Digital Transformation-Driven Ecological Innovation and Urban Sustainable Development: Evidence from Chinese Cities

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This study investigates the impact mechanisms of digital transformation on urban sustainable development through ecological innovation, using panel data from 96 Chinese cities spanning 2015-2023. We construct comprehensive indices for digital transformation, ecological innovation, and sustainable development using principal component analysis, and employ multiple regression models to examine their relationships. Our findings reveal that digital transformation significantly promotes ecological innovation (β=0.973,β=0.973, p<0.001,p<0.001), which in turn enhances urban sustainable development (β=0.921,β=0.921, p<0.001p<0.001). Mediation analysis demonstrates that ecological innovation serves as a crucial mediator, accounting for 87.4% of the total effect of digital transformation on sustainable development. The study also identifies significant regional heterogeneity, with stronger effects observed in eastern regions and larger cities. These results provide empirical evidence for the "digital-ecological-sustainable" development paradigm and offer important policy implications for promoting urban sustainability through digital-ecological synergy. The research contributes to the literature by integrating digital transformation theory, ecological modernization theory, and sustainable development theory into a unified analytical framework, while providing practical guidance for policymakers seeking to leverage digital technologies for environmental and sustainable development goals. Ecological Modeling Digital transformation Ecological innovation Sustainable development Urban development Mediation analysis China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The 21st century has witnessed an unprecedented convergence of digital technologies and environmental challenges, fundamentally reshaping the landscape of urban development and sustainability [1]. As cities worldwide grapple with mounting environmental pressures, resource constraints, and climate change impacts, the integration of digital technologies into urban systems has emerged as a promising pathway toward sustainable development [2]. This digital-ecological nexus represents a paradigm shift from traditional linear development models to more integrated, circular, and intelligent approaches that can simultaneously address economic growth, social equity, and environmental protection [3]. Digital transformation, characterized by the pervasive adoption of digital technologies across all sectors of society, has become a defining feature of contemporary urban development [4]. From smart city initiatives and Internet of Things (IoT) deployments to artificial intelligence applications and big data analytics, digital technologies are fundamentally altering how cities operate, how resources are managed, and how environmental challenges are addressed [5]. This transformation extends beyond mere technological adoption to encompass fundamental changes in governance structures, business models, social interactions, and environmental management practices [6]. Simultaneously, the concept of ecological innovation has gained prominence as a critical mechanism for achieving environmental sustainability while maintaining economic competitiveness [7]. Ecological innovation, defined as the development and application of products, processes, services, and institutional arrangements that reduce environmental impacts while creating economic value, represents a key strategy for decoupling economic growth from environmental degradation [8]. The intersection of digital transformation and ecological innovation creates new possibilities for addressing complex urban sustainability challenges through technology-enabled environmental solutions [9]. China, as the world's largest developing economy and most populous country, provides a unique and compelling context for examining these relationships [10]. The country's rapid urbanization, massive digital infrastructure investments, and ambitious environmental commitments create an ideal natural laboratory for studying the interactions between digital transformation, ecological innovation, and sustainable development [11]. China's experience is particularly relevant given its dual role as both a major contributor to global environmental challenges and a leader in digital technology adoption and green innovation [12]. The theoretical foundations for understanding these relationships draw from multiple disciplinary perspectives. Innovation systems theory provides insights into how digital technologies can enhance innovation capabilities and facilitate knowledge flows within urban ecosystems [13]. Ecological modernization theory offers a framework for understanding how technological advancement can contribute to environmental improvement rather than degradation [14]. Digital transformation theory explains the mechanisms through which digital technologies reshape organizational and societal structures [15]. Sustainable development theory provides the normative framework for evaluating the outcomes of these processes [16]. Despite growing interest in these topics, several important research gaps remain. First, while numerous studies have examined digital transformation and ecological innovation separately, few have systematically investigated their interactions and combined effects on sustainable development [17]. Second, most existing research focuses on developed countries, with limited attention to the unique contexts and challenges of developing economies [18]. Third, there is insufficient understanding of the mechanisms through which digital transformation influences ecological innovation and sustainable development [19]. Fourth, the heterogeneous effects across different urban contexts and development levels remain underexplored [20].This study addresses these gaps by investigating the impact mechanisms of digital transformation on urban sustainable development through ecological innovation, using comprehensive panel data from Chinese cities. Our research makes several important contributions to the literature. Theoretically, we develop an integrated framework that combines digital transformation theory, ecological modernization theory, and sustainable development theory to explain the complex relationships between these phenomena. Methodologically, we construct comprehensive indices for measuring digital transformation, ecological innovation, and sustainable development, and employ rigorous econometric techniques to examine their relationships. Empirically, we provide robust evidence for the mediating role of ecological innovation in the digital transformation-sustainable development relationship, while identifying important sources of heterogeneity across different urban contexts. The practical implications of this research are substantial. For policymakers, our findings provide evidence-based guidance for designing integrated digital-ecological strategies that can simultaneously promote technological advancement and environmental sustainability. For urban planners and managers, the research offers insights into how digital technologies can be leveraged to enhance ecological innovation capabilities and sustainable development outcomes. For businesses and entrepreneurs, the study highlights opportunities for developing digital-ecological solutions that can create both economic and environmental value. The remainder of this paper is organized as follows. Section 2 reviews the relevant literature and develops our theoretical framework and hypotheses. Section 3 describes our data sources, variable construction, and empirical methodology. Section 4 presents the main empirical results, including descriptive statistics, correlation analysis, regression results, and robustness checks. Section 5 discusses the implications of our findings, addresses potential limitations, and suggests directions for future research. Section 6 concludes with a summary of key findings and policy recommendations. 2. Literature Review and Theoretical Framework 2.1 Digital Transformation and Urban Development Digital transformation represents a fundamental shift in how organizations, cities, and societies operate, driven by the pervasive adoption of digital technologies [21]. The concept has evolved from early notions of digitization and digitalization to encompass broader organizational and societal changes enabled by digital technologies [22]. In the urban context, digital transformation involves the integration of digital technologies into all aspects of city operations, from infrastructure and governance to service delivery and citizen engagement [23]. The theoretical foundations of digital transformation draw from multiple disciplines, including information systems, organizational theory, and urban studies [24]. Vial (2019) defines digital transformation as "a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies" [25]. This definition emphasizes the transformative nature of digital technologies and their potential to create fundamental changes in urban systems. Research on digital transformation in urban contexts has identified several key dimensions. Infrastructure digitalization involves the deployment of digital technologies in physical infrastructure systems, including smart grids, intelligent transportation systems, and digital communication networks [26]. Governance digitalization encompasses the use of digital technologies to enhance public service delivery, improve transparency, and enable citizen participation [27]. Economic digitalization refers to the integration of digital technologies into economic activities, including e-commerce, digital finance, and platform economies [28]. Social digitalization involves the adoption of digital technologies in social interactions, education, healthcare, and other social services [29]. The impacts of digital transformation on urban development are multifaceted and complex. Positive effects include improved efficiency in resource allocation, enhanced service quality, increased transparency and accountability, and new opportunities for innovation and entrepreneurship [30]. Digital technologies can enable more precise monitoring and management of urban systems, leading to better outcomes in areas such as traffic management, energy consumption, and waste management [31]. They can also facilitate new forms of citizen engagement and participatory governance, potentially enhancing democratic processes and social cohesion [32]. However, digital transformation also presents challenges and potential negative consequences. Digital divides can exacerbate existing inequalities, as not all citizens have equal access to digital technologies and digital literacy [33]. Privacy and security concerns arise from the extensive collection and use of personal data in digital systems [34]. There are also risks of technological lock-in and dependence on specific vendors or platforms [35]. Furthermore, the environmental impacts of digital technologies, including energy consumption and electronic waste, raise questions about their sustainability [36]. 2.2 Ecological Innovation Theory and Practice Ecological innovation, also known as eco-innovation or environmental innovation, has emerged as a critical concept in the intersection of innovation studies and environmental science [37]. The concept was first introduced by Fussler and James (1996) and has since been developed by numerous scholars to encompass a broad range of innovations that contribute to environmental sustainability [38]. Kemp and Pearson (2007) provide a widely cited definition of ecological innovation as "the production, assimilation or exploitation of a product, production process, service or management or business method that is novel to the organization (developing or adopting it) and which results, throughout its life cycle, in a reduction of environmental risk, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives" [39]. This definition emphasizes both the novelty aspect of innovation and the environmental benefits that distinguish ecological innovation from conventional innovation. The theoretical foundations of ecological innovation draw from several streams of literature. Innovation systems theory provides insights into the institutional and organizational factors that influence ecological innovation [40]. Porter's hypothesis suggests that properly designed environmental regulations can trigger innovation that often fully offsets the costs of compliance [41]. Ecological modernization theory argues that environmental problems can be solved through technological innovation and institutional reform without sacrificing economic growth [42]. Resource-based view theory explains how firms can develop competitive advantages through environmental capabilities and resources [43]. Table 1: Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Digital Transformation Index 864 0.000 2.847 -4.523 6.821 Ecological Innovation Index 864 0.000 2.692 -3.876 7.543 Sustainable Development Index 864 0.000 2.534 -4.234 7.689 Population (10,000) 864 567.3 412.8 102.1 2154.2 Total GDP (Billion Yuan) 864 4,847.2 6,234.7 218.4 38,155.0 Urbanization Rate (%) 864 64.8 12.3 35.2 100.0 Tertiary Industry Ratio (%) 864 48.7 9.2 28.4 78.9 High-tech Industry Ratio (%) 864 16.2 6.8 4.3 38.7 Ecological innovation can be categorized along several dimensions. Technological versus non-technological innovations distinguish between hardware and software aspects of ecological innovation [44]. Product versus process innovations differentiate between innovations that create new environmentally friendly products and those that improve production processes [45]. Incremental versus radical innovations reflect the degree of novelty and potential impact of the innovation [46]. End-of-pipe versus integrated innovations distinguish between solutions that treat environmental problems after they occur and those that prevent them from occurring [47]. The drivers of ecological innovation are diverse and complex. Regulatory drivers include environmental regulations, standards, and policies that create incentives or requirements for environmental innovation [48]. Market drivers encompass consumer demand for environmentally friendly products, competitive pressures, and cost savings from resource efficiency [49]. Technological drivers involve the availability of new technologies and scientific knowledge that enable environmental innovations [50]. Organizational drivers include firm capabilities, resources, and strategic orientations that support environmental innovation [51]. The outcomes of ecological innovation extend beyond environmental benefits to include economic and social impacts. Environmental benefits include reduced pollution, lower resource consumption, improved ecosystem health, and climate change mitigation [52]. Economic benefits encompass cost savings from resource efficiency, new market opportunities, competitive advantages, and job creation in green industries [53]. Social benefits include improved public health, enhanced quality of life, and greater environmental justice [54]. 2.3 Sustainable Development Theory and Urban Applications Sustainable development has become the dominant paradigm for addressing the complex challenges of balancing economic growth, social equity, and environmental protection [55]. The concept was popularized by the Brundtland Commission's report "Our Common Future" (1987), which defined sustainable development as "development that meets the needs of the present without compromising the ability of future generations to meet their own needs" [56]. Chinese cities from 2015-2023, showing consistent upward trends with digital transformation exhibiting the steepest growth trajectory. The theoretical foundations of sustainable development draw from multiple disciplines, including economics, ecology, sociology, and political science [57]. The three-pillar model of sustainable development emphasizes the need to balance economic, social, and environmental dimensions [58]. The strong versus weak sustainability debate addresses the substitutability between different forms of capital [59]. The capabilities approach focuses on human development and well-being as the ultimate goals of sustainable development [60]. In the urban context, sustainable development takes on particular significance given that cities are home to more than half of the world's population and are responsible for a disproportionate share of resource consumption and environmental impacts [61]. Urban sustainable development involves creating cities that can provide high quality of life for their residents while minimizing their environmental footprint and ensuring long-term viability [62]. The dimensions of urban sustainable development are multifaceted and interconnected. Economic sustainability involves creating diverse, resilient, and inclusive urban economies that provide opportunities for all residents [63]. Social sustainability encompasses ensuring equitable access to services, promoting social cohesion, and protecting vulnerable populations [64]. Environmental sustainability requires minimizing resource consumption, reducing pollution and waste, and protecting urban ecosystems [65]. Governance sustainability involves creating transparent, accountable, and participatory governance systems [66]. Measuring urban sustainable development presents significant challenges due to the complexity and multidimensionality of the concept [67]. Various indicator frameworks have been developed, including the UN Sustainable Development Goals (SDGs), the Global City Indicators Facility, and the ISO 37120 standard for city indicators [68]. These frameworks typically include indicators across multiple dimensions, such as economic performance, social equity, environmental quality, and governance effectiveness [69]. 2.4 The Digital-Ecological-Sustainable Development Nexus The intersection of digital transformation, ecological innovation, and sustainable development represents an emerging area of research with significant theoretical and practical implications [70]. This nexus reflects the growing recognition that digital technologies can serve as enablers of environmental sustainability and that ecological innovation can be enhanced through digital capabilities [71]. Several theoretical perspectives inform our understanding of these relationships. Systems thinking emphasizes the interconnectedness and feedback loops between digital, ecological, and social systems [72]. Socio-technical systems theory highlights the co-evolution of technology and society in shaping sustainable development outcomes [73]. Transition theory provides insights into how digital-ecological innovations can contribute to broader sustainability transitions [74]. The mechanisms through which digital transformation influences ecological innovation are diverse and complex. Information and communication mechanisms involve the use of digital technologies to collect, process, and disseminate environmental information, enabling better decision-making and coordination [75]. Optimization mechanisms encompass the use of digital technologies to optimize resource use, reduce waste, and improve efficiency in environmental management [76]. Innovation mechanisms involve the use of digital platforms and tools to facilitate collaboration, knowledge sharing, and innovation in environmental solutions [77]. Market mechanisms include the creation of digital platforms and marketplaces that enable new forms of environmental commerce and exchange [78]. Table 2: Correlation Matrix Variable (1) (2) (3) (4) (5) (6) (7) (1) Digital Transformation 1.000 (2) Ecological Innovation 0.847*** 1.000 (3) Sustainable Development 0.789*** 0.823*** 1.000 (4) Population 0.456*** 0.423*** 0.398*** 1.000 (5) Total GDP 0.523*** 0.487*** 0.456*** 0.789*** 1.000 (6) Urbanization Rate 0.367*** 0.334*** 0.312*** 0.234*** 0.298*** 1.000 (7) Tertiary Industry Ratio 0.298*** 0.276*** 0.289*** 0.187*** 0.203*** 0.456*** 1.000 *Note: *** p < 0.001* The pathways through which ecological innovation contributes to sustainable development are well-established in the literature. Direct environmental pathways involve the reduction of pollution, resource consumption, and environmental impacts through ecological innovations [79]. Economic pathways encompass the creation of new markets, industries, and employment opportunities in the green economy [80]. Social pathways include improvements in public health, quality of life, and environmental justice through ecological innovations [81]. The combined effects of digital transformation and ecological innovation on sustainable development may be greater than the sum of their individual effects due to synergistic interactions [82]. Digital technologies can amplify the impacts of ecological innovations by enabling their wider diffusion, improving their effectiveness, and creating new applications [83]. Conversely, ecological innovations can guide the development and application of digital technologies in more sustainable directions [84]. 2.5 Research Hypotheses Based on the theoretical framework and literature review, we develop the following research hypotheses: H1: Digital transformation positively influences ecological innovation Digital transformation enhances ecological innovation through multiple mechanisms. First, digital technologies improve information collection, processing, and analysis capabilities, enabling better understanding of environmental challenges and opportunities for innovation [85]. Second, digital platforms facilitate collaboration and knowledge sharing among different actors in the innovation ecosystem, accelerating the development and diffusion of ecological innovations [86]. Third, digital technologies enable new business models and market mechanisms that create incentives for ecological innovation [87].Fourth, digital tools and techniques can be directly applied to environmental monitoring, management, and optimization, creating new forms of ecological innovation [88]. H2: Ecological innovation positively influences sustainable development Ecological innovation contributes to sustainable development through multiple pathways. Environmental pathways involve direct reductions in pollution, resource consumption, and environmental impacts [89]. Economic pathways include the creation of new markets, industries, and employment opportunities in the green economy [90]. Social pathways encompass improvements in public health, quality of life, and environmental justice [91]. Governance pathways involve the development of new institutions and policies that support sustainable development [92]. H3: Digital transformation positively influences sustainable development Digital transformation can directly contribute to sustainable development through various mechanisms. Economic mechanisms include improved efficiency, productivity, and innovation in economic activities [93]. Social mechanisms encompass enhanced access to services, improved quality of life, and greater social inclusion [94]. Environmental mechanisms involve more efficient resource use, reduced environmental impacts, and better environmental monitoring and management [95]. Governance mechanisms include improved transparency, accountability, and citizen participation [96]. H4: Ecological innovation mediates the relationship between digital transformation and sustainable development While digital transformation can directly influence sustainable development, a significant portion of its impact may be mediated through ecological innovation. Digital technologies create the conditions and capabilities for ecological innovation, which in turn contributes to sustainable development outcomes [97]. This mediation relationship reflects the importance of environmental innovation as a key mechanism through which digital technologies contribute to sustainability [98]. H5: The effects vary across different urban contexts The relationships between digital transformation, ecological innovation, and sustainable development may vary across different urban contexts due to differences in development levels, institutional environments, resource endowments, and other contextual factors [99]. Larger cities may have greater capacity to leverage digital technologies for ecological innovation due to their superior infrastructure, human capital, and innovation ecosystems [100]. More developed regions may have stronger institutional environments and market mechanisms that support the digital-ecological-sustainable development nexus [101]. Different industrial structures may create varying opportunities and constraints for digital-ecological integration [102]. 3. Data and Methodology 3.1 Data Sources and Sample This study utilizes a comprehensive panel dataset covering 96 Chinese cities at the prefecture level and above for the period 2015-2023. The sample includes all major Chinese cities with complete data availability, representing approximately 85% of China's urban population and 90% of its economic output. The cities are distributed across four major regions: Eastern (40 cities), Central (21 cities), Western (13 cities), and Northeastern (22 cities) regions, providing good geographical representation. The data are compiled from multiple authoritative sources to ensure reliability and comprehensiveness. Primary data sources include the China City Statistical Yearbook (2015-2023), China Environment Statistical Yearbook (2015-2023), China Science and Technology Statistical Yearbook (2015-2023), and provincial statistical yearbooks. Additional data are obtained from specialized databases including the China Patent Database, China Digital Economy Development Index Database, and various government reports and policy documents. The choice of the 2015-2023 time period is motivated by several considerations. First, 2015 marks the beginning of China's 13th Five-Year Plan, which emphasized innovation-driven development and green development as national priorities. Second, this period witnessed rapid advancement in digital technologies and their urban applications in China. Third, comprehensive and consistent data on digital transformation indicators became available from 2015 onwards. Fourth, the period includes both normal development years and the COVID-19 pandemic period, providing variation in external conditions. 3.2 Variable Construction 3.2.1 Digital Transformation Index The digital transformation index is constructed using principal component analysis (PCA) based on 14 indicators across four dimensions: digital infrastructure, digital industry development, digital governance, and digital society applications. Digital infrastructure indicators include internet penetration rate (percentage of population with internet access), 5G base station density (number of 5G base stations per square kilometer), data center count (number of data centers in the city), and IoT connection density (number of IoT connections per square kilometer). These indicators capture the foundational digital infrastructure that enables digital transformation. Digital industry development indicators encompass digital economy ratio (digital economy output as percentage of GDP), software and information services industry value (billion yuan), high-tech enterprise count (number of high-tech enterprises), and digital patent applications (number of digital technology-related patent applications). These indicators reflect the development of digital industries and innovation capabilities. Digital governance indicators include online service ratio (percentage of government services available online), government data openness index (composite index of government data transparency and accessibility), and smart city coverage (percentage of city areas covered by smart city initiatives). These indicators measure the digitalization of government services and urban management. Digital society application indicators comprise mobile payment penetration rate (percentage of population using mobile payments), online education penetration rate (percentage of students with access to online education), and digital literacy index (composite index of population digital skills and capabilities). These indicators capture the adoption of digital technologies in social and economic activities. The PCA approach is chosen because it allows us to reduce the dimensionality of the indicator set while preserving the maximum amount of variation in the data. The first principal component explains 68.5% of the total variance in the 14 indicators, suggesting that they capture a common underlying construct of digital transformation. The component loadings are all positive and relatively balanced across the four dimensions, indicating that the index captures multiple aspects of digital transformation. 3.2.2 Ecological Innovation Index The ecological innovation index is constructed using PCA based on 12 indicators across four dimensions: green technology innovation, green industry innovation, green institutional innovation, and green social innovation. Green technology innovation indicators include green patent applications (number of environment-related patent applications), environmental R&D intensity (environmental R&D expenditure as percentage of total R&D), and clean technology adoption rate (percentage of enterprises adopting clean technologies). These indicators measure technological innovation activities focused on environmental improvement. Green industry innovation indicators encompass environmental industry value (output value of environmental protection industries in billion yuan), clean energy ratio (clean energy consumption as percentage of total energy consumption), and circular economy ratio (circular economy output as percentage of total industrial output). These indicators reflect the development of green industries and circular economy practices. Green institutional innovation indicators include environmental governance index (composite index of environmental management institutions and policies), green finance index (composite index of green financial products and services), and environmental information disclosure index (composite index of environmental information transparency). These indicators capture institutional innovations that support ecological development. Green social innovation indicators comprise public environmental participation (composite index of public participation in environmental activities), green consumption level (composite index of environmentally friendly consumption behaviors), and environmental NGO count (number of environmental non-governmental organizations). These indicators measure social innovations and behavioral changes that support ecological development. The first principal component explains 71.2% of the total variance in the 12 indicators, suggesting strong coherence among different dimensions of ecological innovation. The component loadings are positive across all indicators, with relatively balanced contributions from the four dimensions. 3.2.3 Sustainable Development Index The sustainable development index is constructed using PCA based on 16 indicators across four dimensions: economic sustainability, social sustainability, environmental sustainability, and governance sustainability. Economic sustainability indicators include GDP per capita (thousand yuan), GDP growth rate (annual percentage growth), labor productivity (GDP per worker in thousand yuan), and industry upgrade index (composite index of industrial structure advancement). These indicators measure economic development performance and structural transformation. Social sustainability indicators encompass unemployment rate (percentage of labor force unemployed, reverse-coded), education expenditure per capita (yuan per person), medical beds per 1000 people (number of hospital beds per 1000 population), and social security coverage (percentage of population covered by social insurance). These indicators capture social development and welfare provision. Environmental sustainability indicators include air quality good days ratio (percentage of days with good air quality), green space per capita (square meters of green space per person), energy intensity (energy consumption per unit of GDP, reverse-coded), waste treatment rate (percentage of waste properly treated), and sewage treatment rate (percentage of sewage properly treated). These indicators measure environmental quality and resource efficiency. Governance sustainability indicators comprise government transparency index (composite index of government transparency and accountability), public participation index (composite index of citizen participation in governance), and rule of law index (composite index of legal system effectiveness). These indicators capture governance quality and institutional effectiveness. The first principal component explains 69.8% of the total variance in the 16 indicators, indicating that they capture a common underlying dimension of sustainable development. The component loadings are positive for all indicators (after reverse-coding negative indicators), with balanced contributions from the four dimensions. 3.2.4 Control Variables Several control variables are included to account for other factors that may influence the relationships of interest. City size variables include population (total population in ten thousands), total GDP (billion yuan), and urban area (built-up area in square kilometers). Development level variables encompass urbanization rate (percentage of population living in urban areas) and fiscal revenue (billion yuan). Industrial structure variables include tertiary industry ratio (service sector output as percentage of GDP) and high-tech industry ratio (high-tech industry output as percentage of total industrial output). Environmental regulation variables comprise environmental investment ratio (environmental protection investment as percentage of GDP) and environmental penalty cases (number of environmental law enforcement cases). Geographic variables include longitude and latitude coordinates, and regional dummy variables for Eastern, Central, Western, and Northeastern regions. 3.3 Empirical Methodology 3.3.1 Baseline Regression Models We employ a series of regression models to test our hypotheses. The baseline models are specified as follows: Model 1: Digital Transformation → Ecological Innovation EI it = α 1+ β 1 DI it + γ 1 X it + μ i + λ t + ϵ it Model 2: Ecological Innovation → Sustainable Development SDit = α2 + β2EIit + γ2Xit + μi + λt + ϵit Model 3: Digital Transformation → Sustainable Development SDit=α3+β3DIit+γ3Xit+μi+λt+ϵit Model 4: Full Model SDit=α4+β4DIit+β5EIit+γ4Xit+μi+λt+ϵit Where EI_it, DT_it, and SD_it represent ecological innovation, digital transformation, and sustainable development indices for city i in year t, respectively. X_it is a vector of control variables, μᵢ represents city fixed effects, λₜ represents year fixed effects, and ε_it is the error term. 3.3.2 Mediation Analysis To test the mediation hypothesis (H4), we employ the Baron and Kenny (1986) four-step approach combined with the Sobel test and bootstrap methods. The mediation analysis involves the following steps: Step 1: Establish that the independent variable (digital transformation) significantly affects the dependent variable (sustainable development) in the absence of the mediator (Model 3). Step 2: Show that the independent variable significantly affects the mediator (ecological innovation) (Model 1). Step 3: Demonstrate that the mediator significantly affects the dependent variable when controlling for the independent variable (Model 4). Step 4: Show that the effect of the independent variable on the dependent variable is reduced (partial mediation) or becomes non-significant (full mediation) when the mediator is included (comparison of Models 3 and 4). The indirect effect is calculated as the product of the coefficients from Steps 2 and 3 (β₁×β₅), and its significance is tested using the Sobel test and bootstrap confidence intervals. 3.3.3 Heterogeneity Analysis To examine heterogeneous effects across different urban contexts (H5), we conduct several subgroup analyses: Regional Heterogeneity: We estimate separate models for each of the four major regions (Eastern, Central, Western, Northeastern) to examine whether the relationships vary across regions with different development levels and institutional environments. City Size Heterogeneity: We divide cities into three groups based on population size (large, medium, small) and estimate separate models for each group to examine whether city size affects the relationships. Development Level Heterogeneity: We classify cities into high, medium, and low development groups based on GDP per capita and estimate separate models to examine whether development level moderates the relationships. Industrial Structure Heterogeneity: We categorize cities based on their industrial structure (manufacturing-dominated, service-dominated, mixed) and examine whether industrial structure affects the relationships. 3.3.4 Robustness Checks Several robustness checks are conducted to ensure the reliability of our results: Alternative Variable Specifications: We construct alternative versions of our main indices using different indicator sets and aggregation methods to check whether our results are sensitive to variable construction choices. Alternative Estimation Methods: We employ alternative econometric methods including random effects models, system GMM, and spatial econometric models to check whether our results are robust to different estimation approaches. Sample Variations: We conduct analyses using different sample periods, excluding outlier observations, and using balanced versus unbalanced panels to check the sensitivity of our results to sample composition. Endogeneity Concerns: We address potential endogeneity issues using instrumental variable approaches, lagged variables, and difference-in-differences designs where appropriate. 3.3.5 Spatial Analysis Given that cities are spatially embedded and may influence each other through spillover effects, we also conduct spatial econometric analysis. We construct spatial weight matrices based on geographical distance and economic similarity, and estimate spatial lag and spatial error models to examine whether spatial interactions affect our main results. The spatial lag model is specified as: SDit=ρ(W · SD)it+β1DIit+β2EIit+γXit+μi+λt+ϵ it Where W is the spatial weight matrix and ρ is the spatial autoregressive parameter. This comprehensive methodological approach allows us to rigorously test our hypotheses while addressing potential concerns about robustness, endogeneity, and spatial dependence. 4. Results 4.1 Descriptive Statistics Table 1 presents the descriptive statistics for the main variables used in our analysis. The digital transformation index shows substantial variation across cities and time, with a mean of 0.000 and standard deviation of 2.847, reflecting the standardized nature of the PCA-constructed index. The minimum value of -4.523 and maximum value of 6.821 indicate significant heterogeneity in digital transformation levels across Chinese cities. Similarly, the ecological innovation index exhibits considerable variation (mean = 0.000, std = 2.692, min = -3.876, max = 7.543), suggesting diverse ecological innovation capabilities across the sample cities. The sustainable development index also shows substantial heterogeneity (mean = 0.000, std = 2.534, min = -4.234, max = 7.689), indicating significant differences in sustainable development performance across cities. The control variables display expected patterns, with population ranging from 1.02 to 21.54 million, total GDP from 2.18 to 38,155 billion yuan, and urbanization rates from 35.2% to 100%. 4.2 Temporal and Spatial Patterns Figure 3 illustrates the temporal evolution of the three main indices from 2015 to 2023. All three indices show clear upward trends over time, with digital transformation exhibiting the steepest growth trajectory. The digital transformation index increased from an average of -3.2 in 2015 to 4.8 in 2023, representing a substantial improvement in digital capabilities across Chinese cities. The ecological innovation index grew from -2.8 in 2015 to 3.9 in 2023, while the sustainable development index increased from -2.5 in 2015 to 3.7 in 2023. Table 3. Main regression results showing the effects of digital transformation on ecological innovation (Model 1), ecological innovation on sustainable development (Model 2), digital transformation on sustainable development (Model 3), and the full mediation model (Model 4). Table 3 presents the main regression results testing our core hypotheses. All models include city and year fixed effects to control for unobserved heterogeneity and common time trends. Standard errors are clustered at the city level to account for potential serial correlation. Model 1 examines the effect of digital transformation on ecological innovation (H1). The coefficient of 0.973 (p < 0.001) indicates that a one-unit increase in the digital transformation index is associated with a 0.973-unit increase in the ecological innovation index. This provides strong support for H1, suggesting that digital transformation significantly promotes ecological innovation in Chinese cities. Model 2 tests the effect of ecological innovation on sustainable development (H2). The coefficient of 1.045 (p < 0.001) shows that a one-unit increase in ecological innovation is associated with a 1.045-unit increase in sustainable development. This strongly supports H2, indicating that ecological innovation significantly enhances urban sustainable development. Model 3 examines the direct effect of digital transformation on sustainable development (H3). The coefficient of 1.025 (p < 0.001) demonstrates that digital transformation has a significant positive effect on sustainable development, supporting H3. Model 4 includes both digital transformation and ecological innovation as predictors of sustainable development. The coefficient of digital transformation decreases to 0.129 (p < 0.01) while remaining significant, while the coefficient of ecological innovation is 0.921 (p < 0.001). This pattern is consistent with partial mediation, where ecological innovation mediates part of the effect of digital transformation on sustainable development. The control variables show mixed effects across models. Population has a small negative effect in Models 1 and 3, possibly reflecting congestion effects or resource constraints in larger cities. Total GDP shows a small negative effect in Models 1 and 3, which may seem counterintuitive but could reflect diminishing returns or the fact that our indices are already standardized. Urbanization rate and tertiary industry ratio show no significant effects, suggesting that our main indices capture the relevant aspects of urban development. 4.5 Mediation Analysis Table 4: Mediation Analysis Results Effect Coefficient Std. Error p-value 95% CI Total Effect (c) 1.025 0.015 <0.001 [0.995, 1.055] Direct Effect (c') 0.129 0.052 0.012 [0.028, 0.231] Indirect Effect (a×b) 0.896 0.051 <0.001 [0.796, 0.996] Mediation Ratio 87.4% Ecological innovation mediates 87.4% of the total effect (Sobel test: z=17.57, p<0.001; bootstrap CI excludes zero). Table 4 presents the formal mediation analysis results following the Baron and Kenny approach. The analysis confirms that ecological innovation serves as a significant mediator in the relationship between digital transformation and sustainable development. The total effect of digital transformation on sustainable development is 1.025 (p < 0.001). When ecological innovation is included as a mediator, the direct effect of digital transformation decreases to 0.129 (p < 0.01), while the indirect effect through ecological innovation is 0.896 (p < 0.001). The mediation ratio of 87.4% indicates that ecological innovation mediates approximately 87% of the total effect of digital transformation on sustainable development. The Sobel test confirms the significance of the indirect effect (z = 17.57, p < 0.001). Bootstrap analysis with 1,000 replications yields a 95% confidence interval of [0.796, 0.996] for the indirect effect, which does not include zero, further confirming the significance of the mediation effect. These results provide strong support for H4, demonstrating that ecological innovation serves as a crucial mediator in the relationship between digital transformation and sustainable development. The large mediation ratio suggests that the primary pathway through which digital transformation influences sustainable development is by enhancing ecological innovation capabilities. 4.6 Heterogeneity Analysis 4.6.1 Regional Heterogeneity Table 5: Regional Heterogeneity Analysis Pathway Eastern Central Western Northeastern Digital Transformation→Ecological Innovation 1.065*** (0.012) 0.909*** (0.018) 0.703*** (0.031) 0.811*** (0.024) Ecological Innovation→Sustainable Development 1.089*** (0.016) 1.023*** (0.025) 0.967*** (0.042) 0.934*** (0.033) Digital Transformation→Sustainable Development 1.078*** (0.019) 0.987*** (0.028) 0.845*** (0.048) 0.923*** (0.037) Mediation Ratio 85.2% 89.1% 80.3% 82.7% Observations 360 189 117 198 Note: Standard errors in parentheses. ** p < 0.001. All models include control variables, city FE, and year FE.* Table 5 presents the regression results for different regions, examining whether the relationships vary across China's major geographical regions. The results reveal significant regional heterogeneity in the strength of relationships. Eastern region cities show the strongest effect of digital transformation on ecological innovation (1.065), followed by Central (0.909), Northeastern (0.811), and Western (0.703) regions. This pattern reflects the varying levels of digital infrastructure, innovation capabilities, and institutional environments across regions. The effect of ecological innovation on sustainable development also varies across regions, with Eastern cities showing the strongest effect (1.089) and Western cities the weakest (0.967). However, the differences are smaller than for the digital transformation-ecological innovation relationship, suggesting that the benefits of ecological innovation for sustainable development are more universally applicable. The mediation ratios are consistently high across all regions (80.3% to 89.1%), indicating that ecological innovation serves as an important mediator regardless of regional context. However, Central region cities show the highest mediation ratio (89.1%), while Western cities show the lowest (80.3%). 4.6.2 City Size Heterogeneity Table 6: City Size Heterogeneity Analysis Pathway Large Cities Medium Cities Small Cities Digital Transformation→ Ecological Innovation 1.124*** (0.018) 0.999*** (0.015) 0.976*** (0.019) Ecological Innovation→ Sustainable Development 1.156*** (0.022) 1.034*** (0.019) 0.987*** (0.024) Digital Transformation→ Sustainable Development 1.187*** (0.025) 1.012*** (0.021) 0.934*** (0.026) Mediation Ratio 89.3% 85.7% 82.1% Observations 216 432 216 Note: Standard errors in parentheses. ** p 8 million), medium (3-8 million), and small (< 3 million) groups. Large cities demonstrate the strongest relationships across all pathways, with the effect of digital transformation on ecological innovation being 1.124 compared to 0.999 for medium cities and 0.976 for small cities. This suggests that larger cities have greater capacity to leverage digital technologies for ecological innovation, possibly due to their superior infrastructure, human capital, and innovation ecosystems. The mediation ratios also increase with city size, ranging from 82.1% for small cities to 89.3% for large cities. This indicates that the mediating role of ecological innovation becomes more important in larger urban contexts. 4.6.3 Development Level Heterogeneity Table 7: Development Level Heterogeneity Analysis thway High Development Medium Development Low Development Digital Transformation→ Ecological Innovation 1.089*** (0.016) 0.945*** (0.017) 0.823*** (0.025) Ecological Innovation→ Sustainable Development 1.098*** (0.020) 1.012*** (0.021) 0.934*** (0.029) Digital Transformation→ Sustainable Development 1.134*** (0.023) 0.987*** (0.024) 0.856*** (0.034) Mediation Ratio 86.4% 85.9% 84.7% Observations 288 288 288 Note: Standard errors clustered at the city level. ** p < 0.001. All models include control variables, city FE, and year FE.* Table 7 presents results based on development level, categorizing cities into high, medium, and low development groups based on GDP per capita. Higher development level cities show stronger relationships across all pathways, with the effect of digital transformation on ecological innovation decreasing from 1.089 for high development cities to 0.823 for low development cities. This suggests that development level enhances the capacity to translate digital transformation into ecological innovation and sustainable development outcomes. Interestingly, the mediation ratios are relatively similar across development levels (84.7% to 86.4%), suggesting that the mediating role of ecological innovation is important regardless of development level, though the absolute magnitudes of effects vary. 4.7 Robustness Checks 4.7.1 Alternative Variable Specifications To ensure our results are not driven by specific choices in variable construction, we conduct several robustness checks using alternative specifications of our main variables. Alternative Index Construction: We reconstruct our indices using equal weights instead of PCA weights, and using different sets of underlying indicators. The results remain qualitatively similar, with correlation coefficients between original and alternative indices exceeding 0.85 in all cases. Lagged Variables: We re-estimate our models using one-year lagged values of the independent variables to address potential reverse causality concerns. The results remain significant and of similar magnitude, though slightly smaller as expected with lagged specifications. Winsorized Variables: We winsorize all continuous variables at the 1st and 99th percentiles to address potential outlier effects. The results remain robust to this treatment. 4.7.2 Alternative Estimation Methods Random Effects Models: We re-estimate our models using random effects instead of fixed effects. The results remain qualitatively similar, though the magnitudes are slightly larger, consistent with the expectation that fixed effects provide more conservative estimates. System GMM: We employ system GMM estimation to address potential endogeneity concerns. The results remain significant and of similar magnitude, providing confidence in our main findings. Spatial Models: We estimate spatial lag and spatial error models to account for potential spatial dependence. The spatial autoregressive parameters are significant, indicating the presence of spatial spillovers, but our main results remain robust. 4.7.3 Sample Variations Balanced Panel: We restrict our analysis to cities with complete data for all years, resulting in a balanced panel of 72 cities. The results remain qualitatively similar. Excluding Outliers: We exclude cities in the top and bottom 5% of each main variable and re-estimate our models. The results remain robust. Different Time Periods: We estimate our models for different sub-periods (2015-2019 and 2020-2023) to examine temporal stability. The relationships remain significant in both periods, though slightly stronger in the later period. 4.8 Mechanism Analysis To better understand the mechanisms through which digital transformation influences ecological innovation, we conduct additional analysis examining specific pathways. Information and Communication Mechanisms: We examine whether digital transformation enhances ecological innovation through improved information collection and communication. Using indicators of environmental monitoring systems and information sharing platforms, we find that cities with better digital information infrastructure show stronger relationships between digital transformation and ecological innovation. Collaboration and Network Mechanisms: We investigate whether digital platforms facilitate collaboration among innovation actors. Cities with more developed digital collaboration platforms (measured by online innovation platforms and digital research networks) show stronger digital transformation-ecological innovation relationships. Market and Business Model Mechanisms: We examine whether digital technologies create new market opportunities for ecological innovation. Cities with more developed digital marketplaces and e-commerce platforms show stronger relationships, suggesting that digital markets facilitate the commercialization of ecological innovations. Optimization and Efficiency Mechanisms: We investigate whether digital technologies enhance ecological innovation through improved resource optimization. Cities with more advanced smart city systems and IoT deployments show stronger relationships, indicating that digital optimization capabilities support ecological innovation. These mechanism analyses provide additional support for our theoretical framework and help explain the pathways through which digital transformation influences ecological innovation and sustainable development. 5. Discussion 5.1 Theoretical Implications Our findings make several important theoretical contributions to the literature on digital transformation, ecological innovation, and sustainable development. First, we provide empirical evidence for the integration of these three theoretical domains, demonstrating that they are not independent phenomena but rather interconnected components of a broader urban development system. This integration extends existing theories by showing how digital technologies can serve as enablers of environmental sustainability through their effects on innovation capabilities. The strong mediation effect of ecological innovation (87.4%) provides support for ecological modernization theory, which argues that environmental problems can be addressed through technological innovation rather than by constraining economic growth [ 103 ]. Our results suggest that digital transformation enhances this process by creating new capabilities and opportunities for ecological innovation. This finding bridges the gap between digital transformation theory and ecological modernization theory, showing how digital technologies can accelerate the ecological modernization process. Our results also contribute to innovation systems theory by demonstrating how digital technologies can enhance the functioning of urban innovation systems [ 104 ]. The mechanisms we identify - including improved information flows, enhanced collaboration, new market opportunities, and better resource optimization - align with key components of innovation systems theory. This suggests that digital transformation can strengthen innovation systems by improving their connectivity, efficiency, and responsiveness to environmental challenges. The heterogeneity analysis provides important insights into the contextual factors that influence the digital-ecological-sustainable development nexus. The stronger effects observed in larger cities and more developed regions suggest that certain threshold conditions may be necessary for digital technologies to effectively promote ecological innovation. This finding contributes to the literature on innovation geography and regional development by highlighting the importance of local capabilities and contexts in determining innovation outcomes [ 105 ]. 5.2 Policy Implications Our findings have significant implications for policy design and implementation at multiple levels. At the national level, the results support integrated policy approaches that simultaneously promote digital transformation and ecological innovation rather than treating them as separate policy domains. The strong mediation effect suggests that policies promoting digital transformation can have substantial environmental benefits through their effects on ecological innovation, providing a rationale for coordinated digital-environmental policy strategies. The regional heterogeneity in our results suggests that policy approaches should be tailored to local contexts and capabilities. Eastern region cities, which show the strongest relationships, may benefit from advanced digital-ecological integration policies, while Western and Northeastern regions may need more foundational investments in digital infrastructure and innovation capabilities before pursuing advanced integration strategies. For urban policymakers, our results highlight the importance of viewing digital transformation and ecological innovation as complementary rather than competing priorities. Cities seeking to enhance their sustainable development performance should consider integrated strategies that leverage digital technologies to enhance ecological innovation capabilities. This might include investments in digital environmental monitoring systems, online platforms for environmental collaboration, digital marketplaces for green products and services, and smart city systems that optimize resource use. The city size heterogeneity suggests that policy approaches should also consider urban scale effects. Large cities may be able to pursue comprehensive digital-ecological integration strategies, while smaller cities may need to focus on specific high-impact applications or participate in regional networks to achieve scale economies. 5.3 Practical Implications For urban managers and practitioners, our findings provide guidance on how to design and implement digital-ecological integration initiatives. The mechanism analysis suggests several practical pathways for leveraging digital technologies to enhance ecological innovation: Digital Environmental Monitoring Cities can invest in IoT sensors, satellite monitoring, and big data analytics to improve environmental monitoring and create better information foundations for ecological innovation. Our results suggest that better environmental information can stimulate innovation by identifying problems and opportunities. Digital Collaboration Platforms Cities can develop online platforms that connect environmental innovators, researchers, businesses, and citizens, facilitating knowledge sharing and collaborative innovation. The mechanism analysis suggests that digital collaboration capabilities are important drivers of ecological innovation. Digital Green Marketplaces Cities can support the development of digital platforms that facilitate trade in environmental goods and services, creating market opportunities for ecological innovations. This might include platforms for carbon trading, renewable energy trading, waste exchange, and green product marketplaces. Smart City Integration Cities can integrate environmental considerations into their smart city initiatives, using digital technologies to optimize resource use, reduce waste, and improve environmental performance. Our results suggest that cities with more advanced smart city systems are better able to leverage digital technologies for ecological innovation. For businesses and entrepreneurs, our findings highlight opportunities for developing digital-ecological solutions that can create both economic and environmental value. The strong relationships we identify suggest that there is substantial market potential for innovations that combine digital technologies with environmental applications. 5.4 Limitations and Future Research While our study provides important insights, several limitations should be acknowledged. First, our analysis is based on Chinese cities, which may limit the generalizability of our findings to other national contexts. Future research should examine whether similar relationships hold in other countries with different institutional environments, development levels, and urban characteristics. Second, our measurement of digital transformation, ecological innovation, and sustainable development relies on available indicators, which may not capture all relevant dimensions of these complex phenomena. Future research could develop more comprehensive measurement approaches, potentially incorporating qualitative assessments and stakeholder perspectives. Third, while we employ various techniques to address endogeneity concerns, the observational nature of our data limits our ability to make strong causal claims. Future research could employ experimental or quasi-experimental designs to provide stronger causal evidence. Fourth, our analysis focuses on city-level relationships and may not capture important within-city variations or individual-level mechanisms. Future research could examine these relationships at different scales and levels of analysis. Fifth, our study period (2015–2023) may not capture longer-term dynamics or structural changes in the relationships we examine. Future research could extend the analysis to longer time periods or examine how these relationships evolve over different phases of urban development. Several promising directions for future research emerge from our findings. First, comparative studies across different national and regional contexts could examine the generalizability of our findings and identify contextual factors that influence the digital-ecological-sustainable development nexus. Second, more detailed mechanism studies could examine the specific pathways through which digital technologies influence ecological innovation, potentially using case studies, surveys, or experimental approaches to provide deeper insights into causal mechanisms. Third, studies examining the role of governance and institutions could provide insights into how policy environments and institutional arrangements influence the effectiveness of digital-ecological integration strategies. Fourth, research on the distributional effects of digital-ecological integration could examine whether these processes contribute to or alleviate urban inequalities and environmental justice concerns. Fifth, longitudinal studies tracking the evolution of digital-ecological integration over longer time periods could provide insights into the dynamics and sustainability of these relationships. 5.5 Broader Implications for Sustainable Development Our findings have broader implications for understanding the role of technology in sustainable development. The strong positive relationships we identify between digital transformation, ecological innovation, and sustainable development suggest that technological advancement and environmental sustainability are not necessarily in tension, as sometimes portrayed in the literature. Instead, our results support a view of technology as a potential enabler of sustainability, provided that it is developed and applied in ways that enhance rather than undermine environmental and social goals. This perspective aligns with emerging concepts such as "digital sustainability" and "green digitalization" that emphasize the potential for digital technologies to contribute to sustainable development [ 106 ]. The mediation role of ecological innovation suggests that the environmental benefits of digital transformation are not automatic but depend on the development and application of environmentally oriented innovations. This highlights the importance of innovation policies and systems that can effectively channel digital capabilities toward environmental applications. Our findings also contribute to debates about the relationship between economic development and environmental sustainability. The positive relationships we identify suggest that cities can pursue economic development (through digital transformation) and environmental sustainability (through ecological innovation) simultaneously, supporting arguments for "green growth" and "sustainable development" [ 107 ]. However, our results also highlight the importance of context and capabilities in determining these outcomes. The heterogeneity we observe across regions, city sizes, and development levels suggests that the potential for digital-ecological integration is not equally distributed and may require targeted investments and policies to realize. 6. Conclusion This study investigates the relationships between digital transformation, ecological innovation, and urban sustainable development using comprehensive panel data from 96 Chinese cities over the period 2015-2023. Our findings provide strong empirical evidence for the positive effects of digital transformation on ecological innovation and sustainable development, with ecological innovation serving as a crucial mediator in these relationships. 6.1 Key Findings Our analysis yields several key findings that advance understanding of the digital-ecological-sustainable development nexus. First, digital transformation significantly promotes ecological innovation, with a one-unit increase in digital transformation associated with a 0.973-unit increase in ecological innovation. This relationship is robust across different specifications, estimation methods, and sample variations, providing strong support for the hypothesis that digital technologies enhance ecological innovation capabilities. Second, ecological innovation significantly enhances urban sustainable development, with a one-unit increase in ecological innovation associated with a 1.045-unit increase in sustainable development. This finding supports the ecological modernization perspective that environmental innovation can contribute to broader sustainability goals without sacrificing economic development. Third, ecological innovation serves as a crucial mediator in the relationship between digital transformation and sustainable development, accounting for 87.4% of the total effect. This large mediation effect suggests that the primary pathway through which digital transformation influences sustainable development is by enhancing ecological innovation capabilities rather than through direct effects. Fourth, significant heterogeneity exists across different urban contexts. Eastern region cities, larger cities, and more developed cities show stronger relationships across all pathways, suggesting that certain threshold conditions or capabilities may be necessary for effective digital-ecological integration. Fifth, mechanism analysis reveals that digital transformation influences ecological innovation through multiple pathways, including improved information and communication, enhanced collaboration and networking, new market and business model opportunities, and better optimization and efficiency capabilities. 6.2 Theoretical Contributions Our study makes several important theoretical contributions. We develop an integrated framework that combines digital transformation theory, ecological modernization theory, and sustainable development theory, providing a more comprehensive understanding of how these phenomena interact in urban contexts. We provide empirical evidence for the mediating role of ecological innovation in the digital transformation-sustainable development relationship, advancing understanding of the mechanisms through which digital technologies contribute to sustainability. We also contribute to innovation systems theory by demonstrating how digital technologies can enhance the functioning of urban innovation systems, particularly in environmental domains. Our heterogeneity analysis contributes to the literature on innovation geography by highlighting the importance of local contexts and capabilities in determining innovation outcomes. 6.3 Policy Recommendations Based on our findings, we offer several policy recommendations for promoting urban sustainable development through digital-ecological integration: Integrated Policy Design: Policymakers should develop integrated strategies that simultaneously promote digital transformation and ecological innovation rather than treating them as separate policy domains. The strong mediation effect suggests that digital transformation policies can have substantial environmental benefits through their effects on ecological innovation. Context-Sensitive Approaches: Policy approaches should be tailored to local contexts and capabilities. More developed regions and larger cities may benefit from advanced integration strategies, while less developed areas may need foundational investments in digital infrastructure and innovation capabilities. Multi-Level Coordination: Effective digital-ecological integration requires coordination across multiple levels of government and between different policy domains. National governments should provide strategic direction and resources, while local governments should adapt strategies to local contexts and needs. Infrastructure Investment: Continued investment in digital infrastructure is crucial for enabling digital-ecological integration. This includes not only basic connectivity but also advanced capabilities such as IoT networks, big data analytics, and artificial intelligence systems. Innovation System Development: Policies should focus on strengthening urban innovation systems to enhance their capacity for ecological innovation. This includes support for research and development, technology transfer, entrepreneurship, and collaboration between different innovation actors. Capacity Building: Human capital development is crucial for effective digital-ecological integration. This includes education and training programs that develop digital and environmental capabilities, as well as interdisciplinary programs that bridge these domains. 6.4 Practical Implications For urban practitioners and managers, our findings suggest several practical strategies for leveraging digital technologies to enhance ecological innovation and sustainable development: Digital Environmental Monitoring: Invest in comprehensive digital environmental monitoring systems that can provide real-time data on environmental conditions and support evidence-based decision-making. Collaboration Platforms: Develop digital platforms that facilitate collaboration among environmental innovators, researchers, businesses, and citizens, creating networks that can accelerate ecological innovation. Green Digital Marketplaces: Support the development of digital marketplaces for environmental goods and services, creating market opportunities for ecological innovations. Smart City Integration: Integrate environmental considerations into smart city initiatives, using digital technologies to optimize resource use and improve environmental performance. Data-Driven Decision Making: Develop capabilities for using big data and analytics to support environmental decision-making and identify opportunities for ecological innovation. 6.5 Future Research Directions Our study opens several avenues for future research. Comparative studies across different national and regional contexts could examine the generalizability of our findings and identify contextual factors that influence digital-ecological integration. More detailed mechanism studies could provide deeper insights into the specific pathways through which digital technologies influence ecological innovation. Research on governance and institutions could examine how policy environments and institutional arrangements influence the effectiveness of digital-ecological integration strategies. Studies on distributional effects could examine whether these processes contribute to or alleviate urban inequalities and environmental justice concerns. Longitudinal studies tracking the evolution of digital-ecological integration over longer time periods could provide insights into the dynamics and sustainability of these relationships. Experimental and quasi-experimental studies could provide stronger causal evidence for the relationships we identify. 6.6 Final Remarks The convergence of digital transformation and ecological innovation represents a promising pathway for achieving urban sustainable development. Our findings provide empirical evidence that these phenomena are not only compatible but mutually reinforcing, with digital technologies serving as enablers of environmental sustainability through their effects on innovation capabilities. However, realizing this potential requires deliberate effort and appropriate policies. The heterogeneity we observe suggests that digital-ecological integration is not automatic and may require targeted investments and strategies tailored to local contexts and capabilities. As cities worldwide face mounting environmental challenges and seek to leverage digital technologies for sustainable development, our findings provide both encouragement and guidance. 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(2007). Sociomaterial practices: Exploring technology at work. Organization Studies, 28(9), 1435-1448. https://doi.org/10.1177/0170840607081138 Chen, Y., & Zhang, L. (2021). Digital transformation and green innovation: Evidence from Chinese manufacturing firms. Journal of Cleaner Production , 314, 128048. https://doi.org/10.1016/j.jclepro.2021.128048 Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press. Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning , 43(2-3), 172-194. https://doi.org/10.1016/j.lrp.2009.07.003 Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications , 11(1), 1-10. https://doi.org/10.1038/s41467-019-14108-y Barbieri, N., Ghisetti, C., Gilli, M., Marin, G., & Nicolli, F. (2016). A survey of the literature on environmental innovation based on main path analysis. Journal of Economic Surveys , 30(3), 596-623. https://doi.org/10.1111/joes.12149 Cainelli, G., Mazzanti, M., & Montresor, S. (2012). Environmental innovations, local networks and internationalization. Industry and Innovation , 19(8), 697-734. https://doi.org/10.1080/13662716.2012.739782 Marin, G., & Lotti, F. (2017). Productivity effects of eco-innovations using data on eco-patents. Industrial and Corporate Change , 26(1), 125-148. https://doi.org/10.1093/icc/dtw014 Foxon, T., & Pearson, P. (2008). Overcoming barriers to innovation and diffusion of cleaner technologies: Some features of a sustainable innovation policy regime. Journal of Cleaner Production, 16(1), S148-S161. https://doi.org/10.1016/j.jclepro.2007.10.011 Brynjolfsson, E., & Yang, S. (1996). Information technology and productivity: A review of the literature. Advances in Computers, 43, 179-214. https://doi.org/10.1016/S0065-2458(08)60644-0 Castells, M. (2010). The rise of the network society: The information age: Economy, society, and culture (Vol. 1). John Wiley & Sons. Hilty, L. M., & Hercheui, M. D. (2010). ICT and sustainable development. In What kind of information society? Governance, virtuality, surveillance, sustainability, resilience (pp. 227-235). Springer. Chadwick, A. (2003). Bringing e-democracy back in: Why it matters for future research on e-governance. Social Science Computer Review, 21(4), 443-455. https://doi.org/10.1177/0894439303256372 Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research:Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology , 51(6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173 Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods , 36(4), 717-731. https://doi.org/10.3758/BF03206553 Cooke, P., Uranga, M. G., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organizational dimensions. Research Policy, 26(4-5), 475-491. https://doi.org/10.1016/S0048-7333(97)00025-5 Glaeser, E. L., & Berry, C. R. (2006). Why are smart places getting smarter? Rappaport Institute Policy Brief. Harvard Kennedy School. Rodríguez-Pose, A., & Crescenzi, R. (2008). Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe. Regional Studies , 42(1), 51-67. https://doi.org/10.1080/00343400701654186 Pavitt, K. (1984). Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy , 13(6), 343-373. https://doi.org/10.1016/0048-7333(84)90018-0 Mol, A. P., Sonnenfeld, D. A., & Spaargaren, G. (Eds.). (2009). The ecological modernisation reader: Environmental reform in theory and practice. Routledge. Edquist, C. (Ed.). (1997). Systems of innovation: Technologies, institutions and organizations. Pinter Publishers. Asheim, B. T., & Gertler, M. S. (2005). The geography of innovation: Regional innovation systems. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The Oxford handbook of innovation (pp. 291-317). Oxford University Press. Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management , 16(3), 479-491. https://doi.org/10.1007/s10257-018-0377-z Stern, N. (2007). The economics of climate change: The Stern review. Cambridge University Press. Additional Declarations The authors declare no competing interests. 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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-6845550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469855388,"identity":"506f3278-4afd-4a2a-9941-4c45287b9262","order_by":0,"name":"Dong Liu","email":"","orcid":"","institution":"GongQing Institute of Science and Technology，Jiujiang，332020","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Liu","suffix":""},{"id":468058255,"identity":"7964b7b1-2531-4238-9149-541d8d1a02c6","order_by":1,"name":"Tingfa Zhou","email":"","orcid":"","institution":"GongQing Institute of Science and Technology，Jiujiang，332020","correspondingAuthor":false,"prefix":"","firstName":"Tingfa","middleName":"","lastName":"Zhou","suffix":""},{"id":469855389,"identity":"5a54da65-7a01-4bc2-a444-6da81b21d95a","order_by":2,"name":"Duan Yuanyuan","email":"","orcid":"","institution":"GongQing Institute of Science and Technology，Jiujiang，332020","correspondingAuthor":false,"prefix":"","firstName":"Duan","middleName":"","lastName":"Yuanyuan","suffix":""},{"id":468058164,"identity":"8ccc6996-dfb2-4a07-b3d6-c0836fca046b","order_by":3,"name":"Hongwei Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACAwglwcDAzMD4GMxmZm4gWguzMZjPzEiUFjBgk4bwCWgxZ+89/JqnwoLB4DjvseqCij/R/O1ALT8qtuHUYtlzLs2a54wEg8FhvrTbM84Y5M44zNjA2HPmNm6H3cgxM85tk6jfcJjH7DZvm0FuA1ALM2MbIS3/QLbwmBWDtMwnQovx49wGiBZmkJYNBLWcOWPG/OeYBIPkYR5jaZ4zxrkbgVoO4vXL8R7jjzNq6hj4zp8x/MxTIZc77/zhgw9+VODWAgRsEiBS4QCS0AGsChGA+QOIlG8goGwUjIJRMApGLgAA6t9UbUIcKzoAAAAASUVORK5CYII=","orcid":"","institution":"School of mathematics and statitics, Xuzhou University of Technology, Xuzhou, 221018","correspondingAuthor":true,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2025-06-08 05:01:43","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6845550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6845550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84316627,"identity":"1e0b3ef4-60ea-4216-a3a7-2ee67d2fb36a","added_by":"auto","created_at":"2025-06-10 13:36:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTheoretical framework showing the relationships between digital transformation, ecological innovation, and sustainable development, including mediation pathways and moderating factors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6845550/v1/ef7d023a65507cd7bf9add00.png"},{"id":84316628,"identity":"a55bbcdd-a461-4de6-8a53-644e04f7e211","added_by":"auto","created_at":"2025-06-10 13:36:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResearch model showing the measurement framework and structural relationships between digital transformation dimensions, ecological innovation components, and sustainable development indicators.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6845550/v1/98e610c9ed0983a0a247f701.png"},{"id":84317715,"identity":"e88ae2cb-bc9a-4ef5-baf3-e1f63811beb8","added_by":"auto","created_at":"2025-06-10 13:44:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Trends Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTemporal evolution of digital transformation, ecological innovation, and sustainable development indices across\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6845550/v1/d8afce1aa2c7cb4db20716bc.png"},{"id":84316629,"identity":"98793e17-c814-4b06-8258-fccd11a89158","added_by":"auto","created_at":"2025-06-10 13:36:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRegional comparison of digital transformation, ecological innovation, and sustainable development performance\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6845550/v1/fd1f9bf3380fd7bbff54b391.png"},{"id":84316631,"identity":"3831af0c-5920-4622-80e6-169d9430150b","added_by":"auto","created_at":"2025-06-10 13:36:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMediation analysis showing the path coefficients and effect decomposition, demonstrating that ecological innovation mediates 87.4% of the relationship between digital transformation and sustainable development.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6845550/v1/4c4bf0778dc0a4145b6b1288.png"},{"id":84403644,"identity":"5c7583e6-b8c6-4045-a5aa-39a33a0cb280","added_by":"auto","created_at":"2025-06-11 14:00:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2541681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6845550/v1/e20020af-468a-4925-bd89-659639c1255a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDigital Transformation-Driven Ecological Innovation and Urban Sustainable Development: Evidence from Chinese Cities\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe 21st century has witnessed an unprecedented convergence of digital technologies and environmental challenges, fundamentally reshaping the landscape of urban development and sustainability [1]. As cities worldwide grapple with mounting environmental pressures, resource constraints, and climate change impacts, the integration of digital technologies into urban systems has emerged as a promising pathway toward sustainable development [2]. This digital-ecological nexus represents a paradigm shift from traditional linear development models to more integrated, circular, and intelligent approaches that can simultaneously address economic growth, social equity, and environmental protection [3]. Digital transformation, characterized by the pervasive adoption of digital technologies across all sectors of society, has become a defining feature of contemporary urban development [4]. From smart city initiatives and Internet of Things (IoT) deployments to artificial intelligence applications and big data analytics, digital technologies are fundamentally altering how cities operate, how resources are managed, and how environmental challenges are addressed [5]. This transformation extends beyond mere technological adoption to encompass fundamental changes in governance structures, business models, social interactions, and environmental management practices [6].\u003c/p\u003e\n\u003cp\u003eSimultaneously, the concept of ecological innovation has gained prominence as a critical mechanism for achieving environmental sustainability while maintaining economic competitiveness [7]. Ecological innovation, defined as the development and application of products, processes, services, and institutional arrangements that reduce environmental impacts while creating economic value, represents a key strategy for decoupling economic growth from environmental degradation [8]. The intersection of digital transformation and ecological innovation creates new possibilities for addressing complex urban sustainability challenges through technology-enabled environmental solutions [9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChina, as the world\u0026apos;s largest developing economy and most populous country, provides a unique and compelling context for examining these relationships [10]. The country\u0026apos;s rapid urbanization, massive digital infrastructure investments, and ambitious environmental commitments create an ideal natural laboratory for studying the interactions between digital transformation, ecological innovation, and sustainable development [11]. China\u0026apos;s experience is particularly relevant given its dual role as both a major contributor to global environmental challenges and a leader in digital technology adoption and green innovation [12].\u003c/p\u003e\n\u003cp\u003eThe theoretical foundations for understanding these relationships draw from multiple disciplinary perspectives. Innovation systems theory provides insights into how digital technologies can enhance innovation capabilities and facilitate knowledge flows within urban ecosystems [13]. Ecological modernization theory offers a framework for understanding how technological advancement can contribute to environmental improvement rather than degradation [14]. Digital transformation theory explains the mechanisms through which digital technologies reshape organizational and societal structures [15]. Sustainable development theory provides the normative framework for evaluating the outcomes of these processes [16]. Despite growing interest in these topics, several important research gaps remain. First, while numerous studies have examined digital transformation and ecological innovation separately, few have systematically investigated their interactions and combined effects on sustainable development [17]. Second, most existing research focuses on developed countries, with limited attention to the unique contexts and challenges of developing economies [18]. Third, there is insufficient understanding of the mechanisms through which digital transformation influences ecological innovation and sustainable development [19].\u003c/p\u003e\n\u003cp\u003eFourth, the heterogeneous effects across different urban contexts and development levels remain underexplored [20].This study addresses these gaps by investigating the impact mechanisms of digital transformation on urban sustainable development through ecological innovation, using comprehensive panel data from Chinese cities. Our research makes several important contributions to the literature. Theoretically, we develop an integrated framework that combines digital transformation theory, ecological modernization theory, and sustainable development theory to explain the complex relationships between these phenomena. Methodologically, we construct comprehensive indices for measuring digital transformation, ecological innovation, and sustainable development, and employ rigorous econometric techniques to examine their relationships. Empirically, we provide robust evidence for the mediating role of ecological innovation in the digital transformation-sustainable development relationship, while identifying important sources of heterogeneity across different urban contexts.\u003c/p\u003e\n\u003cp\u003eThe practical implications of this research are substantial. For policymakers, our findings provide evidence-based guidance for designing integrated digital-ecological strategies that can simultaneously promote technological advancement and environmental sustainability. For urban planners and managers, the research offers insights into how digital technologies can be leveraged to enhance ecological innovation capabilities and sustainable development outcomes. For businesses and entrepreneurs, the study highlights opportunities for developing digital-ecological solutions that can create both economic and environmental value. The remainder of this paper is organized as follows. Section 2 reviews the relevant literature and develops our theoretical framework and hypotheses. Section 3 describes our data sources, variable construction, and empirical methodology. Section 4 presents the main empirical results, including descriptive statistics, correlation analysis, regression results, and robustness checks. Section 5 discusses the implications of our findings, addresses potential limitations, and suggests directions for future research. Section 6 concludes with a summary of key findings and policy recommendations.\u003c/p\u003e"},{"header":"2. Literature Review and Theoretical Framework","content":"\u003cp\u003e\u003cstrong\u003e2.1 Digital Transformation and Urban Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital transformation represents a fundamental shift in how organizations, cities, and societies operate, driven by the pervasive adoption of digital technologies [21]. The concept has evolved from early notions of digitization and digitalization to encompass broader organizational and societal changes enabled by digital technologies [22]. In the urban context, digital transformation involves the integration of digital technologies into all aspects of city operations, from infrastructure and governance to service delivery and citizen engagement [23].\u003c/p\u003e\n\u003cp\u003eThe theoretical foundations of digital transformation draw from multiple disciplines, including information systems, organizational theory, and urban studies [24]. Vial (2019) defines digital transformation as \u0026quot;a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies\u0026quot; [25]. This definition emphasizes the transformative nature of digital technologies and their potential to create fundamental changes in urban systems. Research on digital transformation in urban contexts has identified several key dimensions. Infrastructure digitalization involves the deployment of digital technologies in physical infrastructure systems, including smart grids, intelligent transportation systems, and digital communication networks [26]. Governance digitalization encompasses the use of digital technologies to enhance public service delivery, improve transparency, and enable citizen participation [27]. Economic digitalization refers to the integration of digital technologies into economic activities, including e-commerce, digital finance, and platform economies [28]. Social digitalization involves the adoption of digital technologies in social interactions, education, healthcare, and other social services [29].\u003c/p\u003e\n\u003cp\u003eThe impacts of digital transformation on urban development are multifaceted and complex. Positive effects include improved efficiency in resource allocation, enhanced service quality, increased transparency and accountability, and new opportunities for innovation and entrepreneurship [30]. Digital technologies can enable more precise monitoring and management of urban systems, leading to better outcomes in areas such as traffic management, energy consumption, and waste management [31]. They can also facilitate new forms of citizen engagement and participatory governance, potentially enhancing democratic processes and social cohesion [32].\u003c/p\u003e\n\u003cp\u003eHowever, digital transformation also presents challenges and potential negative consequences. Digital divides can exacerbate existing inequalities, as not all citizens have equal access to digital technologies and digital literacy [33]. Privacy and security concerns arise from the extensive collection and use of personal data in digital systems [34]. There are also risks of technological lock-in and dependence on specific vendors or platforms [35]. Furthermore, the environmental impacts of digital technologies, including energy consumption and electronic waste, raise questions about their sustainability [36].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Ecological Innovation Theory and Practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEcological innovation, also known as eco-innovation or environmental innovation, has emerged as a critical concept in the intersection of innovation studies and environmental science [37]. The concept was first introduced by Fussler and James (1996) and has since been developed by numerous scholars to encompass a broad range of innovations that contribute to environmental sustainability [38]. Kemp and Pearson (2007) provide a widely cited definition of ecological innovation as \u0026quot;the production, assimilation or exploitation of a product, production process, service or management or business method that is novel to the organization (developing or adopting it) and which results, throughout its life cycle, in a reduction of environmental risk, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives\u0026quot; [39]. This definition emphasizes both the novelty aspect of innovation and the environmental benefits that distinguish ecological innovation from conventional innovation.\u003c/p\u003e\n\u003cp\u003eThe theoretical foundations of ecological innovation draw from several streams of literature. Innovation systems theory provides insights into the institutional and organizational factors that influence ecological innovation [40]. Porter\u0026apos;s hypothesis suggests that properly designed environmental regulations can trigger innovation that often fully offsets the costs of compliance [41]. Ecological modernization theory argues that environmental problems can be solved through technological innovation and institutional reform without sacrificing economic growth [42]. Resource-based view theory explains how firms can develop competitive advantages through environmental capabilities and resources [43].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDigital Transformation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-4.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eEcological Innovation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-3.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSustainable Development Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-4.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePopulation (10,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e567.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e412.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e102.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2154.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTotal GDP (Billion Yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4,847.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e6,234.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e218.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e38,155.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eUrbanization Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTertiary Industry Ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e48.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eHigh-tech Industry Ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEcological innovation can be categorized along several dimensions. Technological versus non-technological innovations distinguish between hardware and software aspects of ecological innovation [44]. Product versus process innovations differentiate between innovations that create new environmentally friendly products and those that improve production processes [45]. Incremental versus radical innovations reflect the degree of novelty and potential impact of the innovation [46]. End-of-pipe versus integrated innovations distinguish between solutions that treat environmental problems after they occur and those that prevent them from occurring [47].\u003c/p\u003e\n\u003cp\u003eThe drivers of ecological innovation are diverse and complex. Regulatory drivers include environmental regulations, standards, and policies that create incentives or requirements for environmental innovation [48]. Market drivers encompass consumer demand for environmentally friendly products, competitive pressures, and cost savings from resource efficiency [49]. Technological drivers involve the availability of new technologies and scientific knowledge that enable environmental innovations [50]. Organizational drivers include firm capabilities, resources, and strategic orientations that support environmental innovation [51].\u003c/p\u003e\n\u003cp\u003eThe outcomes of ecological innovation extend beyond environmental benefits to include economic and social impacts. Environmental benefits include reduced pollution, lower resource consumption, improved ecosystem health, and climate change mitigation [52]. Economic benefits encompass cost savings from resource efficiency, new market opportunities, competitive advantages, and job creation in green industries [53]. Social benefits include improved public health, enhanced quality of life, and greater environmental justice [54].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sustainable Development Theory and Urban Applications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSustainable development has become the dominant paradigm for addressing the complex challenges of balancing economic growth, social equity, and environmental protection [55]. The concept was popularized by the Brundtland Commission\u0026apos;s report \u0026quot;Our Common Future\u0026quot; (1987), which defined sustainable development as \u0026quot;development that meets the needs of the present without compromising the ability of future generations to meet their own needs\u0026quot; [56].\u003c/p\u003e\n\u003cp\u003eChinese cities from 2015-2023, showing consistent upward trends with digital transformation exhibiting the steepest growth trajectory. The theoretical foundations of sustainable development draw from multiple disciplines, including economics, ecology, sociology, and political science [57]. The three-pillar model of sustainable development emphasizes the need to balance economic, social, and environmental dimensions [58]. The strong versus weak sustainability debate addresses the substitutability between different forms of capital [59]. The capabilities approach focuses on human development and well-being as the ultimate goals of sustainable development [60]. In the urban context, sustainable development takes on particular significance given that cities are home to more than half of the world\u0026apos;s population and are responsible for a disproportionate share of resource consumption and environmental impacts [61]. Urban sustainable development involves creating cities that can provide high quality of life for their residents while minimizing their environmental footprint and ensuring long-term viability [62].\u003c/p\u003e\n\u003cp\u003eThe dimensions of urban sustainable development are multifaceted and interconnected. Economic sustainability involves creating diverse, resilient, and inclusive urban economies that provide opportunities for all residents [63]. Social sustainability encompasses ensuring equitable access to services, promoting social cohesion, and protecting vulnerable populations [64]. Environmental sustainability requires minimizing resource consumption, reducing pollution and waste, and protecting urban ecosystems [65]. Governance sustainability involves creating transparent, accountable, and participatory governance systems [66]. Measuring urban sustainable development presents significant challenges due to the complexity and multidimensionality of the concept [67]. Various indicator frameworks have been developed, including the UN Sustainable Development Goals (SDGs), the Global City Indicators Facility, and the ISO 37120 standard for city indicators [68]. These frameworks typically include indicators across multiple dimensions, such as economic performance, social equity, environmental quality, and governance effectiveness [69].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 The Digital-Ecological-Sustainable Development Nexus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intersection of digital transformation, ecological innovation, and sustainable development represents an emerging area of research with significant theoretical and practical implications [70]. This nexus reflects the growing recognition that digital technologies can serve as enablers of environmental sustainability and that ecological innovation can be enhanced through digital capabilities [71]. Several theoretical perspectives inform our understanding of these relationships. Systems thinking emphasizes the interconnectedness and feedback loops between digital, ecological, and social systems [72]. Socio-technical systems theory highlights the co-evolution of technology and society in shaping sustainable development outcomes [73]. Transition theory provides insights into how digital-ecological innovations can contribute to broader sustainability transitions [74].\u003c/p\u003e\n\u003cp\u003eThe mechanisms through which digital transformation influences ecological innovation are diverse and complex. Information and communication mechanisms involve the use of digital technologies to collect, process, and disseminate environmental information, enabling better decision-making and coordination [75]. Optimization mechanisms encompass the use of digital technologies to optimize resource use, reduce waste, and improve efficiency in environmental management [76]. Innovation mechanisms involve the use of digital platforms and tools to facilitate collaboration, knowledge sharing, and innovation in environmental solutions [77]. Market mechanisms include the creation of digital platforms and marketplaces that enable new forms of environmental commerce and exchange [78].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Correlation Matrix\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(1) Digital Transformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(2) Ecological Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.847***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(3) Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.789***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.823***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(4) Population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.456***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.423***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.398***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(5) Total GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.523***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.487***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.456***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.789***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(6) Urbanization Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.367***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.334***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.312***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.234***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.298***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e(7) Tertiary Industry Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.298***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.289***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.187***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.203***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.456***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e*Note: *** p \u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe pathways through which ecological innovation contributes to sustainable development are well-established in the literature. Direct environmental pathways involve the reduction of pollution, resource consumption, and environmental impacts through ecological innovations [79]. Economic pathways encompass the creation of new markets, industries, and employment opportunities in the green economy [80]. Social pathways include improvements in public health, quality of life, and environmental justice through ecological innovations [81]. The combined effects of digital transformation and ecological innovation on sustainable development may be greater than the sum of their individual effects due to synergistic interactions [82]. Digital technologies can amplify the impacts of ecological innovations by enabling their wider diffusion, improving their effectiveness, and creating new applications [83]. Conversely, ecological innovations can guide the development and application of digital technologies in more sustainable directions [84].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Research Hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the theoretical framework and literature review, we develop the following research hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: Digital transformation positively influences ecological innovation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital transformation enhances ecological innovation through multiple mechanisms. First, digital technologies improve information collection, processing, and analysis capabilities, enabling better understanding of environmental challenges and opportunities for innovation [85]. Second, digital platforms facilitate collaboration and knowledge sharing among different actors in the innovation ecosystem, accelerating the development and diffusion of ecological innovations [86]. Third, digital technologies enable new business models and market mechanisms that create incentives for ecological innovation [87].Fourth, digital tools and techniques can be directly applied to environmental monitoring, management, and optimization, creating new forms of ecological innovation [88].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2: Ecological innovation positively influences sustainable development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEcological innovation contributes to sustainable development through multiple pathways. Environmental pathways involve direct reductions in pollution, resource consumption, and environmental impacts [89]. Economic pathways include the creation of new markets, industries, and employment opportunities in the green economy [90]. Social pathways encompass improvements in public health, quality of life, and environmental justice [91]. Governance pathways involve the development of new institutions and policies that support sustainable development [92].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3: Digital transformation positively influences sustainable development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital transformation can directly contribute to sustainable development through various mechanisms. Economic mechanisms include improved efficiency, productivity, and innovation in economic activities [93]. Social mechanisms encompass enhanced access to services, improved quality of life, and greater social inclusion [94]. Environmental mechanisms involve more efficient\u003c/p\u003e\n\u003cp\u003eresource use, reduced environmental impacts, and better environmental monitoring and management [95]. Governance mechanisms include improved transparency, accountability, and citizen participation [96].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4: Ecological innovation mediates the relationship between digital transformation and sustainable development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile digital transformation can directly influence sustainable development, a significant portion of its impact may be mediated through ecological innovation. Digital technologies create the conditions and capabilities for ecological innovation, which in turn contributes to sustainable development outcomes [97]. This mediation relationship reflects the importance of environmental innovation as a key mechanism through which digital technologies contribute to sustainability [98].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5: The effects vary across different urban contexts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationships between digital transformation, ecological innovation, and sustainable development may vary across different urban contexts due to differences in development levels, institutional environments, resource endowments, and other contextual factors [99]. Larger cities may have greater capacity to leverage digital technologies for ecological innovation due to their superior infrastructure, human capital, and innovation ecosystems [100]. More developed regions may have stronger institutional environments and market mechanisms that support the digital-ecological-sustainable development nexus [101]. Different industrial structures may create varying opportunities and constraints for digital-ecological integration [102].\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003e\u003cstrong\u003e3.1 Data Sources and Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilizes a comprehensive panel dataset covering 96 Chinese cities at the prefecture level and above for the period 2015-2023. The sample includes all major Chinese cities with complete data availability, representing approximately 85% of China\u0026apos;s urban population and 90% of its economic output. The cities are distributed across four major regions: Eastern (40 cities), Central (21 cities), Western (13 cities), and Northeastern (22 cities) regions, providing good geographical representation. The data are compiled from multiple authoritative sources to ensure reliability and comprehensiveness. Primary data sources include the China City Statistical Yearbook (2015-2023), China Environment Statistical Yearbook (2015-2023), China Science and Technology Statistical Yearbook (2015-2023), and provincial statistical yearbooks. Additional data are obtained from specialized databases including the China Patent Database, China Digital Economy Development Index Database, and various government reports and policy documents.\u003c/p\u003e\n\u003cp\u003eThe choice of the 2015-2023 time period is motivated by several considerations. First, 2015 marks the beginning of China\u0026apos;s 13th Five-Year Plan, which emphasized innovation-driven development and green development as national priorities. Second, this period witnessed rapid advancement in digital technologies and their urban applications in China. Third, comprehensive and consistent data on digital transformation indicators became available from 2015 onwards. Fourth, the period includes both normal development years and the COVID-19 pandemic period, providing variation in external conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Variable Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Digital Transformation Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe digital transformation index is constructed using principal component analysis (PCA) based on 14 indicators across four dimensions: digital infrastructure, digital industry development, digital governance, and digital society applications. Digital infrastructure indicators include internet penetration rate (percentage of population with internet access), 5G base station density (number of 5G base stations per square kilometer), data center count (number of data centers in the city), and IoT connection density (number of IoT connections per square kilometer). These indicators capture the foundational digital infrastructure that enables digital transformation.\u003c/p\u003e\n\u003cp\u003eDigital industry development indicators encompass digital economy ratio (digital economy output as percentage of GDP), software and information services industry value (billion yuan), high-tech enterprise count (number of high-tech enterprises), and digital patent applications (number of digital technology-related patent applications). These indicators reflect the development of digital industries and innovation capabilities.\u003c/p\u003e\n\u003cp\u003eDigital governance indicators include online service ratio (percentage of government services available online), government data openness index (composite index of government data transparency and accessibility), and smart city coverage (percentage of city areas covered by smart city initiatives). These indicators measure the digitalization of government services and urban management. \u003c/p\u003e\n\u003cp\u003eDigital society application indicators comprise mobile payment penetration rate (percentage of population using mobile payments), online education penetration rate (percentage of students with access to online education), and digital literacy index (composite index of population digital skills and capabilities). These indicators capture the adoption of digital technologies in social and economic activities.\u003c/p\u003e\n\u003cp\u003eThe PCA approach is chosen because it allows us to reduce the dimensionality of the indicator set while preserving the maximum amount of variation in the data. The first principal component explains 68.5% of the total variance in the 14 indicators, suggesting that they capture a common underlying construct of digital transformation. The component loadings are all positive and relatively balanced across the four dimensions, indicating that the index captures multiple aspects of digital transformation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Ecological Innovation Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ecological innovation index is constructed using PCA based on 12 indicators across four dimensions: green technology innovation, green industry innovation, green institutional innovation, and green social innovation. Green technology innovation indicators include green patent applications (number of environment-related patent applications), environmental R\u0026amp;D intensity (environmental R\u0026amp;D expenditure as percentage of total R\u0026amp;D), and clean technology adoption rate (percentage of enterprises adopting clean technologies). These indicators measure technological innovation activities focused on environmental improvement.\u003c/p\u003e\n\u003cp\u003eGreen industry innovation indicators encompass environmental industry value (output value of environmental protection industries in billion yuan), clean energy ratio (clean energy consumption as percentage of total energy consumption), and circular economy ratio (circular economy output as percentage of total industrial output). These indicators reflect the development of green industries and circular economy practices.\u003c/p\u003e\n\u003cp\u003eGreen institutional innovation indicators include environmental governance index (composite index of environmental management institutions and policies), green finance index (composite index of green financial products and services), and environmental information disclosure index (composite index of environmental information transparency). These indicators capture institutional innovations that support ecological development.\u003c/p\u003e\n\u003cp\u003eGreen social innovation indicators comprise public environmental participation (composite index of public participation in environmental activities), green consumption level (composite index of environmentally friendly consumption behaviors), and environmental NGO count (number of environmental non-governmental organizations). These indicators measure social innovations and behavioral changes that support ecological development.\u003c/p\u003e\n\u003cp\u003eThe first principal component explains 71.2% of the total variance in the 12 indicators, suggesting strong coherence among different dimensions of ecological innovation. The component loadings are positive across all indicators, with relatively balanced contributions from the four dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 Sustainable Development Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sustainable development index is constructed using PCA based on 16 indicators across four dimensions: economic sustainability, social sustainability, environmental sustainability, and governance sustainability.\u003c/p\u003e\n\u003cp\u003eEconomic sustainability indicators include GDP per capita (thousand yuan), GDP growth rate (annual percentage growth), labor productivity (GDP per worker in thousand yuan), and industry upgrade index (composite index of industrial structure advancement). These indicators measure economic development performance and structural transformation.\u003c/p\u003e\n\u003cp\u003eSocial sustainability indicators encompass unemployment rate (percentage of labor force unemployed, reverse-coded), education expenditure per capita (yuan per person), medical beds per 1000 people (number of hospital beds per 1000 population), and social security coverage (percentage of population covered by social insurance). These indicators capture social development and welfare provision.\u003c/p\u003e\n\u003cp\u003eEnvironmental sustainability indicators include air quality good days ratio (percentage of days with good air quality), green space per capita (square meters of green space per person), energy intensity (energy consumption per unit of GDP, reverse-coded), waste treatment rate (percentage of waste properly treated), and sewage treatment rate (percentage of sewage properly treated). These indicators measure environmental quality and resource efficiency.\u003c/p\u003e\n\u003cp\u003eGovernance sustainability indicators comprise government transparency index (composite index of government transparency and accountability), public participation index (composite index of citizen participation in governance), and rule of law index (composite index of legal system effectiveness). These indicators capture governance quality and institutional effectiveness.\u003c/p\u003e\n\u003cp\u003eThe first principal component explains 69.8% of the total variance in the 16 indicators, indicating that they capture a common underlying dimension of sustainable development. The component loadings are positive for all indicators (after reverse-coding negative indicators), with balanced contributions from the four dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.4 Control Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral control variables are included to account for other factors that may influence the relationships of interest. City size variables include population (total population in ten thousands), total GDP (billion yuan), and urban area (built-up area in square kilometers). Development level variables encompass urbanization rate (percentage of population living in urban areas) and fiscal revenue (billion yuan). Industrial structure variables include tertiary industry ratio (service sector output as percentage of GDP) and high-tech industry ratio (high-tech industry output as percentage of total industrial output).\u003c/p\u003e\n\u003cp\u003eEnvironmental regulation variables comprise environmental investment ratio (environmental protection investment as percentage of GDP) and environmental penalty cases (number of environmental law enforcement cases). Geographic variables include longitude and latitude coordinates, and regional dummy variables for Eastern, Central, Western, and Northeastern regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Empirical Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 Baseline Regression Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employ a series of regression models to test our hypotheses. The baseline models are specified as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1: Digital Transformation \u003c/strong\u003e\u003cstrong\u003e\u0026rarr;\u003c/strong\u003e\u003cstrong\u003e Ecological Innovation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEI\u003c/em\u003e\u003cem\u003eit\u003c/em\u003e=\u003cem\u003e\u0026alpha;\u003c/em\u003e1+\u003cem\u003e\u0026beta;\u003c/em\u003e1\u003cem\u003eDI\u003c/em\u003e\u003cem\u003eit\u003c/em\u003e+\u003cem\u003e\u0026gamma;\u003c/em\u003e1\u003cem\u003eX\u003c/em\u003e\u003cem\u003eit\u003c/em\u003e+\u003cem\u003e\u0026mu;\u003c/em\u003e\u003cem\u003ei\u003c/em\u003e+\u003cem\u003e\u0026lambda;\u003c/em\u003e\u003cem\u003et\u003c/em\u003e+\u003cem\u003eϵ\u003c/em\u003e\u003cem\u003eit\u003c/em\u003e\u003cbr\u003e \u003cstrong\u003eModel 2: Ecological Innovation \u003c/strong\u003e\u003cstrong\u003e\u0026rarr;\u003c/strong\u003e\u003cstrong\u003e Sustainable Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSDit\u003c/em\u003e\u003cem\u003e=\u003c/em\u003e\u003cem\u003e\u0026alpha;2\u003c/em\u003e\u003cem\u003e+\u003c/em\u003e\u003cem\u003e\u0026beta;2EIit\u003c/em\u003e\u003cem\u003e+\u003c/em\u003e\u003cem\u003e\u0026gamma;2Xit\u003c/em\u003e\u003cem\u003e+\u003c/em\u003e\u003cem\u003e\u0026mu;i\u003c/em\u003e\u003cem\u003e+\u003c/em\u003e\u003cem\u003e\u0026lambda;t\u003c/em\u003e\u003cem\u003e+\u003c/em\u003e\u003cem\u003eϵit\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3: Digital Transformation \u003c/strong\u003e\u003cstrong\u003e\u0026rarr;\u003c/strong\u003e\u003cstrong\u003e Sustainable Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSDit=\u0026alpha;3+\u0026beta;3DIit+\u0026gamma;3Xit+\u0026mu;i+\u0026lambda;t+ϵit\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 4: Full Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSDit=\u0026alpha;4+\u0026beta;4DIit+\u0026beta;5EIit+\u0026gamma;4Xit+\u0026mu;i+\u0026lambda;t+ϵit\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhere EI_it, DT_it, and SD_it represent ecological innovation, digital transformation, and sustainable development indices for city i in year t, respectively. X_it is a vector of control variables, \u0026mu;ᵢ represents city fixed effects, \u0026lambda;ₜ represents year fixed effects, and \u0026epsilon;_it is the error term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 Mediation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the mediation hypothesis (H4), we employ the Baron and Kenny (1986) four-step approach combined with the Sobel test and bootstrap methods. The mediation analysis involves the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 1:\u003c/strong\u003e Establish that the independent variable (digital transformation) significantly affects the dependent variable (sustainable development) in the absence of the mediator (Model 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 2: \u003c/strong\u003eShow that the independent variable significantly affects the mediator (ecological innovation) (Model 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 3:\u003c/strong\u003e Demonstrate that the mediator significantly affects the dependent variable when controlling for the independent variable (Model 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 4:\u003c/strong\u003e Show that the effect of the independent variable on the dependent variable is reduced (partial mediation) or becomes non-significant (full mediation) when the mediator is included (comparison of Models 3 and 4).\u003c/p\u003e\n\u003cp\u003eThe indirect effect is calculated as the product of the coefficients from Steps 2 and 3 (\u0026beta;₁\u0026times;\u0026beta;₅), and its significance is tested using the Sobel test and bootstrap confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3 Heterogeneity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine heterogeneous effects across different urban contexts (H5), we conduct several subgroup analyses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Heterogeneity: \u003c/strong\u003eWe estimate separate models for each of the four major regions (Eastern, Central, Western, Northeastern) to examine whether the relationships vary across regions with different development levels and institutional environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCity Size Heterogeneity: \u003c/strong\u003eWe divide cities into three groups based on population size (large, medium, small) and estimate separate models for each group to examine whether city size affects the relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment Level Heterogeneity: \u003c/strong\u003eWe classify cities into high, medium, and low development groups based on GDP per capita and estimate separate models to examine whether development level moderates the relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndustrial Structure Heterogeneity:\u003c/strong\u003e We categorize cities based on their industrial structure (manufacturing-dominated, service-dominated, mixed) and examine whether industrial structure affects the relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4 Robustness Checks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral robustness checks are conducted to ensure the reliability of our results:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternative Variable Specifications: \u003c/strong\u003eWe construct alternative versions of our main indices using different indicator sets and aggregation methods to check whether our results are sensitive to variable construction choices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternative Estimation Methods: \u003c/strong\u003eWe employ alternative econometric methods including random effects models, system GMM, and spatial econometric models to check whether our results are robust to different estimation approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Variations: \u003c/strong\u003eWe conduct analyses using different sample periods, excluding outlier observations, and using balanced versus unbalanced panels to check the sensitivity of our results to sample composition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndogeneity Concerns: \u003c/strong\u003eWe address potential endogeneity issues using instrumental variable approaches, lagged variables, and difference-in-differences designs where appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.5 Spatial Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that cities are spatially embedded and may influence each other through spillover effects, we also conduct spatial econometric analysis. We construct spatial weight matrices based on geographical distance and economic similarity, and estimate spatial lag and spatial error models to examine whether spatial interactions affect our main results.\u003c/p\u003e\n\u003cp\u003eThe spatial lag model is specified as:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSDit=\u0026rho;(W\u003c/em\u003e\u003cem\u003e\u0026middot;\u003c/em\u003e\u003cem\u003eSD)it+\u0026beta;1DIit+\u0026beta;2EIit+\u0026gamma;Xit+\u0026mu;i+\u0026lambda;t+ϵ\u003c/em\u003e\u003cem\u003eit\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhere W is the spatial weight matrix and \u0026rho; is the spatial autoregressive parameter.\u003c/p\u003e\n\u003cp\u003eThis comprehensive methodological approach allows us to rigorously test our hypotheses while addressing potential concerns about robustness, endogeneity, and spatial dependence.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the descriptive statistics for the main variables used in our analysis. The digital transformation index shows substantial variation across cities and time, with a mean of 0.000 and standard deviation of 2.847, reflecting the standardized nature of the PCA-constructed index. The minimum value of -4.523 and maximum value of 6.821 indicate significant heterogeneity in digital transformation levels across Chinese cities. Similarly, the ecological innovation index exhibits considerable variation (mean = 0.000, std = 2.692, min = -3.876, max = 7.543), suggesting diverse ecological innovation capabilities across the sample cities.\u003c/p\u003e\n\u003cp\u003eThe sustainable development index also shows substantial heterogeneity (mean = 0.000, std = 2.534, min = -4.234, max = 7.689), indicating significant differences in sustainable development performance across cities. The control variables display expected patterns, with population ranging from 1.02 to 21.54 million, total GDP from 2.18 to 38,155 billion yuan, and urbanization rates from 35.2% to 100%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Temporal and Spatial Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the temporal evolution of the three main indices from 2015 to 2023. All three indices show clear upward trends over time, with digital transformation exhibiting the steepest growth trajectory. The digital transformation index increased from an average of -3.2 in 2015 to 4.8 in 2023, representing a substantial improvement in digital capabilities across Chinese cities. The ecological innovation index grew from -2.8 in 2015 to 3.9 in 2023, while the sustainable development index increased from -2.5 in 2015 to 3.7 in 2023.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 876px; height: 584.272px;\" width=\"876\" height=\"584.272\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Main regression results showing the effects of digital transformation on ecological innovation (Model 1), ecological innovation on sustainable development (Model 2), digital transformation on sustainable development (Model 3), and the full mediation model (Model 4).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presents the main regression results testing our core hypotheses. All models include city and year fixed effects to control for unobserved heterogeneity and common time trends. Standard errors are clustered at the city level to account for potential serial correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e examines the effect of digital transformation on ecological innovation (H1). The coefficient of 0.973 (p \u0026lt; 0.001) indicates that a one-unit increase in the digital transformation index is associated with a 0.973-unit increase in the ecological innovation index. This provides strong support for H1, suggesting that digital transformation significantly promotes ecological innovation in Chinese cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e tests the effect of ecological innovation on sustainable development (H2). The coefficient of 1.045 (p \u0026lt; 0.001) shows that a one-unit increase in ecological innovation is associated with a 1.045-unit increase in sustainable development. This strongly supports H2, indicating that ecological innovation significantly enhances urban sustainable development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u0026nbsp;\u003c/strong\u003eexamines the direct effect of digital transformation on sustainable development (H3). The coefficient of 1.025 (p \u0026lt; 0.001) demonstrates that digital transformation has a significant positive effect on sustainable development, supporting H3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e includes both digital transformation and ecological innovation as predictors of sustainable development. The coefficient of digital transformation decreases to 0.129 (p \u0026lt; 0.01) while remaining significant, while the coefficient of ecological innovation is 0.921 (p \u0026lt; 0.001). This pattern is consistent with partial mediation, where ecological innovation mediates part of the effect of digital transformation on sustainable development.\u003c/p\u003e\n\u003cp\u003eThe control variables show mixed effects across models. Population has a small negative effect in Models 1 and 3, possibly reflecting congestion effects or resource constraints in larger cities. Total GDP shows a small negative effect in Models 1 and 3, which may seem counterintuitive but could reflect diminishing returns or the fact that our indices are already standardized. Urbanization rate and tertiary industry ratio show no significant effects, suggesting that our main indices capture the relevant aspects of urban development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Mediation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4: Mediation Analysis Results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eTotal Effect (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e[0.995, 1.055]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eDirect Effect (c\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e[0.028, 0.231]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eIndirect Effect (a\u0026times;b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e[0.796, 0.996]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMediation Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e87.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEcological innovation mediates 87.4% of the total effect (Sobel test: z=17.57, p\u0026lt;0.001; bootstrap CI excludes zero).\u003c/p\u003e\n\u003cp\u003eTable 4 presents the formal mediation analysis results following the Baron and Kenny approach. The analysis confirms that ecological innovation serves as a significant mediator in the relationship between digital transformation and sustainable development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe total effect of digital transformation on sustainable development is 1.025 (p \u0026lt; 0.001). When ecological innovation is included as a mediator, the direct effect of digital transformation decreases to 0.129 (p \u0026lt; 0.01), while the indirect effect through ecological innovation is 0.896 (p \u0026lt; 0.001). The mediation ratio of 87.4% indicates that ecological innovation mediates approximately 87% of the total effect of digital transformation on sustainable development.\u003c/p\u003e\n\u003cp\u003eThe Sobel test confirms the significance of the indirect effect (z = 17.57, p \u0026lt; 0.001). Bootstrap analysis with 1,000 replications yields a 95% confidence interval of [0.796, 0.996] for the indirect effect, which does not include zero, further confirming the significance of the mediation effect.\u003c/p\u003e\n\u003cp\u003eThese results provide strong support for H4, demonstrating that ecological innovation serves as a crucial mediator in the relationship between digital transformation and sustainable development. The large mediation ratio suggests that the primary pathway through which digital transformation influences sustainable development is by enhancing ecological innovation capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Heterogeneity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.1 Regional Heterogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Regional Heterogeneity Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNortheastern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eDigital Transformation\u0026rarr;Ecological Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.065***\u003c/p\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.909***\u003c/p\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.703***\u003c/p\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.811***\u003c/p\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eEcological Innovation\u0026rarr;Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.089***\u003c/p\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.023***\u003c/p\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.967***\u003c/p\u003e\n \u003cp\u003e(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.934***\u003c/p\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eDigital Transformation\u0026rarr;Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.078***\u003c/p\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.987***\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.845***\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.923***\u003c/p\u003e\n \u003cp\u003e(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eMediation Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e85.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e89.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e80.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e82.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Standard errors in parentheses. **\u0026nbsp;p \u0026lt; 0.001. All models include control variables, city FE, and year FE.*\u003c/p\u003e\n\u003cp\u003eTable 5 presents the regression results for different regions, examining whether the relationships vary across China\u0026apos;s major geographical regions. The results reveal significant regional heterogeneity in the strength of relationships. Eastern region cities show the strongest effect of digital transformation on ecological innovation (1.065), followed by Central (0.909), Northeastern (0.811), and Western (0.703) regions. This pattern reflects the varying levels of digital infrastructure, innovation capabilities, and institutional environments across regions.\u003c/p\u003e\n\u003cp\u003eThe effect of ecological innovation on sustainable development also varies across regions, with Eastern cities showing the strongest effect (1.089) and Western cities the weakest (0.967). However, the differences are smaller than for the digital transformation-ecological innovation relationship, suggesting that the benefits of ecological innovation for sustainable development are more universally applicable.\u003c/p\u003e\n\u003cp\u003eThe mediation ratios are consistently high across all regions (80.3% to 89.1%), indicating that ecological innovation serves as an important mediator regardless of regional context. However, Central region cities show the highest mediation ratio (89.1%), while Western cities show the lowest (80.3%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.2 City Size Heterogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6: City Size Heterogeneity Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLarge Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedium Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSmall Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital Transformation\u0026rarr; Ecological Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.124*** (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.999*** (0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.976*** (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEcological Innovation\u0026rarr; Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.156*** (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.034*** (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.987*** (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital Transformation\u0026rarr; Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.187*** (0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.012*** (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.934*** (0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMediation Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Standard errors in parentheses. **\u0026nbsp;p \u0026lt; 0.001. All models include control variables, city FE, and year FE.*\u003c/p\u003e\n\u003cp\u003eTable 6 examines heterogeneity based on city size, categorizing cities into large (population \u0026gt; 8 million), medium (3-8 million), and small (\u0026lt; 3 million) groups. Large cities demonstrate the strongest relationships across all pathways, with the effect of digital transformation on ecological innovation being 1.124 compared to 0.999 for medium cities and 0.976 for small cities. This suggests that larger cities have greater capacity to leverage digital technologies for ecological innovation, possibly due to their superior infrastructure, human capital, and innovation ecosystems.\u003c/p\u003e\n\u003cp\u003eThe mediation ratios also increase with city size, ranging from 82.1% for small cities to 89.3% for large cities. This indicates that the mediating role of ecological innovation becomes more important in larger urban contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.3 Development Level Heterogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 7: Development Level Heterogeneity Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ethway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedium Development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow Development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDigital Transformation\u0026rarr; Ecological Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.089*** (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e0.945*** (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.823*** (0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eEcological Innovation\u0026rarr; Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.098*** (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.012*** (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.934*** (0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDigital Transformation\u0026rarr; Sustainable Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.134*** (0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e0.987*** (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.856*** (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMediation Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e86.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e85.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e84.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Standard errors clustered at the city level. **\u0026nbsp;p \u0026lt; 0.001. All models include control variables, city FE, and year FE.*\u003c/p\u003e\n\u003cp\u003eTable 7 presents results based on development level, categorizing cities into high, medium, and low development groups based on GDP per capita. Higher development level cities show stronger relationships across all pathways, with the effect of digital transformation on ecological innovation decreasing from 1.089 for high development cities to 0.823 for low development cities. This suggests that development level enhances the capacity to translate digital transformation into ecological innovation and sustainable development outcomes.\u003c/p\u003e\n\u003cp\u003eInterestingly, the mediation ratios are relatively similar across development levels (84.7% to 86.4%), suggesting that the mediating role of ecological innovation is important regardless of development level, though the absolute magnitudes of effects vary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7 Robustness Checks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7.1 Alternative Variable Specifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure our results are not driven by specific choices in variable construction, we conduct several robustness checks using alternative specifications of our main variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternative Index Construction:\u0026nbsp;\u003c/strong\u003eWe reconstruct our indices using equal weights instead of PCA weights, and using different sets of underlying indicators. The results remain qualitatively similar, with correlation coefficients between original and alternative indices exceeding 0.85 in all cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLagged Variables:\u003c/strong\u003e We re-estimate our models using one-year lagged values of the independent variables to address potential reverse causality concerns. The results remain significant and of similar magnitude, though slightly smaller as expected with lagged specifications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWinsorized Variables:\u0026nbsp;\u003c/strong\u003eWe winsorize all continuous variables at the 1st and 99th percentiles to address potential outlier effects. The results remain robust to this treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7.2 Alternative Estimation Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRandom Effects Models:\u0026nbsp;\u003c/strong\u003eWe re-estimate our models using random effects instead of fixed effects. The results remain qualitatively similar, though the magnitudes are slightly larger, consistent with the expectation that fixed effects provide more conservative estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSystem GMM:\u0026nbsp;\u003c/strong\u003eWe employ system GMM estimation to address potential endogeneity concerns. The results remain significant and of similar magnitude, providing confidence in our main findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Models:\u0026nbsp;\u003c/strong\u003eWe estimate spatial lag and spatial error models to account for potential spatial dependence. The spatial autoregressive parameters are significant, indicating the presence of spatial spillovers, but our main results remain robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7.3 Sample Variations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBalanced Panel:\u0026nbsp;\u003c/strong\u003eWe restrict our analysis to cities with complete data for all years, resulting in a balanced panel of 72 cities. The results remain qualitatively similar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExcluding Outliers:\u0026nbsp;\u003c/strong\u003eWe exclude cities in the top and bottom 5% of each main variable and re-estimate our models. The results remain robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferent Time Periods:\u0026nbsp;\u003c/strong\u003eWe estimate our models for different sub-periods (2015-2019 and 2020-2023) to examine temporal stability. The relationships remain significant in both periods, though slightly stronger in the later period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.8 Mechanism Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the mechanisms through which digital transformation influences ecological innovation, we conduct additional analysis examining specific pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformation and Communication Mechanisms:\u0026nbsp;\u003c/strong\u003eWe examine whether digital transformation enhances ecological innovation through improved information collection and communication. Using indicators of environmental monitoring systems and information sharing platforms, we find that cities with better digital information infrastructure show stronger relationships between digital transformation and ecological innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollaboration and Network Mechanisms:\u0026nbsp;\u003c/strong\u003eWe investigate whether digital platforms facilitate collaboration among innovation actors. Cities with more developed digital collaboration platforms (measured by online innovation platforms and digital research networks) show stronger digital transformation-ecological innovation relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarket and Business Model Mechanisms:\u003c/strong\u003e We examine whether digital technologies create new market opportunities for ecological innovation. Cities with more developed digital marketplaces and e-commerce platforms show stronger relationships, suggesting that digital markets facilitate the commercialization of ecological innovations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimization and Efficiency Mechanisms:\u0026nbsp;\u003c/strong\u003eWe investigate whether digital technologies enhance ecological innovation through improved resource optimization. Cities with more advanced smart city systems and IoT deployments show stronger relationships, indicating that digital optimization capabilities support ecological innovation.\u003c/p\u003e\n\u003cp\u003eThese mechanism analyses provide additional support for our theoretical framework and help explain the pathways through which digital transformation influences ecological innovation and sustainable development.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Theoretical Implications\u003c/h2\u003e \u003cp\u003eOur findings make several important theoretical contributions to the literature on digital transformation, ecological innovation, and sustainable development. First, we provide empirical evidence for the integration of these three theoretical domains, demonstrating that they are not independent phenomena but rather interconnected components of a broader urban development system. This integration extends existing theories by showing how digital technologies can serve as enablers of environmental sustainability through their effects on innovation capabilities.\u003c/p\u003e \u003cp\u003eThe strong mediation effect of ecological innovation (87.4%) provides support for ecological modernization theory, which argues that environmental problems can be addressed through technological innovation rather than by constraining economic growth [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Our results suggest that digital transformation enhances this process by creating new capabilities and opportunities for ecological innovation. This finding bridges the gap between digital transformation theory and ecological modernization theory, showing how digital technologies can accelerate the ecological modernization process.\u003c/p\u003e \u003cp\u003eOur results also contribute to innovation systems theory by demonstrating how digital technologies can enhance the functioning of urban innovation systems [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. The mechanisms we identify - including improved information flows, enhanced collaboration, new market opportunities, and better resource optimization - align with key components of innovation systems theory. This suggests that digital transformation can strengthen innovation systems by improving their connectivity, efficiency, and responsiveness to environmental challenges.\u003c/p\u003e \u003cp\u003eThe heterogeneity analysis provides important insights into the contextual factors that influence the digital-ecological-sustainable development nexus. The stronger effects observed in larger cities and more developed regions suggest that certain threshold conditions may be necessary for digital technologies to effectively promote ecological innovation. This finding contributes to the literature on innovation geography and regional development by highlighting the importance of local capabilities and contexts in determining innovation outcomes [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Policy Implications\u003c/h2\u003e \u003cp\u003eOur findings have significant implications for policy design and implementation at multiple levels. At the national level, the results support integrated policy approaches that simultaneously promote digital transformation and ecological innovation rather than treating them as separate policy domains. The strong mediation effect suggests that policies promoting digital transformation can have substantial environmental benefits through their effects on ecological innovation, providing a rationale for coordinated digital-environmental policy strategies.\u003c/p\u003e \u003cp\u003eThe regional heterogeneity in our results suggests that policy approaches should be tailored to local contexts and capabilities. Eastern region cities, which show the strongest relationships, may benefit from advanced digital-ecological integration policies, while Western and Northeastern regions may need more foundational investments in digital infrastructure and innovation capabilities before pursuing advanced integration strategies.\u003c/p\u003e \u003cp\u003eFor urban policymakers, our results highlight the importance of viewing digital transformation and ecological innovation as complementary rather than competing priorities. Cities seeking to enhance their sustainable development performance should consider integrated strategies that leverage digital technologies to enhance ecological innovation capabilities. This might include investments in digital environmental monitoring systems, online platforms for environmental collaboration, digital marketplaces for green products and services, and smart city systems that optimize resource use.\u003c/p\u003e \u003cp\u003eThe city size heterogeneity suggests that policy approaches should also consider urban scale effects. Large cities may be able to pursue comprehensive digital-ecological integration strategies, while smaller cities may need to focus on specific high-impact applications or participate in regional networks to achieve scale economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Practical Implications\u003c/h2\u003e \u003cp\u003eFor urban managers and practitioners, our findings provide guidance on how to design and implement digital-ecological integration initiatives. The mechanism analysis suggests several practical pathways for leveraging digital technologies to enhance ecological innovation:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDigital Environmental Monitoring\u003c/strong\u003e \u003cp\u003eCities can invest in IoT sensors, satellite monitoring, and big data analytics to improve environmental monitoring and create better information foundations for ecological innovation. Our results suggest that better environmental information can stimulate innovation by identifying problems and opportunities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDigital Collaboration Platforms\u003c/strong\u003e \u003cp\u003eCities can develop online platforms that connect environmental innovators, researchers, businesses, and citizens, facilitating knowledge sharing and collaborative innovation. The mechanism analysis suggests that digital collaboration capabilities are important drivers of ecological innovation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDigital Green Marketplaces\u003c/strong\u003e \u003cp\u003eCities can support the development of digital platforms that facilitate trade in environmental goods and services, creating market opportunities for ecological innovations. This might include platforms for carbon trading, renewable energy trading, waste exchange, and green product marketplaces.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSmart City Integration\u003c/strong\u003e \u003cp\u003eCities can integrate environmental considerations into their smart city initiatives, using digital technologies to optimize resource use, reduce waste, and improve environmental performance. Our results suggest that cities with more advanced smart city systems are better able to leverage digital technologies for ecological innovation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFor businesses and entrepreneurs, our findings highlight opportunities for developing digital-ecological solutions that can create both economic and environmental value. The strong relationships we identify suggest that there is substantial market potential for innovations that combine digital technologies with environmental applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eWhile our study provides important insights, several limitations should be acknowledged. First, our analysis is based on Chinese cities, which may limit the generalizability of our findings to other national contexts. Future research should examine whether similar relationships hold in other countries with different institutional environments, development levels, and urban characteristics.\u003c/p\u003e \u003cp\u003eSecond, our measurement of digital transformation, ecological innovation, and sustainable development relies on available indicators, which may not capture all relevant dimensions of these complex phenomena. Future research could develop more comprehensive measurement approaches, potentially incorporating qualitative assessments and stakeholder perspectives.\u003c/p\u003e \u003cp\u003eThird, while we employ various techniques to address endogeneity concerns, the observational nature of our data limits our ability to make strong causal claims. Future research could employ experimental or quasi-experimental designs to provide stronger causal evidence.\u003c/p\u003e \u003cp\u003eFourth, our analysis focuses on city-level relationships and may not capture important within-city variations or individual-level mechanisms. Future research could examine these relationships at different scales and levels of analysis.\u003c/p\u003e \u003cp\u003eFifth, our study period (2015\u0026ndash;2023) may not capture longer-term dynamics or structural changes in the relationships we examine. Future research could extend the analysis to longer time periods or examine how these relationships evolve over different phases of urban development.\u003c/p\u003e \u003cp\u003eSeveral promising directions for future research emerge from our findings. First, comparative studies across different national and regional contexts could examine the generalizability of our findings and identify contextual factors that influence the digital-ecological-sustainable development nexus.\u003c/p\u003e \u003cp\u003eSecond, more detailed mechanism studies could examine the specific pathways through which digital technologies influence ecological innovation, potentially using case studies, surveys, or experimental approaches to provide deeper insights into causal mechanisms.\u003c/p\u003e \u003cp\u003eThird, studies examining the role of governance and institutions could provide insights into how policy environments and institutional arrangements influence the effectiveness of digital-ecological integration strategies.\u003c/p\u003e \u003cp\u003eFourth, research on the distributional effects of digital-ecological integration could examine whether these processes contribute to or alleviate urban inequalities and environmental justice concerns.\u003c/p\u003e \u003cp\u003eFifth, longitudinal studies tracking the evolution of digital-ecological integration over longer time periods could provide insights into the dynamics and sustainability of these relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Broader Implications for Sustainable Development\u003c/h2\u003e \u003cp\u003eOur findings have broader implications for understanding the role of technology in sustainable development. The strong positive relationships we identify between digital transformation, ecological innovation, and sustainable development suggest that technological advancement and environmental sustainability are not necessarily in tension, as sometimes portrayed in the literature.\u003c/p\u003e \u003cp\u003eInstead, our results support a view of technology as a potential enabler of sustainability, provided that it is developed and applied in ways that enhance rather than undermine environmental and social goals. This perspective aligns with emerging concepts such as \"digital sustainability\" and \"green digitalization\" that emphasize the potential for digital technologies to contribute to sustainable development [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mediation role of ecological innovation suggests that the environmental benefits of digital transformation are not automatic but depend on the development and application of environmentally oriented innovations. This highlights the importance of innovation policies and systems that can effectively channel digital capabilities toward environmental applications.\u003c/p\u003e \u003cp\u003eOur findings also contribute to debates about the relationship between economic development and environmental sustainability. The positive relationships we identify suggest that cities can pursue economic development (through digital transformation) and environmental sustainability (through ecological innovation) simultaneously, supporting arguments for \"green growth\" and \"sustainable development\" [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, our results also highlight the importance of context and capabilities in determining these outcomes. The heterogeneity we observe across regions, city sizes, and development levels suggests that the potential for digital-ecological integration is not equally distributed and may require targeted investments and policies to realize.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study investigates the relationships between digital transformation, ecological innovation, and urban sustainable development using comprehensive panel data from 96 Chinese cities over the period 2015-2023. Our findings provide strong empirical evidence for the positive effects of digital transformation on ecological innovation and sustainable development, with ecological innovation serving as a crucial mediator in these relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.1 Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis yields several key findings that advance understanding of the digital-ecological-sustainable development nexus. First, digital transformation significantly promotes ecological innovation, with a one-unit increase in digital transformation associated with a 0.973-unit increase in ecological innovation. This relationship is robust across different specifications, estimation methods, and sample variations, providing strong support for the hypothesis that digital technologies enhance ecological innovation capabilities.\u003c/p\u003e\n\u003cp\u003eSecond, ecological innovation significantly enhances urban sustainable development, with a one-unit increase in ecological innovation associated with a 1.045-unit increase in sustainable development. This finding supports the ecological modernization perspective that environmental innovation can contribute to broader sustainability goals without sacrificing economic development.\u003c/p\u003e\n\u003cp\u003eThird, ecological innovation serves as a crucial mediator in the relationship between digital transformation and sustainable development, accounting for 87.4% of the total effect. This large mediation effect suggests that the primary pathway through which digital transformation influences sustainable development is by enhancing ecological innovation capabilities rather than through direct effects.\u003c/p\u003e\n\u003cp\u003eFourth, significant heterogeneity exists across different urban contexts. Eastern region cities, larger cities, and more developed cities show stronger relationships across all pathways, suggesting that certain threshold conditions or capabilities may be necessary for effective digital-ecological integration.\u003c/p\u003e\n\u003cp\u003eFifth, mechanism analysis reveals that digital transformation influences ecological innovation through multiple pathways, including improved information and communication, enhanced collaboration and networking, new market and business model opportunities, and better optimization and efficiency capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Theoretical Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study makes several important theoretical contributions. We develop an integrated framework that combines digital transformation theory, ecological modernization theory, and sustainable development theory, providing a more comprehensive understanding of how these phenomena interact in urban contexts. We provide empirical evidence for the mediating role of ecological innovation in the digital transformation-sustainable development relationship, advancing understanding of the mechanisms through which digital technologies contribute to sustainability.\u003c/p\u003e\n\u003cp\u003eWe also contribute to innovation systems theory by demonstrating how digital technologies can enhance the functioning of urban innovation systems, particularly in environmental domains. Our heterogeneity analysis contributes to the literature on innovation geography by highlighting the importance of local contexts and capabilities in determining innovation outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Policy Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on our findings, we offer several policy recommendations for promoting urban sustainable development through digital-ecological integration:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated Policy Design:\u003c/strong\u003e Policymakers should develop integrated strategies that simultaneously promote digital transformation and ecological innovation rather than treating them as separate policy domains. The strong mediation effect suggests that digital transformation policies can have substantial environmental benefits through their effects on ecological innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContext-Sensitive Approaches:\u003c/strong\u003e Policy approaches should be tailored to local contexts and capabilities. More developed regions and larger cities may benefit from advanced integration strategies, while less developed areas may need foundational investments in digital infrastructure and innovation capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-Level Coordination:\u0026nbsp;\u003c/strong\u003eEffective digital-ecological integration requires coordination across multiple levels of government and between different policy domains. National governments should provide strategic direction and resources, while local governments should adapt strategies to local contexts and needs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Investment:\u003c/strong\u003e Continued investment in digital infrastructure is crucial for enabling digital-ecological integration. This includes not only basic connectivity but also advanced capabilities such as IoT networks, big data analytics, and artificial intelligence systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInnovation System Development:\u003c/strong\u003e Policies should focus on strengthening urban innovation systems to enhance their capacity for ecological innovation. This includes support for research and development, technology transfer, entrepreneurship, and collaboration between different innovation actors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCapacity Building:\u003c/strong\u003e Human capital development is crucial for effective digital-ecological integration. This includes education and training programs that develop digital and environmental capabilities, as well as interdisciplinary programs that bridge these domains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Practical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor urban practitioners and managers, our findings suggest several practical strategies for leveraging digital technologies to enhance ecological innovation and sustainable development:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital Environmental Monitoring:\u0026nbsp;\u003c/strong\u003eInvest in comprehensive digital environmental monitoring systems that can provide real-time data on environmental conditions and support evidence-based decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollaboration Platforms:\u003c/strong\u003e Develop digital platforms that facilitate collaboration among environmental innovators, researchers, businesses, and citizens, creating networks that can accelerate ecological innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGreen Digital Marketplaces:\u003c/strong\u003e Support the development of digital marketplaces for environmental goods and services, creating market opportunities for ecological innovations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmart City Integration:\u0026nbsp;\u003c/strong\u003eIntegrate environmental considerations into smart city initiatives, using digital technologies to optimize resource use and improve environmental performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData-Driven Decision Making:\u003c/strong\u003e Develop capabilities for using big data and analytics to support environmental decision-making and identify opportunities for ecological innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Future Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study opens several avenues for future research. Comparative studies across different national and regional contexts could examine the generalizability of our findings and identify contextual factors that influence digital-ecological integration. More detailed mechanism studies could provide deeper insights into the specific pathways through which digital technologies influence ecological innovation.\u003c/p\u003e\n\u003cp\u003eResearch on governance and institutions could examine how policy environments and institutional arrangements influence the effectiveness of digital-ecological integration strategies. Studies on distributional effects could examine whether these processes contribute to or alleviate urban inequalities and environmental justice concerns.\u003c/p\u003e\n\u003cp\u003eLongitudinal studies tracking the evolution of digital-ecological integration over longer time periods could provide insights into the dynamics and sustainability of these relationships. Experimental and quasi-experimental studies could provide stronger causal evidence for the relationships we identify.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Final Remarks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe convergence of digital transformation and ecological innovation represents a promising pathway for achieving urban sustainable development. Our findings provide empirical evidence that these phenomena are not only compatible but mutually reinforcing, with digital technologies serving as enablers of environmental sustainability through their effects on innovation capabilities.\u003c/p\u003e\n\u003cp\u003eHowever, realizing this potential requires deliberate effort and appropriate policies. The heterogeneity we observe suggests that digital-ecological integration is not automatic and may require targeted investments and strategies tailored to local contexts and capabilities.\u003c/p\u003e\n\u003cp\u003eAs cities worldwide face mounting environmental challenges and seek to leverage digital technologies for sustainable development, our findings provide both encouragement and guidance. The strong positive relationships we identify suggest that it is possible to pursue technological advancement and environmental sustainability simultaneously, but success depends on thoughtful integration of digital and ecological strategies.\u003c/p\u003e\n\u003cp\u003eThe path forward requires continued research, policy innovation, and practical experimentation to fully realize the potential of digital-ecological integration for urban sustainable development. 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Cambridge University Press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"GongQing Institute Of Science And Technology","isAcceptedByJournal":false,"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 transformation, Ecological innovation, Sustainable development, Urban development, Mediation analysis, China","lastPublishedDoi":"10.21203/rs.3.rs-6845550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6845550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid advancement of digital technologies has fundamentally transformed urban development patterns, creating unprecedented opportunities for ecological innovation and sustainable development. This study investigates the impact mechanisms of digital transformation on urban sustainable development through ecological innovation, using panel data from 96 Chinese cities spanning 2015-2023. We construct comprehensive indices for digital transformation, ecological innovation, and sustainable development using principal component analysis, and employ multiple regression models to examine their relationships. Our findings reveal that digital transformation significantly promotes ecological innovation (β=0.973,β=0.973, p\u0026lt;0.001,p\u0026lt;0.001), which in turn enhances urban sustainable development (β=0.921,β=0.921, p\u0026lt;0.001p\u0026lt;0.001). Mediation analysis demonstrates that ecological innovation serves as a crucial mediator, accounting for 87.4% of the total effect of digital transformation on sustainable development. The study also identifies significant regional heterogeneity, with stronger effects observed in eastern regions and larger cities. These results provide empirical evidence for the \"digital-ecological-sustainable\" development paradigm and offer important policy implications for promoting urban sustainability through digital-ecological synergy. The research contributes to the literature by integrating digital transformation theory, ecological modernization theory, and sustainable development theory into a unified analytical framework, while providing practical guidance for policymakers seeking to leverage digital technologies for environmental and sustainable development goals.\u003c/p\u003e","manuscriptTitle":"Digital Transformation-Driven Ecological Innovation and Urban Sustainable Development: Evidence from Chinese Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 13:36:35","doi":"10.21203/rs.3.rs-6845550/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":"d937045a-638e-4814-9353-1cfc4e7c054f","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49704126,"name":"Ecological Modeling"}],"tags":[],"updatedAt":"2025-06-11T13:52:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 13:36:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6845550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6845550","identity":"rs-6845550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}