The impact of “Digital–Green” integration on urban green transformation performance

As nations increasingly align digital and sustainable development goals, this study empirically investigates the impact of Digital–Green Integration (DGI) on Urban Green Transformation Performance (UGTP) using panel data from 283 Chinese cities (2011–2023). Based on a "government–market–society" framework and spatial econometric models, the findings reveal that DGI currently exerts a significant negative impact on UGTP, although this adverse effect weakens over the long term. Mechanism analysis highlights a complex interaction: while economic agglomeration exacerbates the negative impact, factors such as environmental protection governance, financial development, and the public service environment effectively mitigate it. Notably, public environmental consciousness shifts from intensifying adverse effects in the short term to suppressing them over time. Spatially, DGI exhibits a "Central rise, East-West divergence" pattern, characterized by negative spillovers within a 760 km threshold and positive spillovers between 760–900 km. By elucidating these transmission mechanisms and defining spatial spillover boundaries, this study enriches the theoretical understanding of DGI and offers critical insights for designing region-specific policies to overcome transformation bottlenecks.

1 Introduction

Given the mounting challenges posed by climate change, resource scarcity, and environmental degradation worldwide, the necessity of decoupled economic growth from environmental costs has propelled Urban Green Transformation Performance (UGTP) to the forefront of global sustainability strategies. UGTP is the comprehensive manifestation of a city’s achievement in green and sustainable development amidst its economic growth. This performance is primarily captured by Carbon Emission Efficiency (CEE). CEE represents the ability to maximize economic benefits while minimizing carbon emission costs, making it the core quantitative indicator of UGTP. Therefore, this study adopts CEE to specifically measure UGTP. The central premise of Endogenous Growth Theory is that the non-rivalrous and partially excludable nature of technological innovation generates knowledge spillovers, which in turn drive increasing returns to scale and enable sustained growth. Building on this theoretical foundation, Digital–Green Integration (DGI), through these same knowledge spillovers, endogenously drive enhancements in carbon emission efficiency CEE, propelling the urban transition from ‘exogenous dependence’ to an ‘innovation-driven’ paradigm. While prior research affirms the distinct contributions of GT and DT to economic efficiency and environmental quality, a notable lacuna persists in examining their synergistic, policy-induced effects (Ren et al., 2025). Although studies have probed DT’s role in urban transformation or the alignment of greening and digitalization, few have systematically appraised the influence of DGI policies on UGTP.

In August 2024, led by the Cyberspace Administration of China, ten central departments jointly issued the Implementation Guidelines for Coordinated Digital-Green Transformation Development. These guidelines articulate four core principles for advancing digital-green synergies: innovation-driven growth, collaborative advancement, open collaboration, and practical implementation. They further emphasize three foundational innovations: digital-green capabilities, integrated DT-GT technological architectures, and cohesive DT-GT industrial ecosystems. Overall, these provisions delineate a clear pathway for enhancing UGTP through DT-GT synergies (Fan et al., 2024). To address this scholarly gap, this study conducts a comprehensive empirical investigation using panel data from 283 Chinese prefecture-level cities spanning 2011–2023. The core objective of the study is to assess the efficacy of DGI in advancing UGTP, while elucidating its transmission mechanisms and effect heterogeneities. This study offers following contributions. On the one hand, it constructs a city-level DGI framework, conceptualizing DGI synergies as a dynamic system rather than isolated or static linkages. On the other hand, according to endogenous growth theory, CEE endogenously drives the decoupling of economic growth from carbon emissions via knowledge spillovers, thereby comprehensively capturing the dynamic sustainability of UGTP. The innovations of this study are threefold: Firstly, it proposes a theoretical analysis of the coupling coordination between DT and GT to form DGI; Secondly it examines DGI’s transmission mechanisms on UGTP from the “government–market–society” triadic perspective; Thirdly, it analyzes the DGI–UGTP relationship across short-term (2011–2016), long-term (2017–2023), and overall (2011–2023) periods. By representing UGTP through CEE, we provide empirical validation for the theoretical channels linking DGI to UGTP. Second, employing baseline regressions, the study analyzes the direct impact of DGI on UGTP across short-term, long-term, and overall periods, and leverages spatial econometric models to explore the spatial dependence and spillover effects of DGI on UGTP. Thirdly, this study provides a quintessential Chinese perspective on sustainable development and governance collaboration, holding broader implications for global urban green transformations (UGT).

2 Literature review and research hypotheses

2.1 Review of the literature

Digitalization transcends next-generation ICTs, encompassing digital industrialization and industrial digitalization (Liu et al., 2023). This reshapes production, allocation, and governance, enabling green transitions (Loewen, 2022). GT replace high-carbon modes, boosting efficiency and mitigating degradation (Alkhereibi et al., 2025; Melichercík and Michalíková, 2025). UGTP measurement has evolved from GTFP and CEE to multidimensional frameworks integrating economic, social, and environmental dimensions (Pan et al., 2024). DGI—the reciprocal DT-GT co-innovation—drives UGTP via virtuous loops, optimizing resources, energy, and governance (Du and Zhang, 2025). While affirming DT or GT roles in efficiency and quality, prior studies overlook DGI’s policy-induced synergies, mechanisms for sustained CEE gains, and city-level spillovers on UGTP

Existing scholarship has predominantly examined DGI’s implications for urban and regional development, emphasizing digital infrastructure, green industrial configurations, and policy frameworks as catalysts, with technological innovation, resource optimization, and policy coordination serving as key conduits to UGTP (Fang et al., 2025). Information and communication technologies (ICTs) propel GT dissemination through green innovation, industrial reconfiguration, and inter-regional knowledge spillovers, enhancing DGI’s adaptability amid urban heterogeneity (Wang Z. et al., 2024). The coordinated evolution of the digital economy and GT elevates low-carbon transition efficacy via energy efficiency, behavioral guidance, and economic incentives, highlighting interactions among financial mechanisms, public awareness, and technological progress (Han et al., 2025). Collectively, these studies delineate the foundational levers by which DGI drives UGTP, providing a multifaceted theoretical scaffold for urban greening and evidence-based policymaking. Contemporary research increasingly focuses on DGI’s imprint on urban sustainability and regional equilibrium, elucidating DT’s role in green innovation, low-carbon industrialization, and resource-energy optimization (Wang and Hao, 2024; Yang et al., 2025). Quantitative studies confirm that smart infrastructures, data analytics, and IoT platforms significantly amplify environmental efficacy and carbon mitigation, accompanied by innovative governance paradigms for UGTP (Alshuaibi et al., 2024; Lartey and Law, 2025). Spatially oriented analyses spanning industrial agglomerations, metropolitan areas, and urban–rural linkages identify regional disparities, institutional robustness, and technological absorptivity as key moderators of DGI’s potency (Hu et al., 2025; Shen et al., 2025). Although prior studies affirm the distinct roles of DT and GT, research on DGI’s direct impact on UGTP and its spatial spillover effects remains relatively scarce.

The innovations of this study are primarily manifested in the following four aspects. First, it constructs a novel city-level DGI framework, embedding the “government–market–society” triad, and empirically validating theoretical channels. Second, grounded in the 2024 Guidelines, it offers timely, actionable policy recommendations, particularly for resource integration and heterogeneity management. Third, it uniquely integrates baseline regressions—distinguishing short-term (2011–2016), long-term (2017–2023), and overall periods (2011–2023)—with spatial econometric models to reveal spatial dependence and spillover effects. Fourth, its core theoretical contribution lies in empirically elucidating the specific mediating pathways of the government–market–society framework. This extends existing urban green transformation theory and provides valuable Chinese insights for global sustainable development and governance.

2.2 Theoretical hypotheses

2.2.1 The impact of DGI on UGTP

According to innovation systems theory, Digital–Green Integration (DGI) exerts a significant and complex influence on Urban Green Transformation Performance (UGTP). This dynamic, multi-dimensional process channels governmental regulation, market dynamics, and societal foundations to jointly promote low-carbon progress and resource optimization, thereby affecting UGTP efficiency. DGI aims not only to enhance Carbon Emission Efficiency (CEE), promote resource agglomeration, and drive green industrial development but also to provide a systematic technological and market infrastructure for urban sustainability. However, from the dynamic perspective of endogenous growth theory, the short-term impact of DGI on UGTP often appears negative. This primarily stems from substantial initial investments, infrastructure lags, and technological adaptation frictions. These frictions amplify transformation costs, leading to resource misallocation and policy implementation uncertainty, thus suppressing the immediate enhancement of UGTP (Xia et al., 2025). In the long term, this negative impact may persist but weaken.

This study posits that DGI’s net effect is transmitted to UGTP through three core channels: the “Government–Market–Society” triad (as illustrated in Figure 1):

Figure 1

Figure 1. Framework of DGI empowering UGTP.

The government channel. DGI Impacts UGTP via Policy Guidance and Regulation As the macro-regulator and strategic designer of DGI, the government’s actions directly determine the intensity and direction of DGI’s influence on UGTP. The government establishes the “rules of the game” by formulating policies, providing systemic oversight, and ensuring regulatory standardization, thereby coordinating market and societal interactions (Zuo et al., 2024). The implementation effectiveness of DGI highly depends on the government’s ability to correct market failures—for instance, by utilizing exogenous incentives like fiscal subsidies and tax benefits to lower DGI adoption thresholds, optimize energy structures, and accelerate the market penetration of GT (Yang et al., 2025). Consequently, the government’s top-level design and execution capability are crucial for DGI to successfully empower UGTP.

The market channel. DGI Impacts UGTP by Reshaping Market Mechanisms DGI provides critical digital infrastructures to the market. The market utilizes these tools to execute DGI’s mandates through price mechanisms and competition, optimizing production processes and energy structures, and driving GT innovation to enhance CEE. DGI leverages the market channel to attract resources from both the digital and green domains, generating supply chain synergies and network effects, which should promote the diffusion of sustainable energy and virtuous cycles of low-carbon investment, supporting total factor productivity and the maturation of green industries (Ma et al., 2024).

The society channel. DGI Impacts UGTP by Activating Societal Foundations DGI, through its digital aspects, enhances public environmental consciousness and promotes the diffusion of digital skills. This, in turn, cultivates an intrinsic market pull for low-carbon products and forms human capital clusters by attracting DT and GT experts. Thus, DGI aims to utilize societal forces to drive technological leaps and collaborative innovation, injecting grassroots momentum and normative endorsement into green transformation (i.e., the enhancement of UGTP (Liu et al., 2024; Ren et al., 2024).

Hypothesis 1

Hypothesis 1. DGI has an effective impact on UGTP in the short term.

Hypothesis 2

Hypothesis 2. DGI has an effective impact on UGTP in the long term.

Hypothesis 3

Hypothesis 3. DGI has an effective impact on UGTP.

2.2.2 The transmission mechanisms of DGI influencing UGTP

Within the “government–market–society” triadic framework, DGI not only enhances CEE, catalyzes resource agglomeration, and accelerates green industrial maturation but also constructs institutional scaffolds encompassing policy directives, market incentives, and societal mobilization. Governments, as architects of regulatory ecosystems, formulate policies to steer resource flows toward ecologically sustainable sectors and nurture low-carbon paradigms (Liu and Zhou, 2022). Markets, as innovation vanguards, direct capital toward DGI, fostering technological diffusion and adoption (Venkatesh et al., 2007). Society, as the normative bedrock, amplifies demand through eco-conscious behaviors and oversight, providing legitimacy and iterative feedback to optimize governance (Hou et al., 2024). This interwoven triad, as illustrated in Figure 2, delineates DGI’s conduits to UGTP via governmental regulatory guidance, market resource allocation, and societal participation in green governance.

Figure 2

Figure 2. Transmission mechanism of DGI on UGTP performance.

First, governmental regulatory guidance constitutes a key mediating pathway through which DGI promotes UGTP. As macro-regulators, governments correct market failures by establishing stringent emission standards, carbon pricing mechanisms, and incentive structures. In this transmission chain, DGI does not impact UGTP directly; rather, it exerts an indirect influence by empowering governmental regulatory tools. DGI-enabled real-time data analytics and automated monitoring significantly enhance policy precision, allowing the government to dynamically adjust incentives and reduce barriers to green technology adoption (Wang et al., 2025). Thus, by strengthening governmental regulatory guidance as a mediating mechanism, DGI ensures that market behaviors align with societal imperatives, thereby robustly promoting UGTP.

Second, market resource allocation serves as a core transmission mechanism for DGI’s impact on UGTP. Markets achieve optimal resource distribution through price signals and competitive mechanisms (Ling et al., 2022). On one hand, DGI significantly mitigates information asymmetries, enabling markets to more accurately identify and price green innovations. This directs capital toward the most efficient low-carbon sectors. On the other hand, the network effects spawned by DGI attract capital and talent, accelerating the commercialization of green technologies. By optimizing market resource allocation as a mediator, DGI translates regulatory signals and consumer preferences into efficient green investments and industrial upgrading, ultimately driving UGTP growth through enhanced economic efficiency.

Finally, societal participation in green governance acts as an indispensable normative mediating bridge. Societal participation fosters consensus and agency, providing both legitimacy and grassroots momentum for the green transition. DGI facilitates public collective action and feedback through democratizing digital platforms, while also integrating sustainability principles into daily public services (Kou et al., 2024). This DGI-enabled societal engagement stimulates endogenous demand for green consumption and establishes an iterative feedback loop for policy and markets. Consequently, by activating societal participation as a mediating channel, DGI not only enhances public environmental awareness but also embeds low-carbon norms within the urban fabric. This provides a profound social foundation and sustained momentum for the continuous improvement of UGTP.

Hypothesis 4

Hypothesis 4. DGI can effectively drive UGTP through the three-dimensional mechanism of government regulatory guidance, market resource allocation, and societal participation in green governance.

Figure 3

Figure 3. Research framework.

3 Research design

3.1 Model specification

3.1.1 SBM model

The Slacks-Based Measure (SBM) model, a non-radial approach in Data Envelopment Analysis (DEA), serves as a key tool for evaluating carbon emission efficiency—a core indicator of emission reduction and resource management. Assuming constant returns to scale, the SBM non-radial method seamlessly integrates both desirable and undesirable outputs. In the specific context of this study, the SBM model is explicitly employed to calculate carbon emission efficiency, which is adopted as the proxy indicator for the dependent variable—UGTP. By accounting for slacks in undesirable outputs, this accurate measurement of carbon emission efficiency provides the empirical basis needed to rigorously test the driving effect of DGI. This methodology elucidates inter-city efficiency disparities and identifies targeted improvement pathways under the DGI paradigm.

$\mathit{min}\mathrm{\rho }=\frac{1-\frac{1}{m}{\displaystyle {\sum }_{i=1}^m}{s}_i^{-}/x_{i0}}{1+\frac{1}{s_1+s_2}\left({\displaystyle {\sum }_{r=1}^{s_1}}{s}_r^g/y_{r0}^g+{\displaystyle {\sum }_{r=1}^{s_2}}{s}_r^b/z_{r0}^b\right)}$

$s.t.X\lambda +s_i^{-}=x_k,Y^y\lambda -s_r^g=y_0^g,z^b\lambda +s_r^b=z_0^b,\lambda ,s_i^{-},s_r^g,s_r^b\ge 0(1)$

In Equation (1), $\rho$ represents the mean adjustable shares of real inputs and outputs of a decision-making unit (DMU) relative to the production frontier. Here, $m$, $s_1$ and $s_2$ indicate the quantities of inputs, beneficial outputs, and adverse outputs; $s_i^{-},s_r^g$ and $s_r^b$ correspond to the slack values of inputs, favorable outputs, and unfavorable outputs, respectively; $x$, $y^g$ and $z^b$ are the observed levels of inputs, favorable outputs, and unfavorable outputs; $X$, $Y^g$ and $Z^b$ are the corresponding vector matrices, and $\lambda$ denotes the weight vector. Based on this formulation, a directional distance function is introduced to further measure efficiency, as shown in Equation (2). This approach provides a more precise assessment of DMU efficiency under DGI-facilitated UGTP.

$\begin{array}{c}\overrightarrow{D_0}\left(x,y,b;g_y,-z_b\right)=\mathit{max}\beta \\ s.t.\left\{\begin{array}{c}{\displaystyle \sum _{k=1}^K}{\lambda }_k{y}_{kr}\ge y_{kr}+\beta g_{yj},j=1,2,\cdots ,s_2\\ {\displaystyle \sum _{k=1}^K}{\lambda }_k{b}_{kr}\ge b_{kr}-\beta z_{br},j=1,2,\cdots ,s_1\hfill \\ {\displaystyle \sum _{k=1}^K}{\lambda }_k{x}_{ki}\le x_{ki},i=1,2,\cdots ,n\hfill \end{array}\right.\end{array}(2)$

Finally, carbon emission efficiency is calculated as shown in Equation (3).

$gtf{p}_t^{t+1}={\left[\frac{1+\overrightarrow{D_0^t}\left(x^t,y^t,b^t;g^t\right)}{1+\overrightarrow{D_0^t}\left(x^{t+1},y^{t+1},b^{t+1};g^{t+1}\right)}\times \frac{1+\overrightarrow{D_0^{t+1}}\left(x^t,y^t,b^t;g^t\right)}{1+\overrightarrow{D_0^{t+1}}\left(x^{t+1},y^{t+1},b^{t+1};g^{t+1}\right)}\right]}^{1/2}(3)$

3.1.2 Spatial econometric model

Spatial econometric models are specialized frameworks for examining spatial dependence and correlation within geospatial data, primarily encompassing the Spatial Lag Model (SLM), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). These models effectively identify interdependencies and elucidate the spatial transmission pathways through which various factors influence outcomes. By employing multiple spatial weight matrices to describe multifaceted spatial linkages, this study achieves a nuanced delineation of the spatial effects of DGI on UGTP. The model formulation is presented in Equation (4).

${CEE}_{it}={\beta }_0+\rho {\displaystyle \sum _{j=1}^n}{W}_{ij}{CEE}_{jt}+\theta {\mathit{DGI}}_{it}+\delta {\displaystyle \sum _{j=1}^n}{W}_{ij}{\mathit{DGI}}_{jt}+{\mu }_i+v_t+{\epsilon }_{it}(4)$

In Equation (4), $i$ denotes the city under study, $j$ represents cities that are spatially associated with city$i$, $t$ indicates the year, $n$ denotes the total number of cities.$y_{it}$ corresponds to the carbon emission efficiency of city $i$ in year $t$; $x$ signifies the variables influencing UGTP outcomes; $y_{jt}$ is the dependent variable measuring the DGI coupling coordination degree;$\theta$ represents the estimated coefficients of the influencing factors; $\rho$ and $\delta$ are spatial effect coefficients, and $W_{ij}$ is the spatial weight matrix. ${\mu }_i$ and $v_t$ represent city−specific and time−specific effects, where ${\epsilon }_{it}$ represents the stochastic error term. This model effectively captures the impact of DGI synergies on UGTP performance by accounting for both spatial dependence and temporal effects.

1. Spatial Adjacency Matrix (SAM). The spatial weight matrix is defined according to the geographic closeness of the sampled cities. Let $d_{ij}$ denote the distance between city $i$ and city $j;$ the SAM is defined as follows (Equation 5).

$w_{ij}=\left\{\begin{array}{c}1,\text{if\hspace{0.17em}}{d}_{ij}<d\\ 0,\text{if\hspace{0.17em}}{d}_{ij}⩾d\end{array}\right.(5)$

2. Economic-Geographic Weight Matrix (EGWM). Let ${\overline{Q}}_{i}and{\overline{Q}}_j$ denote the actual economic development levels of cities$i$ and $j$, ${\overline{D}}_i$ and ${\overline{D}}_j$ their geographic coordinates. The EGWM is defined as follows (Equation 6).

$w_{ij}=\left\{\begin{array}{c}{\overline{Q}}_i\times {\overline{Q}}_j/{\left|{\overline{D}}_i-{\overline{D}}_j\right|}^2,i\ne j\\ 0,i=j\hfill \end{array}\right.(6)$

3. Green Finance-Geographic Weight Matrix (GFGWM). Let ${\overline{Q}}_i$ and ${\overline{Q}}_j$ denote the green finance indices of cities $i$ and $j$. The green finance index is calculated via the entropy method, including indicators such as green credit, investment, insurance, bonds, policy support, funds and equity. Denote $d_{ij}$ as the geographic distance between cities$i$ and $j$; the corresponding GFGWM is defined as follows (Equation 7).

$w_{ij}=\left\{\begin{array}{c}{\overline{Q}}_i\times {\overline{Q}}_j/d_{ij}^2,i\ne j\\ 0,i=j\hfill \end{array}\right.(7)$

3.1.3 The entropy method

The entropy method is an objective weighting approach that measures indicator influence via information entropy: lower entropy signifies greater informational value and thus a higher weight. This method avoids subjective bias, enhancing the assessment’s scientific rigor. In this study, it is employed to construct composite indices for urban digital and green technologies to quantitatively assess the impact of DGI on UGTP. By relying on empirical data, it reduces bias and indicator redundancy, identifying key driving factors.

3.1.4 Coupling coordination degree model

According to endogenous growth theory, DGI underscores knowledge spillovers and scale returns, wherein DT accelerates the diffusion of GT, GT enhances DT’s sustainability, and nonlinear positive feedbacks are generated to propel UGTP (Du et al., 2022). The Coupling Coordination Degree (CCD) model constitutes a quantitative paradigm for assessing intersystem coordination among multiple entities or subsystems. Widely applied in ecological, social, and economic research—particularly for evaluating sustainable development and regional equilibrium—the model dissects synergistic dynamics, excelling in holistically depicting DGI’s dynamic balance by directly and objectively proxying integration potential through thresholds. DGI leverages coordination mechanisms to achieve dynamic adaptation and ensure systemic stability, facilitating the urban shift from high-carbon dependency toward low-carbon innovation. The coupling degree (C) quantifies subsystem interdependence and interaction intensity, with higher values indicating enhanced connectivity. Conversely, the coordination degree (D) measures developmental equilibrium and harmony, where higher values signal superior systemic efficacy, and lower values portend disequilibrium, inefficiency, or constraints. The CCD between DT and GT systems is computed as follows:

$C=2\sqrt{\left(\text{DEL}·\text{GDL}\right)/{\left(\text{DEL}+\text{GDL}\right)}^2}(8)$

$T=v\text{DEL}+\gamma \text{GDL}(9)$

$D=\sqrt{C·T}(10)$

In Equations 8–10, DEL and GDL represent the comprehensive indices of DT and GT, and T denotes the integrated synergistic development index. The weights $v,\gamma$ demonstrate the respective influence of the two systems. In this study, DT and GT are considered equally important, hence,$v=\gamma$ = 0.5. Following prior research, the coupling coordination degree D is categorized into ten levels with a 0.1 increment. Drawing on the classification scheme of Xiao et al., the coupling coordination degree in this study is likewise divided into 11 grades (Xiao et al., 2025), as illustrated in Table 1.

Table 1

Table 1. Classification of CCD.

Furthermore, the relative development degree E captures the balance between the two systems, which is calculated as shown in Equation 11.

$E=\text{DEL}/\text{GDL}(11)$

When $E⩽0.8$, DT development lags behind, constraining GT; when $0.8<E<1.2$, DT and GT develop synchronously; and when $E⩾1.2$, GT development lags behind, limiting DT. This metric provides a quantitative basis for assessing the synergistic development of DGI in UGTP.

3.2 Indicator system construction

3.2.1 Dependent variable

CEE is a core indicator for evaluating the performance of UGTP. It not only reflects a city’s effectiveness in reducing environmental pollution and carbon emissions but also captures the coordination aligning economic growth with environmental conservation. Therefore, CEE plays a pivotal role in assessing UGTP performance. Following previous studies, this research calculates CEE using an input-output approach, integrating inputs such as capital, labor, energy, resources, transport capacity, environment, and land with both desirable and undesirable outputs (Zhang et al., 2024). And road density is calculated by dividing the total highway length by the area of the administrative division. Desirable outputs represent economic growth achievements, while undesirable outputs include carbon emissions and environmental pollution. CEE is ultimately computed via a SBM model to quantitatively capture the overall performance of UGTP. As presented in Table 2.

Table 2

Table 2. Dependent variable.

3.2.2 Explanatory variable

The primary explanatory variable in this study is DGI. Drawing on prior scholarship (Kraus et al., 2021; Guo et al., 2025; Shan et al., 2025; Wan et al., 2025), the assessment of DT encompasses three dimensions: digital infrastructure, digital applications, and the digital industry. Digital infrastructure gauges broadband and mobile internet penetration via the number of internet users and mobile phone subscribers (Table 3). Digital applications are quantified using the Digital Inclusive Finance Index. The digital industry captures the information and telecommunications sectors, measured by employment in information technology, computer, and software fields, alongside per capita telecommunications service volume. GT are operationalized across four dimensions: ecological innovation investment, green production, green lifestyle, and green ecology. Ecological innovation investment is indexed by environmental research and development (R&D) expenditures—encompassing government environmental spending and green R&D as shares of GDP. Green production is evaluated through green patent counts, pollution emission intensity, and resource utilization intensity. Green lifestyle integrates behavioral and residential facets: behavior via per capita residential electricity and water consumption and the number of sanitation vehicles; environment via the residential-to-green land ratio and sewage pipeline length. Green ecology draws on resource endowment metrics, the safe treatment rate of residential waste, and the centralized sewage treatment rate. This composite construction of DGI encapsulates the synergistic fusion of DT and GT, furnishing a robust metric for its influence on UGTP.

Table 3

Table 3. Explanatory variables.

3.2.3 Control variables

To bolster analytical robustness, this study incorporates several control variables drawn from extant literature (Bianchini et al., 2023; Wang Y. et al., 2024; Ye et al., 2024). Infrastructure, which facilitates UGTP through DGI, is proxied by the natural logarithm of per capita urban road area. Government expenditure is measured as the ratio of total budgetary spending to GDP, reflecting support for technological innovation and policy execution. Population density, closely related to urbanization and resource demands, is measured as the natural logarithm of the year-end population divided by the administrative area. Furthermore, Openness, captured by total foreign trade as a share of GDP, is used to control for the influence of global linkages on DGI formation. Finally, Environmental regulation intensity is represented by the green coverage rate of urban built-up areas to account for inter-city policy variances, while Technological prowess, quantified by the natural logarithm of employment in scientific research and technical services, controls for innovation capacity and its bearing on DGI synergies. See Table 4 for details.

Table 4

Table 4. Control variables.

3.2.4 Mechanism variables

Drawing on the theoretical framework, this study operationalizes mechanism variables across three dimensions—government regulation, market potential, and social participation—to delineate the multi-channel mechanisms through which DGI impacts UGTP. At the government regulation level, environmental pollution control is indexed by the number of administrative penalties and related legal cases in prefecture-level cities; higher values denote intensified governance. Pollution management intensity is gauged by text-mining local government work reports and computing the frequency of environment-related terms. For market potential level, green structural upgrading is proxied by the ratio of tertiary-to secondary-sector value added. Economic agglomeration is measured by the density of secondary- and tertiary-sector employment relative to the urban built-up area, encapsulating green industry distribution and economic clustering. At the social participation level, public environmental awareness is captured by the Baidu search index for terms where a higher index reflects diminished tolerance for pollution. The public service environment is synthesized using an entropy-weighted composite index incorporating park green area as a share of GDP, industrial pollutant emissions as a percentage of GDP, and the harmless disposal rate of domestic waste. See Table 5 for details.

Table 5

Table 5. Mechanism variables.

3.3 Data sources

This research examines 283 prefecture-level cities in China over the period from 2011 to 2023 to evaluate the impact of DGI on UGTP. Green patent data were collected from the China Research Data Service Platform, whereas energy-related statistics were mainly derived from the China Energy Statistical Yearbook. Pollutant emission data were extracted from the China Industrial Statistical Yearbook and the China Environmental Statistical Yearbook. The number of environmental administrative penalty cases was collected from the Peking University Law Database, and fixed asset investment data were sourced from the China Fixed Asset Investment Statistical Yearbook. Basic indicators for the dependent and independent variables, including the value added of the primary, secondary, and tertiary industries, the number of Internet access users, and total electricity consumption, were retrieved from the China City Statistical Yearbook. Furthermore, data on government environment and energy R&D appropriations expenditure were sourced from each citys’ Government Statistical Yearbook. Due to the lack of data for Tibet, cities in the Tibet Autonomous Region were excluded. All data were handled for missing values to ensure reliability and consistency. The main variables’ descriptive statistics are summarized in Table 6, providing a robust foundation for systematically evaluating how the DGI empowers UGTP.

Table 6

Table 6. Descriptive statistics.

4 Coupling coordination between CEE and DGI

4.1 Measurement results of CEE

Considering regional differences in land resource endowments, economic development, and resident population size, the performance of UGTP reflects significant spatial disparities in CEE across Chinese cities. Figures 4a–f illustrates the spatial configurations and temporal trajectories of CEE from 2011 to 2023. In the broad spatial pattern, eastern cities such as Beijing and Shanghai exhibit superior CEE compared to other regions. Central cities, including Wuhan, Zhengzhou, and Changsha, register CEE levels similar to their western counterparts, such as Nanning, Chongqing, and Chengdu, with the western region holding a marginal edge over the center. By contrast, the northeastern region exhibits marked temporal volatility, whereas the eastern and western areas sustain greater stability. These patterns underscore the divergent regional capacities and potentials in UGTP. Due to space limitations, only the main years’ CEE charts are shown here; the remaining detailed CEE charts for all 13 years are provided in Supplementary Material S1.

Figure 4

Figure 4. CEE from 2011 to 2023. (a) Carbon emission efficiency in 2011; (b) Carbon emission efficiency in 2013; (c) Carbon emission efficiency in 2015; (d) Carbon emission efficiency in 2018; (e) Carbon emission efficiency in 2020; (f) Carbon emission efficiency in 2023.

Spatiotemporal dynamics of CEE unveil marked disparities across urban scales. Mega-cities exhibit pronounced volatility; for instance, Beijing, Tianjin, and Suzhou attained a peak of 0.756 in 2013, tapering to 0.488 by 2023. This trajectory reflects differences in policy shifts, industrial reconfiguration, and the degree of DGI adoption. Metropolitan cities sustain relative stability at approximately 0.540, underscoring DGI’s efficacious role in UGTP. Large cities register elevated CEE, particularly during the 2017–2021 period. Conversely, medium-sized cities, exemplified by Daqing, Baishan, and Tongchuan, peaked at 0.739 in 2017 before plummeting sharply to 0.320 in 2022. Small cities, such as Jiayuguan, Jinchang and Karamay, display erratic fluctuations. Collectively, large and metropolitan cities exhibit resilient green development trajectories; medium-sized counterparts confront acute transformation strains; and small cities harbor untapped enhancement potential. As depicted in Figure 5.

Figure 5

Figure 5. Carbon emission efficiency of cities nationwide and by region and size, 2011–2023. (a) Overall change in CEE. (b) CEE in different regions. (c) CEE of cities of different sizes.

4.2 Results of DGI coupling coordination

4.2.1 Evaluation of city-level DGI trends

From Figure 6 (1), the DGI coordination diagram reveals that prior to 2021, most cities remained in high or moderate disequilibrium stages; post-2021, the number of cities in mild or marginal disequilibrium rose markedly. By 2023, high-CCD cities concentrated predominantly in the eastern region, highlighting stark regional divergences. Beijing attained good synergy status in 2023, maintaining national leadership, with mid-eastern DGI spatial distribution expanding and peak values rising (see Figure 6 (3) 2011 3D kernel density plot and (4) 2023 plot). Between 2011 and 2023, regional average CCD for DGI exhibited a sustained upward trajectory, marked by synchronized fluctuations yet enduring gaps; the eastern region led consistently, while central and western regions followed parallel developmental paths (see Figure 6 (2) DGI spatial heatmap). During the COVID-19 pandemic (2020–2022), national DGI declined notably, attributable to economic contraction, policy recalibrations, fiscal constraints, and project deferrals. The eastern region’s CCD rose from 0.167 in 2011 to 0.291 in 2023, reflecting robust economic vitality, technological innovation, and policy support. Central and western regions advanced from 0.135 to 0.133, respectively, to 0.239 and 0.238 by 2023—trailing the east but demonstrating steady progress, bolstered by governmental interventions, industrial reconfiguration, and incremental DGI infrastructure enhancements. As shown in Figure 7. Due to space limitations, only the main years’ charts are shown here; the remaining detailed CEE charts for all 13 years are provided in Supplementary Material S2.

Figure 6

Figure 6. CCD of DGI in Chinese prefecture-level cities, 2011–2023; (a) DGI Fusion Graph; (b) DGI spatial heatmap; (c) The 2011 3D kernel density plot; (d) The 2023 3D kernel density plot.

Figure 7

Figure 7. CCD of DGI in eastern, central, and western regions of China, 2011–2023.

5 Empirical analysis

5.1 Impact of DGI integration on UGTP

5.1.1 ADF test

The stationarity of spatial econometric data was assessed using the LLC, IPS, and ADF-Fisher tests. The LLC test yielded p-values below 1% for all variables’ t-statistics, evincing high significance and robust overall stationarity. The IPS test affirmed stationarity at levels for all variables except public environmental awareness (pec), which attained significance (p < 0.01) following first-order differencing. The ADF-Fisher test corroborated these results: openness (tra), green industrial structure upgrade (ind), economic agglomeration (eco), public environmental awareness (pec), and public service environment (pub) required first differencing for stationarity, whereas remaining indicators were significant at levels. Collectively, these diagnostics substantiate the dataset’s adherence to stationarity prerequisites, furnishing a solid foundation for subsequent empirical scrutiny of DGI impact on UGTP. Due to space limitations, Table 7 presents only the key information, while the remaining detailed content is provided in Supplementary Material S3.

Table 7

Table 7. ADF test results.

5.1.2 Baseline regression

Employing a two-way fixed effects regression model, this study examines the impact of DGI on UGTP across three periods: the short term (2011–2016), the long term (2017–2023), and the full period (2011–2023). The results are presented in Table 8. Across all three periods, DGI has a significantly negative impact on UGTP, thereby validating Hypotheses1, Hypotheses2 and Hypotheses3. Concurrently, it is evident that this negative impact significantly weakens as the time horizon extends.

Table 8

Table 8. Baseline regression results.

5.1.3 Baseline heterogeneity

1. Regional Heterogeneity Analysis. Based on China’s regional classification, the sample of 283 cities is divided into the Eastern, Central, and Western regions, with the analysis conducted across three periods: the short term (2011–2016), the long term (2017–2023), and the full period (2011–2023). As shown in Table 11, the coefficients of DGI on UGTP are negative for the Eastern and Western cities across all three periods, while the impact coefficient for Central cities is positive. Specifically, in the long term, the negative impact of DGI on UGTP attenuates in the Eastern region but intensifies in the Western region. However, the positive impact of DGI on UGTP in the Central region attenuates in the long term. Existing studies on DGI and UGTP typically report positive regional effects, with Eastern China leading due to advanced infrastructure, while Central and Western regions lag but show convergence,yet few examine temporal variations in negative impacts or patterns of intensification. As shown in Table 9.

2. City Size Heterogeneity Analysis. By classifying cities according to size, this analysis effectively examines how inter-city differences in resource allocation, infrastructure, and technological capacity influence the effect of DGI on UGTP. Following the approach (Yan et al., 2020), city size is measured using the average permanent population from 2011 to 2023 for 283 cities, categorizing them into super-large, mega, large, and small-to-medium cities (see Table 10). The results indicate that across all three time periods, DGI has a positive impact on Small-to-Medium cities, while the effects vary for other city sizes. In the short term, DGI exerts a negative impact on UGTP in Super-Large and Mega cities, whereas it has a positive impact on UGTP in Large cities. In the long term, however, the impact of DGI on UGTP in Super-Large and Mega cities turns positive—with the positive effect being most pronounced in Super-Large cities, while the impact on Large cities becomes significantly negative. For the full 2011–2023 period, DGI has a positive effect on UGTP in Super-Large cities, yet it negatively affects Mega and Large cities.

3. Market integration plays a pivotal role in advancing UGTP by dismantling local protectionism, reducing transaction costs, and facilitating the unfettered flow of capital, technology, talent, and information, thereby providing an effective channel for DGI. Utilizing the market segmentation index derived from the price method, this study categorizes cities into high-, medium-, and low-integration groups and conducts analyses across short-term and long-term periods (see Table 11). The empirical results reveal that in the short term, DGI exerts a negative impact on UGTP in high- and medium-integration cities, whereas it has a positive impact in low-integration cities. In the long term, the impact of DGI on UGTP in high-integration cities turns positive, while its effects on medium- and low-integration cities are negative; furthermore, the negative impact on medium-integration cities attenuates. However, for the 2011–2023 period, DGI has a negative impact on all three market-integration groups.

4. Heterogeneity Analysis by New Economic Growth Momentum. cities characterized by high levels of new economic growth momentum typically possess abundant resources, technological advantages, and innovation capabilities, enabling DGI to effectively promote UGTP. conversely, locales with lower new economic growth momentum grapple with resource scarcity and technological deficits. Grounded in the average per capita economic levels from 2011 to 2023, this study classifies cities into high, medium, and low new economic growth momentum categories and conducts the analysis across both short-term and long-term periods (see Table 12). The results indicate that for the full period (2011–2023) and the short term (2011–2016), DGI exerts a negative impact on UGTP across cities with high, medium, and low new economic growth momentum, with this effect being most significant in high-momentum cities. However, in the long term, the impact of DGI on UGTP in high-momentum cities turns positive, while the negative impact on medium- and low-momentum cities attenuates.

Table 9

Table 9. Regional heterogeneity analysis.

Table 10

Table 10. City size heterogeneity analysis.

Table 11

Table 11. Market integration heterogeneity analysis.

Table 12

Table 12. Heterogeneity analysis by new economic growth momentum.

5.2 Endogeneity test

To address potential endogeneity arising from issues such as sample selection bias, reverse causality, or omitted variables, this study employs the system Generalized Method of Moments (GMM) and Two-Stage Least Squares (2SLS) approaches (Hong et al., 2024). As shown in Table 13, the lagged one-period UGTP exhibits a significantly positive effect on UGTP, while DGI demonstrates a significantly negative impact on UGTP. The AR (1), AR (2), and Hansen tests all affirm the validity of the models. The 2SLS estimation utilizes three distinct instrumental variables (IVs): the first, aver, is a technical instrument constructed as the cube of the difference between the explanatory variable and its mean. The second and third are theory-driven interaction terms: the interaction between the number of fixed telephone subscribers per 10,000 people in 1984 and the contemporary proportion of environmental fiscal expenditure (Iph); and the interaction between the number of post offices per million people in a historical base year and the proportion of environmental pollution control expenditure (Ema). These serve as proxies for the historical foundations of dual-transformation coordination (Nunn, 2021). Regions with more developed telecommunications infrastructure in the past established an early basis for information flow and connectivity; when interacted with indicators of green policy emphasis, these variables emerge as robust predictors of a city’s capacity for modern DGI. The exogeneity condition is satisfied, as these historical metrics are unlikely to directly influence contemporary UGTP. Telecommunications technology from 1984 is largely obsolete, and it is theoretically implausible that the density of telephone lines from 40 years ago would exert a direct causal effect on UGTP, independent of its role in shaping the trajectory of digitalization. The first-stage results indicate that the three instrumental variables have a significant positive effect on UGTP. Their F-statistics far exceed the conventional threshold of 10, confirming their relevance and ruling out weak instrument concerns. In the second stage, all three instrumental variables also yield a significant positive effect on UGTP, indicating that the endogeneity issue has been addressed. See Table 13 for details. Due to space limitations, Table 13 presents only the key information, while the remaining detailed content is provided in Supplementary Material S3.

Table 13

Table 13. Endogeneity test results.

5.3 Robustness checks

5.3.1 Lagged independent variable

To address the potential endogeneity issue of reverse causality between DGI and UGTP, DGI is lagged by one period, and tests are conducted separately for the short term, the long term, and the entire period. As shown in Column (1) of Table 10, the one-period lagged DGI exhibits a significantly negative effect on UGTP across all three time periods, thereby confirming the reliability of the baseline regression results.

5.3.2 Lagged dependent variable

Because the current level is largely influenced by the previous period’s level, the dependent variable is lagged by one period. As reported in Column (2) of Table 14, the effects of DGI on UGTP remain significantly negative across all three time periods, consistent in direction and significance with the results in Column (1).

Table 14

Table 14. Results of robustness checks.

5.3.3 Lagged all variables

Considering the potential for reverse causality between the core, all variables are lagged by one period. As indicated in Column (3) of Table 14, DGI exerts significantly negative effects on UGTP across all three time periods, further corroborating the robustness of the previous regression results.

5.3.4 Excluding provincial capital cities

To examine if the baseline findings are driven by the distinct group of provincial capital cities, thereby ensuring the conclusion’s generalizability. Given the inter-city divergences in economic foundations and industrial structure patterns within the same province, provincial capital cities are excluded for robustness checks (Sharma et al., 2024). As reported in Column (4) of Table 14, DGI maintains a significantly negative effect on UGTP across all three time periods, affirming the consistency of the regression results.

5.4 Transmission mechanisms

Based on the preceding theoretical analysis, this study investigates the mechanisms through which DGI impacts UGTP. To empirically dissect the three-dimensional “government–market–society” interdependencies, this study employs a three-step mediation analysis to examine the impact of DGI on UGTP from 2011 to 2023. The first-step results indicate that the impact coefficient of DGI on UGTP is significantly −0.140. Subsequent analysis will further explore the transmission pathways underpinning this complex relationship.

5.4.1 Governmental environmental improvement mechanism

As shown in Table 15, Columns (1) and (2) represent the first and second steps, respectively. Column (1) indicates that DGI exerts a significantly positive effect on environmental pollution management (pol), environmental protection governance (env), and tax level (tax). In Column (2), under the indirect influence of pol and env, the impact of DGI on UGTP shifts from negative to positive. Meanwhile, under the positive mediating role of tax, the negative impact of DGI on UGTP is attenuated. This demonstrates that environmental pollution management and environmental protection governance exert a full mediation effect, completely suppressing the negative impact of DGI on UGTP. This finding confirms Hypothesis 4.

Table 15

Table 15. Empirical results of the governmental mechanism analysis.

5.4.2 Market resource allocation mechanism

As shown in Columns (1) and (2) of Table 16, DGI exerts a significantly positive impact on industrial structure adjustment (stru), economic agglomeration (eco), and financial development level (fin). This indicates that DGI effectively promotes industrial structure adjustment, economic agglomeration, and financial development. Under the mediating effects of industrial structure adjustment and financial development level, the negative impact of DGI on UGTP is reduced. Simultaneously, mediated by economic agglomeration, the negative impact of DGI on UGTP turns positive. This demonstrates that these three mechanism variables effectively suppress the negative impact of DGI, thereby validating Hypothesis 3. As shown in Table 16.

Table 16

Table 16. Empirical results of the market mechanism analysis.

5.4.3 Societal participation governance mechanism

Table 17, Columns (1) and (2) show the first- and second-step test results for 2011–2023. Columns (3) and (4) present the short-term (2011–2016) mediation results, and Columns (5) and (6) present the long-term (2017–2023) mediation results. Overall, DGI has a significantly positive impact on human capital (hum), whereas its impacts on public environmental consciousness (pec) and public service environment (pub) are significantly negative. This indicates that DGI promotes human capital levels but degrades public environmental consciousness and the public service environment. In the second step, the effect of human capital on UGTP is not significant, indicating this mediation effect is not established. However, public environmental consciousness suppresses the negative impact of DGI on UGTP, necessitating further discussion across short-term and long-term periods. In the 2011–2016 period, DGI’s impact on UGTP is significantly negative, and the insufficient public environmental consciousness in the short term strengthens this negative impact. However, in the 2017–2023 period, as public environmental consciousness increased, pec suppressed the negative impact of DGI on UGTP, causing DGI’s impact coefficient to turn positive. Simultaneously, the public service environment (pub) partially suppresses the negative impact of DGI on UGTP. This confirms Hypothesis 3.

Table 17

Table 17. Empirical results of the social mechanism analysis.

6 Further discussion

6.1 Spatial spillover effect analysis

Drawing on the work of Xing et al., this study employs a spatial panel econometric model to estimate the spatial spillover effects of DGI on UGTP. Identifying these spillover effects is critical for informing specific joint governance policies (Xing et al., 2021). Given the varying forms of spatial dependence, spatial econometric models are categorized into the Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The specific research process is outlined below.

6.1.1 Spatial correlation test

The Moran’s I index was utilized to test spatial autocorrelation. The computed Moran’s I index is 0.083, indicating a positive and statistically significant spatial autocorrelation at the 5% level. The distribution of the Moran’s I index, as shown in the figure, is relatively concentrated, indicating a positive spatial effect on UGTP from 2011 to 2023. Thus, spatial variables are further considered in the analysis. The results are displayed in Table 18.

Table 18

Table 18. Moran’s I index statistical results from 2011 to 2023.

6.1.2 Selection of spatial econometric models

To select the appropriate spatial econometric model for analyzing the spatial spillover effects of DGI on UGTP, this study refers to the methodology of Zeng et al. and conducts tests such as LM and LR to determine the most suitable model specification (Zeng et al., 2025). The study employed three weight matrices: Spatial Distance (SAM), Economic Counter-Geographical Matrix (EGWM), and Green Finance Geographical Matrix (GFGWM). For the SAM and EGWM, the LM error test was insignificant at the 10% level, while the spatial lag LM test was significant at the 1% level. Given that the Hausman test values were also less than 10%, the SAR model with two-way fixed effects was tentatively selected. Furthermore, both the Wald test and the LR test were greater than 10% supporting the adoption of the SAR two-way fixed effects model. Conversely, for the GFGWM, the LM error test, spatial lag LM test, LR test, and Hausman test were all significant at the 1% level. Additionally, both the LR test and the Wald test were significant at the 5% and 10% levels, respectively, leading to the final selection of the SDM with two-way fixed effects. Detailed outcomes appear in Table 19.

Table 19

Table 19. Selection of spatial econometric models.

6.1.3 Spatial spillover effects

6.1.3.1 Decomposition of the effects of explanatory variables

The spatial effects results, derived using Stata 16 software, are presented in Table 20. By comparing the Log-likelihood values across the three spatial weight matrices—SAM, EGWM and GFGWM—the results further confirm that the SAR two-way fixed effects model provides the best fit for the SAM and EGWM matrices, while the SDM two-way fixed effects model provides the optimal fit for the GFGWM matrix. The model results across all three matrices show that DGI exhibits a negative influence on UGTP. Environmental Pollution Control, Economic Agglomeration, and Public Environmental Awareness exert a positive effect on UGTP, with the effect of Public Environmental Awareness being statistically significant across all three matrices. Pollution Management Intensity, Industrial Green Structure Upgrading, and Public Service Environment demonstrate a negative effect on UGTP. However, Industrial Green Structure Upgrading exhibits a significantly positive effect on UGTP under the time-fixed effects specification across all three matrices. Regarding control variables, Government Expenditure and Technological Level have a significantly negative impact on UGTP under both random and fixed effects, but their impact turns significantly positive under the time-fixed effects specification. Conversely, Environmental Regulation consistently shows a negative impact on UGTP. Finally, Infrastructure, Population Density, and Openness all exert a significantly positive influence on UGTP under the time-fixed effects specification.

Table 20

Table 20. Model results.

6.1.3.2 Direct, indirect, and total effects

Spatial econometric models decompose effects into direct, indirect, and total components, facilitating a more accurate understanding of the spatial dependence and spillover effects of DGI on UGTP. As shown in Table 21, Under the SAM and the EGWM, the direct, indirect, and total effects of DGI on UGTP are negative and statistically insignificant. Conversely, only within the GFGWM are DGI’s indirect effect and total effect significantly positive. Among the mechanism variables, Environmental Pollution Control and Public Service Environment consistently exert a negative impact on UGTP across all three effect types and matrices. Pollution Management Intensity shows a positive impact on UGTP only under the direct effect in the EGWM and the indirect and total effects in the GFGWM, with the remaining effects being negative. Industrial Green Structure Upgrading is significantly positive only under the indirect effect in the GFGWM, negative for the direct effect, and negative for all other matrices and effects. Public Environmental Awareness shows a significantly positive impact on UGTP across the direct, indirect, and total effects in all three matrices, except for the indirect effect in the EGWM, which is insignificant. Economic Agglomeration only exhibits a negative and insignificant impact on UGTP under the indirect effect of the GFGWM.

Table 21

Table 21. Direct, indirect, and total effects.

6.1.4 Spatial decay boundary

The UGTP of a single city is influenced not only by its own conditions but also by neighboring cities or regions, a phenomenon referred to as the spatial spillover effect. Analyzing the spatial diffusion effects of DGI is thus critical for understanding its cross-regional interactive mechanisms and its impact on the UGTP of surrounding cities. Following previous studies, this paper employs the SDM, as shown in Equation 4) to examine whether DGI generates significant spatial spillover effects (Chen and Liu, 2025). By incorporating the influence of adjacent cities, the model effectively captures inter-city interactions and allows for a rigorous assessment of whether the spatial effects of DGI exert cross-regional impacts on UGTP performance.

$w_{ij}^1=\left\{\begin{array}{c}1/d_{ij}^2,i\ne j\\ 0,i=j\hfill \end{array}\right.(12)$

$w_{ij}^d=\left\{\begin{array}{c}1/d_{ij},d_{ij}⩾d,d=100,200,300,\cdots ,1000\\ 0,\text{others}\hfill \end{array}\right.(13)$

Based on Equations (12) and (13), different spatial weight matrices ($w_{ij}^1$, $w_{ij}^d$) were constructed, and in conjunction with the LM, LR, and Hausman test results reported in the previous section, a two-way fixed-effects SDM was employed. Table 22 presents the regression results of the spatial spillover effects of DGI on UGTP. In SDM Model (1), the direct impact of DGI on local UGTP is negative and statistically insignificant, suggesting that after controlling for spatial spillovers, a city’s own DGI development has limited direct influence on its local UGTP. Conversely, the spatial spillover effect of DGI on UGTP is significantly negative, indicating that a higher DGI level in one city may suppress the UGTP performance of proximate cities. The spatial autoregressive coefficient is consistently negative and significant, demonstrating a beggar-thy-neighbor dynamic where an improvement in one city’s UGTP may adversely affect its neighbors. Furthermore, robustness checks utilizing alternative spatial weight matrices confirm the stability of the results: although the coefficient of the W $\times$ DGI remains negative but insignificant, the $\rho$ coefficient persists as significant, reinforcing that the negative spillover effects attenuate with distance but fundamentally remain present.

Table 22

Table 22. Regression results of spatial spillover effects.

Following the approach of Zeng et al., this study examine the stage-specific effects of DGT on UGTP via threshold analysis (Zeng et al., 2025). Based on Equations 12 and 13, spatial spillover effects of DGI on UGTP were estimated at 100 km increments, with 90% confidence intervals delineated (Figure 8). Results delineate three distinct distance regimes for DGI’s influence on neighboring cities’ UGTP:

1. Distance <760 km. DGI exhibits an inhibitory effect on the UGTP performance of neighboring cities, with the negative spillover coefficient initially intensifying and then weakening as distance increases. The first stage, covering distances of less than 400 km, sees the negative spillover effect intensify. This is due to frequent economic interactions and factor flows among proximate cities, where core cities leverage their DGI advantages to attract high-skilled talent and technological capital from surrounding areas. This causes “brain drain” and resource depletion, consequently suppressing the UGTP of adjacent cities. In the second stage, spanning 400–760 km, the negative spillover effect attenuates. As direct competition diminishes, weak positive spillovers, such as knowledge diffusion, partially offset the negative influence of DGI on UGTP.

2. Distance 760–900 km. DGI generates a significant positive spillover effect on UGTP in neighboring cities. This indicates that, within moderate distances, positive spatial effects can outweigh negative ones, thereby fostering regional synergy. Through industrial specialization and functional complementarity, cities can mitigate direct inter-city competition and enhance their cooperative potential. Within the 760–860 km range, the spatial spillover effect of DGI on UGTP is significantly positive, with the CI ranging from −2 to 4. This wide CI, spanning negative to positive values, signals considerable uncertainty in the effect’s magnitude and direction. It underscores heterogeneous influences in this transitional zone: while positive synergies propel overall UGTP enhancement, localized frictions introduce variability. Within the 860–900 km range, DGI’s spatial spillover effect on UGTP remains positive and significant, attaining its peak magnitude. This implies an optimal inter-city interaction distance, where geographic proximity suffices to facilitate efficient knowledge and resource flows without the fierce competition characteristic of closer ranges.

3. Distance >900 km. Spillover effects approach zero, consistent with the spatial decay hypothesis. As geographic distance increases, economic linkages and factor flows among cities weaken, rendering intercity impacts negligible.

Figure 8

Figure 8. Spatial distance spillover effects of heterogeneous urban synergistic agglomeration.

Overall, the findings demonstrate a non-linear spatial pattern of DGI’s effects on UGTP: inhibitory at short distances, positive at moderate distances, and negligible at long distances, confirming the existence and attenuation of DGI’s spatial spillover effects. The near-distance inhibition finding necessitates breaking administrative barriers and local protectionism. Policy requires higher-level, coordinated planning, moving beyond individual cities. Cross-regional DGI coordination and ecological compensation mechanisms are essential to transform short-term competition into long-term cooperation. Conversely, long-distance insignificance confirms that no one-size-fits-all national DGI path exists. Top-level design must account for regional heterogeneity, focusing on translating proven models to fit local contexts rather than pursuing direct long-distance spillovers.

7 Conclusions and policy implications

7.1 Conclusions

1. Conclusions on Carbon Emission Efficiency and DGI Coordination Development. Carbon Emission Efficiency (CEE) Results show significant regional disparities. The Eastern region (e.g., Beijing, Shanghai) maintains the highest efficiency and stability, while the Central and Western regions are lower but stable. The Northeast exhibits substantial volatility. Regarding city size, large cities demonstrate resilient green development, whereas medium cities face transformation strains and small cities hold untapped potential. DGI Coordination remains poor for most cities, characterized by high or moderate disequilibrium. High CCD is concentrated almost exclusively in the Eastern region, highlighting a pronounced divergence from the slower-developing Central and Western areas.

2. Impact of DGI on UGTP Performance. Across all three time periods, DGI exerts a negative inhibitory effect on UGTP performance, characterized by a trend of strong short-term impact that weakens over the long term. In the short term, the high costs and institutional frictions associated with DGI implementation predominate, leading to a temporary decline in productivity. However, in the long term, as technology, institutions, and markets mature and adapt, the efficiency-enhancing effects of DGI begin to emerge, gradually offsetting the initial negative shock. While existing studies predominantly report positive impacts on green productivity by focusing on long-term knowledge spillovers and synergies, this study contributes by highlighting the previously overlooked short-term frictional costs and the dynamic attenuation of negative effects over time (Guo et al., 2024).

3. The impact of DGI on UGTP exhibits significant heterogeneity. The study reveals significant multidimensional heterogeneity in the impact of DGI on UGTP. Regionally, DGI exerts a positive promotional effect on Central cities, while predominantly inhibiting Eastern and Western cities. Notably, the negative impact in the Western region intensifies rather than diminishes over the long term, suggesting an inhibition trap. Regarding city size, DGI consistently yields positive benefits for Small-to-Medium cities. Super-Large and Mega cities exhibit a dynamic shift from “initial suppression to subsequent growth”: despite suffering short-term negative shocks, they transition to positive promotion in the long term. Conversely, Large cities display a deteriorating trend, shifting from an initial positive impact to a negative one. Furthermore, cities characterized by high market integration and high new economic growth momentum demonstrate the capacity to overcome short-term frictions, transitioning from short-term inhibition to long-term promotion. In contrast, cities with low market integration lack long-term adaptability, with effects shifting from positive to negative.

4. Transmission Mechanisms. The government and market dimensions function as critical “buffering” mechanisms. Specifically, within the political dimension, environmental pollution management and environmental protection governance exert a full mediation effect, completely offsetting the negative shock of DGI on green productivity. Furthermore, government taxation, along with industrial structure adjustment, financial development, and economic agglomeration in the market dimension, significantly attenuate DGI’s negative inhibitory effect, serving as partial mitigation. The social dimension exhibits a dynamic “double-edged sword” effect. In the short term, insufficient public environmental consciousness exacerbates the negative impact of DGI; however, in the long term, as awareness improves, this role reverses, driving the impact of DGI on UGTP to shift from negative to positive. Additionally, the public service environment contributes to partially mitigating these negative impacts.

5. Although UGTP itself exhibits a positive spatial correlation, the overall impact of DGI on UGTP remains negative. This indicates that DGI has not yet established an effective mode of coordinated regional development. Specifically, public environmental awareness demonstrates a significant and robust positive spatial spillover effect, serving as a key driving force for cross-regional green development. In contrast, the spatial spillover effects of environmental governance and industrial structure/economic agglomeration are largely insignificant or unstable, failing to effectively drive green productivity in neighboring regions. While existing research generally posits that DGI generates positive spillovers for neighboring areas through technology diffusion and demonstration effects, the findings of this study contradict this view (Cheng and Jin, 2022; Lyu et al., 2023). This study highlights that the spatial radiation capacity of DGI is currently weak, failing to achieve the expected positive drive across regions.

6. The spatial decay boundary analysis reveals that the direct driving effect of DGI on local UGTP is limited; however, significant spatial spillover effects exist, exhibiting unique nonlinear characteristics with respect to geographical distance. Specifically, within a range of 760 km, the spillover effect of DGI manifests primarily as negative inhibition. The negative shock is most intense within 400 km, while in the 400–760 km interval, the effect remains negative but gradually attenuates. As the distance extends to the 760–900 km range, the spillover effect undergoes a structural reversal, shifting to significant positive promotion, and reaches its peak magnitude between 860 km and 900 km. Beyond 900 km, the effect tends to vanish. This non-monotonic pattern of initial suppression followed by promotion differs significantly from the conventional monotonic decay conclusion found in existing literature, which posits that the spillover effects of the digital economy simply attenuate as distance increases (Liu and Lian, 2025).

7.2 Policy implications

Drawing on the systematic findings, to advance urban DGI from the current challenging stage to a new phase of high-efficiency, coordinated, and inclusive development, thereby systematically enhancing UGTP performance, the following recommendations are presented.

1. A Fusion-Priority and Regionally Differentiated Strategy to Build a Differentiated Urban Scale Support System. To fundamentally overcome the inherent bottlenecks facing the development of DGI, it is crucial to implement a Strategy of Integrated Prioritization and Regional Differentiation. At the Integrated Prioritization level, the government should decisively dismantle administrative silos between digital and environmental departments by immediately establishing an Inter-Departmental Digital Green Collaboration Office to serve as the highest body for policy coordination and execution. Concurrently, it must mandate the enforcement of a Digital-Green Dual Assessment Standard for all new industrial projects and infrastructure, ensuring concurrent compliance with both digitization and greening metrics. Building on this foundation, addressing regional disparities requires establishing a Central and Western Digital Green Infrastructure Special Transfer Payment Fund, specifically targeting the digital retrofitting of high-energy-consuming industries to strictly prevent the recurrence of a pollute first, clean up later path. For the economically volatile Northeast region, a Digital Green Revitalization Plan for Old Industrial Bases” should be formulated, leveraging Industrial Internet technologies to balance the dual objectives of economic transformation and environmental protection. Finally, leading Eastern cities should be designated to spearhead the establishment of DGI Demonstration Zones, exporting mature management models and technical standards to the rest of the nation, thereby forming a tiered support system.

2. Acknowledging that DGI may exert a negative impact on the economy and employment in the short term, a Time-Phased Adjustment Mechanism of Short-Term Compensation and Long-Term Cultivation must be constructed to smooth the transition pain. For Short-Term Compensation, the government should immediately establish a Digital Green Transformation Guiding Fund to provide direct financial subsidies or tax credits to enterprises for initial equipment upgrades and technological trial-and-error, thereby lowering their switching costs and friction. Simultaneously, the Digital Skills Re-employment Training Voucher system must be implemented to enhance labor force adaptability and effectively alleviate the temporary productivity decline caused by technological substitution. Focusing on Long-Term Cultivation, it is imperative to establish a dynamic DGI Performance Monitoring Platform and Evaluation System to ensure policy outcomes are measurable and traceable. Crucially, in accordance with the Implementation Guidelines for the Coordinated Transformation of Digitization and Greening (2024), green financial instruments should be vigorously promoted to precisely channel credit resources toward high-growth digital green enterprises. This systematic optimization will accelerate technological maturity and knowledge spillover, ensuring the timely release of DGI’s long-term positive effects.

3. To resolve the heterogeneous conflicts among different cities, the strategy of Graded Precision Policy Application must be adopted. At the regional level, Eastern cities should deepen the “Digital Renewal Pilot for Traditional Industries” to counteract long-term negative effects through technological iteration; the Western region needs to establish a “Green East-Data-West-Computing” inter-provincial collaboration mechanism and utilize talent enclave policies to resolve resource constraints; and Central cities should establish an Industry Upgrade Guiding Fund to amplify positive spillover effects. At the urban scale level, Mega and Super-Mega Cities should immediately implement De-Congestion digital governance policies and establish metropolitan area industry alliances to mitigate the siphon effect; Large Cities must focus on optimizing resource allocation efficiency to prevent diseconomies of scale; and Small and Medium-Sized Cities should promote small, fast, light, and precise Software as a Service digital solutions to lower the barrier to transformation. Regarding market integration, high-level cities should unify green financial standards to secure synergy dividends, while low-level cities must prioritize breaking administrative barriers through joint law enforcement and inter-governmental data interoperability to reduce transaction costs.

4. To ensure the effective transmission and execution of DGI policies, it is necessary to smooth the Government-Market-Society three-dimensional transmission channels and construct a comprehensive buffer mechanism. In the Government Dimension, environmental investment must be transformed into the construction of a Smart Environmental Brain, utilizing IoT and big data to enhance the precision and transparency of environmental supervision, ensuring that pollution control investment translates into actual emission reduction performance. Simultaneously, a Green Tax + Digital Tax Refund linkage mechanism must be immediately implemented to guide corporate behavior through fiscal reform. In the Market Dimension, policy banks must establish “Digital Green Technological Transformation Special Loans” to guide social capital toward industrial clusters with high ecological efficiency, correcting resource misallocation through financial leverage. In the Social Dimension, an Individual Carbon Account APP and a Community Digital Environmental Incentive Platform should be developed and promoted to translate public environmental awareness into concrete green consumption behavior. Furthermore, investment in digitalized public service facilities should be increased to alleviate social pressure during the transition period.

5. Given the current negative overall spatial spillover of DGI, the core challenge in overcoming inefficient spatial synergy and obstructed spillover is Spatial Layout Optimization, centered on activating cross-regional factor flow. The government should take the lead in forming a Cross-Regional Green Finance Connectivity Mechanism, utilizing blockchain technology to break down geographical restrictions on capital flow and ensure the unrestricted circulation of funds. Simultaneously, the spatial inefficiency of environmental governance and industrial agglomeration must be corrected by establishing Cross-Administrative Digital Green Industrial Cooperation Parks to guide the rational layout of industrial chains and their upstream/downstream components, fundamentally avoiding homogeneous competition and promoting efficient agglomeration. Ultimately, by utilizing digital means to optimize the cross-regional allocation of public resources and talent, DGI will be established as an effective tool for Regional Collaborative Governance, thereby reversing the current negative spatial correlation.

6. Given the non-linear nature of DGI spillover effects that vary with distance, it is essential to establish a Differentiated Spatial Governance System based on Concentric Circles. In the Strong Competition Circle, the focus should be on establishing Metropolitan Area Interest Compensation Mechanisms and Talent Sharing Agreements to prevent the core city from siphoning resources and exerting negative inhibition on surrounding cities. In the Transition Circle, the window of knowledge spillover should be seized by jointly building and sharing Digital Technology Transfer Platforms to promote factor complementarity rather than zero-sum competition. In the “High-Efficiency Collaboration Circle”, which represents the peak of positive spillover, an Inter-Provincial Digital Green Innovation Corridor should be established to unify policy standards and data interfaces, maximizing positive synergistic effects through joint R&D projects. For the “Long-Distance Circle”, reliance on national-level computing power networks and cloud platforms is necessary to overcome geographical isolation, ensuring the low-cost, long-distance flow of technology and information, preventing the formation of information silos, and achieving nationwide synergistic development.

7.3 Research limitations and future directions

This study makes substantial contributions to the systematic evaluation of the impact, mechanisms, and spatial heterogeneity of DGI on UGTP. Nonetheless, certain unavoidable limitations persist, offering valuable directions for future research. First, in terms of indicator construction, the measurement of DGI—a multifaceted synergistic concept—requires further refinement. In particular, the current analysis falls short in capturing the quality and depth of DGI, and proxy indicators for mechanism variables may not fully encapsulate the depth of public engagement or actual green behaviors. Second, the mechanism analysis remains somewhat simplified, as the employed three-step mediation model struggles to fully delineate the dynamic, nonlinear, and recursive interactions within the “government–market–society” triad. Finally, the spatial spillover analysis is inherently static; while it uncovers the nonlinear spatial spillover patterns of DGI effects, it does not thoroughly explore the temporal dynamic evolution of these spillovers. Building on these limitations, future research could leverage quasi-experimental designs (e.g., Difference-in-Differences) to more rigorously identify the causal impact.” Regarding indicator optimization and data sources, advanced techniques like large language models (LLMs) and machine learning can be harnessed for more nuanced text mining of government work reports, corporate annual reports, and social media data, yielding finer-grained indicators that better reflect DGI’s quality as well as genuine societal awareness and behaviors. For a more profound exploration of dynamic mechanisms, dynamic spatial panel models or time-varying parameter models are recommended to capture the nonlinear temporal shifts in DGI and its transmission pathways, complemented by simultaneous equation models or vector autoregression (VAR) models to precisely unpack the dynamic feedback loops and intricate recursive ties among the “government–market–society” triad. Moreover, studies should broaden to global comparative perspectives, extending samples to urban agglomerations in other developing or developed economies—particularly the EU’s integrated “Digital Europe” and “Green Deal” strategies—for cross-national and cross-regional analyses.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author contributions

LZ: Writing – original draft, Software, Writing – review and editing, Investigation, Methodology, Data curation, Conceptualization, Visualization. LS: Validation, Data curation, Investigation, Conceptualization, Funding acquisition, Writing – review and editing. WY: Writing – review and editing, Resources, Validation, Investigation, Supervision, Software, Visualization. JZ: Writing – review and editing, Funding acquisition, Validation, Resources.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1687504/full#supplementary-material

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