Research on the impact mechanism and spatial spillover effects of digital infrastructure on urban land green use efficiency: evidence from the Yangtze River Delta region

Digital infrastructure is increasingly recognized as a pivotal driver of green and sustainable urban development. This study examines its impact on the spatiotemporal evolution of Urban Land Green Use Efficiency (ULGUE), focusing on the underlying mechanisms and spatial spillover effects.Utilizing panel data from 41 cities in China’s Yangtze River Delta region(YRD) from 2013 to 2022, we employed a multi-period Difference-in-Differences (DID) framework. The “Broadband China” demonstration program served as a quasi-natural experiment. Robustness was assessed through parallel trend tests, Propensity Score Matching-DID (PSM-DID), and policy exclusion checks. Mechanisms were explored via mediation analysis, and spatial effects were analyzed using a Spatial Durbin Model. The analysis reveals that: (1) Digital infrastructure significantly enhances ULGUE, a finding robust across various sensitivity checks. (2) It indirectly improves ULGUE by promoting industrial upgrading and strengthening local governmental environmental awareness. (3) Spatial heterogeneity is notable: the effect is strongest in Anhui Province, slightly inhibitory in parts of Jiangsu Province, and statistically insignificant in Zhejiang Province and Shanghai. (4) Significant positive spatial spillover effects exist, indicating that digital infrastructure benefits both local and neighboring regions’ green land-use efficiency.These findings underscore the multifaceted role of digital infrastructure in advancing sustainable urban land management. They provide theoretical and empirical insights into pathways for low-carbon urban development and offer policy implications for fostering high-quality, regionally coordinated growth in the YRD region.

1 Introduction

Green and low-carbon development aligns with the laws of nature and promotes a harmonious relationship between humans and the environment. Advancing green and low-carbon growth has become a global consensus and an inevitable path toward sustainable development (Liu et al., 2022). In recent years, China has made remarkable progress in its green and low-carbon transition, with accelerated shifts in its development model and tangible achievements in high-quality growth (Fu et al., 2022a). In 2020, China officially announced its “dual-carbon” goals: reaching peak carbon emissions by 2030 and achieving carbon neutrality by 2060. Urban land, as the spatial carrier of economic activities and human settlements (Li et al., 2023; Zhang and Zhang, 2023), serves as a critical platform for implementing the dual-carbon strategy (Li L. et al., 2022; Wang et al., 2022). It plays an essential role in population agglomeration, economic development, and ecological security (Fu et al., 2022b; Chen H. et al., 2022; Hong et al., 2024) and constitutes a key indicator for assessing the rationality and efficiency of regional land use (He and Song, 2023). Conversely, inefficient urban land use can severely constrain sustainable urban development (Wang et al., 2019; Yao and Zhang, 2021). Existing research has primarily focused on identifying the key determinants of green land-use efficiency, including urban green development (Zhou and Lu, 2023), land policies (Koroso, 2023), and industrial agglomeration (Xu and Sun, 2023), although the magnitude of these effects varies across studies. Moreover, the relationship between green land-use efficiency and broader dimensions of sustainability, such as energy transition (Zhang H. et al., 2024), carbon emissions (Huang et al., 2024), sustainable development (Huang et al., 2025), and ecological carrying capacity (Zong et al., 2023), has emerged as an important area of scholarly inquiry.

Infrastructure serves as a fundamental pillar supporting urban operations and development (Pandey et al., 2022). As the material foundation of the digital economy, digital infrastructure leverages emerging digital technologies and characteristics to generate spatial spillover effects (Zhang et al., 2023a), playing an irreplaceable role in promoting high-quality urban economic growth (Haldar and Sethi, 2022) and enhancing urban economic resilience (Balogun et al., 2020). By improving the efficiency of resource allocation, digital infrastructure facilitates the transformation of regional energy structures (Fan et al., 2022). Broadband networks, in particular, constitute a strategic public infrastructure essential for fostering socioeconomic development. Globally, broadband connectivity contributes to a new wave of digitalization, and many countries have elevated broadband development to a national strategic priority (de Clercq et al., 2023; Edquist, 2022), recognizing its significance in securing competitive advantages in the global economy, science, and technology (Hong et al., 2022; Chen and Wang, 2023). Despite notable progress in broadband coverage, transmission capacity, technological innovation, and service quality, a digital divide still persists between China and developed Western countries (Jin et al., 2024). To address this gap, the Chinese government launched the Broadband China Strategy and Implementation Plan in August 2013. With the designation of three successive batches of pilot cities, the strategy has increasingly demonstrated its catalytic effect on urban infrastructure development and its broader role in advancing digital transformation across Chinese cities (Ding C. J. et al., 2024).

Digital infrastructure is closely intertwined with urban land green use efficiency (ULGUE). First, supported by key technologies such as 5G networks, data centers, and the Internet of Things (Nie et al., 2023), digital infrastructure enhances the efficiency of information transmission and processing within cities (Ma and Zhang, 2024), providing robust technological support for high-quality urban development (Lim et al., 2024; Li C. et al., 2024; Song et al., 2023). By enabling green innovation and strengthening public environmental awareness (Du et al., 2022), it contributes to reducing urban carbon intensity and fostering sustainable urban transformation. Second, the powerful capabilities of digital infrastructure in data sensing, analysis, and decision support optimize the spatial allocation of land resources (Qin et al., 2024), enhance regional ecological resilience (Cui et al., 2025), and promote the large-scale integration of renewable energy into economic systems (Abbas et al., 2024; Liao and Liu, 2024; Lai et al., 2024a). Collectively, these mechanisms drive urban land use toward higher efficiency and greater sustainability, establishing digital infrastructure as a key enabler of green and low-carbon urban development (Guo et al., 2024; Liu et al., 2025). Digital infrastructure also enhances ULGUE by accelerating the green transformation of enterprises. Technologies such as the industrial internet and big data analytics reduce information asymmetry in environmental regulation, enabling real-time monitoring of firms’ energy consumption and emissions. This improvement in transparency lowers compliance costs and provides stronger incentives for the adoption of cleaner and more energy-efficient production technologies (Qin et al., 2025). Digitalization further facilitates process optimization and supply-chain coordination, which accelerates the diffusion of green technologies and guides firms away from extensive, high-emission production toward more intensive and low-carbon operational models (Wang et al., 2025). In addition, digital infrastructure enhances firms’ economic performance, green innovation capacity, and environmental outcomes, thereby promoting greener production modes and more efficient resource use. These micro-level improvements ultimately contribute to more intensive and efficient urban land use, reinforcing the overall enhancement of ULGUE (Wang et al., 2021).

Building upon existing research, this study employs a quasi-natural experimental design and takes the “Broadband China” pilot cities in the Yangtze River Delta (YRD) region as the treatment group. The YRD region, one of the most dynamic, open, and innovative regions in China, provides an ideal empirical context for this analysis, as illustrated in Figure 1. A multi-period difference-in-differences (DID) model is applied to examine the impact of urban digital infrastructure on the ULGUE across YRD cities. The research framework chart of this article is shown in Figure 2. Specifically, this study aims to address the following research questions: (1) how does digital infrastructure directly and indirectly influence ULGUE and through which potential mechanisms? (2) Do these effects exhibit heterogeneity among cities with different geographical locations, resource endowments, and scales? (3) Does digital infrastructure generate spatial spillover effects on ULGUE within the YRD region? (4) How can policy interventions be optimized to leverage digital infrastructure in promoting the coordinated and high-quality improvement of ULGUE?

Figure 1

Figure 1. Study area: cities in the YRD region of China.

Figure 2

Figure 2. Article framework chart.

To more clearly articulate the research background and underscore the academic significance of this study, digital infrastructure should be conceptualized not merely as a technological system but as a transformative institutional force reshaping the trajectory of urban sustainable development. As China enters a critical phase in pursuit of its dual-carbon goals, land—serving both as the substrate for economic activities and the ecological foundation of urban systems—has placed improvements in ULGUE at the forefront of policy priorities. Although a growing body of literature has explored the drivers of green land use, the rapid expansion of digital infrastructure has introduced profound structural changes that remain insufficiently integrated into existing research. Digital infrastructure fundamentally alters how cities collect information, allocate resources, and coordinate production, thereby enabling more fine-grained land governance and facilitating greener urban transitions. However, current scholarship still faces three major limitations: (1) it predominantly emphasizes the economic upgrading effects of digitalization while underexploring governance-oriented mechanisms such as enhanced governmental environmental awareness; (2) empirical evidence on whether, and through which channels, digital infrastructure generates spatial spillovers in high-density urban regions remains scarce; and (3) few studies treat the YRD region—China’s most integrated and digitally advanced urban agglomeration—as a natural laboratory for identifying these mechanisms.

The main contributions of this study are as follows: (1) this study provides a deeper investigation into the mechanisms through which digital infrastructure influences ULGUE. Existing research predominantly frames upgrading of the industrial structure as the primary mediating channel linking digital infrastructure to urban land greenness (Li W. et al., 2024; Chang et al., 2023). However, this economically oriented perspective is theoretically narrow and largely overlooks the profound effects of digitalization on governmental decision-making and environmental governance. In response, we innovatively propose and empirically validate a governance-oriented intermediary mechanism: digital infrastructure enhances ULGUE by strengthening governmental environmental awareness. Compared with the conventional industrial structure pathway, this mechanism not only highlights the unique institutional advantages conferred by digitalization in public governance but also provides a novel way of understanding how digital infrastructure supports sustainable land management from a governance perspective, thereby substantially extending the theoretical boundaries of the existing literature. (2) Comprehensive analysis of heterogeneity and regional planning: This study provides a nuanced understanding of the differential effects of digital infrastructure by systematically examining three key dimensions of heterogeneity: regional location, resource endowment, and city scale. By focusing on the economically vibrant and highly integrated YRD region rather than a broader national sample (Ma D. et al., 2024) or individual regions (Xu et al., 2025), the study enhances the contextual relevance of the findings. It also provides targeted insights for formulating regional development policies that promote coordinated growth. (3) Innovative variable selection: The study introduced novel control variables, such as using “environmental regulatory intensity” as an explanatory variable, which increased the originality of the analysis. In addition, compared with the existing literature (Niu et al., 2023), this study selects three different matrices for the spatial Durbin model, and the research results are more comprehensive.

2 Strategic background and research hypotheses

After identifying the research background and issues in Introduction, the following section will delve into the strategic background of the “Broadband China” strategy and propose a series of testable research hypotheses around its potential impact on ULGUE.

2.1 Strategic background

Broadband networks constitute the core component of digital infrastructure, providing high-speed and reliable internet connectivity essential for the development of the digital economy. In August 2013, the Chinese government released the Broadband China Strategy and Implementation Plan, which, for the first time, incorporated broadband networks into the national agenda for public infrastructure development. Between 2014 and 2016, three successive batches of pilot cities were launched to build a high-speed, ubiquitous, and secure digital infrastructure system. Through increased investment, strong governmental guidance, cross-sectoral coordination, and technological innovation, the strategy significantly enhanced network coverage and transmission capacity while fostering deep integration between broadband and other infrastructure systems. Digital infrastructure has since played an indispensable role in improving the overall level of informatization and promoting socioeconomic development. Owing to the Broadband China strategy’s clearly defined policy hierarchy, implementation stages, and regional heterogeneity, it provides an ideal quasi-natural experimental setting for empirical research. Consequently, the policy serves as a reliable proxy for assessing the level of digital infrastructure development and offers a robust identification basis for evaluating its economic and social impacts.

2.2 Research hypotheses

2.2.1 The direct impact of digital infrastructure on ULGUE

From an economic perspective, digital infrastructure represents a new form of development driven by the ongoing digital technology revolution. It constitutes the material foundation for urban production and business activities, while urban land provides the essential spatial conditions for these activities. By supporting the growth of emerging industries and enabling green application scenarios, digital infrastructure promotes the transformation of cities toward green and low-carbon economies (Ma and Lin, 2023). Consequently, it plays an indispensable role in facilitating urban commercial operations and exerts multiple positive effects on ULGUE.

From an environmental perspective, digital infrastructure has stimulated the vitality of the digital economy, increased carbon emission efficiency, and improved the overall environmental quality and performance of cities (Sun, 2023; Li and Diao, 2025). Increasing investment in urban green infrastructure further promotes sustainable development by enhancing ecological resilience and energy efficiency (Barrios-Crespo et al., 2021). By using digital tools such as remote sensing, traffic monitoring systems, and cloud computing platforms, urban managers can significantly improve the accuracy and efficiency of environmental governance (Peng et al., 2024). At the social level, digital infrastructure is increasingly integrated into living and working environments. The widespread use of shared smart services such as shared bicycles, co-working spaces, and smart express lockers has profoundly reshaped urban lifestyles and work patterns (Allam and Jones, 2021). The application of digital infrastructure reduces unnecessary private transportation and personal space requirements, optimizes the utilization efficiency of existing urban space, alleviates the pressure of land expansion, and encourages residents to adopt more environmentally friendly and intensive land-use behaviors (Chen et al., 2025). Based on the above analysis, the following hypotheses are proposed:

Hypothesis 1

Hypothesis 1. Digital infrastructure directly enhances the ULGUE.

2.2.2 The intermediary role of industrial-structure upgrading

Digital infrastructure not only promotes urban economic growth but also plays a significant role in facilitating industrial-structure upgrading (Wang and Shao, 2024). In turn, industrial upgrading enhances the efficiency and performance of digital infrastructure (Hu et al., 2023). Emerging technologies such as cloud computing, big data, and artificial intelligence foster data interconnectivity across industrial chains, enable full-process supervision, and cultivate efficient digital ecosystems. These advancements optimize enterprise resource allocation and ultimately accelerate industrial transformation and upgrading (Gong et al., 2023). Industrial structure upgrading promotes the interaction between urban economic development and ecological protection, guides urban land use to develop in a green and efficient direction, and promotes the formation and development of urban ecological resilience (Zhang Y. et al., 2024). First, upgrading the industrial structure can alleviate the impact of landscape ecological risks in low-urbanized cities while also enhancing urban ecological protection and promoting long-term land environmental sustainability (Cheng et al., 2019; Zeng et al., 2025). Second, the application of clean energy, low-carbon production, and digital management technologies reduces pollutant emissions during production processes and alleviates the environmental burden on land resources (Wu et al., 2025). Third, industrial upgrading fosters the growth of the green economy, mitigating negative environmental externalities, lowering environmental costs per unit of land, and restoring ecological functions (Yang et al., 2025). Therefore, digital infrastructure promotes ULGUE by driving industrial-structure upgrading, which establishes a structural foundation for improving both the economic output and ecological benefits of urban land. This mediating mechanism effectively enhances urban land-use efficiency. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 2

Hypothesis 2. Digital infrastructure can be improved by promoting the upgrading of industrial structure, which in turn enhances ULGUE.

2.2.3 The intermediary role of government in raising environmental awareness

Digital infrastructure not only reshapes economic systems but also fundamentally transforms governmental environmental governance by providing unprecedented capabilities for information acquisition, processing, and decision support (Ding J. et al., 2024; Somanath et al., 2024). Enhanced governmental environmental awareness constitutes a critical yet underexplored mediating mechanism through which digital infrastructure affects ULGUE. First, networks of remote sensing satellites, Internet of Things sensors, and large-scale environmental monitoring platforms significantly strengthen the government’s real-time perception and oversight of urban environmental systems (Li et al., 2025). This technological foundation enables authorities to access environmental information with greater speed, precision, and spatial granularity, exerting a substantial influence on pollution emissions, land-use changes, and ecological quality, thereby enhancing the scientific and anticipatory nature of environmental management. Empirical studies demonstrate that high-resolution remote sensing data can accurately identify industrial pollution sources, providing a basis for targeted enforcement (He et al., 2024). The integrated application of these technologies facilitates the generation of detailed pollution maps and the visualization of environmental conditions (Li and Yang, 2024), improving both data accessibility and interpretability while reinforcing the government’s capacity for dynamic risk perception, early warning, and timely response (Wu et al., 2021).

Second, unified platforms built upon digital infrastructure enable comprehensive environmental data recording and transparent disclosure while simultaneously providing effective channels for public participation and environmental oversight (Fan et al., 2023). Citizens can articulate environmental concerns and monitoring demands through these platforms, generating external pressures that compel governments to prioritize environmental protection in policy formulation and resource allocation (Ma L. et al., 2024; Ye et al., 2022). This process ultimately elevates local governments’ overall environmental awareness and responsiveness.

Strengthened governmental environmental attention serves as a key driver for improving ULGUE. On the one hand, this attention manifests institutionally through the incorporation of environmental performance indicators into official evaluation systems, establishing more binding incentive-compatible mechanisms (Bao and Liu, 2022). On the other hand, heightened governmental environmental focus stimulates corporate-level adjustments, promoting the innovation and adoption of green technologies (Lu and Tao, 2023) and guiding industries toward clean, low-carbon transformation, thereby mitigating the ecological impact of land use at the source (You et al., 2025). Collectively, these dynamics contribute to the effective enhancement of ULGUE, and the following hypothesis is proposed:

Hypothesis 3

Hypothesis 3. Digital infrastructure can be improved by increasing governments’ environmental awareness, which in turn enhances ULGUE.

2.2.4 The spatial spillover effect of digital infrastructure on ULGUE

Digital infrastructure exerts a significant spatial spillover effect on the development of urban agglomerations, which is reflected at both the intra- and inter-city levels. The deployment of digital infrastructure promotes knowledge dissemination, factor optimization, and collaborative innovation among cities (Wang et al., 2024). These spillover effects are not only due to the externalities of digital infrastructure itself but also due to the spatial interdependence of high-quality innovation-driven productivity development (Yao et al., 2025). At the same time, the technology integration system supported by digital infrastructure has accelerated the local absorption of green knowledge and tacit expertise and promoted the transformation of traditional high-carbon industries to more resilient and adaptable low-carbon production models. In addition, digital infrastructure has significantly reduced the cost of cross-regional information transmission and coordination, thereby enhancing the flow of advanced production factors such as knowledge, technology, and capital among cities (Li Y. et al., 2024). Through their advanced management practices, the pilot cities of the “Broadband China” strategy have provided valuable experience to surrounding cities and promoted the formation of a collaborative development mechanism characterized by a standardized digital governance framework and a shared innovation ecosystem. This regional connectivity has prompted cities to prioritize green development and efficient land use, thereby generating positive spatial spillover effects on the surrounding cities and even the entire YRD region. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 4

Hypothesis 4. The impact of digital infrastructure on ULGUE presents a spatial spillover effect.

3 Model setup and variable descriptions

In order to empirically test the aforementioned hypotheses, this section will introduce the econometric model and variable definitions used in the analysis.

3.1 Benchmark modeling

Before applying the econometric models, this study clarifies the underlying statistical rationale and key assumptions. The DID framework relies on the parallel trend assumption, which requires that in the absence of the Broadband China strategy, treatment and control cities would follow comparable temporal trajectories. This assumption is tested through an event-study specification. In addition, the fixed-effects estimator removes unobserved time-invariant heterogeneity across cities, ensuring consistent estimation under potential omitted-variable concerns. For the spatial models, the analysis is grounded in the principles of spatial dependence and spatial error propagation, acknowledging that ULGUE exhibits significant autocorrelation generated by geographic proximity and factor mobility. The spatial Durbin model is selected because it simultaneously captures endogenous interaction effects, exogenous interaction effects, and spatially correlated shocks. These statistical rationales and assumptions provide a solid theoretical foundation, ensuring that the identification strategy and estimated coefficients are both valid and interpretable.

To empirically examine the impact of digital infrastructure on urbanization in the YRD region, this study constructs a quasi-natural experimental framework. The treatment group comprises the “Broadband China” pilot cities within the YRD region, whereas the control group includes non-pilot cities in the same region. Given that the “Broadband China” strategy was implemented in three consecutive batches of pilot cities in 2014, 2015, and 2016, this paper employs a multi-period DID model to capture the intertwined effects of the policy. Following the methodological approaches of prior studies (Li B. et al., 2022; Wu et al., 2024), the empirical specification is formulated as follows:

$ULGU{E}_{it}=\alpha +\beta DI{D}_{it}+\gamma Contro{l}_{it}+{\mu }_i+y_t+{\epsilon }_{it},(1)$

In Equation 1, where $t$ represents the city, $i$ represents the year, $\alpha$ represents the constant term, ${\mu }_i$ represents the city fixed-effect, $y_t$ is a time fixed-effect, and ${\epsilon }_{it}$ is a random disturbance term.

With reference to previous research on land-use efficiency (Yu and Zhou, 2023), the dependent variables, core explanatory variables, and control variables selected in this study and their definitions are shown in Table 1.

Table 1

Table 1. Variable definitions and measurements.

The dependent variable $ULGU{E}_{it}$ represents the ULGUE, defined as the maximized comprehensive benefit that can be achieved within a given period under fixed inputs of urban land, capital, and labor while simultaneously accounting for both economic output and environmental pollution (undesirable output). Following established studies (Li and Chen, 2021; Tone, 2002), this research employs a super-efficiency Slack-Based Measure (SBM) model with undesirable outputs to quantify ULGUE. This approach effectively addresses the limitations of the traditional SBM model, which fails to simultaneously consider input/output slacks and undesirable outputs, and restricts efficiency values to below one. Consequently, the super-efficiency SBM model allows for a more refined discrimination and ranking of decision-making units that are already efficient.

The core explanatory variable ($DI{D}_{it}$) captures digital infrastructure development. Given that digital infrastructure is inherently multidimensional and difficult to measure directly, this study adopts a quasi-natural experimental design based on China’s “Broadband China” pilot city strategy, implemented in three phases by the Ministry of Industry and Information Technology in 2014, 2015, and 2016. Following mainstream policy evaluation practices, this policy is used as a proxy for digital infrastructure development. Accordingly, a multi-period DID dummy variable is constructed. If city $i$ was designated as a “Broadband China” pilot city in year t, its DID value is set to 1 for that year and all subsequent years; otherwise, it is set to 0. The coefficient $\beta$ of this variable thus captures the net causal effect of the digital infrastructure policy on the ULGUE.

Given that the model incorporates both city and year fixed-effects, it effectively absorbs unobserved heterogeneity that is constant across cities or over time. Moreover, clustering standard errors at the city level ensures that the statistical inference remains robust to potential heteroskedasticity and serial correlation. Accordingly, five key control variables ($Contro{l}_{it}$) are included in the analysis: population density (POP), trade openness (TO), industrial structure (IS), level of economic development (ED), and the stringency of environmental regulation (ERS). Population density is measured as the ratio of the resident population to the urban land area. TO is represented by per capita foreign direct investment. Industrial structure is captured by the share of tertiary industry value added in GDP. ED is represented by per capita GDP, and government support is measured by general public budget expenditure. The coefficient $\beta$ reflects the impact of digital infrastructure on ULGUE. A positive and statistically significant $\beta$ indicates that digital infrastructure enhances ULGUE.

3.2 Sample selection and data sources

The YRD region is among the most dynamic and rapidly urbanizing regions in China, characterized by exceptionally intensive land use. It has also been a focal area for the implementation of the “Broadband China” strategy, with a high concentration of pilot cities designated for digital infrastructure development. The advanced and representative nature of the YRD’s digital infrastructure and land-use patterns provides an ideal empirical setting for examining how digital technologies shape green land-use efficiency. To ensure full reproducibility, we document all data sources in detail. City-level data are drawn from the China Urban Statistical Yearbook and the statistical yearbooks of 41 YRD cities for the period 2013–2022. Indicators of digital infrastructure development are obtained from official “Broadband China” strategy documents. Environmental variables, including industrial emissions, green space, and ecological expenditures, are compiled from the China Urban Construction Statistical Yearbook, provincial environmental reports, and local government statistical bulletins. All datasets are publicly accessible and cross-checked for consistency.

Data processing follows standard procedures in urban quantitative research. Continuous variables are winsorized at the 1st and 99th percentiles to mitigate the influence of outliers. Missing values for non-critical indicators are linearly interpolated, whereas key policy or environmental indicators are manually verified using official documents. Treatment variables for the multi-period DID model are coded according to the official announcement years of Broadband China pilot cities. For the spatial Durbin model, the spatial weight matrix is constructed based on the geographic distances between city centroids, with robustness checks conducted using an adjacency matrix. ULGUE for each city from 2013 to 2022 is calculated using a non-radial super-efficiency SBM model in MaxDEA software, which accounts for undesirable outputs (Long, 2021). The specific model is as follows:

$\begin{array}{c}\rho =\min \frac{1+\frac{1}{m}{\displaystyle {\sum }_{i=1}^m}\frac{s_i^x}{x_{i0}}}{1-\frac{1}{s_1+s_2}\left({\displaystyle {\sum }_{k=1}^{s1}}\frac{s_k^y}{y_{k0}}+{\displaystyle {\sum }_{l=1}^{s2}}\frac{s_l^z}{z_{l0}}\right)},\\ s.t.\left\{\begin{array}{l}{\displaystyle \sum _{j=1,j\ne 0}^n}{x}_j{\lambda }_j-s_i^x\le x_{i0},\\ {\displaystyle \sum _{j=1,j\ne 0}^n}{y}_j{\lambda }_j-s_k^y\ge y_{k0},\\ {\displaystyle \sum _{j=1,j\ne 0}^n}{z}_j{\lambda }_j-s_l^z\le z_{l0},\\ 1-\frac{1}{s_1+s_2}\left({\displaystyle \sum _{k=1}^{s1}}\frac{s_k^y}{y_{k0}}+{\displaystyle \sum _{l=1}^{s2}}\frac{s_l^z}{z_{l0}}\right)>0,\\ s_i^x\ge 0,s_k^y\ge 0,\\ s_l^z\ge 0,{\lambda }_j\ge 0,\end{array}\right.\end{array}(2)$

In Equation 2 where $\rho$ denotes the efficiency score of green urban land use, which can be greater than 1; $m$ denotes inputs and outputs; $S_1$ denotes desired outputs; $S_2$ denotes non-desired outputs; $n$ denotes the total number of decision-making units; $x$ denotes the input of the evaluated units; $y$ denotes the desired outputs; and $z$ denotes the non-desired outputs.

Figure 3 illustrates the trend of ULGUE in the YRD region. Overall, ULGUE shows a continuous increase across cities in the region, with significant differences in the growth rates. In the treatment group, ULGUE rapidly increased from 0.47 in 2013 to 0.85 in 2022, reflecting a significantly faster growth rate than both the overall average and the control group, surpassing the ULGUE growth rate of all cities. Although the cities in the control group exhibited some growth, their growth rate was notably slower than that of the treatment group. In general, the ULGUE of demonstration cities consistently remained higher than that of all cities and non-demonstration cities, with the growth rate advantage becoming particularly prominent in the later observation periods. These results suggest that with the implementation of the “Broadband China” strategy, the positive impact of digital infrastructure on ULGUE continues to strengthen, and the gap in ULGUE between the treatment and control groups is widening.

Figure 3

Figure 3. Evolution trend of the average value of green land-use efficiency of urban land in the YRD region, 2013–2022.

3.3 Temporal evolutionary and spatial distribution patterns of ULGUE in the YRD region

3.3.1 Temporal evolutionary patterns of ULGUE in the YRD region

Using the Gaussian kernel density function (Chen Z. et al., 2022), a three-dimensional kernel density map of ULGUE in the YRD region was constructed for the period 2013–2022 using MATLAB software. This analysis provides an evaluation of the temporal evolution characteristics of ULGUE in the region. The model specification is as follows:

$\stackrel{\wedge }{f}\left(x,t\right)=\frac{1}{n·h_x·h_t}\sum \frac{n}{i=1}\left[\frac{1}{2\pi }\exp \left(-\frac{1}{2}\left(\frac{{\left(x-x_i\right)}^2}{h_x^2}+\frac{{\left(t-t_i\right)}^2}{h_t^2}\right)\right)\right],(3)$

In Equation 3 where $n$ denotes the total sample size, $x$ denotes ULGUE, $t$ denotes the year, $h_x$ denotes the ULGUE dimension bandwidth, and $h_t$ denotes the year dimension bandwidth.

Figure 4 reveals three key characteristics of the dynamic evolution of ULGUE in the YRD region. First, the distribution curve shifts rightward over time, indicating a steady annual increase in ULGUE across the region. Second, the peak height of the distribution curve exhibits a fluctuating evolution of “rising–falling–rising–converging” with the width of the main peak widening each year, suggesting that ULGUE is becoming more concentrated while the absolute gap in efficiency between cities is expanding. Third, the polarization degree of the kernel density curve indicates that the overall distribution curve from 2013 to 2022 retains a unimodal shape, reflecting the absence of a trend toward polarization or multi-level differentiation in ULGUE and demonstrating a synchronized improvement in efficiency across cities in the region.

Figure 4

Figure 4. Three-dimensional kernel density curves of ULGUE in the YRD region, 2013–2022.

Overall, from 2013 to 2022, ULGUE in the YRD region has shown a gradual increase, although the rate of improvement has been modest, and the disparities between cities have widened. Addressing ways to enhance the land green-use efficiency in lower-tier cities and reduce the efficiency gap across the region is a critical issue that warrants focused attention.

3.3.2 Spatial evolution patterns of ULGUE in the YRD region

Figure 5 illustrates the spatial evolution of ULGUE, which can be categorized into three distinct phases based on temporal characteristics. First, from 2013 to 2016, the region experienced an initial decentralized equilibrium phase, during which pilot cities were selected in three batches. In 2013, the YRD region predominantly exhibited a large area of light yellow, with cities demonstrating higher efficiency values concentrated in the southern and northeastern parts, showing relatively high spatial homogeneity. By 2016, efficiency values in several cities had slightly improved, with low-efficiency cities disappearing, although the region still remained in a moderate-to-low-efficiency state. Second, from 2016 to 2020, the region entered the mid-term polarization and diffusion phase. In 2018, a medium-to-high-efficiency zone emerged in the eastern part of the region, mainly encompassing cities such as Shanghai, Nantong, Suzhou, and Wuxi. By 2020, this high-efficiency zone had expanded significantly, with cities like Jiaxing and Shaoxing in Zhejiang Province entering the medium-to-high-efficiency range. However, the western and northern edges of the region continued to exhibit large areas of low-efficiency zones, forming a clear gradient contrast with the core area. Third, in the later stage of regional polarization from 2020 to 2022, the number of cities with high land green-use efficiency rapidly increased, with a larger proportion of these cities located in the eastern and southern parts of the region, while cities in the northwest had a smaller share.

Figure 5

Figure 5. Evolution of the spatial distribution of green land-use efficiency in Chinese cities.

This pattern can be attributed to the clear locational advantages of the eastern and southern cities in the YRD region, where “Broadband China” pilot cities are concentrated. Core cities such as Shanghai, Nanjing, and Hangzhou have gradually improved their green land-use efficiency, thereby exerting a demonstration effect and facilitating the gradual emergence of spillover effects. These spillover effects have become increasingly evident. However, cities in the western and northern parts of the region have seen improvements in urban green land-use efficiency but have not yet reached higher efficiency levels due to the lack of locational advantages and policy guidance.

4 Empirical analysis and robustness tests

The empirical results are presented below, first reporting the baseline regression results and then conducting a series of robustness tests to verify the reliability of the research conclusions.

4.1 Benchmark regression results

Table 2 presents the results of the baseline model test. Columns (1) to (6) show the regression outcomes with progressively added control variables. As the control variables are introduced, the coefficients remain stable, indicating that the conclusions are robust and reliable. The results demonstrate that the coefficients for digital infrastructure are all positive and satisfy the 1% significance level, confirming that digital infrastructure has a significant impact on ULGUE and thereby validating Hypothesis 1.

Table 2

Table 2. Benchmark model regression results.

4.2 Robustness check

4.2.1 Parallel trends test

Parallel trend testing is a prerequisite for conducting a multi-period DID model. Its core objective is to verify whether the explanatory variables exhibit the same trend across different groups prior to the policy implementation. If the variable passes the parallel trend test, a DID model can be preliminarily constructed for empirical analysis. The results of the parallel trend test are shown in Figure 6. The “Broadband China” strategy designated the final batch of pilot cities in 2016; thus, 2016 is considered the policy implementation year. Before the policy was implemented, the regression coefficients were not statistically significant and clustered around zero, indicating that both the treatment and control groups satisfy the parallel trend assumption, making them suitable for DID analysis.

Figure 6

Figure 6. Parallel trends and dynamic effect test results.

4.2.2 Placebo testing

To mitigate potential bias arising from unobservable factors and ensure the robustness of the results, a placebo test was conducted by constructing pseudo-policy pilot cities and applying 500 random shocks to the sample. The results, as shown in Figure 7, show that most p-values exceed 0.1, with the average effect coefficients following a normal distribution centered around zero. This distribution significantly differs from the baseline regression coefficients, thereby validating the robustness of the main findings.

Figure 7

Figure 7. Placebo testing.

4.2.3 Propensity score matching: difference-in-differences test

To further address potential sample selection bias and following existing literature (Zhang et al., 2023b), this study selected control samples with characteristics highly similar to those of the treatment group from a sample pool that is less affected by policy interventions. The matched samples were then used as a new analytical group, and a balance test was conducted on these matched samples. As shown in Table 3, the regression coefficients of the DID variables under all three matching methods are significantly positive, indicating that the treatment and control groups are well balanced.

Table 3

Table 3. Balance test results for propensity score matching methods.

In addition, the quality of matching was assessed by examining the degree of overlap between the propensity score distributions of the treatment and control groups. As shown in Figure 8, within the region of common support, the vast majority of propensity scores for both the treatment and control samples fall within the shared value range, indicating that the common support condition is satisfied.

Figure 8

Figure 8. Co-support hypothesis testing.

Figure 9 illustrates the effectiveness of propensity score matching (PSM)–DID under the kernel matching method. The left panel shows the distribution before matching, where the propensity scores of the treatment group are concentrated between 0.6 and 0.7 and those of the control group are centered around 0.3, indicating substantial selection bias in the initial sample. The right panel depicts the distribution after matching, where the control group curve shifts rightward and substantially overlaps with that of the treatment group, demonstrating that the PSM–DID procedure successfully identified comparable samples and significantly improved intergroup balance.

Figure 9

Figure 9. Kernel density plot before and after propensity score matching.

4.2.4 Eliminating policy interference during the same period

To control for the potential influence of other policies on urban green land-use efficiency, this study systematically reviewed relevant policies implemented between 2013 and 2022. The policy analysis identified the “Dual Gigabit Network Collaborative Development Action Plan (2021–2023),” issued by the Ministry of Industry and Information Technology in 2021, as a possible source of interference with the study variables. To effectively mitigate the impact of this competing policy, a sample exclusion approach was adopted by removing observations from 2021 onward. Column (1) of Table 4 presents the robustness test results, which indicate that after excluding the effects of this policy, the core findings remain statistically significant, further confirming the reliability of the results.

Table 4

Table 4. Robustness test results after excluding policy interference and changing regression samples.

4.2.5 Change regression sample

Given the disparities in development levels among the 41 cities in the YRD region—where municipalities directly under the central government, provincial capitals, and sub-provincial cities such as Shanghai, Nanjing, Hangzhou, Hefei, and Ningbo possess higher political and economic capacities and a stronger foundation for digital infrastructure—these five cities were excluded from the sample to mitigate potential bias in policy effectiveness. The empirical analysis was then re-estimated, with the results reported in Column (2) of Table 4. The DID coefficient remains significantly positive at the 1% level, demonstrating considerable robustness and further supporting the validity of the experimental results.

4.3 Mechanism test

Existing research has confirmed the role of industrial-structure upgrading and government environmental awareness in improving urban environmental quality. Drawing on the methodological framework of previous research (Li and Cao, 2025), this study constructs a mediation effect model to examine the underlying mechanisms. Industrial-structure upgrading is measured by the ratio of the value added of the tertiary industry to that of the secondary industry, and the level of urban innovation is represented by the number of patents (Jin et al., 2022). Governmental environmental awareness is represented by the total investment completed in fixed assets for urban public infrastructure construction. The calculation formula is as follows:

$\begin{array}{l}IS{U}_{it}={\alpha }_1+{\beta }_{1}DI{D}_{it}+{\gamma }_{1}Contro{l}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},\\ GE{A}_{it}={\alpha }_2+{\beta }_{2}DI{D}_{it}+{\gamma }_{2}Contro{l}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},\end{array}(4)$

$ULGU{E}_{it}={\alpha }_3+\theta DI{D}_{it}+{\rho }_{1}IS{U}_{it}+{\rho }_{2}GE{A}_{it}+{\gamma }_{3}Contro{l}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},(5)$

In Equations 4, 5 where $IS{U}_{it}$ represents industrial-structure upgrading; $GE{A}_{it}$ represents improvements in governmental environmental awareness; $ULGU{E}_{it}$ represents urban land green-use efficiency; $DI{D}_{it}$ represents digital infrastructure; $Contro{l}_{it}$ represents a set of covariates; $\alpha$ represents the intercept term; $\beta$ represents the marginal effect; $\theta$ represents the direct effect; $\rho$ represents the marginal effects; ${\mu }_i$ and ${\gamma }_t$ represent individual and time fixed-effects, respectively; and ${\epsilon }_{it}$ represents the idiosyncratic disturbance term.

Table 5 reports the results of the mechanism test. The regression coefficients in Columns (1) and (2) are both significantly positive, indicating that digital infrastructure enhances urban green land-use efficiency through two pathways: by facilitating the optimization and upgrading of industrial structure and by improving the level of urban innovation. These findings provide empirical support for Hypothesis 2.

Table 5

Table 5. Mediation effect test results.

4.4 Heterogeneity analysis

4.4.1 Urban location heterogeneity

Based on existing research (Hou et al., 2025), this paper examines the heterogeneity in the impact of digital infrastructure on ULGUE. As shown in Table 6, the effect of digital infrastructure on ULGUE exhibits significant regional variation across the YRD region. Specifically, cities in Anhui Province display a significant positive impact, whereas those in Jiangsu Province show a significant negative impact. In Zhejiang Province, the effect is positive but not statistically significant overall, whereas in Shanghai, the negative impact is statistically insignificant.

Table 6

Table 6. Results of the analysis of urban location heterogeneity.

The heterogeneous effects of digital infrastructure across the provinces of the YRD region can be interpreted within the broader context of systematic differences in development stages, economic structures, and policy environments. Anhui Province, which lags behind in both digital and green economic development within the region, has experienced a marked improvement in green land-use efficiency following the implementation of the “Broadband China” strategy. The accelerated deployment of digital infrastructure in Anhui’s pilot cities has directly enhanced efficiency levels. The short-term negative effects observed in Jiangsu Province cannot be explained solely by the costs of resource reallocation; instead, they are closely linked to the specific industrial composition of the pilot cities. The “Broadband China” pilot cities in Jiangsu are dominated by electronic information, equipment manufacturing, and chemical industries, all of which are characterized by high energy consumption and high emissions. In this context, improvements in digital infrastructure may initially stimulate the further expansion and spatial concentration of these established competitive industries, leading to a rigid increase in energy use and pollutant emissions. At the same time, firms often face substantial transformation costs and technology adaptation periods when integrating digital technologies into green production processes, resulting in a delayed improvement in green efficiency.

Therefore, the empirical findings for Jiangsu can be interpreted as a transitional stage in which the economic expansion effects triggered by digitalization temporarily outweigh the green-efficiency gains under a specific industrial structure. This outcome indicates that the environmental benefits of digital infrastructure are heavily conditioned by the region’s initial industrial configuration. In regions with high environmental burdens from their industrial base, stronger environmental regulation and more targeted guidance on green technologies are required to ensure that digitalization effectively supports the green transition.

Although Zhejiang Province enjoys a clear advantage in digital economy development, its heterogeneous test results suggest that the synergistic effects between digital infrastructure and ULGUE have yet to be fully realized. As for Shanghai, the insignificant result can be attributed primarily to its highly developed economic system and industrial structure, which render the marginal green benefits of digital infrastructure nearly saturated. Additionally, the city’s substantial inflow of foreign investment has led to rigid land demand, thereby constraining the potential for digital infrastructure to further optimize green land-use efficiency.

4.4.2 Heterogeneity of urban resource endowments

The development of digital infrastructure has significantly enhanced ULGUE in the YRD region. The natural resource endowments of YRD cities exhibit pronounced spatial heterogeneity, which directly influences their respective land-use patterns. Resource-based cities, leveraging their abundant natural resources, have long followed a development trajectory dominated by resource-intensive industries. This growth model, however, tends to result in extensive resource utilization and relatively low efficiency in green land use. With the deep implementation of the “Broadband China” strategy, the introduction of digital infrastructure has created new opportunities for industrial transformation in these cities. The deep integration of digital technologies with traditional resource industries has not only optimized resource utilization efficiency but also markedly improved green land-use efficiency, effectively reconciling the long-standing conflict between resource exploitation and environmental protection.

In contrast, non-resource-based cities exhibit lower dependence on natural resources and a more diversified industrial structure. This diversified economic base enables more precise and comprehensive urban land management, thereby minimizing the negative impact on the ecological environment. The implementation of the “Digital China” strategy has further provided non-resource-based cities with advanced technological support, allowing them to optimize spatial layouts and land-use structures through digitalization, thereby achieving sustained improvements in land-use efficiency.

According to the National Plan for the Sustainable Development of Resource-Based Cities (2013–2020) issued by the State Council of China, 12 of the 41 prefecture-level and above cities in the YRD region are officially classified as resource-based cities. As shown in Table 7, although digital infrastructure generally exerts a positive impact on improving ULGUE across the region, the magnitude of this effect varies significantly by city type, with resource-based cities experiencing notably stronger improvements. This differentiated impact can be attributed primarily to the greater marginal effects of digital technology in transforming traditional resource-based industries, along with the synergistic effects generated through institutional innovation and policy support during the digital transformation of resource-based cities.

Table 7

Table 7. Results of the analysis of the heterogeneity of urban resource endowments.

4.4.3 Urban size heterogeneity

The baseline regression results indicate that digital infrastructure significantly promotes ULGUE; however, this promoting effect may vary across cities with different characteristics. Considering the variation in city size, this study further conducts a grouped regression analysis based on the Notice of the State Council on Adjusting the Criteria for City Size Classification in combination with the actual conditions of the YRD region. Accordingly, the full sample is divided into two groups: large- and medium-sized cities (with a permanent population below 5 million) and very large cities (with a permanent population above 5 million). As shown in Table 8, the positive effect of digital infrastructure on ULGUE is more pronounced and statistically stronger in very large cities and megacities, whereas the effect, although still positive, is relatively weaker in large- and medium-sized cities. This pattern reflects a more evident scale agglomeration effect of digital infrastructure in very large cities.

Table 8

Table 8. Results of the analysis of urban-scale heterogeneity.

4.5 Tests for spatial spillover effects

The global Moran’s I is a key metric for measuring spatial autocorrelation. Spatial autocorrelation analysis aims to quantitatively characterize the degree of spatial dependence among sample observations. By calculating the spatial autocorrelation index, it is possible to examine the spatial distribution patterns of a given attribute across spatial units, thereby determining whether its spatial distribution exhibits clustering, randomness, or regularity. Following previous studies (Chen, 2023), the specific computational model is constructed as follows:

$\begin{array}{l}I=\frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{S^2{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}},\\ S^2=\frac{{\sum }_{i-1}^n\left(x_i-\overline{x}\right)}{n},\end{array}(6)$

In Equation 6 where $I$ is the global Moran’s index, $n$ denotes the number of samples, $x_i$ and $x_j$ denote the average value of the attribute values of all spatial units, and $w_{ij}$ is the spatial weight matrix element.

To examine the spatial spillover effects of digital infrastructure in the “Broadband China” pilot cities on ULGUE across the YRD region, this study, following previous research (Xue et al., 2022), constructs three types of spatial weight matrices: a geographic adjacency matrix, a spatial inverse-distance matrix, and an economic distance matrix.

As shown in Table 9, the Moran’s I indices of ULGUE under all three spatial weight matrices are significantly positive, confirming the existence of notable spatial clustering and spillover effects among cities in the YRD region. This result justifies the use of spatial econometric models to capture the interdependent characteristics of regional green land-use efficiency. At the same time, the magnitude of global Moran’s I exhibit a clear downward trend, with the value under the geographic matrix declining from 0.456 in 2013 to 0.186 in 2022. This continuous weakening of spatial dependence reflects several structural dynamics transforming the YRD region over the past decade.

Table 9

Table 9. Global Moran’s I index for ULGUE.

First, regional policy convergence and developmental catch-up have accelerated. The advancement of YRD integration initiatives and the diffusion of the “Broadband China” strategy have enabled previously lagging cities to access enhanced policy support and benefit from digital and environmental technology spillovers. This alignment has reduced the development gap between core and non-core cities, thereby diminishing overall spatial dependence. Second, the region has entered a phase of technological diffusion and relative saturation. In the early stages, digital infrastructure and green technologies were predominantly concentrated in core cities, leading to strong spatial clustering. Over time, knowledge spillovers, labor mobility, and increasing market integration have facilitated the diffusion of these technologies across the region. As a result, the initial technological advantages of leading cities have eroded, contributing to greater convergence in ULGUE. Third, city-specific development trajectories have become increasingly important. With the maturation of regional coordination mechanisms, heterogeneity in resource endowments, industrial structures, and local environmental governance plays a larger role in shaping ULGUE outcomes. As urban development paths rely more heavily on localized strategies rather than neighboring spillovers, geographic proximity naturally loses explanatory power.

Taken together, these trends reveal deeper implications of the declining Moran’s I values. Although ULGUE still demonstrates significant spatial clustering, the weakening spatial dependence suggests a gradual decoupling of inter-city green land-use performance. Cities are increasingly developing independent patterns of land greenness, reflecting diversified development pathways and reduced reliance on regional spillover mechanisms. This evolving spatial structure reinforces the necessity of spatial econometric approaches, as neglecting spatial dynamics would risk overlooking critical regional interactions and lead to biased or incomplete interpretations of policy effects.

To select the most appropriate spatial econometric model, a series of model specification and goodness-of-fit tests were conducted. First, the ordinary least squares regression was employed as a baseline, followed by the Lagrange multiplier (LM) test. The LM test results were significantly positive, strongly indicating the presence of spatial dependence and thus the necessity of adopting spatial regression models for further analysis. Second, the Hausman test was applied to determine the form of model effects, and the results supported the use of a fixed-effects specification. Finally, to discriminate among the spatial Durbin model, spatial lag model, and spatial error model, both the likelihood ratio test and the Wald test were performed. The results of these two tests significantly rejected the null hypothesis, suggesting that the spatial decision model cannot be reduced to a simple spatial lag or spatial error structure. Based on these results, the spatial Durbin model was ultimately selected for the subsequent empirical analysis. Following previous studies (Chen et al., 2018; Elhorst, 2014; Lai et al., 2024b), this study constructs a spatial Durbin DID model to examine the spatial effects of digital infrastructure on ULGUE:

$ULGU{E}_{it}={\gamma }_0+\beta {\displaystyle \sum _j}W{U}_{jt}+{\gamma }_{1}DI{D}_{it}+{\gamma }_2{\displaystyle \sum _j}WDI{D}_{jt}+{\gamma }_3{c}_{it}+{\gamma }_4{\displaystyle \sum _j}W{C}_{jt}+{\mu }_i+{\lambda }_t+{\epsilon }_{it},(7)$

In Equation 7 where $\rho$ is the spatial autoregressive coefficient; $W{U}_{jt}$, $WDI{D}_{jt}$, and $W{C}_{jt}$ are the spatial lag terms of the corresponding variables; and $W$ is the spatial weight matrix.

Table 10 reports the estimated spatial effects of digital infrastructure on ULGUE. Across all three spatial weight matrices, the coefficients of “W × DID” remain significantly positive, which confirms the presence of clear and robust spatial spillover effects across the YRD region. The decomposition into direct and indirect effects further illustrates the transmission mechanisms involved. The direct effects, with values of 0.035, 0.029, and 0.026, are consistently positive and statistically significant at the 1% level. This indicates that improvements in digital infrastructure can substantially enhance the ULGUE of the focal city. These estimates suggest that when other factors are held constant, a one-unit increase in the digital infrastructure variable leads to an increase in local ULGUE of approximately 0.026–0.035.

Table 10

Table 10. Results of the spatial effect test.

The indirect effects are also positive and significant across all three spatial matrices, with values of 0.017, 0.018, and 0.028. This demonstrates that digital infrastructure generates benefits not only for the pilot cities but also for the surrounding cities. The spillovers may occur through channels such as technology diffusion, knowledge transfer, industrial linkages, labor mobility, and cross-city environmental governance. The total effects range from 0.047 to 0.054 and exceed the magnitude of the direct effects in every specification. This pattern shows that the regional spillover component accounts for a substantial proportion of the overall impact. The stability of sign, magnitude, and statistical significance across the three spatial matrices provides strong evidence of the robustness of these findings.

From a policy perspective, the decomposition yields three important insights. First, the significant direct effects highlight the value of continued investment in local digital infrastructure as a means of enhancing land-use greenness. Second, the presence of sizable indirect effects underscores the need for regional coordination because cooperation among cities can extend and amplify the spatial spillovers generated by digitalization. Third, the variation in the relative magnitudes of direct and indirect effects across different spatial matrices suggests that policymakers should carefully consider the specific types of inter-city linkages, such as geographic proximity or economic connectedness, when designing region-specific digital infrastructure and land-use governance strategies.

5 Conclusion and recommendations

This study employs panel data from 41 cities in the YRD region from 2013 to 2022, using a multi-period DID approach and a spatial Durbin model to empirically examine the impact of digital infrastructure on ULGUE. The results indicate that (1) digital infrastructure significantly enhances ULGUE, and this effect remains robust across a series of tests, including parallel trend verification, PSM–DID, and placebo tests. (2) Digital infrastructure indirectly improves ULGUE through two key channels: the upgrading of industrial structure and the enhancement of government environmental awareness. This suggests that its impact is driven not only by economic restructuring but also by the modernization of governance, forming a dual mechanism of technological and institutional empowerment. (3) The effect of digital infrastructure on ULGUE exhibits significant heterogeneity across regions. Its impact is strongest in cities within Anhui Province, whereas a short-term inhibitory effect is observed in Jiangsu Province, likely due to the transitional costs associated with a high-energy-consumption industrial structure. In Zhejiang Province and Shanghai, the effects are not statistically significant, reflecting a diminishing marginal policy impact in more developed regions. (4) Digital infrastructure produces significant spatial spillover effects on ULGUE. The “Broadband China” strategy not only enhances local land green-use efficiency but also promotes green development in neighboring areas and across the urban agglomeration through knowledge diffusion, technology spillovers, and regional coordination, highlighting its value in fostering regional synergistic development.

Based on the research findings, the following policy recommendations are proposed to enhance urban greening and promote high-quality development in the YRD region: (1) Enhancing digital infrastructure development: Deepen the implementation of the “Broadband China” strategy and advance the systematic construction of urban digital infrastructure. Prioritize investments in broadband-based infrastructure, accelerate the development of cutting-edge technologies, and ensure the comprehensive coverage of 5G and gigabit optical fiber networks. Establish a dedicated digital platform for urban land greening utilization to enable real-time, precise, and scientific land management and regulation. The integration of digital infrastructure with land greening strategies will provide a solid digital foundation for the efficient and sustainable use of urban land. (2) Stimulating the green transformation of industrial structure and enhancing governmental environmental awareness: On the one hand, the integration of industrial internet and big data enables precise analysis of enterprise energy consumption and land-use efficiency, thereby facilitating the green and intelligent upgrading of the manufacturing sector and the orderly relocation of energy-intensive industries. This process optimizes the industrial structure at its source and promotes intensive and efficient land-use. On the other hand, leveraging the Internet of Things and remote sensing technologies allows for the establishment of an integrated environmental monitoring platform that dynamically assesses the ecological impact of land development. By incorporating these real-time data insights into governmental performance evaluations, policymakers can strengthen their environmental cognition and decision-making capacity, ultimately improving environmental governance efficiency and achieving high-quality allocation and sustainable development of regional land resources. (3) Differentiated policy implementation: Formulate tailored policies based on the unique characteristics and development potential of each province and city. For Anhui Province, capitalize on the significant role of digital infrastructure in driving regional economic development by creating policies that align with local needs. Strengthen incentive mechanisms to stimulate enterprise investment, maximizing their contribution to regional economic growth. For Jiangsu, Zhejiang, and Shanghai, areas where the impact on economic development is not yet significant, should adopt comprehensive optimization strategies, integrating spatial planning and leveraging economic and resource advantages to establish high-capacity digital platforms and enhance urban management intelligence. This will ensure the efficient and green use of limited land resources. (4) Promoting regional coordinated development: Foster a coordinated development framework for the YRD region, establishing cross-administrative coordination mechanisms, enhancing inter-city transportation and logistics network integration, and breaking down barriers to the flow of data and resources. Strengthen regional economic and geopolitical ties, transform locational advantages into enhanced resource utilization, and fully leverage the spatial spillover effects of digital infrastructure to promote integrated and synergistic development across the region.

Despite providing robust empirical evidence on the impact of digital infrastructure on urban development in the YRD region, this study has several limitations that point to avenues for future research: (1) the analysis primarily relies on city-level panel data, which, while rich in macro-level information, may not fully capture intra-city heterogeneity or underlying micro-level mechanisms. For instance, different regions or types of enterprises may respond differently to digital infrastructure in terms of adoption and land green-use efficiency. Future studies could leverage finer-grained data, such as sub-city or grid-level datasets, to uncover more detailed spatial patterns and potential causal relationships, thereby enabling a deeper understanding of policy effects. (2) Although key variables were measured following established methods, there is room for refinement. In this study, digital infrastructure is represented by a policy dummy, effectively capturing the shock of policy implementation but lacking quantitative detail on the actual level and continuity of infrastructure development. Future research could develop multidimensional indicators that account for the scale, quality, and coverage of digital infrastructure, allowing for more precise impact assessments and a clearer elucidation of the underlying mechanisms. (3) Given that the YRD region is one of China’s most economically developed regions, its specific economic structure, developmental stage, and resource endowments may limit the generalizability of the findings. Subsequent research should empirically test and compare results across regions with different economic conditions, industrial structures, and policy environments to verify robustness and establish a more universally applicable theoretical framework, thereby offering broader guidance for digital infrastructure planning and policy design.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Author contributions

LT: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing. XT: Formal Analysis, Software, Writing – original draft. YJ: Data curation, Resources, Visualization, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This project was funded by the Key Research Base of Humanities and Social Sciences of Higher Education Institutions in Hebei Province (Beijing-Tianjin-Hebei Synergistic Development Management Innovation Research Center), the Shijiazhuang Unmanned Logistics Application Technology Innovation Center, and the Soft Science Research Special Project (25354207D).

Acknowledgements

The authors extend their gratitude to all the reviewers and editors for their comments on this paper.

Conflict of interest

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

Generative AI statement

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

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Abbas, Q., Hong Xing, Y., Ramzan, M., and Fatima, S. (2024). Achieving carbon neutrality through digital infrastructure and public debt. Clean Technol. Environ. Policy 26 (12), 4455–4470. doi:10.1007/s10098-024-02846-7

CrossRef Full Text | Google Scholar

Allam, Z., and Jones, D. S. (2021). Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: Digital twins, immersive realities and new urban economies. Land Use Policy 101, 105201. doi:10.1016/j.landusepol.2020.105201

CrossRef Full Text | Google Scholar

Balogun, A. L., Marks, D., Sharma, R., Shekhar, H., Balmes, C., Maheng, D., et al. (2020). Assessing the potentials of digitalization as a tool for climate change adaptation and sustainable development in urban centres. Sustain. Cities Soc. 53, 101888. doi:10.1016/j.scs.2019.101888

CrossRef Full Text | Google Scholar

Bao, R., and Liu, T. (2022). How does government attention matter in air pollution control? Evidence from government annual reports. Resour. Conservation Recycl. 185, 106435. doi:10.1016/j.resconrec.2022.106435

CrossRef Full Text | Google Scholar

Barrios-Crespo, E., Torres-Ortega, S., and Díaz-Simal, P. (2021). Developing a dynamic model for assessing green infrastructure investments in urban areas. Int. J. Environ. Res. Public Health 18 (20), 10994. doi:10.3390/ijerph182010994

PubMed Abstract | CrossRef Full Text | Google Scholar

Chang, J., Wang, W., and Liu, J. (2023). Industrial upgrading and its influence on green land use efficiency. Sci. Rep. 13 (1), 2813. doi:10.1038/s41598-023-29928-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, Y. (2023). Spatial autocorrelation equation based on Moran’s index. Sci. Reports 13 (1), 19296. doi:10.1038/s41598-023-45947-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, J., and Wang, J. (2023). The impact of broadband speed on innovation: city-level evidence from China. Heliyon 9 (1), e12692. doi:10.1016/j.heliyon.2022.e12692

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, W., Shen, Y., Wang, Y., and Wu, Q. (2018). The effect of industrial relocation on industrial land use efficiency in China: a spatial econometrics approach. J. Clean. Prod. 205, 525–535. doi:10.1016/j.jclepro.2018.09.106

CrossRef Full Text | Google Scholar

Chen, H., Meng, C., and Cao, Q. (2022). Measurement and influencing factors of low carbon urban land use efficiency—based on non-radial directional distance function. Land 11 (7), 1052. doi:10.3390/land11071052

CrossRef Full Text | Google Scholar

Chen, Z., Liu, Y., Feng, W., Li, Y., and Li, L. (2022). Study on spatial tropism distribution of rural settlements in the Loess Hilly and Gully Region based on natural factors and traffic accessibility. J. Rural Stud. 93, 441–448. doi:10.1016/j.jrurstud.2019.02.014

CrossRef Full Text | Google Scholar

Chen, D., Wang, Z., Du, D., and Kong, Q. (2025). How does green-infrastructure investment empower urban sustainable development? —mechanisms and empirical tests. Sustainability 17 (13), 5751. doi:10.3390/su17135751

CrossRef Full Text | Google Scholar

Cheng, J., Yi, J., Dai, S., and Xiong, Y. (2019). Can low-carbon city construction facilitate green growth? Evidence from China’s pilot low-carbon city initiative. Clean. Prod. 231, 1158–1170. doi:10.1016/j.jclepro.2019.05.327

CrossRef Full Text | Google Scholar

Cui, X., Fu, B., Chen, Y., and Bai, S. (2025). Bridging digital and ecological resilience: spatiotemporal dynamics and coordinated development paths in the Yellow River inverted U-Shaped belt metropolitan area. J. Environ. Manag. 388, 125990. doi:10.1016/j.jenvman.2025.125990

PubMed Abstract | CrossRef Full Text | Google Scholar

de Clercq, M., D'Haese, M., and Buysse, J. (2023). Economic growth and broadband access: the European urban-rural digital divide. Telecommun. Policy 47 (6), 102579. doi:10.1016/j.telpol.2023.102579

CrossRef Full Text | Google Scholar

Ding, C. J., Chen, H., Liu, Y., Hu, J., Hu, M., Chen, D., et al. (2024). Unleashing digital empowerment: pioneering low-carbon development through the broadband China strategy. Energy 295, 131034. doi:10.1016/j.energy.2024.131034

CrossRef Full Text | Google Scholar

Ding, J., Ren, C., Wang, J., Feng, Z., and Cao, S. J. (2024). Spatial and temporal urban air pollution patterns based on limited data of monitoring stations. J. Clean. Prod. 434, 140359. doi:10.1016/j.jclepro.2023.140359

CrossRef Full Text | Google Scholar

Du, M., Antunes, J., Wanke, P., and Chen, Z. (2022). Ecological efficiency assessment under the construction of low-carbon city: a perspective of green technology innovation. J. Environ. Plan. Manag. 65 (9), 1727–1752. doi:10.1080/09640568.2021.1945552

CrossRef Full Text | Google Scholar

Edquist, H. (2022). The economic impact of mobile broadband speed. Telecommun. Policy 46 (5), 102351. doi:10.1016/j.telpol.2022.102351

CrossRef Full Text | Google Scholar

Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels. Heidelberg: Springer. doi:10.1007/978-3-642-40340-8

CrossRef Full Text | Google Scholar

Fan, L., Zhang, Y., Jin, M., Ma, Q., and Zhao, J. (2022). Does new digital infrastructure promote the transformation of the energy structure? The perspective of China’s energy industry chain. Energies 15 (23), 8784. doi:10.3390/en15238784

CrossRef Full Text | Google Scholar

Fan, X., Zhou, Y., and Xie, Q. (2023). Performance evaluation, environmental regulation, and urban land green use efficiency: evidence from China. Environ. Prog. and Sustain. Energy 42 (4), e14120. doi:10.1002/ep.14120

CrossRef Full Text | Google Scholar

Fu, J., Ding, R., Zhang, Y., Zhou, T., Du, Y., Zhu, Y., et al. (2022a). The spatial-temporal transition and influencing factors of green and low-carbon utilization efficiency of urban land in China under the goal of carbon neutralization. Int. J. Environ. Res. Public Health 19 (23), 16149. doi:10.3390/ijerph192316149

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, J., Bu, Z., Jiang, D., Lin, G., and Li, X. (2022b). Sustainable land use diagnosis based on the perspective of production–living–ecological spaces in China. Land Use Policy 122, 106386. doi:10.1016/j.landusepol.2022.106386

CrossRef Full Text | Google Scholar

Gong, M., Zeng, Y., and Zhang, F. (2023). New infrastructure, optimization of resource allocation and upgrading of industrial structure. Finance Res. Lett. 54, 103754. doi:10.1016/j.frl.2023.103754

CrossRef Full Text | Google Scholar

Guo, B., Hu, P., and Lin, J. (2024). The effect of digital infrastructure development on enterprise green transformation. Int. Rev. Financial Analysis 92, 103085. doi:10.1016/j.irfa.2024.103085

CrossRef Full Text | Google Scholar

Haldar, A., and Sethi, N. (2022). Environmental effects of information and communication Technology-exploring the roles of renewable energy, innovation, trade and financial development. Renew. Sustain. Energy Rev. 153, 111754. doi:10.1016/j.rser.2021.111754

CrossRef Full Text | Google Scholar

He, T., and Song, H. (2023). A novel approach to assess the urban land-use efficiency of 767 resource-based cities in China. Ecol. Indic. 151, 110298. doi:10.1016/j.ecolind.2023.110298

CrossRef Full Text | Google Scholar

He, G., Jiang, H., and Zhu, Y. (2024). The effect of digital technology development on the improvement of environmental governance capacity: a case study of China. Ecol. Indic. 165, 112162. doi:10.1016/j.ecolind.2024.112162

CrossRef Full Text | Google Scholar

Hong, J., Shi, F., and Zheng, Y. (2022). Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy. Technol. Forecast. Soc. Change 190, 122437. doi:10.1016/j.techfore.2023.122437

CrossRef Full Text | Google Scholar

Hong, Y., Yu, H., Lu, Y., and Peng, L. (2024). Balancing low-carbon and eco-friendly development: coordinated development strategy for land use carbon emission efficiency and land ecological security. Environ. Sci. Pollut. Res. 31 (6), 9495–9511. doi:10.1007/s11356-024-31841-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Hou, X., Jin, J., and Feng, Y. (2025). Does economic freedom affect green economic growth? Analyses of mediation, moderation, and heterogeneity in EU and non-EU countries. Front. Environ. Sci. 13, 1568601. doi:10.3389/fenvs.2025.1568601

CrossRef Full Text | Google Scholar

Hu, J., Zhang, H., and Irfan, M. (2023). How does digital infrastructure construction affect low-carbon development? A multidimensional interpretation of evidence from China. J. Cleaner Production 396, 136467. doi:10.1016/j.jclepro.136467

CrossRef Full Text | Google Scholar

Huang, J., Han, W., Zhang, Z., Ning, S., and Zhang, X. (2024). The decoupling relationship between land use efficiency and carbon emissions in China: an analysis using the socio-ecological systems (SES) framework. Land Use Policy 138, 107055. doi:10.1016/j.landusepol.2024.107055

CrossRef Full Text | Google Scholar

Huang, J., Lu, H., and Du, M. (2025). Regional differences in agricultural carbon emissions in China: measurement, decomposition, and influencing factors. Land 14 (4), 682. doi:10.3390/land14040682

CrossRef Full Text | Google Scholar

Jin, P., Mangla, S. K., and Song, M. (2022). The power of innovation diffusion: how patent transfer affects urban innovation quality. J. Bus. Res. 145, 414–425. doi:10.1016/j.jbusres.2022.03.025

CrossRef Full Text | Google Scholar

Jin, L., Cao, K., Li, J., and Xu, Q. (2024). Information infrastructure construction and optimization of resources allocation among firms: evidence from “Broadband China” strategy. Int. Rev. Econ. and Finance 91, 36–53. doi:10.1016/j.iref.2024.01.007

CrossRef Full Text | Google Scholar

Koroso, N. H. (2023). Urban land policy and urban land use efficiency: an analysis based on remote sensing and institutional credibility thesis. Land Use Policy 132, 106827. doi:10.1016/j.landusepol.2023.106827

CrossRef Full Text | Google Scholar

Lai, A., Li, Z., Hu, X., and Wang, Q. (2024a). Does digital economy improve city-level eco-efficiency in China? Econ. Analysis Policy 81, 1198–1213. doi:10.3389/fenrg.2024.1396872

CrossRef Full Text | Google Scholar

Lai, A., Du, Q., and Wang, Q. (2024b). One region's gain is another's loss: the impact of market segmentation on renewable energy development in China. Energy Policy 192, 114215. doi:10.1016/j.enpol.2024.114215

CrossRef Full Text | Google Scholar

Li, S., and Cao, X. (2025). Can green technology innovation empower urban carbon mission reduction? Evidence from China. Front. Environ. Sci. 13, 1616667. doi:10.3389/fenvs.2025.1616667

CrossRef Full Text | Google Scholar

Li, Y., and Chen, Y. (2021). Development of an SBM-ML model for the measurement of green total factor productivity: the case of pearl river delta urban agglomeration. Renew. Sustain. Energy Rev. 145, 111131. doi:10.1016/j.rser.2021.111131

CrossRef Full Text | Google Scholar

Li, F., and Diao, Z. (2025). The impact of digital infrastructure on the synergistic effects of urban pollution and carbon reduction: empirical evidence from China. Front. Environ. Sci. 13, 1453151. doi:10.3389/fenvs.2025.1453151

CrossRef Full Text | Google Scholar

Li, S., and Yang, T. (2024). The impact of digital infrastructure on air quality: mediating role of public environmental attention and industrial development. J. Environ. Manag. 373, 123893. doi:10.1016/j.jenvman.2024.123893

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, L., Chen, Z., and Wang, S. (2022). Optimization of spatial land use patterns with low carbon target: a case study of Sanmenxia, China. Int. J. Environ. Res. Public Health 19 (21), 14178. doi:10.3390/ijerph192114178

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, B., Han, Y., Wang, C., and Sun, W. (2022). Did civilized city policy improve energy efficiency of resource-based cities? Prefecture-level evidence from China. Energy Policy 167, 113081. doi:10.3390/su17135687

CrossRef Full Text | Google Scholar

Li, L., Huang, X., and Yang, H. (2023). Optimizing land use patterns to improve the contribution of land use planning to carbon neutrality target. Land Use Policy 135, 106959. doi:10.1016/j.landusepol.2023.106959

CrossRef Full Text | Google Scholar

Li, C., Wen, M., Jiang, S., and Wang, H. (2024). Assessing the effect of urban digital infrastructure on green innovation: mechanism identification and spatial-temporal characteristics. Humanit. Soc. Sci. Commun. 11 (1), 1–14. doi:10.1057/s41599-024-02787-y

CrossRef Full Text | Google Scholar

Li, W., Cai, Z., and Jin, L. (2024). Urban green land use efficiency of resource-based cities in China: multidimensional measurements, spatial-temporal changes, and driving factors. Sustain. Cities Soc. 104, 105299. doi:10.1016/j.scs.2024.105299

CrossRef Full Text | Google Scholar

Li, Y., Xu, Y., and Wen, C. (2024). Digital pathways to sustainability: the impact of digital infrastructure in the coordinated development of environment, economy and society. Environ. Dev. Sustain., 1–34. doi:10.1007/s10668-024-05842-5

CrossRef Full Text | Google Scholar

Li, X., Huang, Y., Liu, Y., Yi, H., Li, L., Wu, Y., et al. (2025). Enhancing data integrity in data-driven environmental science research through blockchain technology. Environ. Sci. and Technol. 59, 18696–18705. doi:10.1021/acs.est.5c03461

PubMed Abstract | CrossRef Full Text | Google Scholar

Liao, K., and Liu, J. (2024). Digital infrastructure empowerment and urban carbon emissions: evidence from China. Telecommun. Policy 48 (6), 102764. doi:10.1016/j.telpol.2024.102764

CrossRef Full Text | Google Scholar

Lim, Y., Edelenbos, J., and Gianoli, A. (2024). What is the impact of smart city development? Empirical evidence from a smart City Impact index. Urban Gov. 4 (1), 47–55. doi:10.1016/j.ugj.2023.11.003

CrossRef Full Text | Google Scholar

Liu, J., Feng, H., and Wang, K. (2022). The low-carbon city pilot policy and urban land use efficiency: a policy assessment from China. Land 11 (5), 604. doi:10.3390/land11050604

CrossRef Full Text | Google Scholar

Liu, F. H. M., Lai, K. P. Y., Seah, B., and Chow, W. T. L. (2025). Decarbonising digital infrastructure and urban sustainability in the case of data centres. Npj Urban Sustain 5, 15. doi:10.1038/s42949-025-00203-1

CrossRef Full Text | Google Scholar

Long, L. (2021). Eco-efficiency and effectiveness evaluation toward sustainable urban development in China: a super-efficiency SBM–DEA with undesirable outputs. Environ. Dev. Sustain. 23, 1–16. doi:10.1007/s10668-021-01282-7

CrossRef Full Text | Google Scholar

Lu, X., and Tao, X. (2023). Local government environmental attention and urban land green use efficiency in China: the intermediary role of industrial restructuring. Land 13 (1), 21. doi:10.3390/land13010021

CrossRef Full Text | Google Scholar

Ma, R., and Lin, B. (2023). Digital infrastructure construction drives green economic transformation: evidence from Chinese cities. Humanit. Soc. Sci. Commun. 10 (1), 1–10. doi:10.1057/s41599-023-01839-z

CrossRef Full Text | Google Scholar

Ma, Z., and Zhang, P. (2024). Understanding how digital infrastructure construction could promote sustainable development. Sustain. Cities Soc. 117, 105977. doi:10.1016/j.scs.2024.105977

CrossRef Full Text | Google Scholar

Ma, D., Zhang, J., An, B., Guo, Z., Zhang, F., Yan, Y., et al. (2024). Research on urban land green use efficiency and influencing factors based on DEA and ESTDA models: taking 284 cities in China as an example. Ecol. Indic. 160, 111824. doi:10.1016/j.ecolind.2024.111824

CrossRef Full Text | Google Scholar

Ma, L., Xu, W., Zhang, W., and Ma, Y. (2024). Effect and mechanism of environmental regulation improving the urban land use eco-efficiency: evidence from China. Ecol. Indic. 159, 111602. doi:10.1016/j.ecolind.2024.111602

CrossRef Full Text | Google Scholar

Nie, C., Zhong, Z., and Feng, Y. (2023). Can digital infrastructure induce urban green innovation? New insights from China. Clean Technol. Environ. Policy 25 (10), 3419–3436. doi:10.1007/s10098-023-02605-0

CrossRef Full Text | Google Scholar

Niu, S., Luo, X., Yang, T., and Lin, G. (2023). Does the low-carbon city pilot policy improve the urban land green use efficiency? —investigation based on multi-period difference-in-differences model. Int. J. Environ. Res. Public Health 20 (3), 2704. doi:10.3390/ijerph20032704

PubMed Abstract | CrossRef Full Text | Google Scholar

Pandey, B., Brelsford, C., and Seto, K. C. (2022). Infrastructure inequality is a characteristic of urbanization. Proc. Natl. Acad. Sci. 119 (15), e2119890119. doi:10.1073/pnas.2119890119

PubMed Abstract | CrossRef Full Text | Google Scholar

Peng, W., Li, J., and Ren, X. (2024). Can smart city construction be the answer to sustainable development? Evidence from Chinese corporates. Front. Environ. Sci. 12, 1457801. doi:10.3389/fenvs.2024.1457801

CrossRef Full Text | Google Scholar

Qin, B., Yu, Y., Ge, L., Liu, Y., Zheng, Y., and Liu, Z. (2024). The role of digital infrastructure construction on green city transformation: does government governance matters? Cities 155, 105462. doi:10.1016/j.cities.2024.105462

CrossRef Full Text | Google Scholar

Qin, J., Huang, M., Cheng, Q., and Luo, W. (2025). Does digital infrastructure promote corporate digital and green transformation? Evidence from China’s big data Bureau initiative. J. Chin. Econ. Bus. Stud., 1–29. doi:10.1080/14765284.2025.2579492

CrossRef Full Text | Google Scholar

Somanath, S., Naserentin, V., Eleftheriou, O., Sjölie, D., Wästberg, B., and Logg, A. (2024). Towards urban digital twins: a workflow for procedural visualization using geospatial data. Remote Sens. 16 (11), 1939. doi:10.3390/rs16111939

CrossRef Full Text | Google Scholar

Song, M., Xiao, Y., and Zhou, Y. (2023). How does the smart city policy influence digital infrastructure? Spatial evidence from China. Land 12 (7), 1381. doi:10.3390/land12071381

CrossRef Full Text | Google Scholar

Sun, X. (2023). The impact of digital infrastructure on energy-environmental efficiency: empirical evidence from China. Front. Energy Res. 11, 1277333. doi:10.3389/fenrg.2023.1277333

CrossRef Full Text | Google Scholar

Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Operational Res. 143 (1), 32–41. doi:10.1016/S0377-2217(01)00324-1

CrossRef Full Text | Google Scholar

Wang, L., and Shao, J. (2024). The energy saving effects of digital infrastructure construction: empirical evidence from Chinese industry. Energy 294, 130778. doi:10.1016/j.energy.2024.130778

CrossRef Full Text | Google Scholar

Wang, Y., Feng, Y., Zuo, J., and Rameezdeen, R. (2019). From “Traditional” to “Low carbon” urban land use: evaluation and obstacle analysis. Sustain. Cities Soc. 51, 101722. doi:10.1016/j.scs.2019.101722

CrossRef Full Text | Google Scholar

Wang, A., Lin, W., Liu, B., Wang, H., and Xu, H. (2021). Does smart city construction improve the green utilization efficiency of urban land? Land 10 (6), 657. doi:10.3390/land10060657

CrossRef Full Text | Google Scholar

Wang, K., Li, X., Lyu, X., Dang, D., Dou, H., Li, M., et al. (2022). Optimizing the land use and land cover pattern to increase its contribution to carbon neutrality. Remote Sens. 14 (19), 4751. doi:10.3390/rs14194751

CrossRef Full Text | Google Scholar

Wang, Y., Zhao, Z., Shi, M., Liu, J., and Tan, Z. (2024). Public environmental concern, government environmental regulation and urban carbon emission reduction—Analyzing the regulating role of green finance and industrial agglomeration. Sci. Total Environ. 924, 171549. doi:10.1016/j.scitotenv.2024.171549

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, W., Wang, J., and Wu, H. (2025). Assessing the impact of new digital infrastructure on enterprise green transformation from a triple performance perspective. J. Knowl. Econ. 16 (1), 5594–5633. doi:10.1007/s13132-024-01976-z

CrossRef Full Text | Google Scholar

Wu, H., Xue, Y., Hao, Y., and Ren, S. (2021). How does internet development affect energy-saving and emission reduction? Evidence from China. Energy Econ. 103, 105577. doi:10.1016/j.eneco.2021.105577

CrossRef Full Text | Google Scholar

Wu, J., Lai, A., Li, Z., and Wang, Q. (2024). Investment efficiency of renewable energy enterprises when exposed to air pollution: evidence from China. Int. Rev. Econ. and Finance 96, 103722. doi:10.1016/j.iref.2024.103722

CrossRef Full Text | Google Scholar

Wu, M., Liu, Y., Huang, L., and Wang, Y. (2025). Impact of the low-carbon city pilot policy on green total factor productivity: a quasi-natural experimental design using an epsilon-based measure. Front. Environ. Sci. 13, 1531065. doi:10.3389/fenvs.2025.1531065

CrossRef Full Text | Google Scholar

Xu, B., and Sun, Y. (2023). The impact of industrial agglomeration on urban land green use efficiency and its spatio-temporal pattern: evidence from 283 cities in China. Land 12 (4), 824. doi:10.3390/land12040824

CrossRef Full Text | Google Scholar

Xu, C., Ren, Y., and Kong, F. (2025). Spatial correlation network, driving factors and planning application of land green use efficiency in Zhejiang Province, China. Environ. Sustain. Indic. 28, 100963. doi:10.1016/j.indic.2025.100963

CrossRef Full Text | Google Scholar

Xue, D., Yue, L., Ahmad, F., Draz, M. U., Chandio, A. A., Ahmad, M., et al. (2022). Empirical investigation of urban land use efficiency and influencing factors of the yellow River basin Chinese cities. Land Use Policy 117, 106117. doi:10.1016/j.landusepol.2022.106117

CrossRef Full Text | Google Scholar

Yang, R., Wang, J., Xu, M., and Xu, K. (2025). Road to green urbanization: how does urbanization process affect the green land use efficiency. Front. Environ. Sci. 13, 1581107. doi:10.3389/fenvs.2025.1581107

CrossRef Full Text | Google Scholar

Yao, M., and Zhang, Y. (2021). Evaluation and optimization of urban land-use efficiency: a case study in Sichuan Province of China. Sustainability 13, 1771. doi:10.3390/su13041771

CrossRef Full Text | Google Scholar

Yao, L., Li, A., and Yan, E. (2025). Research on digital infrastructure construction empowering new quality productivity. Sci. Rep. 15 (1), 6645. doi:10.1038/s41598-025-90811-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Ye, X., Du, J., Han, Y., Newman, G., Retchless, D., Zou, L., et al. (2022). Developing human-centered urban digital twins for community infrastructure resilience: a research agenda. J. Plan. Literature 38 (2), 187–199. doi:10.1177/08854122221137861

PubMed Abstract | CrossRef Full Text | Google Scholar

You, C., Luo, Q., and Liu, W. (2025). The role of urban digital intelligence in fostering sustainable collaborative innovation: an analysis of spillover effects. Sustainability 17 (5), 1890. doi:10.3390/su17051890

CrossRef Full Text | Google Scholar

Yu, B., and Zhou, X. (2023). Urban administrative hierarchy and urban land use efficiency: evidence from Chinese cities. Int. Rev. Econ. and Finance 88, 178–195. doi:10.1016/j.iref.2023.06.033

CrossRef Full Text | Google Scholar

Zeng, C., Lin, C., Liang, Y., Stringer, L. C., Tian, Z., Mak, H. W. L., et al. (2025). Unraveling the complex relationship between urbanization and landscape ecological risk: insights from Chinese cities. Ecosyst. Health Sustain 11, 0448. doi:10.34133/ehs.0448

CrossRef Full Text | Google Scholar

Zhang, X., and Zhang, D. (2023). Urban carbon emission scenario prediction and multi-objective land use optimization strategy under carbon emission constraints. J. Cleaner Production 430, 139684. doi:10.1016/j.jclepro.2023.139684

CrossRef Full Text | Google Scholar

Zhang, W., Fan, H., and Zhao, Q. (2023a). Seeing green: how does digital infrastructure affect carbon emission intensity? Energy Econ. 127, 107085. doi:10.1016/j.eneco.2023.107085

CrossRef Full Text | Google Scholar

Zhang, W., Cui, R., Li, C., Ge, H., Zhang, Z., and Tang, X. (2023b). Impact of urban agglomeration construction on urban air quality–empirical test based on PSM–DID model. Sci. Rep. 13 (1), 15099. doi:10.1038/s41598-023-42314-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, H., Song, Y., Zhang, M., and Duan, Y. (2024). Land use efficiency and energy transition in Chinese cities: a cluster-frontier super-efficiency SBM-based analytical approach. Energy 304, 132049. doi:10.1016/j.energy.2024.132049

CrossRef Full Text | Google Scholar

Zhang, Y., Wang, J., Liu, Y., and Zhao, J. (2024). The impact of the digital economy on urban ecological resilience: empirical evidence from China’s comprehensive big data pilot zone policy. Sustainability 16, 3611. doi:10.3390/su16093611

CrossRef Full Text | Google Scholar

Zhou, Y., and Lu, Y. (2023). Spatiotemporal evolution and determinants of urban land use efficiency under green development orientation: insights from 284 cities and eight economic zones in China, 2005–2019. Appl. Geogr. 161, 103117. doi:10.1016/j.apgeog.2023.103117

CrossRef Full Text | Google Scholar

Zong, B., Ling, H., Hui, L., Liangzhi, L., and Xuhai, J. (2023). Assessment of coordinated development between urban land use efficiency and ecological carrying capacity: case study of the cities in Inner Mongolia. Ecol. Indic. 155, 110933. doi:10.1016/j.ecolind.2023.110933

CrossRef Full Text | Google Scholar