Spatial Dependence Effects Research Articles

Research Analysis on the Impact of China's Digital Economy Development on Carbon Emissions

The global economy has been transformed by the digital economy (DE). This study deeply explores the relationship between DE development and its impact on carbon emissions, which is not straightforward. By synthesizing multiple papers, this article aims to discuss key issues under climate changes background and sustainable development. Through the study of various econometric models, including the Spatial Durbin Model. There is spatial dependence and spillover effects of digital economic activities on carbon emissions are revealed in this paper. The primary findings suggest that DE and carbon emissions are not linear and have an inverted U-shaped curve. Data centers and network infrastructure have increased energy consumption, which may result in carbon emissions from the digital sector's initial growth. After reaching a certain threshold, the DE appears to help reduce carbon emissions. This research provides comprehensive insights for policymakers, industry leaders, and academic researchers, advocating for a balanced approach to the development of the DE. At the same time, environmental sustainability should be given priority, and digital technology should be fully utilized to promote economic growth.

FDI and industrial development in a mega-city region: a modelling study on the Pearl River Delta

ABSTRACT Foreign direct investment (FDI) and regional development are mutually interwoven phenomena. This paper introduces an analytical framework to investigate the interaction mechanism between FDI and regional industrial structure. This framework posits first that FDI generally has a significant influence on the industrial structure in the region concerned, specifically through intervening mechanisms such as capital supply, active selection, passive competition, product linkage, imitation and demonstration and employee flows. Secondly, the industrial structure, in turn, also affects FDI flows through mechanisms including market demand, factor supply and policy or institutional factors. Finally, there are bi-directional interactions; using a spatial interaction framework, the present study applies and tests a simultaneous equation model and a spatial Durbin model to analyse empirical data from 15 urban regions in the Pearl River Delta mega-city region (PRD) in China spanning the period 2005–2018. The key findings from this multi-scalar analysis are: (i) there are substantial interactive effects between FDI and industrial structure, with the rationalisation and upgrading of the industrial structure significantly promoting FDI inflows into the PRD; (ii) FDI inflows, in turn, contribute to the reinforcement and upgrading of the industrial structure in the PRD; and (iii) the interactions between FDI and industrial structure exhibit significant spatial-economic growth effects. We also find that moderator factors, including independent R&D level, market size, marketisation level, labour cost, infrastructure and government expenditure, have discernible impacts on FDI inflows and the rationalisation and upscaling of the industrial-economic structure in the area concerned. Finally, we note that, in our study, significant spatial dependence effects among urban areas inside the PRD area are observed and modelled. The discussion of the empirical findings leads to the identification of several important policy implications and lessons for regional development strategies from an FDI perspective.

Spatiotemporal evolution and key driver analysis of ozone pollution from the perspectives of spatial spillover and path-dependence effects in China

In recent years, ozone (O3) has emerged as the primary air pollutant in China, superseding PM2.5. Previous studies have concentrated on the spatiotemporal variation of ozone pollution, the analysis of its sources and drivers, as well as its environmental impacts and health benefits. Since ozone pollution can be dynamically transferred through industrial activities and meteorological factors, it is crucial to scientifically identify the spatial spillover and path-dependent effects of ozone pollution in China. However, existing studies have not yet addressed this issue. Therefore, we investigate the spatiotemporal evolution and the spatial spillover of ozone pollution by means of the exploratory spatial data analysis (ESDA) using panel data of 30 Chinese provinces from 2013 to 2020 in this study. The dynamic spatial Durbin model (SDM) was employed to reveal the key drivers of ozone pollution from the perspectives of spatial spillover and path-dependence effects. The direct and spillover effects of each driver on ozone pollution are systematically analyzed. The results show that from 2013 to 2020 ozone concentrations followed a fluctuating upward trend at national and provincial scales. Ozone pollution presented significant spatial spillover and path dependence effects. The direct effects indicated that economic growth, technological level, industrial structure, energy structure, ventilation coefficient, relative humidity and precipitation were the key drivers of local ozone pollution. The spillover effects indicated that population density, technology level, industrial structure, environmental regulations, ventilation coefficient, sunshine hours and relative humidity had significant spatial spillover effects on ozone pollution of surrounding regions. These findings will contribute to the understanding of the spatial spillover and path-dependent effects of O3 pollution, and provide scientific guidance for regional synergy and long-term ozone control policies in China.

The impact of natural hazards on migration in the United States and the effect of spatial dependence

In this paper, we analyze the effect of natural hazards on migration in the United States (US) and the importance of spatial dependence in such assessments. We use two measures of migration: migration rates and flows. The model for migration flows is estimated using the gravity model, whereas out- and in-migration rates are analyzed using the spatial Durbin model. Our results indicate there is a major and significant impact of economic damage caused by natural hazards on out-migration rates and outward migration flows. In the spatial Durbin model and in the gravity model, a $1,000 dollar damage per capita is associated with an increase in out-migration of 16.0% and 9.1%, respectively. However, when spatial dependence is not accounted for, the effect of natural hazards on migration is substantially overestimated: the coefficients are 1.5–2 times larger when spatial dependence is not considered.

River system changes under urban expansion: Insights from the Suzhou-Wuxi-Changzhou metropolitan area, China

Study regionThe Suzhou-Wuxi-Changzhou (SWC) metropolitan area, China. Study focusUrban expansion is a natural product of growing cities at the expense of river systems. In this study, we evaluated river system changes that come with urban expansion on a grid scale. We also utilized the spatial analysis models to examine the impact of urban expansion on river system changes. New hydrological insights for the regionOur results indicate that the cities in the metropolitan area had a similar pattern of urban expansion from the 1960s to the 2010s, with Changzhou, Wuxi, and Suzhou losing 45.8%, 32.3%, and 42.2% of their rivers, respectively. A negative correlation was detected between the urban expansion area (UEA) and the river density variation (∆Rd). During the rapid urban expansion period, ∆Rd significantly decreased as UEA increased. We then identified the spatial dependence and spatial spillover effect between river system changes and urban expansion. In our analysis, the river density was degraded as a result of urban expansion both in the in-situ location and in the neighboring locations. The river system has seen significant changes due to the impervious areas and infrastructures caused by urban expansion; thus, an appropriate pattern of urban development is required to mitigate the negative effects of river loss.

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.

THE EFFECTS OF TRANSACTION HOTSPOTS AND FLIPPING HOTSPOTS ON HOUSING PRICES

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions.

Evaluation and dynamic mechanism of green low-carbon transformation of agriculture in Yangtze River Delta region

Abstract To break through the development dilemma of traditional agriculture, the green and low-carbon transformation of agriculture (GLCTA) has gradually become a key engine to drive the construction of ‘dual carbon’ targets and the strategy of agricultural green development. Based on the panel data of 41 cities in the Yangtze River Delta (YRD) from 2010 to 2021, this study estimates the level of GLCTA in each city, discusses its spatiotemporal evolution characteristics, and uses the spatial panel error model and the geodetector model to reveal the dynamic mechanism of GLCTA process. The results show the following: (1) The overall level of GLCTA in the YRD shows a steady upward trend from 2010 to 2021, but the overall development level is low and the room for improvement is large. The development level of different cities differs greatly, showing a general situation of ‘high in the east and low in the west’, and the high level of GLCTA continues to deepen to the west. (2) The GLCTA in the YRD has spatial dependence and spatial spillover effect, and the number of cities with high–high and Low–Low development level is relatively large. (3) Except the urbanization level and the degree of rural industrialization that negatively affect the level of GLCTA in the YRD, other dynamic factors have a positive effect. Among them, the influence of fiscal support policy and economic level on the GLCTA is increasing. This paper discusses the current situation and dynamic mechanism of GLCTA in the YRD, which is innovative in terms of research content and perspective, and puts forward corresponding policy suggestions to promote GLCTA, providing important insights and practical basis for researchers and implementers in related fields.

Pemodelan Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) pada Kasus HIV di Indonesia

In general, spatial regression is used to model one of the spatial effects, namely spatial dependency or heterogeneity. For the effects of spatial dependencies, the models that have been used frequently follow Elhost's taxonomy, with the spatial dependencies being on the response, predictor, or error. Whereas for the effect of spatial heterogeneity generally use geographically weighted regression models (GWR) or if there are global predictors use mixed geographically weighted regression (MGWR). The data used in this study are cases of Human Immunodeficiency Virus (HIV) per 100,000 population as a response variable, and key populations, positive cases in pregnant women, tuberculosis patients, poverty rate, and unemployment rate as predictors. In the data used, there are spatial dependencies and heterogeneity. The MGWR-SAR is a model that can be used if the data has both spatial effects. This study aims to determine the factors influencing HIV cases in districts/cities in Indonesia using a spatial model. The results showed that the combined model of GWR and spatial autoregressive regression (SAR) was the best model. Key population explanatory variables have a global and significant influence. Other explanatory variables that have local influence are positive cases in pregnant women, tuberculosis patients, poverty rates, and unemployment rates.

Factors affecting tourist visits to archaeological sites in Turkey: A spatial regression analysis

The study focuses on the factors affecting visitor numbers to archaeological sites in Turkey. The aim is to investigate the geographical, economic, and demographic factors underlying the visits using statistical methods. The study covers 117 archaeological site visits in 2019. Although existing studies analysed determinants of visits to archaeological sites of different countries, the evidence needs to be explicit. Methodologically, the classical linear regression models are primarily applied in the literature, whereas the incorporation of spatial dependence has largely been ignored. This study contributes to the literature by employing demographic, economic, and climatic factors and spatial relations between the sites. Therefore, spatial autoregressive (SAR) and spatial error models (SEM) are developed in the analyses. According to the results, WHL inscription and distance to the city centre are crucial factors for the visits. In addition, the study emphasizes the significant negative effect of spatial dependence on visitor numbers of archaeological sites near each other.

The Spatial Effects of Regional Poverty: Spatial Dependence, Spatial Heterogeneity and Scale Effects

Recognizing the spatial effects of regional poverty is essential for achieving sustainable poverty alleviation. This study investigates these spatial effects and their determinants across three distinct administrative levels within Hubei Province, China. To analyze the spatial patterns and heterogeneity of multi-scale regional poverty, we employed various spatial analysis techniques, including the global and local Moran’s I statistics, the Lineman, Merenda, and Gold (LMG) method, as well as Multiscale Geographically Weighted Regression (MGWR). We found that: (1) Regional poverty exhibits significant spatial dependence across various scales, with a higher level of spatial dependence observed at higher administrative levels. (2) The spatial distribution of poverty is primarily influenced by geographical factors, encompassing first-, second-, and third-nature geographical elements. Notably, first-nature geographical factors make substantial contributions, accounting for 36.99%, 42.23%, and 23.79% at the county, township, and village levels, respectively. (3) The influence of geographical factors varies with scale. Global effects of various factors may transcend scales or remain confined to specific scales, while the local impacts of different factors also exhibit variations across scales. These results underscore the necessity for collaborative efforts among government entities at different levels with the anti-poverty measures tailored to local contexts.

An Analysis of the Effects of Spatial Dependence on Economic Growth Among Regencies and Cities in Java

Java is inarguably essential for Indonesia’s economy with its 59% contribution to the country’s GDP. However, behind the tremendous participation lies high regional disparity, poverty, and unemployment, which have become challenges to its economic development. This research aims to analyze the role of spatial dependence on regional economic growth in 119 regencies and cities in Java during the 2015- 2019 period. Exploratory Spatial Data Analysis (ESDA) and Spatial Durbin Model (SDM) were used to analyze the determinants of the spatial dependence and its impact on regional economic growth. The travel time among regions was utilized for the spatial weight matrix. The existence of spatial dependence in inter-regional economic growth can be identified from all models. The positive value of Moran’s I in the ESDA analysis indicates that the spatial pattern of the growth is clustering. The lambda parameter in the SDM estimation indicates the occurrence of backwash spillover in the effect of the spatial dependence on economic growth. The direct effects of initial per capita income, physical capital investment, road infrastructure, population growth, and education are significant on economic growth. Furthermore, the spillover effect of initial per capita income and education is also significant on the inter-regional economic growth.

Spillover effects from inland waterway transport development: Spatial assessment of the Rhine-Alpine Corridor

Inland waterways have the potential to enhance port-hinterland connectivity and foster sustainable freight transport. Their development as transport corridors is highly dependent on geography, yet spatial spillovers of port throughput are often overlooked. In Europe, the configuration of inland container terminals can lead to cross-border spillovers. This paper analyzes inland container throughput between 2007 and 2021 in the Rhine-Alpine Corridor to quantify its spatial dependence and economic spillover effects. The assessment involves 43 regions from Belgium, France, Germany, Netherlands, Luxemburg, and Switzerland. A Spatial Durbin Model is developed accounting for economic conditions, fixed effects, and time trends. The results suggest that throughput exhibited spatial concentration patterns, which intensified after water levels dropped in 2018. Second, the spatial dependence is heterogeneous between transport performance in tonne-km vs. TEU-km. Third, technological resources and population density were positively associated with throughput, while the opposite was found for employment and motorways. Notably, the spillovers of technological resources and employment were considerably higher than the local impact. Based on these findings, the paper advises on spatially oriented policies to address low water levels, workforce decline, and the need for new business models.

An Analysis of the Effect of CPTPP on Japan's Export Using the Spatial Panel Gravity Model

The purpose of this paper is to quantitatively measure the effect of the CPTPP on Japan's exports to member countries, and to find out various factors affecting Japan's exports. In this study, we adopt the spatial Durbin gravity model to consider the spatial dependency and spillover effects of trade flows between Japan and CPTPP member countries during the period from 2017 to 2021. While the CPTPP did not directly increase Japan's exports, it had an indirect spillover effect due to increased trade among other CPTPP member countries, which decreased Japan's exports. The factors that have a more direct impact on Japan's exports to CPTPP member countries are currently GDP of both countries, distance between countries, and the exchange rate. In particular, the direct effect of the exchange rate is most significant, as a depreciation of the Japanese yen enhances the price competitiveness of Japanese products, resulting in increased exports.

Spatial spillover impact of determinants on child mortality in Pakistan: evidence from Spatial Durbin Model

BackgroundChild mortality is a major challenge to public health in Pakistan and other developing countries. Reduction of the child mortality rate would improve public health and enhance human well-being and prosperity. This study recognizes the spatial clusters of child mortality across districts of Pakistan and identifies the direct and spatial spillover effects of determinants on the Child Mortality Rate (CMR).MethodData of the multiple indicators cluster survey (MICS) conducted by the United Nations International Children’s Emergency Fund (UNICEF) was used to study the CMR. We used spatial univariate autocorrelation to test the spatial dependence between contiguous districts concerning CMR. We also applied the Spatial Durbin Model (SDM) to measure the spatial spillover effects of factors on CMR.ResultsThe study results showed 31% significant spatial association across the districts and identified a cluster of hot spots characterized by the high-high CMR in the districts of Punjab province. The empirical analysis of the SDM confirmed that the direct and spatial spillover effect of the poorest wealth quintile and MPI vulnerability on CMR is positive whereas access to postnatal care to the newly born child and improved drinking water has negatively (directly and indirectly) determined the CMR in Pakistan.ConclusionThe instant results concluded that spatial dependence and significant spatial spillover effects concerning CMR exist across districts. Prioritization of the hot spot districts characterized by higher CMR can significantly reduce the CMR with improvement in financial statuses of households from the poorest quintile and MPI vulnerability as well as improvement in accessibility to postnatal care services and safe drinking water.

How Does the Built Environment in Urban Villages Affecting Ride-Sourcing Commuting Trips Interacting with the Spatial Dependence Effect

Urban renewal provides opportunities to improve urban transport structures during the process of improving built environments. It is necessary to clarify the impact of different elements of the built environment on travel behaviors in the context of urban village renewal. This paper presents a microscopic perspective of individual travel behavior by proposing analytical frameworks to investigate travel behavior in terms of DiDi commuting trips. Considering the effect of spatial dependence, a Spatial Durbin Error Model was established, incorporating a spatial lag and spatial error. Traveling information was employed from the ride-sourcing company DiDi during the morning and evening peaks within in the urban village areas and workplaces of Shenzhen, and the variables of a built environment were scaled within travel analysis zones (TAZs). The results show that the impacts of the built environment on ride-sourced commuting were different between job and housing locations, with more influential factors in residential locations (urban villages). On the other hand, working locations had an influential magnitude more than twice that of residential locations. Alongside that, due to the spillover effect, it was more effective to hinder ride-sourced commuting and promote green traveling modes by increasing the number of bus stops. The findings provide some insights into transit-oriented urban renewal. Therefore, when transforming urban villages, an emphasis should be placed on the enhancement of transit availability, and the mixed land use could be considered last due to limited time and funds.

Spatio-Temporal Evolution and Influencing Factors of the Resilience of Tourism Environmental Systems in the Yangtze River Economic Belt of China

The resilience of a tourism environmental system (TESR) is an important aspect of sustainable tourism growth. Based on the construction of an evaluation system for the TESR, this study used 126 prefecture-level cities in the Yangtze River Economic Belt (YREB) as a case study and attempted to explore the spatio-temporal evolution features and influencing mechanism of the TESR. The primary conclusions are as follows: (1) Despite significant improvement in TESR in the YREB, the overall resilience level and growth rate remain relatively low, with ample potential for improvement. (2) Positive spatial correlation and type agglomeration impact are evident in the urban TESR. (3) Relatively frequent transitions of the TESR occur with spatial dependence and spillover effects in the transition paths, i.e., high-level cities can improve the TESR of neighboring cities through positive spillover effects. (4) Several factors, such as city economic, social, industrial, and policy factors, jointly impact the evolution of the pattern of the TESR in the YREB, with heterogeneous effects.

Spatiotemporal associations of mental distress with socioeconomic and environmental factors in Chicago, IL, 2015–2019

Mental distress is an epidemic that endangers global well-being and contributes to various illnesses. In the United States, the prevalence of mental distress has risen rapidly in recent years. However, this topic is understudied in spatial information research, as current literature lacks focus on spatially varying relationships between mental distress and relevant factors, which leads to impediment of prevention and mitigation efforts. Therefore, this study aims for investigating the spatiotemporal relationships of mental distress with crime, housing cost, poverty, air quality. Using the space–time scan statistic, we illustrate the spatiotemporal distribution of mental distress in Chicago, IL. In addition, we employ geographically and temporally weighted regression (GTWR) to find the varying relationships between aforementioned factors and mental distress. Lastly, we compare GTWR to a linear ordinary least squares model to assess the effect of spatial and temporal dependence in found relationships. Our findings indicate that, while the crime rate, housing costs, and poverty explain the prevalence of mental distress over time and space, the space–time variation of PM2.5 is not a predominant determinant of mental distress in Chicago. The practical implications of our work are that planners and policymakers are encouraged to identify spatiotemporal patterns of mental distress so that resources can be directed to the most vulnerable communities. Spatiotemporal modelling, the identification of geographic patterns and relationships, enables novel understanding of societal issues, and is an integral part of spatial information science.

Digital Economy and Urban Carbon Emissions in China: An Empirical Study Based on The Spatial Dubin Model

Starting from the Chinese city level, combined with the panel data of 281 cities in China from 2011 to 2019, this paper empirically examines the spatial impact of digital economy development on urban carbon emissions, and the results show that there is a significant spatial dependence and spatial spillover effect between digital economy and urban carbon emissions, and the region-wide inspection finds that the digital economy shows a significant promotion effect on local carbon emission levels, while the adjacent effect of digital economy shows "U" shaped characteristics. There is a "U" shaped relationship between the digital economy and the carbon emissions of the region, and the "U" relationship between the digital economy and the carbon emissions of neighboring areas is only within a specific distance. This paper grasps the general laws of digital economy development and urban carbon emissions from the medium and micro scales, and provides policy suggestions for solving the carbon emission reduction problems faced by high-quality development.

The effect of proximity and spatial dependence on the house price index for Dar es Salaam

PurposeThis study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.Design/methodology/approachUsing the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.FindingsThe inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.Originality/valueThe paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.