policies to promote the low-carbon development of physical industries.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Theoretical Analysis and Research Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Mechanism of Digital Economy Driving Low-Carbon Development of Industry from a Spatial Perspective\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDigital technology has strong penetration, wide coverage, substitution, and synergy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which affects the low-carbon development level of local industries, breaks through spatial constraints, and produces spatial spillover effects on the low-carbon development level of physical industries in other regions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Mechanism of Digital Economy Driving Low-Carbon Development of Local Industry\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe digital economy exerts a direct spillover effect on the low-carbon development of local industries through the following channels: (1) Optimizing the element structure. The application of digital technology replaces the physical elements and improves the cohesion and coordination between the elements [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. (2) Improving the efficiency of resource allocation. Through the deep integration of digital technology with R\u0026amp;D, production, marketing, branding, and other links, it helps to improve the allocation efficiency of resources in all links of the industrial chain. (3) Reducing costs. Big data analysis reduces the search and matching cost of transactions, the Internet of things greatly shortens the switching time between processes, and digital trade reduces logistics and marketing costs by breaking through the constraints of time and space. (4) Technological innovation. Through simulation experiments, digital technology helps to improve the probability of success in research and development, customizes personalized innovation schemes according to consumer needs, and reduces resource consumption [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. (5) Scale expansion. China\u0026rsquo;s physical industries mainly require high investment, high consumption, and high emissions. The increase in capacity brought about by digitalization further aggravates resource consumption. The expansion of the digital economy and the continuous improvement in industry for low-carbon development demonstrate that the digital economy has improved low-carbon development of local industry.\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes research hypothesis 1: the digital economy promotes low-carbon development of local physical industries.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Spatial Spillover Mechanism of the Digital Economy Driving Low-Carbon Development of Local Industry\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe path and direction of the spatial spillover mechanism for low-carbon development of physical industries driven by the digital economy is refined from the local area, adjacent area, and interactions between regions. (1) Technology spillover from the digital economy. In the process of promoting deep integration of industry and digital technology, the region voluntarily or involuntarily spills over the digital economy, enabling its effect to adjacent areas, thus affecting the resource input and energy consumption of industry in the adjacent areas. (2) \u0026ldquo;Imitation effect\u0026rdquo; of adjacent areas. Promotion of the digital economy to the low-carbon development of industry in this region stimulates neighboring regions to learn and imitate, actively promote the integration of digital technology into industry, and promote the low-carbon development of industry. (3) Inter-regional spatial \u0026ldquo;interaction effect\u0026rdquo;. Development of the digital economy and the low-carbon development of physical industries in one region inevitably causes strategic interactions in other regions. This competitive situation helps each region to enhance the spatial spillover effect of the digital economy.\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes research hypothesis 2: the digital economy promotes the low-carbon development of physical industries in adjacent areas through spatial spillover.\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.2. The Impact of Urban Characteristics on the Spatial Spillover Effect of Low-Carbon Development of Industries Driven by the Digital Economy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eUsing three indicators of geographical location, population size, and the degree of marketization to describe urban characteristics, this research sorted out the role of urban characteristics in the low-carbon development of industries driven by the digital economy. (1) Geographical location. In areas with relatively perfect information technology infrastructure, the digital economy has stronger penetration and synergy effects on physical industries in the surrounding areas, and the spatial spillover effect is positively correlated with the development level of the digital economy. (2) Urban population size. The larger the population size, the larger the number of customers for the development of the digital economy; on the other hand, it also increases the demand for digital technology in surrounding areas, improving the spatial spillover effect of the digital economy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. (3) The degree of market integration. The higher the degree of market integration, the smoother factors circulate, including digital technology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which is conducive to the spillover effect of digital economy technology and the imitation effect in adjacent areas.\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes research hypothesis 3: the spatial spillover effect of the digital economy that drives the low-carbon development of physical industries is affected by the characteristics of cities; there are differences in the spatial spillover effect of the digital economy in cities with different geographical locations, population sizes, and market integration degrees.\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.3. The Moderating Effect of Intangible Asset Equity Protection on the Spatial Spillover Effect of the Digital Economy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe regulatory role of intangible asset equity protection includes the following: (1) Intangible asset equity protection plays a crucial role in enhancing the spillover effects of the digital technology. A good intangible asset equity protection system can optimize the environment for contract performance, encourage R\u0026amp;D and innovation of digital technology in the region [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and enhance the spillover effect of digital technology. (2) Intangible asset equity protection strengthens the \u0026ldquo;demonstration effect\u0026rdquo; of adjacent areas. In the era of the digital economy, enterprises\u0026rsquo; innovation achievements are more likely to be occupied [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The intangible asset equity protection system affords digital technology enterprises a certain technological monopoly, which forces enterprises in adjacent regions to accelerate their progress in digital technology and the development of the digital economy. (3) Intangible asset equity protection promotes \u0026ldquo;spatial interaction\u0026rdquo; between regions. Improvements in intangible asset equity protection helps to dispel the concerns of enterprises about digital technology innovation, promotes cross regional digital technology R\u0026amp;D cooperation, protects the interests of cross-regional digital element flow, and forms a competitive mechanism for digital technology innovation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes research hypothesis 4: intangible asset equity protection has a regulatory effect on the spatial spillover effect of the digital economy, and improvements in intangible asset equity protection strengthen the role of spatial spillover effects of the digital economy in promoting the low-carbon development of physical industries.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Attenuation of Spatial Spillover Effects of the Digital Economy\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatial diffusion impact of the digital economy is anticipated to diminish progressively with escalating geographical separation and the presence of administrative delineations, indicative of the prevalence of spatial diffusion effects within regional confines. (1) Geographical distance weakens the spatial spillover effect. The transmission of invisible knowledge and technology to industry tends to decline with increases in geographical distance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; thus, the spillover of digital technology and the \u0026ldquo;imitation effect\u0026rdquo; of adjacent areas will decrease with geographical distance. (2) Administrative boundaries weaken the spatial spillover effect of the digital economy. Regional barriers formed by local protection increase the cost of factor circulation, which is not conducive to cross-regional circulation of factors including digital technology, and increases the difficulty of cooperation between regional enterprises in the field of digital industrialization and industrial digitalization, thus weakening the spatial spillover effect of the digital economy.\u003c/p\u003e \u003cp\u003eTherefore, this paper proposes research hypothesis 5: the spatial spillover effect of the digital economy that drives the low-carbon development of physical industries in surrounding areas attenuates over distance, and there is a certain regional boundary to spatial spillover.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Spatial Econometric Model Setting\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatial Durbin model (SDM) reflects the spatial interdependence of explained variables among regions, and also reflects the spatial influence of explained variables in other regions; moreover, the estimation results are unbiased. Therefore, this research constructed the following SDM model to estimate the spatial impact of digital economic development on the low-carbon total factor development of industry:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{I}\\text{n}LTF{P}_{it} = \\delta {\\sum }_{j=1}^{N}{w}_{ij}\\text{I}\\text{n}LCTF{P}_{jt}+\\theta \\text{I}\\text{n}DID{E}_{it}+\\rho {\\sum }_{j=1}^{N}{w}_{ij}\\text{I}\\text{n}DID{E}_{jt}+{\\gamma }_{\\text{I}}\\text{I}\\text{n}Contro{l}_{jt}+\\varphi {\\sum }_{j=1}^{N}{w}_{ij}\\text{I}\\text{n}Contro{l}_{jt}+{\\mu }_{i}+{\\lambda }_{i}+{\\epsilon }_{it}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i,\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003emean city; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e is the year; \u003cem\u003eN\u003c/em\u003e is the number of cities; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(LCTF{P}_{jt}\\)\u003c/span\u003e\u003c/span\u003e means low-carbon total factor productivity; the coefficient to be estimated, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\delta\\)\u003c/span\u003e\u003c/span\u003e, measures the spatial spillover effect of low-carbon of urban industrial development; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(DID{E}_{jt}\\)\u003c/span\u003e\u003c/span\u003e represents the development level of the digital economy; the coefficient to be estimated, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta\\)\u003c/span\u003e\u003c/span\u003e, measures the impact of the digital economy on the low-carbon development of local industries. A coefficient to be estimated, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e, represents the spatial spillover effect of the digital economy; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents a spatial weight matrix that including three types: the geographical distance spatial weight matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e, the economic distance spatial weight matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e, and the economic and geographical distance-nested matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Control\\)\u003c/span\u003e\u003c/span\u003e is the set of control variables; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{i}\\)\u003c/span\u003e\u003c/span\u003eis the urban effect; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda }_{i}\\)\u003c/span\u003e\u003c/span\u003e is the time effect; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{it}\\)\u003c/span\u003e\u003c/span\u003e stands for a random error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Direct and Indirect Effects\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatial Durbin model (SDM) encompasses the spatial spillover impact of the explained variables among regions, and also incorporates the spatial spillover effect of the explained variables among regions into the dependent variables. The estimated coefficient contains interactive information between regions, and cannot directly explain the relationship between dependent variables and independent variables. This research used partial differential matrix analysis to decompose the total effect of the independent variables on the dependent variables into direct effects (intra-regional spillover effects) and indirect effects (spatial spillover effects) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This can accurately reflect the impact of spatial spillover effects under the SDM, and then correctly interpret the estimated coefficient of the SDM. The partial derivative matrix of the expected \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(lnLCTFP\\)\u003c/span\u003e\u003c/span\u003e of low-carbon development of urban industry to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(lnDIDE\\)\u003c/span\u003e\u003c/span\u003e of the digital economy level can be written as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\frac{\\partial E\\left(lnLCTFP\\right)}{\\partial lnDID{E}_{1}}\\frac{\\partial E\\left(lnLCTFP\\right)}{\\partial lnDID{E}_{2}}\\dots \\frac{\\partial E\\left(lnLCTFP\\right)}{\\partial lnDID{E}_{N}}= {\\left(1-\\delta \\sum _{j=1}^{N}{w}_{ij}\\right)}^{-1}\\left[\\begin{array}{cccc}\\theta \u0026amp; {w}_{12}\\rho \u0026amp; \\cdots \u0026amp; {w}_{1N}\\rho \\\\ {w}_{21}\\rho \u0026amp; \\theta \u0026amp; \\cdots \u0026amp; {w}_{2N}\\rho \\\\ ⋮\u0026amp; ⋮\u0026amp; \\ddots \u0026amp; ⋮\\\\ {w}_{N1}\\rho \u0026amp; {w}_{N2}\\rho \u0026amp; \\cdots \u0026amp; \\theta \\end{array}\\right]$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe direct effect is the mean value of all elements on the main diagonal of the matrix in Formula (2), which is the impact of the digital economy on the low-carbon development of local industries. The spatial spillover effect of the digital economy on the low-carbon development of industries in other regions is expressed in terms of the mean of the column sum of the non-diagonal elements. The total effect of the digital economy on the low-carbon development of industry is obtained by summing up the direct effect and the indirect effect.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Variables and Data Selection of GML Index Model\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Explained Variable: Low-Carbon Development Level of Industry\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLow-carbon total factor productivity is used to measure the low-carbon development level of urban industry, and the GML index model based on the SBM distance function is used to measure it. First of all, the real industrial sectors of the city are taken as the decision-making unit m, along with a combination of production factors of real industry input x = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{1} {\\cdots x}_{n}\\)\u003c/span\u003e\u003c/span\u003e), producing an \u0026ldquo;expected\u0026rdquo; output y = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{1 }\\cdots { y}_{n}\\)\u003c/span\u003e\u003c/span\u003e) and an \u0026ldquo;unexpected\u0026rdquo; output b = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{1}\\cdots {b}_{n}\\)\u003c/span\u003e\u003c/span\u003e); data envelopment analysis (DEA) is used to construct a production possibility set, including both the \u0026ldquo;expected\u0026rdquo; output and \u0026ldquo;unexpected\u0026rdquo; output. Secondly, the directional distance function of the SBM that considers the \u0026ldquo;unexpected\u0026rdquo; output is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{V}^{G}({x}^{t{m}^{{\\prime }}}, {y}^{t{m}^{{\\prime }}},{b}^{t{m}^{{\\prime }}};{g}^{x},{g}^{y},{g}^{b})\\)\u003c/span\u003e\u003c/span\u003e, with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({g}^{x},{g}^{y},{g}^{b}\\right)\\)\u003c/span\u003e\u003c/span\u003e denoting the direction vector. Then, the GML index is constructed to measure the dynamic change in low-carbon total factor productivity of industries from period t to t\u0026thinsp;+\u0026thinsp;1, specifically as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${GML}_{t}^{t+1}=\\frac{1 +{ D}_{v}^{G}({x}^{t},{y}^{t},{b}^{t};g)}{{1 + D}_{v}^{G}({x}^{t+1},{y}^{t+1},{b}^{t+1};g)}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere x is the input factor of industry, including capital, labor, and energy; y is the \u0026ldquo;expected\u0026rdquo; output, expressed in real GDP; b is the \u0026ldquo;unexpected\u0026rdquo; output, including industrial wastewater discharge, industrial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{S}\\text{O}}_{2}\\)\u003c/span\u003e\u003c/span\u003e discharge, and industrial dust discharge.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Core Explanatory Variable, Urban Digital Economy Development Level\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe digital economy development index is used to measure the digital economic development level of cities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Considering the availability of data, the digital economy development index includes the inclusive development of digital finance and the development of the Internet [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The entropy method and principal component analysis are used to measure the digital economy development index at the city level, which are used for benchmark regression and robustness testing, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Control Variables\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThere are seven control variables: (1) Economic development level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{co}\\)\u003c/span\u003e\u003c/span\u003e), measured via urban per capita GDP; (2) factor endowment structure (\u003cem\u003eFE\u003c/em\u003e), expressed as the ratio of fixed assets of industrial enterprises above a designated size to the number of employed persons at the end of the year; (3) industrial structure (\u003cem\u003eIS\u003c/em\u003e), expressed as the proportion of the added value of the secondary industry in GDP; (4) (\u003cem\u003eInno\u003c/em\u003e) is expressed by the proportion of the sum of science and technology expenditure and education expenditure in the general public budget for expenditures; (5) the level of foreign investment (\u003cem\u003eFI\u003c/em\u003e), measured by the proportion of actually used foreign capital in urban GDP; (6) environmental regulation (\u003cem\u003eER\u003c/em\u003e), expressed by the comprehensive utilization rate of general industrial solid waste; and (7) the degree of marketization (\u003cem\u003eMK\u003c/em\u003e), expressed as the proportion of employment in non-public enterprises.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Data Sources\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn view of the availability of data, the sample constructed in this study is the panel data of 241 cities from 2012 to 2021. The original data come from the China Urban Statistics Yearbook, China Energy Statistics Yearbook, China Environmental Statistics Yearbook, and the China Digital inclusive finance index. In order to eliminate data volatility and heteroscedasticity, natural logarithms were taken for the above variables. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the statistical description of the variables.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMeanTitle\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStandard deviation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMinimum value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMaximum value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariable low-carbon total factor productivity (\u003c/em\u003eIn\u003cem\u003eLCTFP)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.0407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDigital economy development(\u003c/em\u003eIn\u003cem\u003eDIDA)(Entropy Method)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;2.4528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.9431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDigital economy development(\u003c/em\u003eIn\u003cem\u003eLCTFP )(Principal Component Analysis)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.1552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;7.7769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEconomic development level (\u003c/em\u003eIn\u003cem\u003eEco)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.7745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.2193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.6752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFactor endowment structure(\u003c/em\u003eIn\u003cem\u003eFE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.2266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.1849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndustrial structure (\u003c/em\u003eIn\u003cem\u003eIS)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.7829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.0477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.1978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInnovation capability(\u003c/em\u003eIn\u003cem\u003eInno)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;4.4386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;12.9371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.2551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eForeign investment level (\u003c/em\u003eIn\u003cem\u003eFI)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;4.1381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;4.8841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.7713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental regulation(\u003c/em\u003eIn\u003cem\u003eER)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.8032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDegree of marketization(\u003c/em\u003eIn\u003cem\u003eMK)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;2.4592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;4.8061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Change Characteristics of Digital Economy Development Level and Low-Carbon Development Level of Industry","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Change Characteristics of Digital Economy Development Level\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatiotemporal evolution trends in the urban digital economy development index are as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (1) from the perspective of time, the development level of the digital economy in each city continuously improved. In 2012, only a few cities such as Beijing and Shenzhen had a digital economy development index above 0.15. In 2014, the digital economy development index of Hangzhou, Nanjing, and Guangzhou exceeded 0.2. In 2016, the digital economy development indexes of Suzhou, Xiamen, Dongguan, and other cities exceeded 0.2. In 2019, the digital economy development indexes of Shanghai and Shenzhen exceeded 0.5, and the digital economy development indexes of major cities in the central and western regions also increased significantly. (2) In terms of spatial dimensions, there are spatial heterogeneity and disequilibrium in the development level of the digital economy. The top 20% of cities in the digital economy development index are mainly concentrated in coastal areas. Driven by these cities, the digital economic development level of surrounding cities is also high. In 2019, although the digital economy development index of Wuhan, Nanchang, Chengdu, Xi\u0026rsquo;an, and other central and western cities also entered a high level, these cities limited the use of the digital economy development belt in their provinces, and did not develop digital economy agglomeration.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Temporal and Spatial Evolution of Low-Carbon Development Level of Industry\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTemporal and spatial evolution trends of the low-carbon total factor productivity of industries in Chinese cities are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Generally, the low-carbon total factor productivity of urban industries from 2012 to 2021 showed an initial trend of decline and then increase, and the spatial distribution also showed obvious heterogeneity and imbalance. In 2012, the number of cities with a low-carbon total factor productivity from industries greater than 1.02 was large and widely distributed. In 2016, the low-carbon total factor productivity of industry in Beijing, Suzhou, and other strong manufacturing cities declined, and Shijiazhuang, Zhuzhou, Liuzhou, Lhasa, and other cities in the central and western regions also began to decline, indicating that the decline in total factor productivity from industry during this period shows a trend of diffusion. By 2019, the low-carbon total factor productivity of industries in cities turns upward, which is related to the implementation of measures to promote the low-carbon development of industries in the \u0026ldquo;made in China 2025\u0026rdquo; strategy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical Results and Analysis","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Spatial Correlation Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this research, the Moran index is used to describe spatial autocorrelation. It can be seen from the estimation results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that the Moran index of low-carbon total factor productivity of industry is positive and significant, indicating that the low-carbon development of industry among cities shows a strong positive spatial correlation; that is, cities with similar low-carbon development levels for industry are geographically close to each other. The development of the digital economy also shows a positive spatial correlation, indicating that development of the digital economy among cities shows mutual influence.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMoran\u0026rsquo;s I test results of manufacturing green development and digital economic development.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn\u003cem\u003eLCTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn\u003cem\u003eDIDE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIn\u003cem\u003eLCTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn\u003cem\u003eDIDE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.318***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.389***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.207***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.349***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.197***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.358***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.212***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.384***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.183***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.354***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.201***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.372***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.160***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.209***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.361***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNote: ***, *, and * represent significance at the 1%, 5%, and 10% significance levels, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Selection of Spatial Econometric Model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe results of the spatial model selection test show the following: (1) For the LM_ spatial error and robust LM_, the spatial error statistics were 23.147 and 9.725, respectively, which were significantly positive, indicating that the selected spatial econometric model should include the spatial error term; simultaneously, the LM_ spatial lag and robust LM_ spatial lag statistics are 58.726 and 16.284, respectively, which are also significantly positive. The spatial econometric model should also include the spatial lag term of the dependent variable. (2) After checking the more general SDM model, it was found that the LR_ spatial error (15.379), Wald_ spatial error (22.153), LR_ spatial lag (16.262), and the Wald_ spatial lag (23.174) were significantly positive, indicating that the SDM is the preferred model. (3) The Hausman test showed that the fixed effect model of the SDM should be selected. (4) The LR test shows that the time and space double fixed effect model should be selected. Therefore, the optimal model is the spatiotemporal double fixed effect model of the SDM.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Spatial Econometric Estimation Results and Analysis\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1. Benchmark Estimation Results of the Spatial Durbin Model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUnder the three different spatial weight matrices, the estimated coefficients of urban digital economy development (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(lnDIDE\\)\u003c/span\u003e\u003c/span\u003e) are significantly positive, indicating that the development of the local digital economy promoted low-carbon total factor productivity in local industries. The estimation coefficient of the spatial lag variable for urban digital economy development (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(w\u0026middot;lnDIDE\\)\u003c/span\u003e\u003c/span\u003e) is also positive, which indicates that the development of the digital economy affects the low-carbon development of physical industries in surrounding areas through a spatial spillover effect. The spatial lag coefficient of low-carbon development of industry is significantly positive, which means that there is a positive spatial interaction effect of low-carbon development on industry in cities. Therefore, in addition to the impact of local digital economy development on the low-carbon development of local industry, the spatial spillover effect of the low-carbon development of industry and digital economic development in the surrounding areas cannot be ignored.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation results of SDM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eDIDE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.815***(3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.195***(2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236***(3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eDIDE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.083**(1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076**(2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.068*(1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eEco\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.262***(2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297***(3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.345***(3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eEco\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.129*(1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.156*(1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.190**(1.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.137*(1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145*(1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.164*(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.089(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.109(1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eIS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.135***(-2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.176***(-2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.182***(-3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eIS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045(0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.095(0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.082(0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eInno\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364***(3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.376***(3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.332***(3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eInno\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.256**(2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.279***(2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.293***(3.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eFI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.234**(-2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.248***(-2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.224**(-2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eFI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.157**(1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.198***(2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.185**(2.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eER\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.557***(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.589***(4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576***(3.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eER\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.125**(-1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.112**(-1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.156**(-2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn\u003cem\u003eMK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.225***(3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263***(3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.471***(3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026middot;\\)\u003c/span\u003e\u003c/span\u003eIn\u003cem\u003eMK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123*(1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.135**(2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.187***(3.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.463***(4.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.478***(4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.492***(4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLog-L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e345.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e457.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e521.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2. Direct Effect, Indirect Effect, and Total Effect of Digital Economy\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNext, the total effect of the digital economy on the low-carbon development of industries was divided into direct effects (intra-regional spillover effects) and indirect effects (spatial spillover effects), in order to correctly interpret the impact of digital economy development. It can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that under different spatial weight matrices, the impact of the same explanatory variable on the low-carbon development of industries is basically the same. The direct effect of the digital economy on the low-carbon development of industry is significantly positive, indicating that the development of a local digital economy is an important driving force for the low-carbon development of industry. The spatial spillover effect of digital economic development on low-carbon industrial development is less than the direct effect, but it is also positive, indicating that developing the digital economy in surrounding areas also promotes the low-carbon development of local industries through spatial interaction. Therefore, hypothesis 1 and hypothesis 2 are verified.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation results of direct effects, indirect effects, and the total effect of the digital economy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWeight Matrix\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eType\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn\u003cem\u003eDIDE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIn\u003cem\u003eEco\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIn\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn\u003cem\u003eIS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIn\u003cem\u003eInno\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn\u003cem\u003eFI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIn\u003cem\u003eER\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIn\u003cem\u003eMK\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.196***(3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163**(2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161*(1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.188***(\u0026minus;\u0026thinsp;2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.388***(3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.345**(\u0026minus;\u0026thinsp;3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.436***(4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.311***(3.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114**(2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118*(1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078(1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.293***(3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.123*(1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.102*(\u0026minus;\u0026thinsp;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.145*(1.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.130**(2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.281*(1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234*(1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.110**(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.681***(3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.222**(\u0026minus;\u0026thinsp;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.334**(2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.456**(2.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164***(2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185***(3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183*(1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.162***(\u0026minus;\u0026thinsp;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.392***(4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.211**(\u0026minus;\u0026thinsp;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.478***(3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.256***(3.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.109*(1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122*(1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099(1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.256**(2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.168**(2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.123**(\u0026minus;\u0026thinsp;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.155**(2.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273**(2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.307**(2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.282*(1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.117**(\u0026minus;\u0026thinsp;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.648***(3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.043(1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.355**(2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.411***(3.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.211***(3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231***(3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155*(1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.049***(\u0026minus;\u0026thinsp;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312***(3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.288***(\u0026minus;\u0026thinsp;2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.485***(4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.401***(4.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.127***(3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217**(2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094(0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.189***(2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.199***(3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.147**(\u0026minus;\u0026thinsp;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.183***(3.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.338***(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448***(4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241*(1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.055**(\u0026minus;\u0026thinsp;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.501***(4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.089*(\u0026minus;\u0026thinsp;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.338**(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.584***(4.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Robustness Test\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo assess the resilience of the benchmark regression outcomes, principal component analysis was employed to gauge the city-level digital economic development index; subsequently, the independent variable index was substituted for robustness testing. Across three distinct spatial weight matrices, the influence of digital economic development observed for both low-carbon advancement within local industry and its equivalent in neighboring regions demonstrates a notably positive correlation. Furthermore, the estimated coefficient for the temporal lag associated with low-carbon development in industry exhibits a statistically significant positive trend. A decomposition of the total effect reveals both a direct spillover effect and a spatial spillover effect, which stem from digital economic development. Thus, the original findings from the benchmark regression remain largely unaffected by the alteration in the measurement approach used for the independent variables, thereby affirming the robustness of the spatial measurements.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.5. Heterogeneity Test\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.5.1. Geographical Location Heterogeneity Test\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo investigate the heterogeneity of geographical location, Chinese cities were divided into eight groups for regression. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The direct and indirect effects of digital economic development in the eastern and southern coastal areas are greater. The reason is that the digital economy of Shanghai, Hangzhou, and other cities in the Yangtze River Delta urban agglomeration is developed, and there are a large number of industrial enterprises in Suzhou, Ningbo, Wuxi, and other cities in the region. The developed digital economy and perfect manufacturing network produce spatial interaction, which enhances the spillover and spatial spillover effects in the region. Similarly, the digital economy and industry are the two wheels driving the economic development in Shenzhen and Guangzhou. Foshan and Dongguan also have developed production and manufacturing systems, so the intra-regional and spatial spillover effects of the digital economy highly promote the low-carbon development of industries. The direct promotional effect of the digital economy on the low-carbon development of industry in the northern coastal area is strong, but the spatial spillover effect is less than that in the eastern and southern coastal areas. The spatial spillover effect of urban digital economic development in southwest China is stronger than that in the middle reaches of the Yangtze River and the Yellow River. The reason is that the digital economies in Chengdu, Chongqing, and Guiyang are developing rapidly, and development of the big data industry has natural advantages. Northeast China and northwest China are two regions with low spatial spillover effects from the digital economy. As a result of the low level of digital economic development in these two regions, the proportion of modern industries is small, and the degree of regional integration is low.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation results of SDM in different regions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNortheast region\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135**(2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088*(1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138**(2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093*(1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.196**(2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.101*(1.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNorthern coastal areas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.226***(3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201**(2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271***(3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.174**(2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.254***(3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.145**(2.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEastern coastal areas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.371***(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.312***(3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322***(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245***(3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.423***(4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.371***(3.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSouthern coastal areas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.321***(4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289***(3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345***(4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289***(4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.371***(3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.321***(3.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle Yellow River region\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.155**(2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107**(2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138**(2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.111*(1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.189**(2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.116**(2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle Yangtze River region\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.189***(3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123**(2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174***(2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118**(2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.222***(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.134***(3.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSouthwest region\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.202***(3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.145**(2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198***(3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156**(2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.234***(3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.145***(3.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNorthwest region\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116**(2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095*(1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123***(2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092*(1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118**(2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.096*(1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.5.2. Population Size Heterogeneity Test\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eChina\u0026rsquo;s cities are divided into four categories: small cities (with a permanent resident population of less than 500,000 in urban areas), medium-sized cities (with a permanent resident population of 500,000\u0026ndash;1\u0026nbsp;million in urban areas), large cities (with a permanent resident population of 1 million\u0026ndash;5\u0026nbsp;million in urban areas), and cities above mega cities (with a permanent resident population of more than 5\u0026nbsp;million in urban areas). The estimated results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. As far as the direct effect is concerned, the digital economy\u0026rsquo;s role in promoting the low-carbon development of local industries is relatively significant, and there is no obvious change with changing urban population size. However, the spatial spillover effect is quite different. With the gradual increase in urban population size, the spatial spillover effect of digital economic development increases, which shows that the spatial spillover effect is in direct proportion to the urban population size. The larger the urban population, the more diversified are the figures required for low-carbon development of industrial enterprises, which increases the input demand for digital elements in surrounding cities, thus improving the spatial spillover effect of the digital economy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation results of SDM model for cities with different population sizes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSmall cities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.398***(4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177*(1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.336***(4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.158*(1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.369***(4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.143*(1.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMedium-sized cities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.293***(3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.274**(2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.242***(3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.207**(2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.232***(2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.205**(2.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLarge cities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.332***(3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.335***(3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.311***(3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296***(3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309***(3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.275***(3.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMega cities\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438***(4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.364***(4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.412***(4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322***(3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.456***(4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.333***(3.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.5.3. Heterogeneity Test of Market Integration Level\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe price index method is used to measure the degree of market integration of each city. The degree of market integration is divided into three levels: low, medium, and high. The estimated results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Under different degrees of market integration, the direct and spatial spillover effects of the digital economy that drives the low-carbon development of industry are heterogeneous. With improvements in the degree of market integration, the direct and spatial spillover effects increase. Enhancing market integration serves as a catalyst for the seamless dissemination of digital components. This, in turn, diminishes the operational costs incurred by enterprises that leverage sophisticated digital technologies beyond their immediate locales. Furthermore, such heightened integration optimally facilitates harnessing of the spillover effects intrinsic to the digital economy, thereby elevating the low-carbon development quotient within both local and international industries. The heterogeneity test shows that the spatial spillover effect of the digital economy is related to the geographical location, population size, market integration level, and other urban characteristics; thus, research hypothesis 3 is verified.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation results of SDM model for cities with different degrees of market integration.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLow level of market integration\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.129**(2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115*(1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134**(2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104*(1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.155**(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119*(1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMedium level of market integration\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.243***(3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118***(2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255***(3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.175***(2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.281***(3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.184***(2.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigh level of market integration\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.483***(4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.412***(3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.464***(4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.408***(3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.483***(4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.433***(3.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.6. Extended Analysis\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.6.1. Regulatory Role of Intangible Asset Equity Protection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDigital technology has the characteristics of virtuality, replication and sharing, and is prone to infringement in the process of market transaction and use. Therefore, intangible asset equity protection has an important impact on the development of the digital economy and its spatial spillover effect. In this section, the cross term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(lnDIDE\u0026middot;lnIP\\)\u003c/span\u003e\u003c/span\u003e and its spatial lag variable of the digital economy and intangible asset equity protection are included in the spatial econometric model (1) to examine the regulatory role of intangible asset equity protection in the low-carbon development of industries driven by the digital economy, specified as follows:\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1707401144.png\"\u003e\u003c/p\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(lnIP\\)\u003c/span\u003e\u003c/span\u003e represents the level of urban intangible asset equity protection; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{j=1}^{N}{w}_{ij}(lnDIG{I}_{jt}\u0026middot;lnIP)\\)\u003c/span\u003e\u003c/span\u003e is a spatial lag variable of the interaction between the digital economy and intangible asset equity protection, which is used to capture the impact of intangible asset equity protection on the spatial spillover effect of digital economic development. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the moderating effect of intangible asset equity protection on the low-carbon development of physical industries driven by the digital economy. The direct and indirect effects of are still significantly positive. The direct effect of focusing on the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}\\text{n}DIDE\\)\u003c/span\u003e\u003c/span\u003e\u0026middot;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}\\text{n}IP\\)\u003c/span\u003e\u003c/span\u003e is positive, indicating that intangible asset equity protection strengthens the intra-regional spillover effect of the digital economy driving low-carbon development of physical industries. The indirect effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}\\text{n}DIDE\\)\u003c/span\u003e\u003c/span\u003e\u0026middot;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}\\text{n}IP\\)\u003c/span\u003e\u003c/span\u003e is significantly positive, indi \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}\\text{n}DIDE\\)\u003c/span\u003e\u003c/span\u003e cating that in regions with higher levels of intangible asset equity protection, the level of digital economic development has a greater spatial spillover effect on the low-carbon development of physical industries in that region. Therefore, improving the level of intangible asset equity protection strengthens the spatial spillover effect of the digital economy on the low-carbon development of physical industries. Therefore, research hypothesis 4 is validated.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe moderating role of intangible asset equity protection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{d}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{d{eco}_{ij}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eIndirect effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(InDIDE\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185***(3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.155**(2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.194***(3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.174**(2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158***(2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132**(2.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(InDIDE\u0026middot;InIP\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.093***(3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087***(2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078***(3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068***(2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085***(3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062***(3.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***, **, and * represent significant values at the 1%, 5%, and 10% significance levels, respectively, with () indicating the estimated z-values of the parameters.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.6.2. Relationship between Spatial Spillover Effect and Geographical Distance\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to test the relationship between the spatial spillover effect of the digital economy and geographical distance, the spatial econometric model was estimated for every 20 km increase in the geographical distance between cities, and the estimated spatial spillover effect coefficients under different geographical distance thresholds were recorded. It can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that within a distance of 300 km, the spatial spillover effect of the digital economy is strong and the downward trend is slow, mainly because it is generally within 300 km of provincial administrative boundaries. Compared with outside the province, the mobility of digital elements among cities within the province is better, which better promotes the low-carbon development of physical industries in cities within the province through the spatial spillover effect. However, when the geographical distance exceeds 300 km, the spatial spillover effect of the digital economy suddenly decreases, and the decline rate is faster, indicating that beyond the provincial boundary, the barrier effect is stronger than the spatial spillover effect of the digital economy. When the geographical distance exceeds 520 km, the rate that the spatial spillover coefficient of the digital economy slows down declines, which may be due to a reduction in spatial units with spatial relationships among regions. Therefore, the spatial spillover effect of the digital economy attenuates with the increase in geographical distance, and there is a certain regional boundary to spatial spillover. Thus, research hypothesis 5 is verified.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Conclusions and Policy Recommendations","content":"\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThis study yields several key findings: (1) The digital economy propels the low-carbon evolution of local physical industries and stimulates low-carbon advancements in proximate industries through the spatial spillover effect. (2) The spatial spillover effect of the digital economy is contingent upon geographic location, population size, and the degree of market integration, among other urban attributes. Notably, municipalities in the southeast coastal regions, those with larger populations, and those boasting higher market integration levels exhibit more pronounced spatial spillover effects. (3) Intangible asset equity protection, as a crucial institutional underpinning for digital technology innovation, amplifies the spatial spillover effect of the digital economy, thereby steering the low-carbon development trajectory of physical industries. (4) The spatial spillover effect of the digital economy adheres to a discernible attenuation pattern. Specifically, the impact diminishes abruptly beyond a geographical distance of 300 km, which is attributed to provincial administrative boundaries. Notably, the rate of decline in the spatial spillover coefficient decelerates once the geographical distance surpasses 520 km.\u003c/p\u003e\n\u003cp\u003eThis study has the following policy implications: (1) Accelerate the establishment of a unified factor market, promote the efficient, reasonable, and safe flow of data elements in a wider range, and reduce the cost of using digital elements in physical industries. Maximize the expansion, superposition, and multiplication of digital technology, and improve the spatial spillover effect of the digital economy to enable the low-carbon development of physical industries. (2) Taking into account the geographical location, population size, market integration level, and other urban characteristics, we should take measures to improve the spatial spillover effect of the digital economy. Cities with relatively lagging digital economic development in northwest and northeast China should strengthen their cooperation with cities that have developed digital economies. Cities with small populations should increase the number of employed people in the digital economy, improve their level of market integration, and expand the spatial spillover effect dividend of the digital economy. (3) We should improve the data intangible asset equity protection system, encourage digital technology innovation, and strengthen the protection of the whole process of data production, circulation, and consumption. Safeguard the rights and interests of owners and users of digital elements, and strengthen the spatial spillover effect of the digital economy. (4) Promote the development of regional economic integration, delay the decay rate of the spatial spillover effect, and expand the spatial spillover radius of the digital economy.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e(1)\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e(2)\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e(3)\u003cstrong\u003eAnonymization of article files\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eCompletely anonymize all my article files.\u003c/p\u003e\n\u003cp\u003e(4)DATA AVAILABILITY STATEMENT\u0026nbsp;：Data sharing is not applicable to this research as no data were generated or analysed.\u003c/p\u003e\n\u003cp\u003e(5)\u003cstrong\u003eCOMPETING INTERESTS/CONFLICTS STATEMENT\u003c/strong\u003e：The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen,\u003cem\u003e \u003c/em\u003eM.G.;\u003cem\u003e \u003c/em\u003eZhou,\u003cem\u003e \u003c/em\u003eY.R.\u003cem\u003e \u003c/em\u003eThe\u003cem\u003e \u003c/em\u003eimpact\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003edigitalization\u003cem\u003e \u003c/em\u003eon\u003cem\u003e \u003c/em\u003elabor\u003cem\u003e \u003c/em\u003ecosts\u003cem\u003e \u003c/em\u003ein\u003cem\u003e \u003c/em\u003eenterprises.\u003cem\u003e Popul. 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Environ. \u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e,\u003cem\u003e 28\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e117\u0026ndash;127.\u003c/li\u003e\n\u003cli\u003eGuo,\u003cem\u003e \u003c/em\u003eW.;\u003cem\u003e \u003c/em\u003eSi,\u003cem\u003e \u003c/em\u003eM.H.\u003cem\u003e \u003c/em\u003eHas\u003cem\u003e \u003c/em\u003efinancial\u003cem\u003e \u003c/em\u003eagglomeration\u003cem\u003e \u003c/em\u003eimproved\u003cem\u003e \u003c/em\u003ethe\u003cem\u003e \u003c/em\u003elow-carbon\u003cem\u003e \u003c/em\u003etotal\u003cem\u003e \u003c/em\u003efactor\u003cem\u003e \u003c/em\u003eproductivity\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003ephysical\u003cem\u003e \u003c/em\u003eindustries.\u003cem\u003e Mod. Econ. 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Res. \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e,\u003cem\u003e 8\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e57\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eHeberer,\u003cem\u003e \u003c/em\u003eT.;\u003cem\u003e \u003c/em\u003eSenz,\u003cem\u003e \u003c/em\u003eA.\u003cem\u003e \u003c/em\u003eStreamlining\u003cem\u003e \u003c/em\u003elocal\u003cem\u003e \u003c/em\u003ebehavior\u003cem\u003e \u003c/em\u003ethrough\u003cem\u003e \u003c/em\u003ecommunication,\u003cem\u003e \u003c/em\u003eincentives\u003cem\u003e \u003c/em\u003eand\u003cem\u003e \u003c/em\u003econtrol:\u003cem\u003e \u003c/em\u003eA\u003cem\u003e \u003c/em\u003ecase\u003cem\u003e \u003c/em\u003estudy\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003elocal\u003cem\u003e \u003c/em\u003eenvironmental\u003cem\u003e \u003c/em\u003epolicies\u003cem\u003e \u003c/em\u003ein\u003cem\u003e \u003c/em\u003eChina.\u003cem\u003e J. Curr. Chin. 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Stud. \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e,\u003cem\u003e 30\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e2446\u0026ndash;2477.\u003c/li\u003e\n\u003cli\u003eWen,\u003cem\u003e \u003c/em\u003eJ.;\u003cem\u003e \u003c/em\u003eYan,\u003cem\u003e \u003c/em\u003eZ.J.;\u003cem\u003e \u003c/em\u003eCheng,\u003cem\u003e \u003c/em\u003eY.;\u003cem\u003e \u003c/em\u003eThe\u003cem\u003e \u003c/em\u003eenhancement\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003edigital\u003cem\u003e \u003c/em\u003eeconomy\u003cem\u003e \u003c/em\u003eand\u003cem\u003e \u003c/em\u003eregional\u003cem\u003e \u003c/em\u003einnovation\u003cem\u003e \u003c/em\u003ecapabilities.\u003cem\u003e Explor. Econ. Issues \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e,\u003cem\u003e 11\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e112\u0026ndash;124.\u003c/li\u003e\n\u003cli\u003eQi,\u003cem\u003e \u003c/em\u003eY.D.;\u003cem\u003e \u003c/em\u003eDu,\u003cem\u003e \u003c/em\u003eB.;\u003cem\u003e \u003c/em\u003eYe,\u003cem\u003e \u003c/em\u003eS.\u003cem\u003e \u003c/em\u003eCollaborative\u003cem\u003e \u003c/em\u003eDriving\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003eIntangible\u003cem\u003e \u003c/em\u003eAsset\u003cem\u003e \u003c/em\u003eEquity\u003cem\u003e \u003c/em\u003eand\u003cem\u003e \u003c/em\u003eTechnical\u003cem\u003e \u003c/em\u003eStandards\u003cem\u003e \u003c/em\u003efor\u003cem\u003e \u003c/em\u003eDigital\u003cem\u003e \u003c/em\u003eIndustry\u003cem\u003e \u003c/em\u003eInnovation:\u003cem\u003e \u003c/em\u003eMechanisms\u003cem\u003e \u003c/em\u003eand\u003cem\u003e \u003c/em\u003ePaths.\u003cem\u003e Ind. 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World \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e,\u003cem\u003e 36\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e65\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eHuang,\u003cem\u003e \u003c/em\u003eQ.H.;\u003cem\u003e \u003c/em\u003eYu,\u003cem\u003e \u003c/em\u003eY.Z.;\u003cem\u003e \u003c/em\u003eZhang,\u003cem\u003e \u003c/em\u003eS.L.\u003cem\u003e \u003c/em\u003eThe\u003cem\u003e \u003c/em\u003eDevelopment\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003ethe\u003cem\u003e \u003c/em\u003eInternet\u003cem\u003e \u003c/em\u003eand\u003cem\u003e \u003c/em\u003ethe\u003cem\u003e \u003c/em\u003eProductivity\u003cem\u003e \u003c/em\u003eEnhancement\u003cem\u003e \u003c/em\u003eof\u003cem\u003e \u003c/em\u003ethe\u003cem\u003e \u003c/em\u003ePhysical\u003cem\u003e \u003c/em\u003eIndustry:\u003cem\u003e \u003c/em\u003eIntrinsic\u003cem\u003e \u003c/em\u003eMechanisms\u003cem\u003e \u003c/em\u003eand\u003cem\u003e \u003c/em\u003eChina\u0026rsquo;s\u003cem\u003e \u003c/em\u003eExperience.\u003cem\u003e China Ind. Econ. \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e,\u003cem\u003e 8\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e5\u0026ndash;23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital economy, space overflow, regional industries, low-carbon total factor productivity, degree of marketization, population size, intangible asset equity","lastPublishedDoi":"10.21203/rs.3.rs-3844460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3844460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUtilizing data that encompass municipalities and regions within China at the prefectural level and beyond, spanning the period from 2012 to 2021, this study employed the spatial Durbin model to assess the spatial spillover impact of the digital economy in propelling low-carbon advancement within regional physical industries. This investigation elucidates the spatial spillover mechanism that underlies the low-carbon evolution of regional industries catalyzed by the digital economy and offers nuanced insights. The findings delineate the following observations: (1) The digital economy propels the low-carbon progression of indigenous physical industries and stimulates the low-carbon development of proximate regions\u0026rsquo; physical industries through discernible spatial spillover effects. (2) The spatial spillover ramifications of the digital economy manifest a substantive correlation with urban attributes, including geographical positioning, population size, and market integration levels. Notably, municipalities situated in the southeast coastal region, those characterized by larger population sizes, and those exhibiting heightened market integration levels show greater spatial spillover effects attributable to the digital economy. (3) The safeguarding of intangible asset equity property, a pivotal institutional underpinning for fostering digital economic development, amplifies the spatial spillover effect of the digital economy in propelling low-carbon development within regional industries. (4) As geographical and spatial distances expand, the spatial spillover effect of the digital economy attenuates, indicating a diminishing influence with increasing spatial separation.\u003c/p\u003e","manuscriptTitle":"The Spatial Effect of Low-Carbon Development of Regional Industries Driven by the Digital Economy: Evidence from Chinese Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-08 14:13:11","doi":"10.21203/rs.3.rs-3844460/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d762c069-39b4-4cd8-9c76-40b425606502","owner":[],"postedDate":"February 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-31T00:28:46+00:00","versionOfRecord":{"articleIdentity":"rs-3844460","link":"https://doi.org/10.1057/s41599-024-03215-x","journal":{"identity":"humanities-and-social-sciences-communications","isVorOnly":false,"title":"Humanities and Social Sciences Communications"},"publishedOn":"2024-05-30 00:28:46","publishedOnDateReadable":"May 30th, 2024"},"versionCreatedAt":"2024-02-08 14:13:11","video":"","vorDoi":"10.1057/s41599-024-03215-x","vorDoiUrl":"https://doi.org/10.1057/s41599-024-03215-x","workflowStages":[]},"version":"v1","identity":"rs-3844460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3844460","identity":"rs-3844460","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}