Spatial Correlation Research Articles

Understanding the spatial patterns of atmospheric ammonia trends in South Asia

Ammonia (NH3) is the most abundant alkaline gas in the atmosphere, mainly emitted by agricultural activities. NH3 readily reacts with other atmospheric acidic pollutants, such as the oxidation products of sulfur dioxide (SO2) and nitrogen oxides (NOₓ), to create fine particulate matter, which has far-reaching effects on human health and ecosystems. Here, we investigated long-term atmospheric NH3 trends in South Asia (SA) using satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI). We analyzed 15 years (2008–2022) of IASI-NH3 retrievals against climate, biophysical, and chemical variables using an ensemble of multivariate statistical methods to identify the major factors driving the observed patterns in the region. Trend analysis of IASI-NH3 data reveals a significant rise in atmospheric NH3 over 51 % of SA plains, but a downward trend over 31 % of the region. Spatial correlation analysis reveals that biophysical factors, representing cropland expansion and agriculture intensification, have the highest positive correlation over 56 % of SA plains experiencing positive NH3 trends. However, our results reveal that the chemical conversion of NH3 to ammonium compounds, driven by the positive trends in NOₓ and SO2 pollution, is driving the apparently declining trend of NH3 in the other regions. Our results provide important insights into the NH3 trends detected by satellite data and can better inform the policy design aimed at reducing NH3 emissions and improving air quality for developing regions of the world.

Correlation structures of statistically isotropic stiffness and compliance TRFs through upscaling

This paper reports a procedure to develop random fields of material properties on a mesoscale level, coarser than the microscale level of heterogeneous material microstructure. Since the anisotropy of properties at the mesoscale level is unavoidable, tensor-valued random fields (TRFs) need to be constructed. The construction satisfies three criteria: (i) the passage from the micro to mesoscale must be conducted according to micromechanics, (ii) any anisotropic properties must be grasped, and (iii) full (spatial) correlation structure must be accounted for. The construction is illustrated in the example of a linear elastic microstructure modeling a planar interpenetrating phase composite (IPC), where each phase is interconnected throughout the microstructure. The most general representation of a statistically homogeneous and isotropic correlation structure of the TRFs of stiffness and compliance is employed. Four material functions of the representation are determined through scale-dependent homogenization, which grasps any auto- and cross-correlations within/among different components of TRFs. The Gaussianity of the TRFs is also assessed with a finding that, overall, the smaller is the mesoscale and the more pronounced is the microstructural randomness, the more non-Gaussian are the mesoscale TRFs.

Resilience evaluation and spatio-temporal evolution analysis of China's science and technology financial ecosystem

ABSTRACT This paper discusses the resilience of science and technology (sci-tech) financial ecosystem. It constructs an evaluation system from four dimensions: resistance, adaptability, recovery and innovation. The data are from 29 provinces in China from 2010 to 2020. It is evaluated using entropy weights–TOPSIS and dynamic evaluation model. The spatial evolution law is revealed by spatial autocorrelation analysis. The results show that: (1) The overall resilience level of sci-tech financial ecosystem is not high. Its evolution and development are generally in a relatively stable state. It has significant regional heterogeneity, showing a distribution pattern of ‘high east and low west’. (2) It has significant spatial positive correlation. The structure of the sci-tech financial ecosystem in the eastern region is highly complex, most provinces are in high–high agglomeration area. Provinces in other regions are mainly in low–low agglomeration area. Only a few provinces have experienced changes in the spatial distribution. (3) The dynamic value in the eastern region shows polarisation characteristics. Although the static value of resilience in the central and western regions is low, the dynamic evaluation value in some provinces in the central and western regions is high. The subsequent growth momentum of resilience in the north-eastern region is insufficient.

Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Exploring the influencing factors of scrub typhus in Gannan region, China, based on spatial regression modelling and geographical detector

Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants. However, in order to effectively eradicate scrub typhus, it is crucial to identify the specific factors that contribute to its incidence at a detailed level. Therefore, the objective of our study is to identify these influencing factors, examine the spatial variations in incidence, and analyze the interplay of two factors on scrub typhus incidence, so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals. Additionally, spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021. The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation. Among the global spatial regression models, the spatial lag model was found to be the best fitting model (log likelihood ratio = −319.3029, AIC = 666.6059). The results from the SLM analysis indicated that DEM, mean temperature, and mean wind speed were the primary factors influencing the occurrence of scrub typhus. For the local spatial regression models, the multiscale geographically weighted regression was determined to be the best fitting model (adjusted R2 = 0.443, AICc = 726.489). Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan, while the southern region was found to be more susceptible to scrub typhus due to mean wind speed. The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index. Additionally, the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus (q = 0.357). This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus; and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships. The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.

Accounting for continuous correlations among alternatives in the context of spatial choice modelling using high resolution mobility data

Accounting for similarity among alternatives is important for having unbiased estimates and behaviourally reasonable substitutions. Capturing similarity in a spatial context is a challenging task and the common approach of discretising space into a number of disjoint nests generally leads to uncaptured spatial correlations. On the other hand, relying on more complex error structures quickly leads to computational issues. In the present paper, we propose an alternative approach, where a Cross-Nested Logit (CNL) modelling framework with a flexible correlation structure is used, where space is treated as continuous, while the allocation can be parameterised based on a range of similarity factors. The proposed structure is applied in the context of mode and destination choices of shopping trips using a smart-phone GPS panel survey from Leeds, UK. Results indicate that in addition to the improvements in model fit, the proposed CNL specification is able to uncover interesting findings about individual mobility behaviour.

Spatial spillover effects and correlation network analysis of green construction development efficiency in China

Green construction has been implemented globally to address resource depletion and environmental degradation. The effectiveness of green construction development depends on the efficiency of resource utilization and the common progress of all regions. However, existing studies have paid limited attention to green construction development efficiency (GCDE) and its spatial correlation. This study uses the super-efficiency slacks-based measure (Super-SBM) model, spatial econometric models, and social network analysis (SNA) method to reveal the spatial distribution, spatial spillover effects, and spatial correlation of GCDE across 30 provinces in China. The results indicate significant disparities in GCDE among regions, with the eastern region exhibiting considerably higher GCDE than the western and northeastern regions. There is evidence of spatial spillover effects, and the spatial correlation of GCDE is strengthening. Provinces with high GCDE occupy central positions in the GCDE network and exert spillover effects on other provinces, while underdeveloped provinces have opportunities to benefit from regional cooperation. The findings provide policy insights for the implementation and enhancement of green construction development actions. Moreover, they provide Chinese experience for green construction practices in other areas of the world.

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model

Carbon dioxide (CO2) serves as a crucial greenhouse gas that traps heat and regulates the Earth's temperature. High spatiotemporal resolution CO2 estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO2 (XCO2) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO2 by comprehensively considering the complex relationships between XCO2 and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO2 dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO2 across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO2 dataset for China is made publicly available at [https://doi.org/10.6084/m9.figshare.25272868], offering substantial potential for fine-scale carbon research.

Variogram models reconstruction for damaged ERT profiles

Reconstructing signals which are embedding spatial patterns such as Electrical resistivity tomography, is a process that should require to reconstruct first the spatial correlation of the damaged signals. This paper proposes an approach that implements an Unstationary Kriging (UNK) to reconstruct the experimental variogram of a damaged synthetic pseudo section within a set of pseudo sections coming from the same survey. We used and compared 02 other simple methods which are Linear Regression (LR) and Ordinary Kriging (OK), to test the hypothesis we formulate to link the experimental variograms coming from the same ERT survey. We implemented the UNK using Discrete Fourier Transforms (DFT) for trend modeling. After an implementation of the hybrid process (UNK) on 02 sets of data which are synthetics, we observed that the LR and the UNK methods present an interest. They both reconstruct signals with a +90% rate of accuracy, but when there is no structure or spatial correlation within the data, the LR is unstable. DFT was also tested alone for reconstruction but was mainly used in this study to help in computing the trends for each set of variographic signals. In the end, we conclude on an evidence that is: the proposed hybrid process is a promising way to reconstruct variographic signals, since we can improve it after more time invested to dig deep into the modeling of each of his components.

An Improved Spatiotemporal Time Series Modelling Procedure with Application to Forecasting of Solar Radiation

The demand for energy and associated services to meet sustainable agricultural and economic growth and improve human health and lifestyle is increasing day by day. Hence, there is a need for systematic and scientific prediction of solar and other renewable sources of energy to meet these requirements. The main purpose of this study is to propose a hybrid Space-Time Autoregressive Moving Average Artificial Neural Network (STARMA-ANN) model for the precise and accurate forecasting of solar radiation for better planning and policy making. This approach has been implemented at seven geographical locations of Bihar in India. Spatial weight matrices have been used to describe all seven geographical locations and incorporated into the STARMA model to reflect the spatial and temporal correlation. To deal with nonlinear dynamics in the spatiotemporal data, ANN technique has been applied on residuals of the fitted STARMA model. The results have demonstrated that the proposed hybrid model performs better prediction accuracy than using conventional STARMA model, especially for spatiotemporal data with nonlinear characteristics of solar radiation.

Spatial insights for sustainable transportation based on carbon emissions from multiple transport modes: A township-level case study in China

Understanding the CO2 emissions and influencing factors of travelers' multiple modes can provide direction for energy conservation and emission reduction, which is of great significance for developing sustainable cities. Previous studies focused on the CO2 emissions of the transportation sector or individual modes. Which has overlooked the variations of emissions within the transport system. Hence, this study focuses on multiple modes (i.e., car, subway, bus, and bike) in the township in the Guangdong-Hong Kong-Macao Greater Bay Area. This study proposes a framework for exploring the spatial autocorrelation of urban transport emission structure based on ratios (i.e., CO2 emissions from each mode divided by total emissions) and key factors by combining spatial econometric model (i.e., Moran's I index and Spatial Error Model) and machine learning model (i.e., Random Forest and SHAP model). In addition, the spatial autocorrelation of ratios at different spatial scales is investigated. The results indicate the high spatial dependence in the ratios from each transport mode and Moran's I indices for four ratios are 0.883, 0.886, 0.706, and 0.776, respectively. In addition, subway and car ratios exhibit a negative spatial correlation (−0.798), and subway and bike show a positive correlation (0.570). Population density, road length, and land use diversity are the key drivers of CO2 emission ratios and have different effects on various transport modes. Furthermore, as the spatial scales expand from townships to distinct and city, the spatial autocorrelation of the ratios decreases. This study could provide policy implications for optimizing urban transport strategies and reducing CO2 emissions.

Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network

In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method.

Spatial and statistical predictors of voter purge rates in Michigan

AbstractObjectiveVoter purges can protect election integrity by ensuring deceased or moved individuals are removed from election rolls. But they have been used to diminish voting power of marginalized groups, often by anti‐majoritarian forces seeking to undemocratically retain power. Little research has examined “who gets purged?” at the state level, especially with respect to local‐level differences.MethodsWe leverage Michigan's voter purge database from 2014 to 2018. Records are geocoded to their exact address, and a range of spatial correlates are identified to answer the above question. We then used generalized structural equation modeling to incorporate patterns of mobility and mortality.ResultsInitial results showed that more Democratic leaning areas, denser/more urban areas, and areas with more Black residents had higher purge rates. Notably, while these mediation effects were significant, racial composition and median income (i.e. more black and poorer communities) remained a significant factor in voter purge rates. These results suggest a potentially troublesome underlying element in Michigan's pattern of voter purges. We suggest this is an important first step in future research in other states and with subsequent databases, which can help strengthen the case that purges may be being used to uphold discriminatory and anti‐majoritarian goals.

Light-weight neural network for intra-voxel structure analysis.

We present a novel neural network-based method for analyzing intra-voxel structures, addressing critical challenges in diffusion-weighted MRI analysis for brain connectivity and development studies. The network architecture, called the Local Neighborhood Neural Network, is designed to use the spatial correlations of neighboring voxels for an enhanced inference while reducing parameter overhead. Our model exploits these relationships to improve the analysis of complex structures and noisy data environments. We adopt a self-supervised approach to address the lack of ground truth data, generating signals of voxel neighborhoods to integrate the training set. This eliminates the need for manual annotations and facilitates training under realistic conditions. Comparative analyses show that our method outperforms the constrained spherical deconvolution (CSD) method in quantitative and qualitative validations. Using phantom images that mimic in vivo data, our approach improves angular error, volume fraction estimation accuracy, and success rate. Furthermore, a qualitative comparison of the results in actual data shows a better spatial consistency of the proposed method in areas of real brain images. This approach demonstrates enhanced intra-voxel structure analysis capabilities and holds promise for broader application in various imaging scenarios.

Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development

Since 2010, the Chinese economy has transitioned from a high-speed growth model to a high-quality development model. During this period, the logistics industry has witnessed rapid growth, leading to significant carbon emissions and posing severe threats to the ecological environment. To investigate the spatiotemporal variations in carbon emissions in China's logistics industry, we conducted a correlation analysis using Moran's I index and a bivariate spatial autocorrelation model from 2010 to 2021. Additionally, we employed a geographically and temporally weighted regression model （GTWR） to examine the spatial heterogeneity of factors influencing provincial-level logistics-related carbon emissions. The results indicated that over the study period, there was a shift from insignificant spatial relationships to significant positive spatial correlations among provincial-level logistics carbon emissions in China. Furthermore, varying degrees of spatial clustering were observed. The findings regarding factor heterogeneity revealed that freight turnover volume, per capita GDP of the logistics industry, and infrastructure level exhibited positive spatial correlations with logistics-related carbon emissions, whereas energy intensity showed negative spatial correlations with such emissions. Comparing the results from the geographically weighted regression （GWR） and ordinary least squares regression （OLS）, it was evident that the adjusted R-squared values for the OLS, GWR, and GTWR models were 0.541, 0.567, and 0.838, respectively. This suggests that our adopted GTWR model provided a superior fit and offered better explanations for spatiotemporal heterogeneity between various influencing factors and logistics-related carbon emissions. These research findings can serve as valuable references for formulating province-specific strategies to reduce carbon emissions within China's economy under its high-quality development context.

Spatial Correlation of Land Use Carbon Budget Based on Social Network Analysis： A Case Study of Chongqing Metropolitan Area

Clarifying the temporal and spatial changes in the carbon budget in the Chongqing metropolitan area and exploring the spatial correlation of land use carbon budget are of great significance for realizing the regional "double carbon" goal. Using 21 districts and counties in Chongqing metropolitan area as the research scale, the IPCC inventory method, carbon emission coefficient method, Gini coefficient, gravity model, and social network analysis were used to estimate the net carbon emissions from land use in Chongqing metropolitan area from 2000 to 2020, and the spatial correlation of the carbon budget was obtained. The results revealed that： ① In the past 20 years, the carbon budget of the Chongqing metropolitan area showed an overall upward trend, with an average annual growth rate of 2.83%, and the spatial distribution was "higher in the north and south, higher in the east and west, and lower in the middle." ② During the past 20 years, the spatial difference of net carbon emissions in the Chongqing metropolitan area became highly average, and the overall Gini coefficient decreased by 11.42%, whereas the intra-group difference was the largest in key development zones. ③ In the past 20 years, the overall structure of the spatial correlation network of land use carbon budget in the Chongqing metropolitan area has become stable and complex, and the network density and network correlation number have increased by 0.43 and 180, respectively, the network correlation degree has increased to 1, and the network health degree has improved. ④ In the individual network structure of land use carbon budget in the Chongqing metropolitan area, the degree centrality of each district and county has increased, and the increase in the central urban area was the most significant, with an increase of 81, whereas the decline in intermediate centrality and proximity centrality has promoted the regional coordinated development and integration process in the metropolitan area. ⑤ In the past 20 years, the density of net carbon emissions in the Chongqing metropolitan area has increased as a whole, with the density of the core area increasing by 0.35 and the density of the core-edge area increasing by 0.34. By exploring the change and spatial correlation of land use carbon budget in the Chongqing metropolitan area, this study clarified the spatial distribution difference of carbon budget and provided support for regional green development.

Carbon Emission Effects of Spatially Connected Networks in the Digital Economy： Dynamic Evolution and Mechanism of Action

In the context of the "double carbon" goal and the construction of the "East Counts, West Counts" project, this study investigated the characteristics of the spatial and temporal evolution of the digital economy spatial correlation network, and the effects and mechanisms of carbon emission reduction and carbon rebound effects by selecting data at the level of 254 prefectural-level cities from 2011 to 2021 and using the UCINET software and empirical econometric modeling. The results indicated that： The spatial network of the digital economy represented the development pattern of multiple synergies and had the characteristics of non-equilibrium spatial evolution in geographic regions. Secondly, the spatial network of the digital economy in the sample selection period had a positive contribution to carbon emission reduction, and it led to the carbon rebound effect, which was still valid after the robustness test. Furthermore, the spatial correlation network of the digital economy could reduce carbon emissions by promoting the energy efficiency effect under technological progress and led to the carbon rebound effect. Finally, the empirical results were heterogeneous under the four dimensions of geographic region, administrative level, urban environmental governance objectives, and resource endowment.

Promotion or inhibition? Exploring the influence of the digital economy on urban-rural integration: A case study of yellow River basin

The digital economy actively disrupts the "urban depression effect" and the "wealth gap" between urban and rural areas, playing a crucial role in advancing urban-rural integration. Based on this, a scientifically constructed performance evaluation indicator system for the digital economy and urban-rural integration was developed. Using the Yellow River Basin as an empirical case study area, the projection pursuit model, simulated annealing algorithm and spatial Durbin model were employed to explore the impact of the digital economy on urban-rural integration in the Yellow River Basin from 2011 to 2021. The findings reveal the following: 1) The degree of digital economy and urban-rural integration display a characteristic pattern resembling that of a downstream > midstream > upstream watershed, showcasing discernible spatial correlation features between them. 2) The digital economy in the Yellow River Basin directly fosters urban-rural integration development. Government behavior stand out as the most influential factor in promoting urban-rural integration among the control variables. 3) The impact of the digital economy on urban-rural integration in the Yellow River Basin displays regional heterogeneity, with the most pronounced effects observed in downstream areas. Moreover, the spatial spillover effect of the digital economy exhibits a distinct "good-neighborliness" characteristic. 4) The digital economy in the Yellow River Basin substantially advances urban-rural integration development through the adjustment effect of industrial structure rationalization and the mediating effect of entrepreneurial activity. These research findings shed light on the impact and spatial spillover pathways of the digital economy on urban-rural integration, aligning with the national strategy of ecological protection and high-quality development in the Yellow River Basin.

Spatial correlation network and influencing factors analysis of supply efficiency of basic public service (SEBPS) in rust belt regions of China: An empirical study from Northeast China

Equalizing basic public service is crucial for achieving social fairness and justice. Promoting the improvement of public service can be achieved through the improvement of supply efficiency and cross-regional coordinated development. We employed the Super-SBM model, modified gravity model, and social network analysis method to empirically study the spatial correlation network characteristics of supply efficiency of basic public service (SEBPS) based on the data of 34 prefecture-level cities in the three provinces of Northeastern China from 2011 to 2020. The results revealed the overall efficiency of SEBPS in the study area is relatively low, and the supply efficiency is polarized. There is significant non-equilibrium between regions, and the regional difference moment gradually expands over time. There exists a strong spatial correlation between SEBPS in each city, and the overall correlation effect is weak but the network stability has improved. Each city had different roles and statuses in the spatial correlation network, creating a hierarchical structure. The level of geospatial proximity had a positive impact on the spatial correlation and spillover of the network. The level of economic development and infrastructure configuration hurt the network, and there is obvious heterogeneity between cities. The differences in industrial structure, opening up level, urbanization level, population density, the level of fiscal decentralization and technological innovation and personal income level among cities affected the SEBPS network. This study not only provides a new network research perspective and theoretical foundation for public service policies in China's rust belt region but also serves as a reference for urban development in similar regions.

Effect of government environmental attention on green transformation: Empirical analysis from a spatiotemporal perspective in China

As the primary policymaker and implementer, the government plays an essential role in driving regional green transformation development (GTD). The study of the impact mechanisms of government environmental attention (GEA) on GTD can offer a scientific basis and reference for policy-making and regional governance. However, the varying focus of local governments on environmental issues and the externalities of environmental governance underscore the complexities in the influence of GEA on GTD. This paper proposes a method that integrates multi-source data to explore the impact of GEA on GTD from a spatiotemporal perspective. A text mining framework is first proposed to provide a more accurate measurement of the GEA, and the geographically and temporally weighted regression model is used to analyze the spatiotemporal characteristic and mechanism of GEA's impact. Additionally, the spatial correlation effect is explored based on the gravity model. The results indicate that the level of GTD in China demonstrates an increasing trend with an average annual growth rate (AAGR) of 3.22%, while the variance between provinces is also increasing. GEA has a significant positive effect on GTD (R2 = 0.866), with an average growth rate of 18.1% following the implementation of major environmental policies. The spatial distribution pattern of GEA's impact shows the characteristics of “central > eastern > western > northeastern”. GEA promotes GTD by increasing environmental governance expenditure and enhancing public environmental services, while the transmission pathway of promoting green innovation has not yet worked. The spatial correlation and spillover effects of GEA have shown an overall increasing trend, with the AAGR of 8.92% in the gravity value. Inter-provincial cooperation can promote GTD, and certain collaborative clusters among provinces have been formed. This paper provides novel insights into the theoretical connection between GEA and GTD. The research findings can serve as a reference for the government to propose targeted policies for promoting regional green transformation.