An empirical analysis of spatial effects and their driving factors in urban green development using spatial econometric models

Evaluation of the spatial effect of urban green development can provide spatial clues for solving urban governance problems. The existing research lacks an assessment of urban green development from the perspective of productivity. In this study, 285 cities in China from 2008 to 2020 were selected as research objects. An indicator system for urban green development was innovatively constructed from a productivity perspective. Meanwhile the spatial autocorrelation effects of its development under the adjacency relationship weight matrix (ARWM), geographical distance weight matrix (GDWM), and economic distance weight matrix (EDWM) were clarified. Spatial Durbin Models (SDM) partial differential method was used to decompose the spatial effects of the influencing factors of urban green development, and the spatial correlation effects and spatial spillover effects of urban green development were clarified. The findings reveal that: (1) From the perspective of spatial correlation, urban green development has global spatial autocorrelation, which indicates that the level of urban green development is affected by at least 13% of cities with similar economic levels, 84% by cities with close geographical distance, and 63% by adjacent cities. (2) From the perspective of spatial spillover effect, urban green development has significant spatial spillover effect under ARWM and GDWM, specifically, the level of economic development under different spatial weight matrices has a significant spillover effect on urban green development. There are spillover effects on the impact of industrial structure, innovation level, infrastructure conditions, government intervention and foreign trade openness on urban green development under the influence of the ARWM. There is a significant spillover effect of environmental regulation on urban green development under the influence of EDWM.

1 Introduction

As human demand for natural resources continues to intensify, associated challenges such as the reduction of arable land, resource depletion, ecological damage, and low energy utilization rates grow increasingly severe (Yurtkuran and Pata, 2024). Globally, it is estimated that each person generates approximately 1.2 kg of waste daily, culminating in approximately 2.1 × 10¹² kg per year (Crome et al., 2023). As the world’s largest industrial power and second largest economy, China consumes more energy and emits more carbon than any other nation, primarily due to its high-energy, high-pollution production methods. In 2019 alone, China accounted for 24.3% of global primary energy consumption and 27% of global CO₂ emissions (Liu and Zhang, 2025). Despite implementing various environmental policies to enhance governance, these measures have been insufficient in fundamentally altering China’s widespread high-energy-consumption, low-output production model. Productive forces, as intrinsic drivers of economic development, are pivotal in shaping social progress. Similarly, productivity is crucial for achieving sustainable urban development and economic growth. However, there is currently limited research on urban green development and productivity, lacking analysis of the driving factors and spatial characteristics of urban green development from a productivity perspective, which is not conducive to comprehensively grasping and improving regional green development capabilities. Scholars typically explore urban green development from perspectives like sustainable development, urban planning, ecological welfare, and urbanization (Peng et al., 2024; Anguelovski et al., 2024; Opbroek et al., 2024; Yuan, 2025; Waqar et al., 2025), yet often overlook the direct link to productivity. For instance, Bian et al. (2024) demonstrated the impact of carbon emission trading system on urban green transformation development from the perspective of sustainable development such as carbon emission reduction, pollution reduction, green expansion and growth, and then constructed an urban green transformation evaluation system. Eben et al. (2025) designed a realistic path to enhance urban sustainable development based on the design and planning of urban drainage system. From the perspective of urban development, productivity, as the fundamental driving force of urban development, is an important indicator to measure urban economic development and directly determines the competitiveness of cities. However, rapid urbanization tends to disrupt existing ecological systems, leading to issues like traffic congestion, excessive carbon emissions, and ecological degradation. Data show that cities are the concentration of human activities and greenhouse gas emissions. Among them, residential energy consumption has become the second largest greenhouse gas emission sector after industrial energy consumption. With cities consuming nearly 67% of the world’s energy and producing 71% of the world’s CO₂ emissions annually, it is imperative that cities take action to reduce carbon emissions. Practice has proved that green development is the inevitable path and fundamental way of urban development. As the fundamental driving force for urban carbon emission reduction, productivity can effectively solve the problems in the process of urban development. For example, the growth of green productivity alleviates ecological degradation and has a more significant impact on ecological protection. Therefore, exploring urban green development from the perspective of productivity will undoubtedly become a new engine for cities to solve the problems of “urban diseases” such as environmental pollution and resource shortage.

In real life, as economic structures evolve into networked patterns, cities are no longer limited to the closed development of individual cities, manifesting in denser urban spaces and tighter inter-city economic interconnections. Despite clear geographical boundaries, the exchange of resources, information, and production factors such as population and capital flows seamlessly across administrative boundaries, indicating that the development of cities will also shift with the spatial flow of population, capital, and other production factors, thereby affecting the ecological environment and economic development of other cities and even the entire region (Deng et al., 2025). This spatial interaction has been verified in the field of geography, that is, the first law of geography points out that the closer the spatial distance, the closer the material and information exchange between different regions (Tobler, 1979). With the development of spatial econometrics, scholarly attention has expanded to include these spatial effects. Lokhande and Xie (2023) analyzed the spillover effect of urban decline in southeast Michigan, but only around the spatial lag model. Chen S. Y. et al. (2025) analyzed the spatial spillover effects and nonlinear impacts of urban digital economy on urban innovation. On the whole, although scholars have made great research progress on the structure and evolution of urban development, but studies on the spatial effects of urban green development from the perspective of productivity remain limited. Therefore, will the boosting effect of productivity on urban green development break through the constraints of geographical distance, and then have a spatial impact on the production and life of the surrounding areas, that is, whether urban green development has a spatial spillover effect, and what is the specific impact mechanism? The core objective of this study is to clarify the driving factors affecting green development from the perspective of productivity and reveal the spatial spillover effect of urban green development.

In addition, geographic variables exhibit changes that are both scale-dependent and scale-independent across temporal and spatial dimensions (Khomenko et al., 2023). Therefore, when describing the spatial correlation and heterogeneity of urban green development, it is important to acknowledge that urban green development arises from multidimensional factors. Current research predominantly approaches urban green development from a narrow input-output or single-factor perspective, lacking a broader or more systematic exploration of the underlying mechanisms. For example, Tian et al. (2021) focused solely on the positive impact of financial factors (traditional financial agglomeration and technological financial agglomeration) on the green development of urban agglomerations. Wang D. et al. (2023) analyzed the spatial spillover effect of urban green and smart development, but only analyzed the infrastructure factors. On the whole, there is a lack of research on the spatial effect of urban green development and its influencing factors from the perspective of productivity. In view of this, this study aims to fill this gap by utilizing a geospatial framework to not only assess the direct impact of independent variables but also to explore the spillover effects of independent variables through a spatial transmission mechanism.

From the perspective of research methods, the practical application mode of spatial measurement has been relatively mature. The dominant idea of the spatial econometric model is that the dependent variables are affected by the independent variables and are also affected by the factors of the adjacent regions. Different spatial weight matrix studies explain things from different perspectives, and the spatial interdependence and correlation expressed by them are different. In this study, the SDM was adopted in the present study, accounting for both spatial errors and correlations, to examine the spillover effects of urban green development. This approach breaks through traditional econometric models by incorporating spatial dynamics, thereby providing a more accurate and realistic analysis of how spatial factors influence key variables and ultimately supporting the development of scientifically sound urban green development policies.

As discussed above, the existing research still has notable research gaps: (1) Previous studies on urban green development have primarily focused on technological innovation, the digital economy, energy conservation, and emission reduction, among other areas. However, productivity is most directly connected to urban green development, and previous studies have overlooked the crucial role of urban productivity in fostering high-quality urban development. (2) In the analysis of the influencing factors of urban green development, scholars only investigate the role of a single factor in isolation and lack comprehensiveness. (3) There is a lack of research on the spatial effects of various factors on urban green development. The novel contribution lies in three aspects. Firstly, from the perspective of productivity, this paper constructs an index system for urban green development and analyzes the spatial effect of green development between cities, so that the research is no longer limited to a single limited area, but systematically analyzes the spatial spillover effect of the influencing factors of urban green development from the spatial dimension, which is universal and can provide new ideas for the green development and value co-creation of cities and regions. Secondly, this study reveals the spatial correlation effect of urban green development. In particular, the direct and indirect effects decomposition method is used to test the spatial spillover effect of urban green development, building both methodological and empirical bases for crafting targeted urban green development improvement policies. In addition, based on the existing research, this study establishes a comprehensive framework for these factors. It further elucidates their spatial impacts and mechanisms acting on urban green development. It enhances the theoretical understanding of the internal relationship of urban green development and is an important supplement to the existing research.

2 Research hypothesis

In recent years, the focus of spatial econometric research has increasingly shifted towards urban green development. However, existing studies predominantly concentrate on the efficiency of city development and carbon emissions (Yang J. H. et al., 2025; Lokhande and Xie, 2023), lacking research on the spatial effects of urban green from a productivity perspective. For example, Lokhande and Xie. (2023) analyzed the spatial spillover effect of urban decline in Southeast Michigan. Furthermore, the scope of such research is generally limited to individual urban agglomerations, provinces, and metropolitan areas (Wang Y. J. et al., 2023). Taking Hubei Province as an example, Xiao et al. (2022) employed the ‘driver-pressure-state-impact-response’ model and entropy weight method to explore the spatial effects of regional green development. In efforts to fill these gaps, the present study examines 285 cities in China as a more holistic research sample to explore the impact of interactions between cities and regions on urban green development from the productivity perspective. Therefore, the following hypothesis is proposed in this study:

Hypothesis 1. Urban green development has spatial correlation and spatial spillover effect.

Moreover, while prior research has established that foreign direct investment and technological progress significantly influence green development (Yu and Zheng, 2024; Helfaya and Bui, 2025), these studies have tended to overlook the spatial dimensions of such effects as intrinsic mechanisms. This oversight has led to inconsistencies in research findings. Real-world regional economic integration facilitates factor mobility (Yang B. et al., 2024), yet it remains unclear how variables like economic development and government intervention influence urban green development. Human capital is an important indicator of a city’s labor quality, which also significantly impacts its productivity. The higher the score of human capital, the more concentrated the human capital of the city, which can drive the development of the city (Majeed et al., 2024). As the main body of productivity, whether the increase of human capital will have a positive spillover effect and whether it will promote the green development in surrounding cities deserves further analysis. Again, research indicates that the transformation and upgrading of industrial structures significantly enhance regional economic performance (Guo et al., 2025). Such changes not only benefit the urban economy but also boost the efficiency of urban energy use and reduce pollutant emissions. Consequently, there may be spillover effects in the industrial structure. In addition, the effects of environmental regulations on green development, however, are less clear. Lin et al. (2022) found a U-shaped impact of environmental regulations on urban green development efficiency. Moreover, innovation is regarded as crucial for enhancing green total factor productivity (GTFP) and sustainable development (Degirmenci et al., 2025), especially in the “new normal” economy, supported by theories of endogenous growth and sustainable development. Studies further suggest that stricter environmental regulations, improved infrastructure, and increased spatial economic agglomeration could enhance green development. Conversely, expanding the industrial base might impede its growth. Additionally, while government support—including tax incentives and reduced trade barriers—is vital for boosting urban total factor productivity (TFP), the direct impact of foreign direct investment on reducing air pollution appears to be minimal (Su, 2025). Nevertheless, some research highlights foreign direct investment as a potential air pollution reducer through mechanisms such as income effects, pollution halos, and technology spillover. Considering the availability, operability and comparability of index data, this study synthesizes the driving factors of urban green development to include economic development level, human capital, industrial structure, innovation, infrastructure conditions, government intervention, foreign trade openness, and environmental regulations. These are quantified using metrics like per capita GDP (Yan et al., 2019), the number of college students (Qiu et al., 2021), the proportion of secondary industry in GDP, science and technology expenditures, road network density (Li and Wu, 2018), and government spending as a percentage of local GDP (She et al., 2020). The resulting econometric model considers urban green development as the dependent variable with these factors as independent variables. Control variables include urbanization level, market potential, energy intensity, and industrialization degree. This study proposes the following hypothesis:

Hypothesis 2. Various factors affect urban development.

Hypothesis 3. Various factors exert spatial spillover effects on urban green development.

Based on the existing research on influencing factors, the theoretical model constructed in this study is shown in Figure 1.

Figure 1

Figure 1. Spatial effect framework model of urban green development.

3 Methods

3.1 Data source and sample distribution

This study takes the panel data of 285 cities in China as the research object, with a time span of 2008–2020. The data are derived from ‘China Urban Statistical Yearbook’, ‘National Statistical Yearbook’, ‘China Health and Family Planning Statistical Yearbook’, ‘China Energy Statistical Yearbook’, ‘China Labor Statistical Yearbook’, ‘China Social Statistical Yearbook’, ‘China Environmental Statistical Yearbook’, ‘China Population and Employment Statistical Yearbook’, provincial statistical yearbooks, municipal statistical yearbooks and statistical bulletins of national economic and social development of cities. Use the interpolation method of SPSS software to fill in missing values.

Sample selection in this study, in terms of regions, strictly adheres to the classification of China’s eastern, central, western, and northeastern regions by the National Bureau of Statistics. And the initial sample covers 293 prefecture-level cities across all regions of China. Considering the availability and continuity of data, this study excluded cities with substantial missing data, such as Lhasa City, Changdu City, Nyingchi City, Sansha City, Danzhou City, and Bijie City. The final sample comprises 285 prefecture-level cities in China, including 87 in the Eastern Region, 80 in the Central Region, 84 in the Western Region, and 34 in the Northeast Region. The complete list of cities is provided in Supplementary Appendix 1. Furthermore, based on the Official Notice on Adjusting the Criteria for Classifying City Sizes issued by the State Council of China and the Seventh National Population Census, this study classified the sample cities by size within the aforementioned research scope, as presented in Table 1.

Table 1

Table 1. The distribution of sample cities by region and size.

In addition, sample characteristics are critical considerations when assessing spatial effects. To avoid their impact on the measurement of spatial effects, three weight matrices—ARWM, GDWM, and EDWM—were constructed for model estimation, ensuring the stability of spatial coefficients and coefficients of key independent variables. Second, control variables were incorporated to eliminate the influence of non-spatial effects. Third, a variance inflation factor was used to conduct a multicollinearity test, which mitigated pseudo-regression disturbances and further ensured result stability. Finally, the same sample was employed in previous research to verify the spatial heterogeneity of urban green productivity via group regression (Hou et al., 2024). Thus, based on the above work, the sample selection in this study does not affect the estimation of the overall spatial effect of urban green development.

3.2 Measurement of urban green development

From the perspective of productivity, urban green development pays more attention to the fundamental driving forces of urban development. For instance, the health level of the workforce directly affects production efficiency. This study starts from the perspective of productivity, based on the composite system theory of “economy society environment energy health” and sustainable development theory. Building on previous research, the fuzzy rough set theory and methods are used to quantitatively screen the measurement index system of urban green development from the economic, environmental, energy consumption, social security, and health levels, in order to avoid the influence of noise on the indicators, clarify the object membership relationship, and reduce information loss. At the same time, this method can more accurately capture the complexity and variability of urban green development. The specific steps include: Firstly, standardizing the data to avoid the influence of dimensionality on the indicators (Yuan et al., 2025). Two equations are defined.

$\text{Positive\hspace{0.17em}indicator}:E_{ij}=\frac{X_{ij}-\underset{1\le i\le m}{\min }\left(X_{ij}\right)}{\underset{1\le i\le m}{\max }\left(X_{ij}\right)-\min \left(X_{ij}\right)}$

$\text{Negative\hspace{0.17em}indicator}:E_{ij}=\frac{\underset{1\le i\le m}{\max }\left(X_{ij}\right)-X_{ij}}{\underset{1\le i\le m}{\max }\left(X_{ij}\right)-\min \left(X_{ij}\right)}$

Among them, $E_{ij}$ is the standardized value of the j-th index of the i-th evaluation object, and $X_{ij}$ is the value of the j-th index of the i-th evaluation object; m is the number of evaluated objects. Secondly, fuzzy similarity classes are calculated. The classical rough set requires the conversion of continuous data into discrete data when processing it, which leads to information loss. Although the fuzzy set itself retains information, the final decision-making process requires the “defuzzification” of fuzzy membership degrees; if this process is not properly executed, information loss will also occur. This study employs the fuzzy similarity relationship in fuzzy rough sets, which not only retains the complete information of continuous data—thus avoiding discretization—but also minimizes information loss. In this study, the similarity of 0.3 was selected as the critical value, which has been widely used in previous studies (Guo and Zhu, 2007). In theory, the setting of α refers to the extent to which a degree of membership qualifies as belonging or relevance. Setting α = 0.3 ensures the inclusion of the maximum amount of relevant information, thereby reducing the risk of omission; it also includes any object with a similarity level of at least 30% to the target in the initial consideration set. Thirdly, through the lower approximation operation, the neighbor information is aggregated to generate a new and more robust membership degree for each object, which can clarify the fuzzy relationship and generate a more reliable and more suitable measurement for decision-making, so as to reduce the influence of the model on noise sensitivity. In this study, the accuracy value of 0.9 was set as the critical value (Zhang and Chi, 2012), meaning that when the new classification results achieve a 90% confidence interval, the new classification is deemed to adequately reflect the information of the original classification. Theoretically, a 90% confidence level in statistics represents a critical point that achieves an optimal balance between rigor and information retention, which is more flexible than 95% or 99% (thereby preserving more useful information) while stricter than 80% or 70% (effectively filtering out most noise). This ensures that even if a small number of objects are misclassified due to noise interference, such objects can still be correctly assigned to the lower approximation set as long as most similar objects are classified correctly. This significantly enhances the model’s robustness and improves its adaptability to imperfect data. The setting of these two threshold parameters enables the use of a low standard to establish a broad indicator candidate pool and a strict high standard to extract indicators. Specifically, the low standard minimizes the risk of missing important indicators, while the high standard enhances the reliability of the final indicators; this dual-standard approach thus strikes an optimal balance between retaining valuable information and filtering out harmful noise. Finally, the approximate classification quality coefficient is calculated. 16 green development evaluation indicators were obtained, as shown in Figure 2. The specific steps can be found in relevant literature (Hou et al., 2024; Cheng et al., 2018).

Figure 2

Figure 2. Measurement system for urban green development.

Based on this, the entropy weight method was used to calculate the weights of the indicators in this study. Firstly, the proportion of the j-th index value of the i-th evaluation object is calculated. Secondly, the entropy value of the index is calculated. Finally, the entropy weight of the index is calculated.

3.3 Construction of spatial weight matrix

From the perspective of adjacency relationship, geographical distance and economic distance, this study constructs the spatial weight matrix of urban green development on the basis of extracting urban coordinates. This matrix helps enhance the scientific rigor and accuracy of the evaluation results. It also offers targeted strategies and novel approaches for enhancing urban green development. Specifically, ARWM reflects the spatial adjacency relationship, which can appropriately reflect the spatial interaction relationship of green development among cities. It is set as a significant interaction between cities bordering each other, and the interaction between non-adjacent cities is not significant. Specifically, the matrix is set as a symmetric matrix with the main diagonal element being 0, and the elements in the matrix indicate whether two cities are geographically adjacent, if adjacent, then the weight is 1, if not, then the weight is 0. The GDWM is constructed based on the longitude and dimension of each city. D_ij represents the geographical distance between city i and city j. The closer the distance is, the stronger the spatial autocorrelation effect is. The EDWM is obtained by calculating the reciprocal of the mean GDP gap, and the greater the gap between two cities, the greater the weight.

$\left\{\begin{array}{l}{Q}_{ij}=\left\{\begin{array}{l}1Regioniisadjacenttoregionj.\\ 0Regioniisnotadjacenttoregionj.\end{array}\right.\\ Q_{ij}=\left\{\begin{array}{c}\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$d_{ij}$}\right.i\ne j\\ 0i=j\hfill \end{array}\right.\\ Q_{ij}=\left\{\begin{array}{c}\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\left|\left.\overline{G{p}_i}-\overline{G{p}_j}\right|\right.$}\right.i\ne j\\ 0i=j\hfill \end{array}\right.\end{array}\right.$

$\overline{G{p}_i}$ is the value-added GDP of the i-province during the study period.

3.4 Spatial autocorrelation model

The global spatial autocorrelation reveals the spatial pattern of the entire study area, and the Moran’s I index is used to characterize the degree of autocorrelation in that area. The local spatial correlation index can reveal the similarity or correlation between the attribute feature values of spatial reference units and their adjacent spatial units, identify spatial clustering and spatial isolation features, and detect spatial heterogeneity. Therefore, this study selected Moran’s I index to measure the spatial autocorrelation effect of urban green development, and the spatial autocorrelation model is defined:

$\left\{\begin{array}{l}Globalspatialcorrelation:M\mathrm{o}ra{n}^{\prime }sI=\raisebox{1ex}{$\left[{\displaystyle {\sum }_{i=1}^n}{\displaystyle {\sum }_{j\ne i}^r}{Q}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)\right]$}\!\left/ \!\raisebox{-1ex}{$T^2{\displaystyle {\sum }_{i=1}^n}{\displaystyle {\sum }_{j=1}^r}{Q}_{ij}$}\right.\\ Localspatialcorrelation:M\mathrm{o}ra{n}^{\prime }s{I}_i=\frac{n\left(x_i-\overline{x}\right){\displaystyle {\sum }_{j\ne 1}}{Q}_{ij}\left(x_j-\overline{x}\right)}{{\displaystyle {\sum }_i}{\left(x_i-\overline{x}\right)}^2}\end{array}\right.$

Among them, $x_i$ and $x_j$ represents the urban green development index, $\overline{x}$ represents the mean of each city’s green development index, $T^2$ represents the mean square error, $Q_{ij}$ represents the spatial weight matrix, and $I_i$ represents the product of the observed value at spatial position i and the weighted values of adjacent observations. The range of Moran’s I values is [-1, 1]. When Moran’s I > 0, it indicates the existence of spatial positive correlation characteristics in urban green development; When Moran’s I < 0, it indicates that there is a spatial negative correlation in urban green development.

3.5 Spillover effect model

According to different representations of spatial overflow, spatial econometric models can be divided into Spatial Lag Models (SLM), Spatial Error Models (SEM), and SDM. The choice of different spillover models has a significant impact on the results. In order to more accurately verify the spillover effects of various driving factors on urban green development, we tested the models. Specifically, this study first uses the Lagrange Multiplier (LM) test in the Ordinary Least Squares (OLS) model to determine which spatial panel regression model is most suitable. Secondly, we used Hausman test to determine whether to use fixed effects or random effects, and verified the robustness of the spatial regression model through Likelihood Ratio (LR) test. We also determined that the SDM is the most suitable and cannot be converted into SEM or SLM. The model test results are shown in Table 2.

Table 2

Table 2. Test results of spatial econometric model.

As shown by the LM test, its significance indicates that the presence of either a spatial error effect or a spatial lag effect. This warrants rejecting the null hypothesis of OLS, making it appropriate to adopt a spatial econometric model. Additionally, the LM test reveals that under the ARWM and GDWM, both the spatial error effect and spatial lag effect tests are significant, thus rejecting the “no spatial correlation” hypothesis. Although none of the tests under the EDWM passed the “no spatial correlation” test, the results of the Moran’s I index test confirm that urban green development exhibits both spatial error effects and spatial lag effects. We therefore conclude that the spatial panel regression model is the most appropriate for this study. Second, the Hausman test shows that the p-values for all weight matrices are significant. Accordingly, the fixed-effects design captures more spatial interaction effects and fixed-effects model yields the optimal performance. Thirdly, the Wald test and LR test results showed that the P values of LR passed the significance test (P = 0.000) regardless of the spatial weight matrix, indicating that SDM had higher goodness of fit and effect than SLM and SEM. The SDM model can provide more information and cannot be replaced by SLM and SEM. In addition, the SDM includes both the spatial lag term of the dependent variable and that of the independent variable, and exhibits superior adaptability and explanatory power across various spatial weight matrices. In contrast, the SEM is limited to the spatial autoregressive term for the error component, and the SLM is restricted to the spatial lag term of the dependent variable. Given the test results confirming the presence of spatial effects, this study integrated spatial correlation and spatial spillover effects, with particular consideration of the impacts from endogenous interaction, exogenous interaction, and error term interaction. Accordingly, a spatial Durbin model for urban green development was innovatively developed, which offers a comprehensive perspective for analyzing urban green development. Combined with the research object and related variables of this study, the SDM is defined:

$g{p}_{it}=\rho Q_{ij}g{p}_{it}+{\beta }_{1}ed{l}_{it}+{\beta }_{2}h{c}_{it}+{\beta }_{3}i{s}_{it}+{\beta }_{4}i{l}_{it}+{\beta }_{5}i{c}_{it}+{\beta }_{6}g{i}_{it}+{\beta }_{7}df{t}_{it}+{\beta }_{8}e{r}_{it}+\lambda Q_{ij}{X}_{it}+{\mu }_i+{\sigma }_i+{\epsilon }_{it}$

In the equation, $g{p}_{it}$ represents the green development level of the i-th city in period t. $Q_{ij}g{p}_{it}$ represents the spatial lag term of the dependent variable, which describes the endogenous interaction effect between the dependent variables. $\rho \in \left[-1,1\right]$ is a spatial autoregressive coefficient, which is used to describe the interaction between the dependent variables of the research object and the dependent variables of the adjacent object. $Q_{ij}$ is a spatial weight matrix, which quantifies the spatial dependence between different cities. This study mainly involves the ARWM, the GDWM and the EDWM. $\beta$ is the estimated parameter of the independent variable, that is, the regression coefficient of the independent variable. $X_{it}$ is the observation value of the independent variable in the t period of the i-th research object. $Q_{ij}{X}_{it}$ is the spatial lag term of the independent variable, which is used to describe the exogenous interaction effect between the independent variables of other adjacent objects and the dependent variables of the research object. $\lambda$ is the spatial autoregressive coefficient of the independent variable of the research object. ${\mu }_i$ is spatial fixed effect, ${\sigma }_i$ is time fixed effect, and ${\epsilon }_{it}$ is random error term. In addition, $ed{l}_{it}$ $h{c}_{it}$ $i{s}_{it}$ $i{l}_{it}$ $i{c}_{it}$ $g{i}_{it}$ $df{t}_{it}$ $e{r}_{it}$ represents the independent variable value of the i-th city in period t, which are economic development level, human capital, industrial structure, innovation level, infrastructure conditions, government intervention, foreign trade openness and environmental regulation.

3.6 Model robustness test

To mitigate the effects of multicollinearity and pseudo-regression, this study employs the variance inflation factor (VIF) to evaluate the panel data. Typically, a VIF threshold of 10 is used to judge the severity of multicollinearity: A VIF below 10 indicates an absence of multicollinearity, a VIF between 10 and 100 suggests strong multicollinearity and VIF of 100 or more indicates severe multicollinearity. The results of multicollinearity test are shown in Table 3.

Table 3

Table 3. Multicollinearity test.

The analysis reveals that the VIFs for all independent variables are below 3, and their tolerance levels are less than 1. Additionally, all variables passed the significance test at the 1% level. These results indicate that the model is free of multicollinearity issues, the data is robust, and the model construction process is sound.

4 Results and discussion

4.1 Spatial autocorrelation analysis of urban green development

This study uses Stata 16.0 to conduct a global Moran’s I analysis of the urban green development index from 2008 to 2020, and the results are shown in Figure 3.

Figure 3

Figure 3. Moran index analysis of urban green development under different spatial weights.

From the distribution of Moran’s I index, the Moran’s I index for urban green development under the ARWM and GDWM is greater than 0, and the overall value is 0.028–0.193, and passes the significance test. This indicates that there is significant spatial correlation and agglomeration effect in the green development of adjacent cities and cities with similar geographical distances, which can drive the green development of local cities. It may be because the city’s borders are connected and the geographical distance is close, making it more vulnerable to the influence of surrounding cities. For example, the negative externality of environmental pollution. It may also be because convenient transportation closely links urban green development with the development of neighboring cities (Deng et al., 2024; Hysa et al., 2025). In addition, when formulating development policies, the government is also relatively easy to refer to and imitate the development policies of neighboring cities or regions, resulting in learning effect and policy interaction, this in turn makes their green development levels more likely to exhibit spatial agglomeration (Yang Y. et al., 2025). This enlightens us that in the practice of urban governance, the inter-urban synergy should be emphasized and the spatial spillover effect of urban green development should be fully leveraged. At the same time, a city-level platform for sharing data, knowledge, and technology dedicated to urban green development should be established, thereby allowing more advanced green development cities to drive the green transformation and development of surrounding cities. The Moran’s I index of urban green development under the EDWM was greater than 0 during the period of 2008–2013 (Moran’s I index is 0.163-0.300), and through significance testing, it was shown that cities with similar economic development exhibited spatial correlation and agglomeration of green development levels between 2008–2013. However, this spatial agglomeration gradually disappeared during the period of 2014–2020, and the spatial agglomeration effect no longer existed. One plausible explanation is that between 2008 and 2013, China’s economy was in a phase of rapid growth driven by resources and investment. During this period, the industrial structure—predominantly industrial—and energy consumption patterns of economically similar cities were similar, leading to the emergence of similarity and agglomeration in the level of urban green development. From 2014 to 2020, however, China’s economic development gradually entered a new normal, and the construction of ecological civilization was elevated to a national strategy (People’s Network, 2015). Differentiated urban development strategies and technological innovations enabled economically similar cities to pursue distinct paths of green development, thereby weakening or even eliminating the spatial correlation rooted in economic distance. Therefore, the government should promote the coordinated layout of green industries in the economic network, create cross regional green industry chain clusters, and set up regional green innovation community funds to transform competition from cities with similar economies into cooperation.

In addition, the local Moran’s I values of each city under different spatial weight matrices are shown in Figure 4.

Figure 4

Figure 4. Local moran scatter diagram of green development of cities under different spatial weight matrices. (a) Local moran scatter plot of green development of cities under the adjacency relation weight matrix; (b) local moran scatter plot of green development of cities under the geographic distance weight matrix; (c) local moran scatter plot of green development of cities under the economic distance weight matrix.

On the whole, the trend line of urban green development scatter plot under different spatial weight matrices is located in the first quadrant and the third quadrant, which means that the local Moran’s I index is mainly characterized by ‘high-high’ clustering (hot spots areas, that is, cities with high levels of green development are surrounded by other high-level cities) or ‘low-low’ agglomeration (cold spot areas, that is, cities with low levels of green development are surrounded by other low-level cities), that is, the green development between cities affects or attracts each other. The local Moran’s I test further confirms that urban green development among the sample cities exhibits spatial correlation. Specifically, based on the local spatial autocorrelation analysis of urban green development under the ARWM, cities such as Dongguan City and Guangzhou City fall within high-high agglomeration clusters, exhibiting a high level of green development both locally and in their surrounding areas—classed as hot spots. In contrast, cities including Anshan City, Baishan City, and Wenzhou City are situated in low-low agglomeration clusters—classed as cold spots. The distribution of these hot and cold spots is closely linked to cities’ geographical location, industrial structure, and economic development level. Therefore, the spatial agglomeration of urban green development is prominent, which further confirms that research on urban green development requires the application of spatial econometric models.

Furthermore, this study uses the econometric model to calculate the spatial correlation degree of urban green development under different weight matrices, and the results are shown in Table 4.

Table 4

Table 4. Regression analysis of spatial effect under different weight matrix.

The data demonstrates that urban green development is affected by at least 13% of the green development of cities with similar economic levels. The impact of cities with close geographical distance reached 84%, and the impact of green development of adjacent cities reached 63%. Hypothesis 1 is verified. Therefore, urban green development has spatial correlation and agglomeration. This pattern echoes Li’s (2019) findings concerning spatial correlations environmental quality, environmental governance, development quality, and green living across various provinces and cities, but the study only analyzes the spatial correlation of ecological civilization construction in each province under the ARWM in 2016. This study constructs the spatial weight matrix of adjacency relationship, geographical distance and economic distance, reveals the spatial effect of urban green development from 2008 to 2020, and can comprehensively reflect the spatial impact of urban green development. In addition, some studies only analyzed and verified the spatial autocorrelation of urban green transformation under the ARWM and the GDWM (Li, 2019; Wang D. et al., 2023), while this study included the EDWM, which could more comprehensively analyze the spatial effect of urban green development. In addition, this conclusion is also similar to the research conclusions of Lin et al. (2023) and Ding et al. (2025). That is, the urban green development level of the Yangtze River Economic Belt has spatial dependence and spatial spillover effects. In contrast to previous studies, this study scientifically quantifies the spatial correlation of urban green development and derives specific quantitative outcomes, which explicitly reveals the agglomeration characteristics of urban green development, thereby extending and complementing existing research. Moreover, it can effectively and targetedly enhance urban green development levels. For policy implications, policymakers should prioritize the establishment of intercity cooperation platforms, encourage neighboring cities to accelerate the flow of factors such as information and technology, and thereby leverage the spatial spillover effects of cities with high green development levels.

4.2 Analysis of spatial spillover effects of urban green development

Considering that the spatial econometric model is easily affected by the spatial weight matrix, this study uses the ARWM, the GDWM and the EDWM to test the robustness of the spatial regression results. The results of econometric regression analysis of urban green development are shown in Table 5.

Table 5

Table 5. Regression analysis of influencing factors of urban green development.

The findings reveal that the regression results of urban green development under the ARWM and the GDWM are largely consistent, confirming that the model results are highly robust and reliable. In addition, results from the ARWM and GDWN indicate that urban green development has a spatial spillover effect, suggesting that urban green development is affected by neighboring cities and cities with close geographical distances. These effects likely stem from the rapid circulation of production factors facilitated by proimity and interconnected urban areas, forming a “convenience circle” that promotes mutual urban developmemt (Yang X. L. et al., 2024; García-Chan et al., 2025). This is similar to the research conclusion of Xiao et al. (2022), who used SDM to analyze the spatio-temporal evolution pattern and spatial spillover effect of green development level in Hubei Province, and found that there is a significant spatial spillover effect of green development, and the regional spillover effect is larger than the intra-regional spillover effect. However, the research only analyzes the spillover effect of green development level in Hubei Province. This study expands the research scope to 285 prefecture-level cities in China, and finds that the spatial spillover effect of urban green development under the geospatial weight matrix is higher than that under the adjacency weight matrix, which makes the research results universal. Additionally, this study indicates that the spillover effects of urban green development are more substantial under the GDWM than the ARWM. This supports the view that spatial correlation of urban green development is stronger when measured by geographical distance rather than mere adjacency. At the same time, this conclusion also verifies that the flow of factors brought about by the rapid development of regional economic integration will promote the agglomeration of industries and economies. Therefore, it is crucial to consider spatial spillover effects in establishing urban sustainable development strategies.

Specifically, taking the level of economic development and industrial structure as examples, the estimated coefficients of the level of economic development and industrial structure are significantly positive under the ARWM and the GDWM, indicating that the rapid development of urban economy and the adjustment of industrial structure can promote the green development of the city. It is worth noting that this study found that after considering the spatial lag term, the spatial effect interaction coefficient of economic development level and industrial structure is negative, indicating that the green development of the city has a significant negative impact on the green development of other cities. In order to further reveal the reasons behind this phenomenon, this paper analyzes the action path of the spillover effect of each driving factor, so as to more accurately reflect the relationship between each variable and urban green development. This study uses Stata16.0 software to decompose the spatial effects of urban green development under different weight matrices, as shown in Table 6.

Table 6

Table 6. Direct effect, indirect effect and comprehensive effect under the action of spatial weight matrix.

Table 6 reveals that, from the perspective of spillover effect, the level of economic development has a significant negative spillover effect on adjacent cities and cities with close geographical distances, but has a significant positive spillover effect on cities with similar levels of economic development. Urban siphon and competition effects may cause economically developed cities to absorb high-quality resources from surrounding regions; alternatively, these effects may constrain the improvement of adjacent cities’ green development levels through industrial competition (Duan et al., 2025; Aydin et al., 2025). This is similar to the research conclusion of Duan et al. (2025), that is, due to the existence of inter city competition and pollution shelter effect, developed cities may use their good economic conditions to provide more preferential policies to attract foreign investment in order to maintain their economic growth advantages after development and expansion, which will form huge competitive pressure on surrounding cities while enhancing their own strength. In addition, developed cities attract high-quality production factors such as capital, talents and technology from surrounding areas, which will also produce a “siphon effect”, thus promoting the green development of the city and inhibiting the green development ability of surrounding cities (Yang Y. et al., 2025). Therefore, the government should focus on the construction of regional coordinated development mechanisms, such as ecological compensation and enclave economy, to avoid the plunder of surrounding resources by core cities. The industrial structure and infrastructure level have a significant negative spillover effect on the growth of green development in adjacent cities. In addition, cities with similar economic development levels are more likely to learn from each other’s experiences and adopt effective practices, and subsequently form industrial alliances or regional cooperation bodies—such as those within the Yangtze River Delta and the Guangdong-Hong Kong-Macao Greater Bay Area. Through these bodies, initiatives like the co-construction of green standards and joint research and development of green technologies have achieved the scale effect and synergy effect, thereby generating the positive spatial spillover effect (National Development and Reform Commission, 2022). Therefore, it is necessary to give full play to the positive spillover effect of economic level and improve the level of green development in cities and regions.

This study reveals that the industrial structure significantly impacts improvements in green development within cities and exerts a considerable negative spillover effect on adjacent cities. The reason is that the upgrading of industrial structure can directly promote the green development of the city by reducing pollution sources, improving efficiency and introducing technology (Hou, 2025; Erdogan et al., 2025). The negative spillover effect of industrial structure upgrading reveals that a city’s green development improvement comes at the cost of greater environmental burden on adjacent cities (Chen B. et al., 2025). Specifically, following a city’s industrial structure upgrading, existing polluting enterprises choose neighboring cities as ideal locations to sustain their operations, which in turn hampers the green development of these cities. This conclusion is also similar to the research findings of Xiao et al. (2022), Khan et al. (2022) and Sadiq et al. (2024), that is, industrial structure has a negative spillover effect on the green development of cities and industrial structure also shed inverse impacts on environmental quality. Taking 285 cities as the research object, this study comprehensively clarifies the negative spillover effect of industrial structure on urban green development, which is an in-depth supplement to the existing research. This suggests that government should prioritize industrial division of labor and collaboration among adjacent cities, avoid homogeneous competition, guide the orderly flow and optimal allocation of production factors within the regional cluster, and work to convert the “siphon effect” into the “diffusion effect”, so as to achieve the common green transformation of these cities. A city’s innovation level drives sustainable economic growth and profoundly influences its development of green development (Alnafrah et al., 2023). The level of innovation has a significant improvement effect on the urban green development, and has a significant positive spillover effect on the green development of adjacent and geographically close cities. This is similar to the research conclusion of Liao et al. (2024) and Degirmenci et al. (2025), that is, green innovation is conducive to improving the gross economic product of local and neighboring cities and innovation can also promote the sustainable development of Organization for Economic Co-operation and Development (OECD) countries. The researches of Duan et al. (2025) and Zhu et al. (2025) confirm that the quality of green innovation is crucial for the sustainable development of urban agglomerations and reveal that it can also promote green development in neighboring cities. This also enlightens that we should not ignore the role of spatial dependence or spatial spillover in research and development (R&D) activities, especially the spatial spillover effect and diffusion effect caused by R&D personnel flow and technical information exchange (Anselin, 2013; Chen L. et al., 2025). Current trends indicate that R&D activities in China are experiencing unprecedented growth marked by increases in expenditures and patent filings. Therefore, city policymakers are encouraged to bolster support for R&D, optimize the allocation of innovation resources, and implement scientifically informed strategies to enhance green development in urban areas.

The level of infrastructure has a significant role in promoting the improvement of green development in the city, and has a significant negative spillover effect on the growth of green development in adjacent cities. The reason is that the convenience and accessibility of transportation are conducive to the flow of resources from adjacent cities into the city, thus improving the economic development level of the city and promoting the improvement of people’s living standards (Abdolpour and Marut, 2025). However, evacuating the development resources of adjacent cities and possibly shifting the environmental costs lead to the shortage of resources in adjacent cities and hindering the improvement of green development in adjacent cities. This also enlightens government that when planning infrastructure projects, it should start from the perspective of regional coordination, build a networked, multi-center pattern, and enable surrounding cities to share convenience. The results of this study are different from those of Wang Y. J. et al. (2023), which emphasizes that information infrastructure has a significant positive spillover effect on the development of urban green intelligence. This also enlightens us to further analyze the differences in the impact of different infrastructures (such as new infrastructure, green infrastructure, etc.) on the level of urban green development in the future. Government intervention tends to have a positive spillover effect on green development in adjacent cities, indicating that the relevant policies and measures of the city government can play a positive demonstration effect (Yi et al., 2025). This is similar to the conclusion of Kwilinski et al. (2024) that investment of government funds can promote industrial technology innovation, so as to promote sustainable development. However, different from the research conclusion of Liu and Han (2024), They made contrasting observations in examining the influencing factors of green innovation efficiency in Guangdong-Hong Kong-Macao Greater Bay Area based on the SDM, where government support exerted a negative spillover effect on efficiency values in neighboring areas. The disparity between Liu and Han’s results and the findings of this analysis may be that the Guangdong-Hong Kong-Macao has particularly high green innovation efficiency as China’s most open and strongest market economy.

Foreign trade openness also exhibits a positive spillover effect on adjacent cities, indicating that the knowledge, technologies, management, and standards derived from foreign trade openness break through administrative boundaries, drive the flow of talents and technology, and facilitate the diffusion of advanced green production concepts and technologies to these cities (Wang and Zhao, 2025). Zamani and Tayebi (2021), observation that trade and foreign direct investment exert a significant spillover effect on economic growth across OECD member countries. Meanwhile, this conclusion is consistent with Chen’s (2024) finding that open and shared development inhibit carbon emission intensity. This may be because neighboring cities absorb the spillover of a city’s foreign-funded enterprises, driving the development of green development in those surrounding areas. This suggests that government should encourage opening-up, plan regional open economy, strengthen industrial division and cooperation among cities, and maximize the effect of knowledge and technology spillover. In addition, environmental regulation has a significant positive spillover effect on the level of green development in cities with similar levels of economic development. It may be that cities with similar levels of economic development have similar industrial structure, pollution types, and financial capabilities. Therefore, the relevant policies for the green improvement of a city can provide high reference value and operability for cities with similar levels of economic development. In addition, when multiple cities with similar economic development levels collectively adopt strict regulations, this thereby jointly fosters the formation of a sizeable regional green technology market and enhances the collective green development level of these agglomerations (Gong and Zhang, 2020; Feng, 2024). The results of this study are different from those of Aydin et al. (2025), which emphasizes that strict environmental regulation can promote green growth and the development of low-carbon energy. Therefore, in the process of urban green development, a normalized environmental policy exchange platform between cities with similar economic development levels should be established. At the same time, for urban agglomerations with similar economic levels, the government should actively explore and formulate relatively unified environmental standards to avoid vicious competition. Hypothesis 2 is verified.

5 Conclusion

Cities, as spatial concentrations of various elements, represent the most direct and closely linked units within regional systems. They are interdependent and exert mutual influences, manifesting notable spatial spillovers that significantly impact regional development. This makes analyses of the dynamic spatial patterns of urban green development over specific periods of time crucial for both urban and regional sustainable development. From the perspective of urban green productivity, which is a fundamental driver of urban growth, this study uses the SDM to verify spatial dependencies and uncover the spatial spillover effects of urban green development. This analysis not only deepens the current understanding of urban green development but also addresses gaps in the existing literature, as discussed above. This study is particularly significant as it seeks to elucidate both the drivers and the spatial effects of urban green development, offering valuable insights that can inform targeted policy recommendations and contribute to more effective urban planning and sustainability strategies in China and abroad. This study draws two key conclusions: (1) From the perspective of spatial correlation, urban green development has global spatial autocorrelation. The level of urban green development is affected by at least 13% of cities with similar economic levels, 84% by cities with close geographical distances, and 63% by neighboring cities. (2) From the perspective of spatial spillover effect, urban green development has significant spatial spillover effect under ARWM and GDWM, specifically, the level of economic development has a significant negative spillover effect on the green development growth of adjacent cities and cities with similar geographical distances, but a significant positive spillover effect on the green development of cities with similar levels of economic development. The industrial structure and infrastructure level have a significant negative spillover effect on the green development growth of adjacent cities. The level of innovation has a significant positive spillover effect on the green development level of cities that are close to it and geographically distant. Environmental regulations have significant positive spillover effects on the green development level of cities with similar economic development levels. Government intervention and foreign trade openness have significant positive spillover effects on the green development of neighboring cities.

Limitations of this study should be acknowledged: (1) The data supporting this study were primarily sourced from national, local, and urban statistical yearbooks, along with statistical bulletins on urban national economic and social development. However, the release of statistical data typically exhibits a certain degree of delay. Considering the availability and integrity of data, this study only uses the data from 2008 to 2020. This limited time frame restricted the ability to thoroughly trace long-term trends in the spatial effects of urban green development. (2) Endogeneity issues are a common challenge in econometric analyses. In this study, rather than the ordinary least squares method, the SDM was employed to assess spatial effects followed by the VIF to check their robustness. These approaches helped mitigate, but did not fully eliminate, endogeneity issues.

In the future, firstly, expanding data collection channels will enhance the ability to delineate spatial effects of urban green development over more extended timeframes. Secondly, while improvements have been made, there remains significant room for improvement in the treatment of endogeneity within spatial econometric models. In addition, future research should focus on integrating various city characteristics (e.g., local resources and geographical factors) to provide a more nuanced analysis of urban sustainability from multiple perspectives. Such a holistic approach would better inform strategies for high-quality urban development.

6 Policy recommendations

Based on the research conclusions, targeted policy recommendations are proposed: (1) Radiation strategy. The findings of this study indicate that urban green development exerts a spatial spillover effect. Cities with high levels of green development should proactively extend their influence to neighboring cities and to cities with similar geographical and economic profiles to foster regional development. Specifically, strengthen the city’s unique advantages of the city, give play to the positive spillover effect of its elements, and increase the “feeding” of neighboring cities, so as to promote the two-way flow of high-quality resources, and drive the green development of the surrounding area or even the region. (2) Cooperation strategy. The findings also demonstrate spatial correlation in urban green development. Therefore, establishing an interactive mechanism for the high-quality development of urban green development can create a “win-win” scenario. Specifically, it is essential to dismantle administrative barriers and encourage inter-regional cooperation to ensure equitable resource distribution and minimize regional disparities. Moreover, cities should capitalize on their inherent strengths to create a siphoning effect, attracting superior resources to not only boost local green development but also facilitate regional integration through spatial correlation and spillover. Such strategies can activate new sources of spillover effects, leading to the diffusion of innovative practices and industries across adjacent areas, thereby supporting the coordinated advancement of urban green development.

Data availability statement

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

Author contributions

CH: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Methodology, Project administration, Validation, Writing – original draft. YM: Software, Supervision, Visualization, Writing – review and editing.

Funding

The authors declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

Author YM was employed by Xuzhou Surveying & Mapping Institute Co., Ltd.

The remaining author declares that the research 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 authors declare that no Generative AI was 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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1664513/full#supplementary-material

Abbreviations

ARWM, Adjacency Relationship Weight Matrix; GDWM, Geographical Distance Weight Matrix; EDWM, Economic Distance Weight Matrix; SDM, Spatial Durbin Models; SLM, Spatial Lag Models; SEM, Spatial Error Models; LM, Lagrange Multiplier; OLS, Ordinary Least Squares; LR, Likelihood Ratio; VIF, Variance Inflation Factor; OECD, Organization for Economic Co-operation and Development; R&D, Research and Development; GDP, Gross Domestic Product; GTFP, Green Total Factor Productivity; TFP, Total Factor Productivity.

References

Abdolpour, H., and Marut, M. (2025). Parametric design for sustainable marine infrastructure. B. Pol. Acad. Sci-Tech, 154289. doi:10.24425/bpasts.2025.154289

CrossRef Full Text | Google Scholar

Alnafrah, I., Okunlola, O., Sinha, A., Abbas, S., and Dagestani, A. A. (2023). Unveiling the environmental efficiency puzzle: insights from global green innovations. J. Environ. Manage. 345, 118865. doi:10.1016/j.jenvman.2023.118865

PubMed Abstract | CrossRef Full Text | Google Scholar

Anguelovski, I., Oscilowicz, E., Connolly, J. J., García-Lamarca, M., Perez-del-Pulgar, C., Cole, H. V., et al. (2024). Does greening generate exclusive residential real estate development? Contrasting experiences from North America and Europe. Urban Urban Gree 101, 128376. doi:10.1016/j.ufug.2024.128376

CrossRef Full Text | Google Scholar

Anselin, L. (2013). Spatial econometrics: methods and models. Berin: Springer.

Google Scholar

Aydin, M., Degirmenci, T., Bozatli, O., Radulescu, M., and Balsalobre-Lorente, D. (2025). Do green energy, command and control-based environmental regulations, and green growth catalysts for sustainable development? New evidence from China. J. Environ. Manage. 373, 123620. doi:10.1016/j.jenvman.2024.123620

PubMed Abstract | CrossRef Full Text | Google Scholar

Bian, Z., Liu, J., Zhang, Y., Peng, B., and Jiao, J. (2024). A green path towards sustainable development: the impact of carbon emissions trading system on urban green transformation development. J. Clean. Prod. 442, 140943. doi:10.1016/j.jclepro.2024.140943

CrossRef Full Text | Google Scholar

Chen, B., Zhu, H., and Dai, J. (2025). The impact of urban digital economy on urban innovation: the mediating role of trade openness. Asian Geogr. 42, 195–218. doi:10.1080/10225706.2025.2478913

CrossRef Full Text | Google Scholar

Chen, Y. Y., Li, Q., and Liu, J. Y. (2024). Innovating sustainability: VQA-based AI for carbon neutrality challenges. J. Organ. End. User Com. 36 (1), 337606. doi:10.4018/JOEUC.337606

CrossRef Full Text | Google Scholar

Chen, L., Gao, M., Gao, J. H., and Chen, W. X. (2025). Spatial correlation network structure and its influencing factors on municipal solid waste carbon emissions in three major urban agglomerations in China. Energy 328, 136537. doi:10.1016/j.energy.2025.136537

CrossRef Full Text | Google Scholar

Chen, S. Y., Mu, S. Y., Feng, Y. D., and Tan, Z. X. (2025). Can industrial robot adoption improve the green total factor productivity in Chinese cities? Systems 13 (4), 215. doi:10.3390/systems13040215

CrossRef Full Text | Google Scholar

Cheng, X., Long, R., Chen, H., and Li, W. B. (2018). Green competitiveness evaluation of provinces in China based on correlation analysis and fuzzy rough set. Ecol. Indic. 85, 841–852. doi:10.1016/j.ecolind.2017.11.045

CrossRef Full Text | Google Scholar

Crome, C., Graf-Drasch, V., Hawlitschek, F., and Zinsbacher, D. (2023). Circular economy is key! designing a digital artifact to foster smarter household biowaste sorting. J. Clean. Prod. 423, 138613. doi:10.1016/j.jclepro.2023.138613

CrossRef Full Text | Google Scholar

Degirmenci, T., Aydin, M., Bozatli, O., and Ahmed, Z. (2025). Consolidating sustainable development in OECD countries: the role of green energy transition, green innovation, environmental policy stringency, and human capital. Sustain. Dev. doi:10.1002/sd.70034

CrossRef Full Text | Google Scholar

Deng, Y. M., Li, X. M., and Zhu, J. M. (2024). Effect of planning and construction of intercity railways on the economic development of the pearl river Delta urban agglomeration: an analysis based on the spatial durbin model. Sustainability 16 (2), 738. doi:10.3390/su16020738

CrossRef Full Text | Google Scholar

Deng, S., Wang, H., and Yao, Z. (2025). The role of low-carbon technology innovation incentive programs in achieving sustainable development goals in cities: empirical evidence from 277 cities in China. Environ. Technol. and Innovation 39, 104296. doi:10.1016/j.eti.2025.104296

CrossRef Full Text | Google Scholar

Ding, Y., Zhang, F., and Wang, X. H. (2025). Digital economy enabling low-carbon Development—spatial spillover and heterogeneity. Empir. Econ. 69, 639–659. doi:10.1007/s00181-025-02756-7

CrossRef Full Text | Google Scholar

Duan, S. J., Chen, H., and Han, J. (2025). Green innovation quality in center cities and economic growth in peripheral cities: evidence from the yangtze river Delta urban agglomeration. Systems 13 (8), 642. doi:10.3390/systems13080642

CrossRef Full Text | Google Scholar

Eben, P., Stinshoff, P., Knoll, S., Busse, L., Kick, D., Duthweiler, S., et al. (2025). Are infiltration swales all-rounders? An interdisciplinary field study on multifunctionality. Ecol. Eng. 215, 107597. doi:10.1016/j.ecoleng.2025.107597

CrossRef Full Text | Google Scholar

Erdogan, S., Pata, U. K., and Kartal, M. T. (2025). Does economic transition enhance environmental sustainability? The case of Russia. Environ. Dev. Sustain., 1–20. doi:10.1007/s10668-025-06461-4

CrossRef Full Text | Google Scholar

Feng, L. (2024). Temporal and spatial evolution and influencing factors of eco-efficiency in resource-based cities: a case study of Heilongjiang province. Ecol. Econ. 40 (4), 87–93.

Google Scholar

García-Chan, N., Alvarez-Vázquez, L. J., Martínez, A., and Vázquez-Méndez, M. E. (2025). A nonconservative macroscopic traffic flow model in a two-dimensional urban-porous city. Math. Comput. Simulat. 233, 60–74. doi:10.1016/j.matcom.2025.01.016

CrossRef Full Text | Google Scholar

Gong, C. J., and Zhang, X. Q. (2020). The spatial effect of China's interregional environmental regulation on green economic efficiency and its decomposition. Discuss. Modern Economy 4, 41–47.

Google Scholar

Guo, M., and Zhu, J. F. (2007). The performance evaluation in logistics service supply chain based on fuzzy-rough set. Syst. Eng. 25, 48–52.

Google Scholar

Guo, B., Hu, J., and Guo, X. (2025). Can the industrial transformation and upgrading demonstration zones policy improve urban green technology innovation? An empirical test based on old industrial cities and resource-based cities in China. Front. Environ. Sci. 12, 1505177. doi:10.3389/fenvs.2024.1505177

CrossRef Full Text | Google Scholar

Helfaya, A., and Bui, P. (2025). Pursuing a corporate sustainable identity: green governance strategy, hybrid vehicle development, knowledge and sustainability performance. J. Innov. Knowl. 10 (2), 100660. doi:10.1016/j.jik.2025.100660

CrossRef Full Text | Google Scholar

Hou, C. M. (2025). Analysis of the factors promoting urban green productivity using a system dynamics simulation. Sci. Rep. 15 (1), 27928. doi:10.1038/s41598-025-13699-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Hou, C. M., Chen, H., and Huang, X. R. (2024). Measurement and distribution characteristics of urban green productivity: based on fuzzy rough set and spatial autocorrelation analysis. Environ. Dev. Sustain., 1–29. doi:10.1007/s10668-024-05440-5

CrossRef Full Text | Google Scholar

Hysa, A., Löwe, R., and Geist, J. (2025). Waterfront usage trends across German metropolitan areas: a social-ecological perspective to urban blue-green infrastructure connectivity. Landsc. Urban Plan. 260, 105369.

CrossRef Full Text | Google Scholar

Khan, S., Akbar, A., Nasim, I., Hedvičáková, M., and Bashir, F. (2022). Green finance development and environmental sustainability: a panel data analysis. Front. Environ. Sci. 10, 1039705. doi:10.3389/fenvs.2022.1039705

CrossRef Full Text | Google Scholar

Khomenko, S., Pisoni, E., Thunis, P., Bessagnet, B., Cirach, M., Iungman, T., et al. (2023). Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment. Lancet Public Health 8 (7), e546–e558. doi:10.1016/S2468-2667(23)00106-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Kwilinski, A., Lyulyov, O., and Pimonenko, T. (2024). Green economic development and entrepreneurship transformation. Entrepr. Bus. Econ. Rev. 12 (4), 157–175. doi:10.15678/EBER.2024.120409

CrossRef Full Text | Google Scholar

Li, Q. (2019). Spatial autocorrelation analysis on construction of ecological civilization. China Popul. Resour. Environ. 29 (9), 91–98.

Google Scholar

Li, D. S., and Wu, R. W. (2018). A dynamic analysis of green productivity growth for cities in Xinjiang. Sustainability 10, 515. doi:10.3390/su10020515

CrossRef Full Text | Google Scholar

Liao, B., Li, L., and Li, C. (2024). Does the innovation community cultivation promote urban green development? Evidence from the urban agglomeration in the middle reaches of the yangtze river. Environ. Dev. Sustain. 26, 32023–32043. doi:10.1007/s10668-024-04809-w

CrossRef Full Text | Google Scholar

Lin, L. M., Lai, Y. B., Xie, J. L., and He, X. L. (2022). The impact of environmental regulation on urban green development efficiency: an empirical analysis based on super-efficiency EBM model and system GMM model. J. Nanjing Univ. Technol. 21 (5), 102–114.

Google Scholar

Lin, Y. Y., Xu, X. B., and Wang, W. (2023). Spatial-temporal characteristics and influencing factors of green development level in the yangtze river economic belt. Resour. Environ. Yangtze Basin 32 (9), 1822–1833. doi:10.11870/cjlyzyyhj202309005

CrossRef Full Text | Google Scholar

Liu, P. F., and Han, X. L. (2024). The impact and mechanism of digital technology development on regional carbon emissions: a case study of the yangtze river economic belt. Ecol. Econ. 40 (4), 26–35.

Google Scholar

Liu, Z., and Zhang, S. (2025). Exploring the relationship between urban green development and heat island effect within the yangtze river Delta urban agglomeration. Sustain. Cities. Soc. 121, 106204. doi:10.1016/j.scs.2025.106204

CrossRef Full Text | Google Scholar

Lokhande, T., and Xie, Y. (2023). Spatial spillover effects of urban decline in southeast Michigan. Appl. Geogr. 158, 103031. doi:10.1016/j.apgeog.2023.103031

CrossRef Full Text | Google Scholar

Majeed, A., Wang, J., Zhou, Y., and Muniba, M. (2024). The symmetric effect of financial development, human capital and urbanization on ecological footprint: insights from BRICST economies. Sustainability 16 (12), 5051. doi:10.3390/su16125051

CrossRef Full Text | Google Scholar

National Development and Reform Commission (2022). The regional economic department of the national development and reform commission organized a meeting on the coordination mechanism for the construction of major cooperation platforms in Guangdong Hong Kong Macao greater Bay area. Available online at: https://www.ndrc.gov.cn/fzggw/jgsj/qys/sjdt/202211/t20221104_1340886.html (Accessed September 12, 2025).

Google Scholar

Opbroek, J., Barboza, E. P., Nieuwenhuijsen, M., Dadvand, P., and Mueller, N. (2024). Urban green spaces and behavioral and cognitive development in children: a health impact assessment of the Barcelona “Eixos Verds” plan (green axis plan). Environ. Res. 244, 117909. doi:10.1016/j.envres.2023.117909

PubMed Abstract | CrossRef Full Text | Google Scholar

Peng, Q. L., He, W. J., Kong, Y., Shen, J. Q., Yuan, L., and Ramsey, T. S. (2024). Spatio-temporal analysis of water sustainability of cities in the yangtze river economic belt based on the perspectives of quantity-quality-benefit. Ecol. Indic. 160, 111909. doi:10.1016/j.ecolind.2024.111909

CrossRef Full Text | Google Scholar

People's Network (2015). People's essentials: deeply understand the new normal of China's economic development. Available online at: http://theory.people.com.cn/n/2015/0602/c40531-27088968.html (Accessed October 12, 2025).

Google Scholar

Qiu, L. C., Zhong, X. L., and Wang, Z. F. (2021). Has human capital externality promoted the stabilization and expansion of employment. J. Shanxi Univ. Finance Econ. 43 (11), 87–101. doi:10.13781/j.cnki.1007-9556.2021.11.007

CrossRef Full Text | Google Scholar

Sadiq, M., Paramaiah, C., Dong, Z., Nawaz, M. A., and Shukurullaevich, N. K. (2024). Role of fintech, green finance, and natural resource rents in sustainable climate change in China. Mediating role of environmental regulations and government interventions in the pre-post COVID eras. Resour. Policy. 88, 104494. doi:10.1016/j.resourpol.2023.104494

CrossRef Full Text | Google Scholar

She, S., Wang, Q., and Zhang, A. C. (2020). Technological innovation, industrial structure and urban GTFP—Channel test based on national low-carbon city pilots. Res. Econ. Manag. 41 (8), 44–61. doi:10.13502/j.cnki.issn1000-7636.2020.08.004

CrossRef Full Text | Google Scholar

Su, R. (2025). Spatial effects of environmental factors on foreign direct investment: evidence from Chinese cities. Front. Environ. Sci. 13, 1577657. doi:10.3389/fenvs.2025.1577657

CrossRef Full Text | Google Scholar

Tian, Y., Wang, R., Liu, L., and Ren, Y. (2021). A spatial effect study on financial agglomeration promoting the green development of urban agglomerations. Sustain. Cities. Soc. 70, 102900. doi:10.1016/j.scs.2021.102900

CrossRef Full Text | Google Scholar

Tobler, W. R. (1979). Philosophy in geography. Berlin: Springer press.

Google Scholar

Wang, S., and Zhao, C. (2025). Study on the impact of expanding imports on the upgrading of the industrial structure: evidence from 285 Chinese cities. Singap. Econ. Rev. Press, 1–25. doi:10.1142/S0217590825500298

CrossRef Full Text | Google Scholar

Wang, D., Xu, L., and Du, J. (2023). The direct and indirect spatial spillover effects of infrastructure on urban green and smart development. Front. Environ. Sci. 11, 1197048. doi:10.3389/fenvs.2023.1197048

CrossRef Full Text | Google Scholar

Wang, Y. J., Chen, H., Long, R., Wang, L., Yang, M., and Sun, Q. (2023). How does population aging affect urban green transition development in China? An empirical analysis based on spatial econometric model. Environ. Impact Asses. 99, 107027. doi:10.1016/j.eiar.2022.107027

CrossRef Full Text | Google Scholar

Waqar, A., Othman, I., and Shafiq, N. (2025). The role of sustainable procurement in attaining sustainable development goals: insights from sustainable logistics and sustainable metamorphosis practices. J. Clean. Prod. 500, 145285. doi:10.1016/j.jclepro.2025.145285

CrossRef Full Text | Google Scholar

Xiao, Y. T., Chen, J., Wang, X. L., and Lu, X. Y. (2022). Regional green development level and its spatial spillovereffects: empirical evidence from Hubei Province, China. Ecol. Indic. 143, 109312. doi:10.1016/j.ecolind.2022.109312

CrossRef Full Text | Google Scholar

Yan, T., Zhang, X. P., Chen, H., and Li, R. K. (2019). Evolution of regional differences in urban economic development in China from 2001 to 2016. Econ. Geogr. 39 (12), 11–20. doi:10.15957/j.cnki.jjdl.2019.12.002

CrossRef Full Text | Google Scholar

Yang, B., Wang, Y., Yang, H., and Chen, F. (2024). How does regional economic integration affect carbon emission efficiency? Evidence from the yangtze river Delta, China. Environ. Sci. Pollut. R. 1-14, 23766–23779. doi:10.1007/s11356-024-32663-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, J. H., Liu, P. X., Zhong, F. L., and Han, N. (2025). Subway opening enables urban green development: evidence from difference-in-differences and double dual machine learning methods. J. Environ. Manage. 375, 124177. doi:10.1016/j.jenvman.2025.124177

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, X. L., Jin, K., Duan, Z., Gao, Y., Sun, Y. W., and Gao, C. (2024). Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities-A case study of 247 prefecture-level cities. Sci. Total Environ. 928, 172325. doi:10.1016/j.scitotenv.2024.172325

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, Y., Wu, S. N., Zhang, X. L., and Hao, L. L. (2025). Provincial border effects on capital flows in China: a study based on enterprise cross-regional investment data. Cities 162, 105984. doi:10.1016/j.cities.2025.105984

CrossRef Full Text | Google Scholar

Yi, M., Xu, L., Zhang, T., Ao, L., and Sun, M. (2025). The impact of foreign direct investment on green technology progress in China on two-carbon background: taking trade openness into consideration. Front. Environ. Sci. 12, 1533146. doi:10.3389/fenvs.2024.1533146

CrossRef Full Text | Google Scholar

Yu, C., and Zheng, H. (2024). Efficiency of green and low-carbon coordinated development for mega urban agglomerations: an empirical study. Front. Environ. Sci. 12, 1336322. doi:10.3389/fenvs.2024.1336322

CrossRef Full Text | Google Scholar

Yuan, R. (2025). The synergistic effect of digital economy and environmental governance on urban green development. Front. Environ. Sci. 13, 1506794. doi:10.3389/fenvs.2025.1506794

CrossRef Full Text | Google Scholar

Yuan, L., Zhou, Z. J., He, W. J., Wu, X., Degefu, D. M., Cheng, J., et al. (2025). A fuzzy logic approach within the DPSIR framework to address the inherent uncertainty and complexity of water security assessments. Ecol. Indic. 170, 112984. doi:10.1016/j.ecolind.2024.112984

CrossRef Full Text | Google Scholar

Yurtkuran, S., and Pata, U. K. (2024). The effect of nuclear and fossil fuel energy R&D expenditures on environmental qualities in Canada. Sustain. Energy Techn. 68, 103872. doi:10.1016/j.seta.2024.103872

CrossRef Full Text | Google Scholar

Zamani, Z., and Tayebi, S. K. (2021). Spillover effects of trade and foreign direct investment on economic growth: an implication for sustainable development. Environ. Dev. Sustain. 24 (3), 3967–3981. doi:10.1007/s10668-021-01597-5

CrossRef Full Text | Google Scholar

Zhang, K., and Chi, G. T. (2012). Establishment of ecological evaluation indicators system based on correlation analysis-rough set theory. J. Syst. Eng. 27 (1), 119–128.

Google Scholar

Zhu, X., Che, J., Niu, X., Cao, N. N., and Liu, M. Y. (2025). Study on the effect of technological innovation on carbon emission intensity in 278 prefecture-level cities in China. Sci. Rep. 15, 14917. doi:10.1038/s41598-025-99370-5

PubMed Abstract | CrossRef Full Text | Google Scholar