Spatial Durbin Model Research Articles

Digital dividend or digital divide? – Evidence from China

ABSTRACT This paper examines the impact of the digital economy on the urban‒rural income gap. Based on Chinese provincial panel data from 2011 to 2022, this paper uses the entropy weight-TOPSIS method to construct a comprehensive index of the digital economy and explores the spatial spillover and heterogeneity of the impact of the digital economy on the urban‒rural income gap through the spatial Durbin model and partially linear functional-coefficient panel model. The results indicate that at the national level, the digital economy widens the urban‒rural income gap in the region and neighbouring regions. At the regional level, the digital economy contributes to widening the urban‒rural income gap in the eastern region and narrowing the gap in the western region. Furthermore, there is a nonlinear relationship between the digital economy and the urban‒rural income gap, and different dimensions of the digital economy have different impacts. This paper aims to provide insights and references for China and other developing countries to achieve integrated urban‒rural development with the support of the digital economy.

Spatial Correlation Structure and Influencing Mechanism of Development Level of Zero-waste Cities in China

Based on the entropy weight TOPSIS method to measure the development level of "zero-waste cities" in China from 2004 to 2021, the social network analysis method and spatial Durbin model were used to explore the spatial correlation network structure and impact mechanism of the development level of "zero-waste cities." The results showed that： ① The development level of "zero-waste cities" was generally on the decline in the whole country and the eastern and central regions. However, it was on the rise in the western regions. ② The spatial correlation of the development level of "zero-waste cities" presented a core-edge structure, with an overall upward trend in network density and a stable state in the overall network. ③ Beijing, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong were at the center and dominant position of the network. ④ Beijing, Tianjin, Shanghai, and Jiangsu belonged to the "net benefit" sector； Zhejiang, Fujian, and Guangdong belonged to the "broker" sector； and the other provinces belonged to the "net overflow" sectors. ⑤ The level of urbanization, economic development, technological innovation, foreign investment, environmental regulations, government intervention, and population size had a significant impact on the development level of "zero-waste cities" in local or neighboring provinces, respectively. The research results can provide a reference for the proposal of policies for constructing and coordinating the development of "zero-waste cities" in various regions.

Environmental shadows in the age of progress: The toll of economic globalization on China's climate

This study investigates the impact of economic globalization on China's climate using city-level data from 2005 to 2019. This study employs the Spatial Durbin Model (SDM) to reveal an inverted U-shaped relationship between globalization and climate impact, with carbon emissions initially increasing due to scale and composition effects. Surprisingly, the technology effect has limited effectiveness, particularly in second and third-tier cities. Another significant finding of this study is the observed positive correlation between carbon emissions in the previous and current periods. However, this trend leads to lower carbon emissions in neighbouring regions. The results of this study call for technology-focused policies and international collaborations to promote sustainable development in Chinese cities.

An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.

The Impact of Green Finance on Carbon Emissions: An Empirical Study Based on Spatial Metrology

Green finance plays a crucial role in driving industrial transformation and upgrading, crucial for China’s dual carbon goals. This study constructs comprehensive green finance indicators from market and policy perspectives using the entropy method. Panel data from 30 Chinese provinces (2007-2020) is analyzed via spatial Durbin model to assess the impact of green finance policies on carbon emissions, considering driving mechanisms and geographical heterogeneity. Findings show a steady increase in green finance development across Chinese provinces, with the east leading. Local green finance significantly correlates with carbon reduction, especially in the east and central regions. Policy recommendations are derived from these insights. The final policy recommendations come from these insights.

Institutional Performance and Carbon Reduction Effect of High-Quality Development of New Energy: China’s Experience and Policy Implication

Based on the policy text from 1999 to 2022, this paper quantitatively analyzes photovoltaic power, wind power and new energy policies in mainland China by keyword capture and policy strength and establishes a spatial Durbin model to study the carbon reduction effects. The results show the following: (1) The development of new energy is primarily project-based and concentrated in rural areas. (2) Financial support is a double-edged sword. (3) After the implementation of the Renewable Energy Law in 2015, the change trend in policy quantity, total policy intensity, and average policy intensity is generally consistent. (4) The increase in the strength of new energy policies has effectively reduced the intensity of provincial carbon dioxide emissions. (5) New energy policies introduced in a single region are less effective in reducing carbon emissions than joint regional regulation. Therefore, it is necessary to improve the quality and coordination of new energy policies through the effective convergence of policies and regular evaluations to enhance the positive guidance of the policies. Policy measures for new energy are refined in three areas: improving the amount and quality of new energy policies, strengthening new energy policies and establishing cooperation mechanisms for the cross-regional consumption of new energy.

Spatial spillover effect of service industry agglomeration on industrial structure upgrading: Evidence from China

The industrial structure upgrading is a significant indicator in the High-quality economic development. Despite the crucial role that the cluster of service sectors play in improving the industrial system, the research on the impact of service industry agglomeration on industrial structure upgrading is scant, especially the spatial correlation between them. This paper aims to discuss this objective. The current study employs the Spatial Durbin Model to examine the association between service industry gathering and the advancement of industrial systems based on the panel data of Chinese provincial units for the years 2000 to 2020. Findings from the empirical analysis disclose that the concentration of service-related activities in the same province plays a notable role in the optimization of the industrial framework. Moreover, service industry agglomeration demonstrates a noteworthy spatial spillover effect. The gathering of the service industry breaks through geographical constraints and facilitates the enhancement of industrial framework in adjacent regions through radiation effect. Subsequently, the enhancement of the industrial framework in neighboring areas further stimulates the local industrial configuration through spill-over impacts. This research provides a theoretical framework for analyzing the spatial correlation between the service industry group and the enhancement of the industrial framework, and presents practical reference for local authorities to foster the transformation of industrial structure.

Human activities and ecosystem health: A historical analysis of Poyang Lake

Urbanization, industrialization, land development, and other human activities have led to ecological issues such as shrinking lake wetlands, fragmented cropland, and reduced biodiversity. Taking the Poyang Lake urban agglomeration as the study area, the textual analysis method was used to analyze the typical facts of the historical succession of human activities since 1949, and then the index method and the spatial analysis method were adopted to characterize the spatial and temporal evolution of the ecosystem health and human activities in the period of 2003–2020. On this basis, the spatial spillover effects of human activities on ecosystem health were investigated based on the spatial Durbin model in four dimensions. The results of the study show that since 1949, human activities in the Poyang Lake area have gone through the management path from “agricultural retreat and fishery advancement” and “dike reclamation” to “sand ban” and “returning the lake”. Ecological protection has gone through the sublimation from “enlightenment” to “full implementation” to “strengthening and upgrading”. The ecosystem health of Poyang Lake urban agglomeration from 2003 to 2020 has been in the range of 0.37709 ∼ 0.3709 ∼ 0.3709. 0.37709 ∼ 0.48485, with the spatial characteristics of “low in the center and high in the periphery”. The city expansion and agricultural activities in the county are generally distributed in the pattern of “high center, low periphery”, while the spatial distribution of water body area is characterized by “near-lake” and “near-river”, and the newly registered enterprises show a change from “point” to “surface”. The spatial distribution of water body area is characterized by “proximity to the lake” and “proximity to the river”, and the newly registered enterprises show changes from “point” to “surface”. There is a significant spatial competition effect on the effects of city expansion on ecosystem health, while there is a significant negative spatial collaboration effect on the effects of industrial activities, and agricultural activities show a significant spatial spillover effect. The results of the study can provide more detailed empirical support for the regulation and management of ecosystem health in global lakeside urban agglomerations.

The Carbon Emissions from Public Buildings in China: A Systematic Analysis of Evolution and Spillover Effects

Public buildings, being the primary source of carbon emissions in China’s construction sector, present a pressing need for emission control. This imperative task not only ensures the sustainable progression of China’s building industry but also holds pivotal significance in the realm of global energy conservation and emission curtailment. This study analyzed the spatiotemporal evolution of carbon emissions from public buildings in China and assessed the spatial influence of related factors using a dataset covering 30 provincial units from 2006 to 2021. The analysis employed Theil’s index, Moran’s I index, standard deviation ellipse, and the spatial Durbin model. The study revealed an upward trajectory in carbon emissions from public buildings in China, although the growth rate was generally decreasing. Disparities in emission distribution among provincial units stem largely from intra-regional distinctions, notably prominent in the Low-Intensity High-Economy regions. Provincial carbon emissions from public buildings exhibited significant spatial correlation, manifesting as clusters of high–high and low–low patterns, indicative of mutual influence among adjacent areas. Additionally, the shift in carbon emission focal points from the northeast to the southwest underscored a more pronounced surge in the southwestern regions. Variables such as energy intensity, energy structure, per capita public building area, disposable income per capita, openness level, and environmental governance directly impact carbon emissions from public buildings. Among these, energy intensity, energy structure, disposable income per capita, and environmental governance also had spatial spillover effects. These findings provide a scientific reference and a foundation for policy-making, aiding local administrations in crafting strategies to mitigate carbon emissions from public buildings and foster sustainable progress.

The impact of internet development on carbon emission efficiency: Evidence from China

Modern information and communication technology presented by the Internet has become the significant driving force to promote the realization of “carbon peak and carbon neutrality” target in China. Taking China’s 30 provinces over the period 2006–2019 as research object, this paper investigates the effect of Internet development on carbon emission efficiency (CEE) and its impact mechanism by using the dynamic spatial Durbin model (DSDM) and mediation effect model. The regression results indicate that: (1) Internet development can significantly improve the carbon emission efficiency in the region and its neighboring areas. (2) The positive effect of the Internet development on CEE in long-term is greater than that in short-term. (3) Internet development can directly improve CEE, as well as indirectly increase CO2 emission efficiency by promoting technological innovation and industrial structure upgrading, as well as reducing the level of labor and capital resource mismatch. The conclusions of this paper are reliable after multiple robustness checks. Finally, we have put forward several policy implications to be conducive to achieving carbon neutrality.

Can low-carbon transportation construction improve urban green total factor productivity? A quasi-natural experiment based on 282 prefecture-level cities in China.

Total factor productivity in the green economy is a major step forward in fostering robust growth in the economy. Urban green total factor productivity (GTFP) may be increased and carbon emissions decreased through the low-carbon transformation of the transportation sector. To analyze how the pilot policy of low-carbon transportation system construction affects urban GTFP, this study builds a DID model and a spatial Durbin model using panel data from 282 Chinese cities between 2006 and 2021. The findings show that low-carbon transportation systems boost urban GTFP. In addition, the mechanism test shows that low-carbon transportation development increases urban GTFP by optimizing urban industrial structure, expediting economic agglomeration, and encouraging technological innovation. Lastly, the geographical correlation test shows that GTFP is geographically associated and that spatial spillover reduces the impact of low-carbon transportation growth on GTFP. The findings affirm the need to vigorously promote low-carbon transportation systems to aid in the achievement of the carbon neutrality target.

MODELING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION

The high crime rate will create unrest and losses for the community. One of the provinces with high crime rates is East Java. This study aims to analyze the factors that influence criminality in East Java to ensure appropriate crime prevention and control measures can be taken. The factors that potentially influence crime in East Java studied include population density, the number of poor people, unemployment, Human Development Index (HDI), Gross Regional Domestic Product (GRDP), and per Capita Expenditure, which are associated with geographical conditions in each region (regency/city) collected from BPS East Java in 2022. Meanwhile, the number of crimes is collected from the East Java Regional Police. This research uses a statistical method, namely the Spatial Durbin Model (SDM), which is a particular form of the Spatial Autoregressive Model (SAR) method with Queen Contiguity weighting by analyzing geographically (spatial processes). Based on the results of the analysis, it was found that the influential factors were unemployment, HDI, GRDP, and per Capita Expenditure, and the R-square result was obtained at 85.18%. This shows a relationship between spatial accessibility and crime, where unemployment, HDI, GRDP, and per Capita Expenditure in an area can affect regional vulnerability to crime

Digital economy empowered entrepreneurship

PurposeAs an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of the effect of the digital economy on entrepreneurship remain unanswered. This study examines how the digital economy influences entrepreneurship in China using provincial data from 2011–2020, applying convergence tests and spatial econometric models.Design/methodology/approachBased on theoretical analysis and using macro provincial data covering the period of 2011–2020, we adopt a diversified empirical analytical method and apply a combination of the convergence trend test, spatial auto correlation test, and spatial Durbin model to test the research hypotheses.FindingsFirst, there is spatial correlation between the digital economy and entrepreneurship. Second, the overall trend of China’s digital economy shows s convergence, with the whole country and the eastern region showing absolute β convergence and the whole country as well as the central and western regions showing β conditional convergence. Third, the digital economy can significantly promote entrepreneurship and has spatial spillover effects. Moreover, higher education has a negative moderating effect on the process of digital economy empowering entrepreneurship.Research limitations/implicationsStudying the spatially correlated impacts of the digital economy on entrepreneurship enhances our understanding of its contribution to economic growth. Policy-makers can use these findings to develop targeted digital infrastructure investments in lagging provinces, guide entrepreneurs to better grasp the opportunities of the digital economy, and provide support for innovation and entrepreneurship. The findings also could offer Chinese experience that can be used to guide developing countries in utilizing the digital economy to enable entrepreneurship.Originality/valueThis paper expands and enriches the analytical focus on digital economy-empowered entrepreneurship and complements the current theoretical research on the moderating effect of the digital economy in empowering entrepreneurship.

Digital Economy and Green and Low-Carbon Transformation of Land Use: Spatial Effects and Moderating Mechanisms

The green and low-carbon transformation of land use (GLTLU) is a pressing global issue that requires urgent attention. The digital economy has emerged as a new driver for the GLTLU. However, current research mainly focuses on the measurement and environmental effects of the digital economy, with less exploration of how the digital economy influences the spatial effects and regulatory mechanisms of GLTLU, particularly regarding the differential impacts and specific mechanisms at the regional level. This study uses panel data from 283 cities in China from 2011 to 2019, employing the spatial Durbin model (SDM) and the panel threshold model to examine the spatial and regulatory mechanisms of the digital economy’s impact on GLTLU. The findings reveal that digital economy promotes GLTLU not only within cities but also in surrounding regions. Robustness analyses support this conclusion. Notably, the digital economy’s positive impact on GLTLU in surrounding areas is confined to the central region of China. In contrast, the Yangtze River Delta urban agglomeration experiences a significant negative impact on GLTLU in nearby regions due to the digital economy. The study also identifies that the positive spatial spillover effect of the digital economy on GLTLU reaches its peak at a distance of 450 km. Additionally, the digital economy’s ability to promote GLTLU is contingent upon financial agglomeration levels exceeding 9.1728. Moreover, the local government’s emphasis on the digital economy and intellectual property protection enhances the digital economy’s impact on GLTLU. The promotion effect is maximized when these factors surpass the thresholds of 27.8054 and 3.5189, respectively. Overall, this study contributes to the understanding of how the digital economy influences sustainable land development, highlighting the critical role of regional factors and regulatory mechanisms in amplifying the digital economy’s positive effects on GLTLU.

Impact and Spatial Effect of Government Environmental Policy on Forestry Eco-Efficiency—Examining China’s National Ecological Civilization Pilot Zone Policy

Can government environmental policy harmonize environmental protection with economic output? We explore this issue from the perspective of forestry eco-efficiency, using China’s National Ecological Civilization Pilot Zone Policy (NECP), an environmental policy promulgated by the government of China, as the subject of this study. The study introduces forestry eco-efficiency as an indicator to assess the balance between economic development in the forestry sector and environmental conservation. The indicator, grounded in sustainable development theory, employs a super-efficiency SBM model that includes undesirable outputs to evaluate efficiency. Additionally, we empirically analyze the impact of NECP on forestry eco-efficiency by using the difference-in-difference (DID) model with provincial panel data from 2011 to 2020. Ultimately, we analyze the effects of spatial spillover by employing the spatial Durbin model (SDM). Our study yields the following conclusions. (1) In this paper, through hotspot clustering analysis, forestry eco-efficiency in each province is categorized into three categories: effective, semi-effective and ineffective. Our findings suggest that China’s average forestry eco-efficiency falls into the ineffective category, highlighting the need to optimize resource allocation within the sector. (2) NECP significantly enhances forestry eco-efficiency, with robust findings across various stability tests. Thus, implementing government environmental policies can have a multiplier effect on forestry, i.e., it can synergize its economic development with environmental protection. (3) In provinces with a strong ecological foundation, the NECP significantly enhances forestry eco-efficiency. However, in other provinces, the improvement is only moderate. Furthermore, while the NECP has a substantial positive impact in the eastern region, it has yet to show a discernible effect in other regions. (4) The positive impacts of NECP implementation on forestry eco-efficiency have spatial spillover effects due to demonstration effects and comparative advantages.

The impact of digital inclusive finance on environmental pollution: A case study of air pollution.

This study delves into the impact of digital inclusive finance on environmental pollution, with a specific focus on air pollution. Utilizing data from 265 Chinese cities, advanced econometric methods such as the bi-directional fixed effects model, threshold model, spatial Durbin model, and multi-period difference-in-differences model are employed, incorporating a variety of control variables. The empirical findings indicate that digital inclusive finance significantly reduces air pollution. This mechanism chiefly operates through enhancing public environmental consciousness and fostering green technological innovation. The study also uncovers the spatial spillover effect and non-linear characteristics of digital inclusive finance on air pollution, along with its interactive effects with specific policies (e.g., smart city pilot policies and the "major protection, no major development" initiative). Moreover, heterogeneity analysis reveals regional variations in the environmental effects of digital inclusive finance. These insights provide a novel perspective on the relationship between financial technology and environmental protection and offer crucial guidance for policymaking.

Spatial dependence and poverty factors: A study of Pantura Regions in East Java Province, Indonesia

Poverty is still a problem in various countries in the world, so eradicating poverty and hunger by 2030 is the backbone of the goals of the sustainable development agenda. Poverty, which is the main goal of the Millennium Development Goals (MDGs), is again the main goal in the Sustainable Development Goals (SDGs). This research aims to determine the spatial dependence of poverty in the Pantura Region of East Java Province and to determine the influence of economic growth, education, unemployment, natural resources, health and accessibility on poverty in the Pantura Region of East Java Province. This research uses a quantitative approach, with data analysis methods using Moran’s index analysis, and spatial panel regression by selecting the best model from four models, namely the spatial autoregressive model (SAR), the spatial error model (SEM), the spatial Durbin model (SDM), and spatial autoregressive combined (SAC) model. The research results show that there is spatial interaction between coastal areas through endogenous and exogenous interactions between independent variables. Based on the selection of the best model, the spatial Durbin model (SDM) was selected for the Pantura Region. Economic growth has a negative effect on poverty. Education has a negative effect on poverty. Unemployment has no effect on poverty. Natural resources have a positive effect on poverty. Health has no effect. Accessibility has a negative effect on poverty in the Pantura Region. This research provides a new and important contribution in understanding the spatial patterns of poverty and the factors that influence it in the Pantura Region, which is a strategic and vulnerable area in East Java. This research also faces several limitations and challenges, such as data quality, model assumptions, and spatial validity. Future research could expand this analysis using more complete data, more flexible models, and more sophisticated techniques.

Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

Linkages between digital economy and ecosystem services: Revealing the spatiotemporal relationship based on U-shaped effect

This study employs geographically and temporally weighted regression and spatial Durbin models to investigate the impact of digital economy index (DEI) on ecosystem service value (ESV). Estimates of DEI are obtained from night-time light data, and ESV is assessed by the equivalent factor method. The findings indicate significant spatial agglomeration of both DEI and ESV in China. Overall, the impact of DEI on ESV exhibits a U-shaped effect: initially restrained, then promoted. Moreover, the analysis detects spatiotemporal heterogeneity in the DEI-ESV interaction. Spatially, higher DEI positively impacts lower ESV in eastern China, while lower DEI negatively impacts higher ESV in western China. Temporally, most cities in eastern China, unlike those in western China, surpassed the U-shaped curve's inflection point during the study period. Furthermore, the number of cities exceeding the inflection point has increased, amplifying the dual positive and negative impacts of DEI on ESV over time. By revealing the multi-staged and geographically diverse impact of DEI on ESV, this study helps to address the environmental sustainability challenges posed by the rapid advancement of digital technologies.

Spatiotemporal characteristics of ecological resilience and its influencing factors in the Yellow River Basin of China

Maintaining and improving ecological resilience is of great practical significance for the Yellow River Basin to reduce potential ecological risks and deliver sustainable development. Based on the essential characteristics of evolutionary resilience, this paper developed an ecological resilience index system of “resistance-recovery-reconstruction-renewal” and calculated the ecological resilience of 75 prefecture-level cities in the Yellow River Basin from 2007 to 2021 with the improved TOPSIS method. Then the spatiotemporal evolution characteristics of ecological resilience were analyzed using the gravity center-standard deviation ellipse, Dagum Gini coefficient decomposition, and spatial autocorrelation analysis. Furthermore, the dynamic spatial Durbin model (DSDM) was used to investigate the influencing factors of ecological resilience. The main results are as follows: (1) The ecological resilience of the Yellow River Basin showed an overall fluctuating upward trend, and the average annual growth rate in the downstream region was larger than in the upstream and midstream regions. (2) Cities with similar levels of ecological resilience were distributed in a “large settlement, small scattered” pattern. The center of gravity shifted to the southeast, and the spatial distribution exhibited a “northwest-southeast” pattern and a trend towards an “east–west” pattern. The primary source of spatial differences was the intensity of transvariation. (3) The ecological resilience in the Yellow River Basin showed significant spatial clustering, with the H–H clustering area shifting from the Hubao-Eyu urban agglomeration to the Shandong Peninsula urban agglomeration, and the L–L clustering area mainly distributed around the Central Plains city cluster. (4) The ecological resilience of the Yellow River Basin exhibited significant snowball, spillover, and siphon effects in time, space, and space-time dimensions, respectively. In the short and long term, population density and openness significantly positively affected the ecological resilience of local and surrounding cities. Urbanization had a long-term effect on ecological resilience without a short-term effect. GDP per capita and industrial structure only imparted a significant positive influence on local ecological resilience. The negative spatial spillover of the intensity of financial investment in technological innovation gradually turned into a positive effect.