Spatial Lag Effects Research Articles

Spatial Evolution and Driving Factors of Ecological Well-Being Performance in the Yellow River Basin

Ecological well-being performance (EWP) is a key indicator of sustainable development and has garnered significant research attention. This study measures the overall and stage-by-stage efficiency of the urban agglomerations in the Yellow River Basin based on the ends–means framework of steady-state economics. This study then delves into the spatiotemporal transfer characteristics of EWP through Moran’s I, and spatial Markov chains. Additionally, this research investigates the factors influencing EWP using a random forest model. The findings indicate a notable enhancement in EWP in the urban agglomerations in the YRB from 2006 to 2021, showing clear spatial agglomeration patterns. The shift in EWP types displays a “path dependence” effect, with distinct evolutionary paths influenced by spatial lag effects. Ecological input emerges as a key internal driver of EWP, while urbanization and technological advancements are highlighted as significant external factors. Industrial agglomeration and industrial structure also contribute to improving EWP. The findings of this study help to clarify the spatial and temporal characteristics of ecological welfare performance and its driving mechanisms in the urban agglomerations of the Yellow River Basin. This is conducive to the achievement of high-quality urban transformation and regional green development, and it provides a reference for the construction of an ecological civilization.

Spatiotemporal Variations of COVID-19 Variants in 100 Countries: Exploring Spatial Patterns and Time-Lag Effects

Learning from the COVID-19 pandemic is vital for future global health crises. Forecasting pandemic spread across countries in the presence of viral mutations, such as SARS-CoV-2 variant strains, remains a challenge. Previous studies indicate spatial and temporal regularities in SARS-CoV-2 variant distribution, yet limitations persist: (1) static geographical patterns overlook dynamic changes and their causes, and (2) the association between temporal variations in SARS-CoV-2 lineages and epidemic outbreaks is underexplored. To address these gaps, we propose an analysis framework using more than 10 million SARS-CoV-2 genome sequence metadata from 100 countries. Our framework identifies spatial heterogeneity patterns in the relative frequency of SARS-CoV-2 variants through clustering, examines factors influencing spatial heterogeneity using explainable machine learning, and analyzes the time lag effect of temporal variation on COVID-19 infection waves based on spatial patterns. Our findings demonstrate the following: (1) The distribution of SARS-CoV-2 variant strains among 100 countries exhibits a spatial pattern characterized by geographic proximity, with observed dynamic changes. (2) Bilateral distance consistently dominates, and factors related to globalization and response policy contribute to nongeographic proximity and temporal variation of the spatial pattern. (3) Leveraging this spatial pattern allows for the forecast of time lags (mainly ranging from twenty to thirty-eight days) between the emergence of new strains and subsequent infection waves. Our study clarifies the spatial pattern and time lag effect of the dynamic distribution of SARS-CoV-2 variant strains. It offers a spatial basis and temporal reference for early warning in future global public health crises from a geographical perspective.

The Construction of Economic Education Model Based on the Spatial Durbin Model

This research attempts to address the complexities and the nuances that are a part of the economic education by proposing an innovative model that is grounded in the Spatial Durbin Model (SDM). The spatial interdependencies which are crucial for the understanding of the regional variations in the economic implications and the efficacy of the educational policies are sometimes neglected by the conventional main frames of the economic education. The integration of the SDM, which is a sophisticated spatial econometric tool, aims to offer a thorough and detailed understanding of the intricate relationships between the education and the economic development in context of the diverse geographics. The model that is proposed in this research study extends beyond the traditional linear regressions as it accounts for the spatial autocorrelation and the spatial lag effects which thereby captures the spatial spillover and the diffusion of the educational investments and policies. With the help of the empirical analysis which moves forward by the utilization of the spatial econometric techniques such as the spatial lag and the spatial error models, strives to explore the spatial dynamics of the economic education while identifying the spatial patterns, the dependencies involved and the disparities that come up in the attainment of the educational and the economic performance. By incorporation of the spatial dimensions into the economic education model, the policymakers and the educators are able to get their hands on the in depth insights. This is into the spatially differentiated impacts of the educational interventions which tends to enable a more targeted and a more effective design and implementation of the policy. Furthermore, the proposed model also aims to facilitate the identification of the spatially targeted strategies in order to address the regional disparities with respect to the educational outcomes and the economic growth. Hence, it contributes to a more inclusive and a sustainable development trajectory. Through the interdisciplinary effort of bridging the economics, education and the spatial analysis, this research extends a valuable main frame for the policymakers, educators and the researchers which helps in attaining better understanding and helps to address the multifaceted challenges as well as the opportunities in the economic education and the regional development. Ultimately, this has enhanced the understanding of the spatial dimensions of the economic education which leads to the evidence based policies and interventions which are aimed at nurturing the equitable and resilient economic growth. In this paper, we utilize the spatial Durbin model as a framework to investigate the role that advanced degrees play in driving technological innovation in various parts of the world. Spatial self-correlation and instability in the geographic distribution of post-graduates in China were shown by data from the regional panel in China between 2004 and 2018. Advanced degrees facilitated the advancement of cutting-edge technologies.

Research on charging demands of commercial electric vehicles based on Voronoi diagram and spatial econometrics model: An empirical study in Chongqing China

In order to address the mismatch between the current charging demand of electric vehicles and the layout of charging stations, this paper collects multi-source heterogeneous data that may affect charging demand, and explores their spatiotemporal distributions in depth. Then, this paper proposes a Voronoi polygon spatial partitioning method based on charging demand clustering and two spatial econometric models, spatial lag model (SLM) and spatial error model (SEM), to quantitatively analyze the influences of various elements on charging demands. Empirical research in Chongqing, China reveals that the spatiotemporal distribution of charging demand is uneven, with peak areas and periods for charging, and the division of Voronoi polygon can better reflect the spatial heterogeneity of charging demands than regular grid division. The positive factors include the low SOC, parking lot density, population density, and road network density, especially the low SOC and parking lot density, and the negative items contain consumption related Point Of Interest (POI), tourism related POI, and transportation hub related POI. Among them, population density and road network density, as static data, have similar influences on charging demands at different intervals, while the impact of other factors on charging demand varies with periods, with obvious peak-valley characteristics. There is spatial dependence and lag effect between charging demands within polygons, causing SLM outperforms SEM. The research results are beneficial for the rational siting and optimization of charging stations.

Private Health Insurance in the Post-Pandemic Era: Spatial Econometric Market Development Analysis

The post-COVID era has posed unprecedented challenges to healthcare systems worldwide, urging a reevaluation of healthcare financing mechanisms. This study investigates the evolving landscape of private health insurance markets in the aftermath of the pandemic while considering the influence of international sanctions. Employing a spatial lag model (SLM), data from Albania, Russia, and Malaysia were analyzed to uncover the intricate interplay of factors shaping private health insurance coverage rates. The findings reveal nuanced patterns and disparities across these countries. The significance of variables varies: while private insurance share and government health expenditure consistently exhibit positive and substantial impacts on coverage rates, the unemployment rate presents differential effects. COVID-19 deaths influence coverage in Russia, emphasizing the pandemic's specific impact. Moreover, hospital beds’ significance in Malaysia underscores healthcare infrastructure's varying importance. Importantly, the spatial lag effect is consistently significant, emphasizing regional interdependence. Collectively, our study highlights the multifaceted determinants of private health insurance coverage, revealing insights crucial for policymakers navigating post-pandemic healthcare financing challenges among international sanctions. Doi: 10.28991/ESJ-2023-07-06-013 Full Text: PDF

Spatial location and agglomeration economies in exports: empirical evidence by technological intensities in Brazil

ABSTRACT This study investigates spatial autocorrelation and the formation of spatial export clusters of Brazilian mesoregions based on technological intensity. It also analyzes how regional knowledge and agglomeration economies explain regional exports, using spatial econometric techniques. Brazilian exports exhibit a stronger spatial autocorrelation. Non-manufacturing sectors, which are less technology-intensive, exhibit lower spatial dependence than manufacturing sectors. The results also show an autocorrelation between exports and R&D expenditure, identifying the formation of clusters in the southeastern and southern regions. R&D expenditure fosters the growth of regional exports and the spatial lag effects of exports and R&D expenditure are significant in the high technology-intensive manufacturing sector.

Association between immigrant concentration and mental health service utilization in the United States over time: A geospatial big data analysis.

Immigrants (foreign-born United States [US] citizens) generally have lower utilization of mental health services compared with US-born counterparts, but extant studies have not investigated the disparities in mental health service utilization within immigrant population nationwide over time. Leveraging mobile phone-based visitation data, we estimated the average mental health utilization in contiguous US census tracts in 2019, 2020, and 2021 by employing two novel outcomes: mental health service visits and visit-to-need ratio (i.e., visits per depression diagnosis). We then investigated the tract-level association between immigration concentration and mental health service utilization outcomes using mixed-effects linear regression models that accounted for spatial lag effects, time effects, and covariates. This study reveals spatial and temporal disparities in mental health service visits and visit-to-need ratio among different levels of immigrant concentration across the US, both before and during the pandemic. Tracts with higher concentrations of Latin American immigrants showed significantly lower mental health service utilization visits and visit-to-need ratio, particularly in the US West. Tracts with Asian and European immigrant concentrations experienced a more significant decline in mental health service utilization visits and visit-to-need ratio from 2019 to 2020 than those with Latin American concentrations. Meanwhile, in 2021, tracts with Latin American concentrations had the least recovery in mental health service utilization visits. The study highlights the potential of geospatial big data for mental health research and informs public health interventions.

Does information infrastructure and technological infrastructure reduce carbon dioxide emissions in the context of sustainable development? Examining spatial spillover effect

AbstractTo cope with global climate change and realize sustainable development, China has invested heavily in the construction of information infrastructure and technological infrastructure (IT) in recent years. In this research, we first find out the effect of IT on CO2 emissions and how IT impacts CO2 emissions, considering an expanded endogenous growth model. Then, using the dynamic spatial lag model and the system generalized method of moments (SGMM), this study investigates the impact of IT on CO2 emissions and their mechanisms in 30 provinces of China from 2005 to 2019. The results are: (1) CO2 emissions have a spatial lag effect, a temporal lag effect, and a spatio‐temporal lag effect. (2) IT reduces local CO2 emissions. (3) IT promotes the decrease of CO2 emissions in local areas over the short and long terms. In the long term, IT promotes the decrease of CO2 emissions in the surrounding regions. Both in the short and long terms, the direct effect is greater than the indirect effect. (4) IT reduces CO2 emissions through optimizing the energy consumption structure (ENS) and enhancing the level of green technology innovation (GI). This study provides a theoretical basis for realizing the dual carbon goals of emissions peak and carbon neutrality.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.

Observations and modeling of shear stress reduction and sediment flux within sparse dune grass canopies on managed coastal dunes

AbstractWind flow over coastal foredunes adapts to vegetation, resulting in spatial gradients in bed shear stresses that contribute to the formation of localized bedforms. Understanding, and having the capability to numerically predict, the distribution of sediment deposited within sparsely vegetated dune complexes is critical for quantifying the ecological, protective, and economic benefits of dune management activities. Data from wind tunnel experiments have indicated that there is a spatial lag from the canopy leading edge to a downwind location where sediment deposition first occurs. The length scale of this deposition lag is further quantified here using new field measurements of aeolian sediment transport across sparsely vegetated managed dune systems in Oregon, USA. We develop a deposition lag length scale parameter using both lab and this new field data and then incorporate this parameter into the process‐based aeolian sediment transport model, Aeolis, which also includes a new far‐field shear stress coupler. Results from numerical simulations suggest that the spatial deposition lag effect is significant for model skill in sparsely vegetated dunes. We observe with field and laboratory observations that, as canopy density increases, the length of the deposition lag decreases. As such, within the model framework the implementation of the deposition lag length does not affect the results of models of coastal dune geomorphological evolution within higher density canopies. Dune canopy density can vary due to natural (e.g., storm overwash, burial, die‐off) or anthropogenic (e.g., managed plantings, dune grading) processes.

Analysis of a diffusive epidemic system with spatial heterogeneity and lag effect of media impact.

We considered an SIS functional partial differential model cooperated with spatial heterogeneity and lag effect of media impact. The wellposedness including existence and uniqueness of the solution was proved. We defined the basic reproduction number and investigated the threshold dynamics of the model, and discussed the asymptotic behavior and monotonicity of the basic reproduction number associated with the diffusion rate. The local and global Hopf bifurcation at the endemic steady state was investigated theoretically and numerically. There exists numerical cases showing that the larger the number of basic reproduction number, the smaller the final epidemic size. The meaningful conclusion generalizes the previous conclusion of ordinary differential equation.

Spatio-Temporal Heterogeneity of Carbon Emissions and Its Key Influencing Factors in the Yellow River Economic Belt of China from 2006 to 2019.

The Yellow River Economic Belt (YREB) performs an essential function in the low-carbon development of China as an important ecological protection barrier, and it is of great importance to identify its spatio-temporal heterogeneity and key influencing factors. In this study, we propose a comprehensively empirical framework to conduct this issue. The STIRPAT model was applied to determine the influencing factors of carbon emissions in the YREB from 2006 to 2019. The results show that the carbon emissions in the YREB had significant clustering characteristics in the spatial auto-correlation analysis. In addition, the estimation results of the spatial panel analysis demonstrate that the carbon emissions showed a distinct spatial lag effect and temporal lag effect. Moreover, the three traditional factors including population, affluence, technology are identified as the key influencing factors of carbon emissions in the YREB of China. Furthermore, the spatio-temporal heterogeneity is illustrated vividly by employing the GTWR-STIRPAT model. Finally, policy implications are provided to respond to the demand for low-carbon development.

Unified M-estimation of matrix exponential spatial dynamic panel specification

In this paper, a unified M-estimation method in Yang (2018) is extended to the matrix exponential spatial dynamic panel specification (MESDPS) with fixed effects in short panels. Similar to the STLE model which includes the spatial lag effect, the space-time effect and the spatial error effect in Yang (2018), the quasi-maximum likelihood (QML) estimation for MESDPS also has the initial condition specification problem. The initial-condition free M-estimator in this paper solves this problem and is proved to be consistent and asymptotically normal. An outer product of martingale difference (OPMD) estimator for the variance-covariance (VC) matrix of the M-estimator is also derived and proved to be consistent. The finite sample property of the M-estimator is studied through an extensive Monte Carlo study. The method is applied to US outward FDI data to show its validity.

A spatial rank-ordered probit model with an application to travel mode choice

Ranking data provide important additional information related to valuation because of the implied preference sequence among all alternatives, rather than just the top choice preference. This additional information from a preference ranking can be exploited to achieve a certain desired precision in choice model estimation with a much smaller sample size, making ranked data surveys much more cost-effective than first-choice surveys. In this paper, we propose a spatial rank-ordered probit (SROP) model that accommodates both spatial lag effects as well as spatial drift effects. To our knowledge, this is the first such formulation and application of an SROP model in the econometric and transportation literature. An application of the proposed model is demonstrated in a travel mode choice ranking experiment among seven alternatives, including autonomous vehicle (AV) private ride-hailing and AV pooled ride-hailing.

STUDY ON SPATIAL STRESS EFFECT OF PC CONTINUOUS THIN-WALLED BOX GIRDER BRIDGE

In order to study the influence of spatial stress effect and shear lag effect on the cracking of PC continuous thin-walled box girder bridge, a spatial model was established by using ANSYS finite element software to analyze the internal stress distribution of the bridge. The test results are compared with the analysis results of spatial model and plane link system model through the load test of real bridge. The results show that the longitudinal stress is evenly distributed along the width direction, which means that the spatial stress effect and the shear lag effect have little influence on the downdeflection of the bridge. The shear lag coefficient at the longitudinal axis of midspan bottom plate and the intersection of bottom plate and web are larger than other positions, which is most likely to produce cracks caused by stress concentration, and should be strengthened here in practical engineering. The results of load test show that the results of spatial finite element analysis are more reliable than those of plane link system calculation, and the design and construction based on the results of spatial finite element analysis is safer.

Does industrial agglomeration affect the regional environment? Evidence from Chinese cities

This study examines the impact of industrial agglomeration on the regional environment using panel data of 13 prefecture-level cities in the Beijing-Tianjin-Hebei region from 1998 to 2018. The empirical results show a strong spatial autocorrelation and a strong spatial lag and spatial error effect on the environmental pollution level in the Beijing-Tianjin-Hebei region. The results further show that the manufacturing agglomeration significantly aggravates the regional pollution; service agglomeration significantly alleviates the regional environmental pollution; the synergistic agglomeration of manufacturing and service industry has a significant inverted "U" type relationship on the regional environmental pollution. More specifically, the development of synergistic agglomeration intensifies regional environmental pollution at the early stage, and after reaching the inflection point, synergistic agglomeration alleviates regional environmental pollution. The inflection point occurs when the level of synergistic agglomeration is around 2.85. At the same time, there is no significant spillover effect of synergistic agglomeration on neighboring regions after calculating the feedback effect. Study findings provide insights for promoting the strategy of "two-wheel-drive development" and alleviating the contradictory relationship between industrial development and environmental pollution.

The Environmental Effects of International Trade in China: Measuring the Mediating Effects of Technology Spillovers of Import Trade on Industrial Air Pollution

China is in a strategic phase of an industrial green transformation. Industrial air pollution is a key environmental target for governance. Because import trade is a core channel through which advanced environmental protection technology is absorbed, the question of whether technology spillovers brought about by import trade can reduce industrial air pollution emissions is a topic worth exploring. This paper uses a generalized spatial two-stage least-square (GS2SLS) model to explore the impact of import trade technology spillovers on industrial air pollution emission intensities using panel data from 30 provinces and cities between 2000 and 2017. Economic scale, industrial structure, and technological innovation are used as intermediary variables to test whether they play mediating effects. The results show that: (1) capital and intermediate goods technology spillovers directly reduce industrial air pollution emission intensity and (2) import trade technology spillovers indirectly reduce emission intensities by expanding economic scale, optimizing industrial structure, and enhancing technological innovation through mediating variables. Furthermore, industrial structure optimization and technological innovation have the largest mediating effects on industrial SO2, while economic expansion has the most significant mediating effect on industrial smoke and dust. The mediating effects of technology spillovers from intermediate goods exceed those of capital technology spillovers. Finally, industrial air pollution emission intensity demonstrates both spatial agglomeration and time lag effects. Environmental regulations and energy structure are shown to increase industrial air pollution emissions, while urbanization and foreign direct investment reduce industrial air pollution. Based upon these research results, some pertinent policy implications are proposed for China.

Reexamining the Impact of Industrial Structure on Haze Pollution Based on the Yangtze River Delta

In this study, the static and dynamic spatial Durbin model between industrial structure and haze pollution in Yangtze River Delta is constructed. Later, the spatial spillover effect and time lag effect of haze pollution in Yangtze River Delta are analyzed. The impact of rationalization and upgrading of industrial structure on haze pollution and its spatial spillover effect are discussed. The results show that: (i) PM2.5 has a significant positive spatial spillover effect and time lag effect; (ii) in the short run, the rationalization and upgrading of industrial structure has no inhibitory effect on haze pollution, while the rationalization and upgrading of industrial structure of surrounding cities has an inhibitory effect on local haze pollution; (iii) in the long run, the rationalization and upgrading of industrial structure of surrounding cities have an inhibitory effect on local haze pollution; (iv) economic growth, FDI, the number of Industrial Enterprises above Designated Size, and population density also have spatial spillover effects on haze pollution. Therefore, considering the spatial spillover effect of haze pollution from the perspective of urban agglomeration and long-term, strengthening the joint prevention and control and comprehensive treatment among cities, further promoting the rationalization and upgrading of industrial structure is conducive to reducing haze pollution.

Numerical and theoretical analysis of spatial shear lag effect in through wide box bowstring arch bridge main girder

The shear lag of the box girder section of a wide box bowstring arch bridge was investigated in this study. The finite element analysis software Midas Civil was used to discretize the bridge into separate entities, establish its spatial model, and analyze the law of shear lag effect in the longitudinal direction during the main construction stage and the completion stage separately. The shear lag effect is more prominent at certain key points due to the influence of the tension of the boom and the weight of the arch foot during the construction stage. After the bridge is completed, the axial pressure of the arch foot drives the shear lag effect near the midpoint of the main girder section to its maximum; the axial force is transmitted along the longitudinal bridge direction in a V shape along which it gradually decreases. Under the action of uniformly distributed load, the cross-section of the box girder near the middle fulcrum is affected by the axial force of the arch foot; there is both a positive and negative shear lag effect. Under the eccentric load, the positive and negative shear lag effects appear simultaneously at the end fulcrum. The shear lag coefficient is higher in the web on the eccentric load side than the non-load side. There is obvious and widely fluctuating positive/negative shear lag effect in the vicinity of the middle fulcrum. There is significant tensile stress on the roof under the action of the middle fulcrum. The cross-sectional stress of a single-box seven-cell box girder under compression-bending load action was analyzed according to the finite element analysis results. The stress was decomposed into a superposition of bending moment action and axial force action. The shear lag coefficient λM under the action of bending moment was also solved via energy variation method. Assuming that the axial force is only borne by the compressed area, the shear lag coefficient λN under the action of the axial force was obtained; λM and λN were superimposed to determine the comprehensive shear lag effect coefficient λ.

Numerical and theoretical research on spatial shear lag effect of self-anchored suspension bridge steel box girder

Numerical and theoretical research on spatial shear lag effect of self-anchored suspension bridge steel box girder