Spatial Weight Matrices Research Articles

The spatial spillover impact of artificial intelligence on energy efficiency: Empirical evidence from 278 Chinese cities

Within the framework of global sustainable development, improving energy efficiency (EE) is recognized as a crucial strategy to address environmental challenges and foster economic growth. This research employs the Spatial Durbin Model to investigate the spatial spillover effects of advancements in artificial intelligence (AI) technology on EE across 278 Chinese cities. The analysis reveals several key findings: first, the advancement of AI technology significantly enhances urban EE and generates a positive spatial spillover effect across different spatial weight matrices; second, there is a nonlinear, inverted U-shaped relationship between AI levels and urban EE; third, industrial structure upgrades play a significant moderating role in the impact of AI technology on urban EE. These insights not only illuminate the complex role of AI in improving regional EE but also provide critical recommendations for policymakers aiming to enhance urban EE and achieve sustainable development goals.

The Spatial Spillover Effects of Transportation Infrastructure on Regional Economic Growth—An Empirical Study at the Provincial Level in China

This study examines the spatial spillover effects of transportation infrastructure on regional economic growth, utilizing panel data from 31 provincial-level administrative divisions in China from 2003 to 2022. Using the spatial Durbin model (SDM) and three distinct spatial weight matrices—0–1 adjacency, spatial economic–geographical nested, and GDP-based economic distance matrices—this study comprehensively analyzes the multifaceted impacts of transportation infrastructure. The results show that transportation infrastructure significantly promotes economic growth in both local and neighboring regions across all spatial weight matrices. The total effect is most pronounced in geographically proximate regions, with a coefficient of 7.845 (p < 0.01). Regions with similar economic development levels also show strong collaborative effects, with a coefficient of 2.074 (p < 0.01), although the marginal effect of transportation infrastructure diminishes. Furthermore, adjustments in industrial structure and innovation inputs demonstrate a short-term inhibitory effect on economic growth, highlighting the need for synchronized development of transportation infrastructure alongside industrial and innovation policies. This study incorporates multi-dimensional spatial weight matrices to systematically reveal the direct and indirect impacts of transportation infrastructure on regional economies, providing essential empirical support for regional coordination and infrastructure investment policies. The findings offer valuable insights for infrastructure planning in other regions, particularly in formulating policies that promote cross-regional economic cooperation and optimize resource allocation.

High-resolution full-coverage ozone (O3) estimates using a data-driven spatial random forest model in Beijing-Tianjin-Hebei region, China

The Beijing-Tianjin-Hebei (BTH) region is severely polluted by ozone (O3). Accurate O3 estimates are essential for identifying high-polluted zones and developing targeted interventions to relieve the burden of diseases. Although many studies have estimated high-resolution O3 concentrations in BTH, the estimation accuracies are still insufficient. In this study, we incorporated data-driven spatial weight matrices (DDWs) into a random forest (RF) model to fully utilize both the spatial homogeneity and heterogeneity of maximum daily 8-h ozone concentration (MDA8O3), and obtained full-coverage MDA8O3 concentrations at 1km×1km in BTH from 2014 to 2022. DDW-RF exhibited satisfactory accuracy (10-fold cross-validation R2=0.937, RMSE=13.919 μg/m3). Overall O3 level presented a spatial pattern of lower in the north and higher in the southeast and showed a distinct temporal trend, i.e., first increasing and then decreasing during 2014-2021 and increasing slightly in 2022. The accurate MDA8O3 estimates indicates that more attention and resources should be poured into the areas adjacent to Bohai Rim, Shandong and Henan. Regulated operation of factories under specific meteorological conditions and upgrading industrial structure and production modes are recommended to mitigate the formation of O3 precursors and reduce O3 generation. Our findings provide evidence and reference for environmental cleaning policies and targeted interventions.

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.

Analysis of multiple gravitational influences on interregional population migration in South Korea

ABSTRACT Migration is the result of complex interactions between different socio-economic factors in different areas. This study analyses the multiple gravitational influences on interregional migration in South Korea. Spatial econometric models are employed to incorporate spatial interactions between multiple socio-economic gravitational influences into spatial weight matrices. Using Bayesian posterior probabilities to evaluate models incorporating different gravitational influences, this study finds that the primary driver of migration is the difference in human capital conditions between areas. Furthermore, it suggests that the simultaneous consideration of differences in both economic and human capital conditions across areas increases the attractiveness of certain areas for migration.

Spatial Nonlinear Effects of Street Vitality Constrained by Construction Intensity and Functional Diversity—A Case Study from the Streets of Shenzhen

As an important part of urban vitality, street vitality is an external manifestation of street economic prosperity and is affected by the built environment and the surrounding street vitality. However, existing research on the formation mechanism of street vitality focuses only on the built environment itself, ignoring the spatial spillover effect on street vitality. This study uses 5290 street segments in Shenzhen as examples. Utilizing geospatial and other multisource big data, this study creates spatial weight matrices at varying distances based on different living circle ranges. By combining the panel threshold model (PTM) and the spatial panel Durbin model (SPDM), this study constructs a spatial autoregressive threshold model to explore the spatial nonlinear effects of street vitality, considering various spatial weight matrices and thresholds of construction intensity and functional diversity. Our results show the following: (1) Street vitality exhibits significant spatial spillover effects, which gradually weaken as the living circle range expands (Moran indices are 0.178***, 0.160***, and 0.145*** for the 500 m, 1000 m, and 1500 m spatial weight matrices, respectively). (2) Construction intensity has a threshold, which is 0.1466 under spatial matrices of different distances. Functional diversity has two thresholds: 0.6832 and 2.2065 for the 500 m spatial weight matrix, and 0.6832 and 1.4325 for the 1000 m matrices, and 0.6832 and 1.2724 for 1500 m matrices. (3) As an international metropolis, street accessibility in Shenzhen has a significant and strong positive impact on its street vitality. This conclusion provides stakeholders with spatial patterns that influence street vitality, offering a theoretical foundation to further break down barriers to street vitality.

Bayesian Spatio-Temporal Multilevel Modelling of Patient-Reported Quality of Life following Prostate Cancer Surgery.

Globally, prostate cancer is the second leading cause of cancer deaths among males. It is the most commonly diagnosed cancer in Australia. The quality of life of prostate cancer patients is poorer when compared to the general population due to the disease itself and its related complications. However, there is limited research on the geographic pattern of quality of life and its risk factors in Victoria. Therefore, an examination of the spatio-temporal pattern and risk factors of poor quality of life, along with the impact of spatial weight matrices on estimates and model performance, was conducted. A retrospective study was undertaken based on the Prostate Cancer Outcome Registry-Victoria data. Patient data (n = 5238) were extracted from the Prostate Cancer Outcome Registry, a population-based clinical quality outcome assessment from 2015 to 2021. A Bayesian spatio-temporal multilevel model was fitted to identify risk factors for poor quality of life. This study also evaluated the impact of distance- and adjacency-based spatial weight matrices. Model convergence was assessed using Gelman-Rubin statistical plots, and model comparison was based on the Watanabe-Akaike Information Criterion. A total of 1906 (36.38%) prostate cancer patients who had undergone surgery experienced poor quality of life in our study. Belonging to the age group between 76 and 85 years (adjusted odds ratio (AOR) = 2.90, 95% credible interval (CrI): 1.39, 2.08), having a prostate-specific antigen level between 10.1 and 20.0 (AOR = 1.33, 95% CrI: 1.12, 1.58), and being treated in a public hospital (AOR = 1.35, 95% CrI: 1.17, 1.53) were significantly associated with higher odds of poor quality of life. Conversely, residing in highly accessible areas (AOR = 0.60, 95% CrI: 0.38, 0.94) was significantly associated with lower odds of poor prostate-specific antigen levels. Variations in estimates and model performance were observed depending on the choice of spatial weight matrices. Belonging to an older age group, having a high prostate-specific antigen level, receiving treatment in public hospitals, and remoteness were statistically significant factors linked to poor quality of life. Substantial spatio-temporal variations in poor quality of life were observed in Victoria across local government areas. The distance-based weight matrix performed better than the adjacency-based matrix. This research finding highlights the need to reduce geographical disparities in quality of life. The statistical methods developed in this study may also be useful to apply to other population-based clinical registry settings.

Optimizing Spatial Weight Matrices in Spatial Econometrics: A Graph-Theoretic Approach Based on Shortest Path Algorithms

Optimizing Spatial Weight Matrices in Spatial Econometrics: A Graph-Theoretic Approach Based on Shortest Path Algorithms

The Impact of Official Promotion Incentives on Urban Ecological Welfare Performance and Its Spatial Effect

Economic growth, environmental protection, and welfare improvement are closely related to local officials. Therefore, exploring the paths that affect ecological welfare performance (EWP) from the perspective of official promotion incentives has certain practical significance. EWP is an indicator that measures the welfare relationship between ecological resource inputs and outputs, reflecting the sustainable development status of a region. First, considering the background of the dual carbon goals, the EWPs of 284 cities from 2007 to 2020 were measured by constructing an indicator system and using the super-SBM-DEA method. Second, by constructing a theoretical framework of “official promotion incentives-fiscal responsiveness-EWP”, we empirically tested the impact and mechanism of official promotion incentives on urban EWP; finally, based on regional and official age heterogeneity, we explored the differential impacts of official promotion incentives on urban EWPs. The results show that official promotion incentives inhibit the improvement of urban EWP by reducing financial responsiveness; in terms of spatial spillovers, under three types of spatial weight matrices, promotion incentives for officials in neighboring areas can significantly improve the EWPs of local cities. From the perspective of regional differences, official promotion incentives in the eastern region significantly improved urban EWP, official promotion incentives in the western and northeastern regions significantly inhibited urban EWP, and official promotion incentives in the central region had no significant impact on urban EWP. From the perspective of official age, local officials aged 53 and 54 intensified the inhibitory effect of promotion incentives on urban EWP. By constructing a theoretical framework and empirical testing, this paper provides theoretical support and empirical evidence for the impact of official promotion incentives on urban EWP. The results help to understand the relationship between official promotion incentives and sustainable development, and promotes sustainable urban development.

The distance decay effect and spatial reach of spillovers

This paper quantifies and graphically illustrates the distance decay effect and spatial reach of spillover effects derived from a spatial Durbin (SD) model with parameterized spatial weight matrices. Building on attributes of the concept of spatial autocorrelation developed by Arthur Getis, we adopt a distance-based negative exponential spatial weight matrix and parameterize it by a decay parameter that is different for each spatial lag in this model, both of the regressand and of all regressors. The quantification and illustration are applied to the spatially augmented neoclassical growth framework, which we estimate using data for 266 NUTS-2 regions in the EU over the period 2000–2018. We find distance decay parameters ranging from 0.233 to 2.224 and spatial reaches ranging from 700 to more than 1500 km for the different growth determinants in this model. These wide ranges highlight the restrictiveness of the conventional SD model based on one common spatial weight matrix for all spatial lags.

Access Weight Matrix: A Place and Mobility Infused Spatial Weight Matrix

This study introduces the Access Weight Matrix (AWM) to capture the spatial dependence of access across a geographical surface. AWM is a nonsymmetry, nonzero diagonal matrix with elements to be a function of (i) the spatial distribution of places, (ii) the number of places, and (iii) the travel‐time threshold to reach places rather than distance, contiguity, or adjacency. AWM is tested and validated to examine the spatial dependence of transit access to employment opportunities in the City of Chicago. Three observations are noticed. First, the degree of spatial dependence between the access of geographical units is not necessarily proportional to their proximity and is better explained by AWM than traditional spatial weight matrices regardless of the travel‐time threshold. Second, the time‐dependence feature of AWM improves the accuracy of capturing spatial dependence, particularly in short travel‐time thresholds. Third, near geographical units are not necessarily more related than distant geographical units even for access that is proved to be spatially highly correlated with neighboring units. With the increased ease of measuring access, research is expanding to explore the socioeconomic, demographic, and built‐environment correlates of access. AWM can be employed in developing more accurate spatial econometrics models.

Optimized ST-Moran’s I Model for Characterizing the Dynamic Evolution of Terminal Airspace Congestion

This study aims to unveil the spatiotemporal evolution of congestion within terminal airspace, offering an in-depth analysis of congestion concerns to effectively utilize airspace resources and devise targeted control strategies, thereby enhancing airspace operation safety and efficiency. Initially, converting segment flow rates into equivalent speeds serves as a quantitative benchmark for operational status. Subsequently, an enhanced version of the ST-Moran’s I index model, specifically tailored to terminal airspace, is developed by incorporating improvements across spatial weight matrices, standard state parameters, and temporal dimensions. Validating this model with actual operational data from Chengdu’s terminal airspace, the research demonstrates significant advancements. Compared to conventional models, the proposed model enhances recognition rates for congestion in spatial and temporal dimensions by 62.5% and 43.61%, respectively. Congestion within terminal airspace predominantly occurs at the intersection of departure-climb and approach-departure segments, exhibiting evident spatiotemporal migration behavior. The proposed model accurately delineates the spatiotemporal characteristics of segment congestion, offering support for tailored congestion management strategies.

Direct and spillover effects of short‐ and long‐term land pricing drivers: Evidence from Italian districts, 1992−2019

AbstractThe global economic and food crisis has increased the demand for land and rekindled the interest in farmland market investments worldwide. This study explores the Italian farmland market, investigating its main influencing factors from 1992 to 2019 using a spatial econometric framework. Traditional land characteristics and location‐specific agricultural factors, as well as non‐agricultural factors, are assessed. The average level of farmland prices and their growth are analyzed by modeling the potential types of spatial interactions, and the results are corroborated by considering different configurations of spatial weight matrices. The results show that farmland markets are influenced by land's current net returns as well as by its potential alternative uses. Therefore, factors considered external to the agricultural dimension, such as population pressure, climate change, and speculative expectations, increasingly shape farmland prices.

Study on the spillover effect of digital economy development on CO2 emissions

To study the spillover effect of the digital economy development on carbon emissions, this study innovatively constructs different spatial weight matrices, based on 2011–2020 panel data covering 30 Chinese provinces, and it explores the direct spillovers, conducted spillovers, and spillovers from different spillover channels, such as human capital, service industry development, and information development of digital economy development on carbon emissions through the spatial Durbin model combined with a mediating effect model. The results show that there is significant spatial heterogeneity in digital economy development; in terms of regions, the eastern region has the highest average development level and the central region has the highest average annual growth rate. Digital economy development can directly suppress carbon emissions, and it can also indirectly suppress carbon emissions by driving technological innovation and optimizing the energy consumption structure, and there exists a spatial spillover effect. Under human capital, service industry development and information development matrices, the spatial spillover effect of digital economy development on carbon emissions is significantly negative. Regions with the same level of information development are more likely to exert a spatial spillover effect of digital economy development on carbon emissions.

Study on the impact of digital finance on agricultural carbon emissions from a spatial perspective: an analysis based on provincial panel data

Abstract China is committed to achieving the goal of ‘carbon peaking and carbon neutrality’, which requires reducing carbon emissions from various sectors, especially agriculture. Agriculture is a major source of greenhouse gas emissions, and its carbon intensity is influenced by many factors, including the development of digital finance. Digital finance, as an important driver of economic growth and technological innovation, can affect the carbon emissions of agricultural production and consumption through various channels. However, the existing literature has not paid enough attention to the spatial effects of digital finance on agricultural carbon emissions, which may lead to biased estimates and policy implications. Therefore, this paper aims to fill this gap by using the Spatial Durbin Model to analyze the impact of digital finance on agricultural carbon emissions from a spatial perspective, based on the provincial panel data of 28 provinces (municipalities and autonomous regions) in China from 2011 to 2020. The main findings are as follows: (1) The development of digital finance has a positive and significant effect on China’s agricultural carbon emissions, and this effect is robust to different spatial weight matrices. (2) The impact mechanism of digital finance on agricultural carbon emissions is mainly through affecting the spatial spillover of carbon emissions and the cross-regional financial coordination. Based on these results, this paper provides some policy suggestions for achieving the goal of ‘carbon peaking and carbon neutrality’ in China’s agricultural sector.

Comparison of distance-based spatial weight matrix in modeling Internet signal strengths in Tasikmalaya regency using logistic spatial autoregressive model

To ensure that national development objectives in rural areas are achieved evenly and sustainably, the Government of Indonesia applies the principles of Village Sustainable Development Goals (SDGs), which are derivative programs of SDGs. One of the indicators in measuring the progress and independence of villages in Indonesia is the availability of cellular phone signal access. Cellular phone signals have a vital role because most internet users in Indonesia rely on mobile data connections from cellular operators. However, the signal emitted by a provider tower has a limited range. According to the data of the Developing Villages Index in 2022, Tasikmalaya Regency is one of the regencies with the highest number of villages that have weak signal strength in West Java Province, Indonesia. To examine the effect of distance and height difference between the placement of the nearest provider tower and the location of the Village Office on the internet signal strength category in Tasikmalaya Regency, Logistic Spatial Autoregressive modeling is needed. In this study, the Bayesian Markov-Chain Monte Carlo estimation method was used, because it has advantages in flexibility and computational efficiency. In spatial modeling, there is a spatial weight matrix determined by the researcher’s understanding of the observed phenomenon. The variable observed in this study is signal strength, which has an orientation at a distance. However, there are several types of distance-based spatial weight matrices, such as K-nearest neighbor, radial distance, power distance, and exponential distance. To determine the most suitable distance-based spatial weight matrix in internet signal strength modeling, the four (4) weight matrices were compared based on the goodness of fit measure models, calculated from the confusion matrix. The results of the analysis showed that the radial distance weight matrix with a threshold distance of d = 1.7km is the most suitable use of distance-based spatial weight matrix in internet signal modeling in Tasikmalaya Regency. The weight matrix exerted a positive spatial autocorrelation effect of 57.141%. In addition, the height difference factor between the location of the provider tower with the location of the village office has a greater effect than the horizontal distance.

Does the Collaborative Agglomeration of Logistics and Manufacturing have an Inhibitory Effect on Carbon Emissions? Based on the Spatial Econometric Analysis of Panel Data of 30 Provinces in China

In this paper, panel data from 30 provinces in the Chinese Mainland (excluding Tibet, Hong Kong, Macao, and Taiwan) spanning from 2006 to 2022 is utilized to investigate the relationship between logistics industry agglomeration, manufacturing industry agglomeration, and carbon emissions using spatial econometric analysis methodologies. Specifically, the study focuses on investigating the relationship between the synergistic agglomeration of the logistics and manufacturing industry, and their impact on carbon emissions using the Spatial Durbin Model. The research findings indicate that an increase in manufacturing industry agglomeration significantly reduces carbon emissions, an increase in logistics industry agglomeration result in higher carbon emissions, and the synergistic agglomeration of the two industries plays a significant role in mitigating regional carbon emissions through spatial and geographical mechanisms. Robustness tests, which include the control of additional variables and the use of alternative spatial weight matrices, further support the above conclusions.

Spatio-temporal evolution of provincial ecological footprint and its determinants in China: A spatial econometric approach

The increasing demand for ecosystem services in regional development has presented significant challenges to environmental sustainability. To alleviate environmental pressure, it is crucial to employ the provincial ecological footprint to gauge regional ecological pressure and research the determinants that contribute to reducing environmental pressure. This underscores the significance of examining and addressing the intricate relationships between ecological resources and regional development. While prior studies often focus on national data, provincial ecological footprints have been relatively ignored. Moreover, scant research exists on the influence of variables such as resource rents and industrial structures. This study employs the ecological footprint model to assess the per capita ecological footprint of Chinese provinces from 1990 to 2020, intending to gauge escalating ecological pressures and pinpoint their drivers. We use spatial panel data analysis to investigate the association among economic growth, natural resource rents, industrial structures and ecological footprint. Leveraging spatial weight matrices, we detected provincial footprint spatial autocorrelation and further analyzed it using the spatial Durbin model. Results underscore that economic growth profoundly shapes the ecological footprint, but an explicit Environmental Kuznets Curve remains elusive. Both natural resource rents and industrial shifts considerably influence the ecological footprint. Distinguishing from earlier non-spatial studies, our research merges spatial panel data analysis, emphasizing the regulatory role of juxtaposing natural resource rents with industrial structure adjustment can decrease the impact of economic growth on the ecological footprint. Advocating robust policies that favor renewables over non-renewables is pivotal, emphasizing the imperative for rigorous environmental standards in sectors shifting to renewables.

Assessing the short-term effects of PM2.5 and O3 on cardiovascular mortality using high-resolution exposure: a time-stratified case cross-over study in Southwestern China.

Air pollution is a major risk factor of cardiovascular disease (CVD). To date, limited studies have estimated the effects of ambient air pollution on CVD mortality using high-resolution exposure assessment, which might fail to capture the spatial variation in exposure and introduce bias in results. Besides, the three-year action plan (TYAP, 2018-2020) was released; thus, the constitution and health effect of air pollutants may have changed. In this study, we estimated the short-term effect exposed to particulate matters with parameter less than 2.5µm (PM2.5) and ozone (O3) with 0.05° × 0.05° resolution on CVD mortality and measured the influence of TYAP in the associations. We used random forest models with spatial weight matrices to attain high-resolution pollutant concentrations and conditional Poisson regression to assess the relationship between air pollution and cardiovascular mortality. With an increase of 10µg/m3 in PM2.5 and O3 during 2018-2021 in the Sichuan Basin (SCB), CVD mortality increased 1.0134 (95% CI 1.0102, 1.0166) and 1.0083 (95% CI 1.0060, 1.0107), respectively, using high-resolution air pollutant concentration, comparing to 1.0070 (95% CI 1.0052, 1.0087) and 1.0057 (95% CI 1.0037, 1.0078) using data from air quality monitoring stations (AQMs). After TYAP, the relative risk (RR) due to PM2.5 rose up to 1.0149 (95% CI 1.0054, 1.0243), and the RR due to O3 rose up to 1.0089 (95% CI 1.0030, 1.0148) in Sichuan Province. We found significantly positive association of cardiovascular mortality and air pollution in Sichuan Province. And using high-resolution exposure would be more accurate to estimate the effect of air pollution on CVD. After TYAP, the cardiovascular mortality risk estimation due to PM2.5 decreased in elderly in SCB, and the risk due to O3 increased in Sichuan Province.

How FinTech affects total factor energy efficiency? Evidence from Chinese cities

The advancement of Financial Technology (FinTech) is crucial for government entities, the National Grid, and various energy corporations to facilitate the transition towards sustainable and green production methods. This study investigates the relationship between FinTech and Total Factor Energy Efficiency (TFEE) using data from a selected sample of 254 city groups in China. We examine how the development of FinTech impacts TFEE from both non-spatial and spatial perspectives. The results from the non-spatial panel model indicate that FinTech development has a significant positive impact on TFEE. Comparative studies were conducted using fixed effects (FE), feasible generalized least squares (FGLS) models, and system generalized method of moments (GMM) models, and the main findings remained consistent, confirming the robustness of our conclusions. Spatial autocorrelation results reveal a significant positive spatial spillover effect on TFEE. Both the spatial Durbin model and the dynamic spatial Durbin model demonstrate that FinTech also has a significant positive impact on TFEE, and this effect increases over time. These conclusions remain robust even after considering various spatial weight matrices and alternative methods for calculating TFEE. Additionally, we discovered that the digital economy plays a vital role in strengthening the relationship between FinTech and TFEE. Heterogeneity analysis indicates that, compared to cities without resource-based economies, FinTech development in growing resource-based cities has a more substantial impact on TFEE.