Spatial Econometric Perspective Research Articles

The Spatial and Temporal Research on the Coupling and Coordinated Relationship between Social Economy and Energy Environment in the Belt and Road Initiatives

The Belt and Road Initiative (BRI) was presented by China’s central government to achieve regional economic cooperation under the background of economic globalization. Energy and environmental problems, as by-products of economic success, are fundamental concerns in this grand project. An integrated method that combined the coupling coordinated degree model, Moran’s I, and the spatial panel data model was used in the research zone. Results of the coupling coordinated degree model indicated that the statuses of the social economy and the energy environment of the countries in the research zone improved from 1997 to 2014, with the changes in China and Eastern Europe being particularly significant. From the spatial econometric perspective, the coupling coordinated degrees of the countries in the research zone appeared in the spatial cluster in accordance with Moran’s I. The result of the spatial panel data model indicated that the coupling coordinated degree of one country positively affected those of its neighboring countries.

Income distribution and environmental quality in China: A spatial econometric perspective

Paying heed to a surge of concern about the serious and continuous environmental degradation that is currently being witnessed in China, in this paper we investigate whether income distribution contributes to environmental deterioration. Environmental degradation can be linked not only to income growth, we argue, but also to increases in income inequality. The study calculated the Gini coefficient of 31 province using a data set of 403 city level data in mainland China and estimated the linkage between income distribution and environmental deterioration utilizing balanced data for 31 Chinese provinces during the period 1996–2015. First, the degree of environmental pollution was calculated by constructing a comprehensive index system, which was formulated using the entropy method, based on six environmental indicators. Then, a spatial panel regression model was also employed in order to estimate whether income inequality is likely to be a relevant factor in environmental deterioration in China. The estimation results showed that: using the index and spatial analysis techniques, significant spatial positive agglomeration were found existing between Chinese provinces in terms of their levels of environmental degradation; the influencing factors estimation results substantiated the existence of an invert U-shaped relation, lending support to the Environmental Kuznets curve theory; more importantly, the study found that income inequality played a significant role in preventing environmental degradation, suggesting that an appropriate level of income inequality over a delimited period is beneficial to improving environmental quality. It should, however, also be emphasized that optimizing the country's income distribution is remains an important long-term task for the Chinese government; other factors were found to exert different types and magnitudes of impacts on environmental pollution. Results showed that many factors—for instance, energy intensity, the share of secondary industry, population density, and foreign direct investment—contribute to environmental deterioration. Conversely, our findings also indicate that rising urbanization levels can have a positive effect on environmental quality. In addition, no clear evidence was found to suggest that local government expenditure is related to environmental degradation.

Agglomeration effect of CO2 emissions and emissions reduction effect of technology: A spatial econometric perspective based on China's province-level data

To clarify the spatial characteristics of CO2 emissions, economic externalities of spatial units are transplanted into CO2 emissions research. Furthermore, to identify emissions reduction pathways, spatial econometric models are constructed based on the patent data about energy conservation and emissions reduction. The key results are: (1) The kernel density plot of China's CO2 emissions shows an obvious right-averse state, and the peak is getting lower. Therefore, we can conclude that CO2 emissions in China are increasing, and polarisation of CO2 emissions is serious; (2) Due to the similarity and connectivity exist in spatial units, China's CO2 emissions have shown a stable spatial agglomeration effect from global and local perspectives; (3) Based on adjacent, geographic, and economic distance matrices, energy technological progress has played a positive emissions reduction role on China's CO2 emissions from the perspective patent data about energy saving and emissions reduction. Finally, on the basis of the above conclusions, some policy implications have been proposed accordingly.

The impact of education expenditures on growth in the EU28 – a spatial econometric perspective

This paper analyses spatial impact of government expenditures on education on economic growth in the EU28 countries during the period 2004–2013. Employing a novel econometric technique that allows for the estimation of spatial spillovers, our results indicate that government expenditures on education significantly and positively infl uence GDP growth. Moreover, the indirect i.e. the spillover effects are quite large suggesting that the growth models should account for spatial interdependencies. Precisely, we find that education expenditures in one country affect GDP growth in the neighbouring countries, meaning that these spillovers are geographical in nature. Moreover, we find that the degree of interdependence among countries varies according to the average GDP per capita even if their geographical distances are identical. Additionally, immigration is found to be an important channel of spatial transmission.

Exploring the spatial spillover effects of industrialization and urbanization factors on pollutants emissions in China's Huang-Huai-Hai region

The diffusion effect makes air pollution become a regional problem. The purpose of this paper is to explore the spatial spillover effects of most pollutants emissions and identify industrialization and urbanization factors affecting emissions from spatial econometric perspective. In recent years, air pollution in China's Huang-Huai-Hai region is the most serious in the whole nation. Based on the geographical weight matrix introduced in this paper, the spatial Durbin model (SDM) is employed to explore the direct and spatial spillover effects of industrialization and urbanization factors on 7 pollutant emissions in 53 cities of China's Huang-Huai-Hai region over the period 2009–2014. The results show that CO is the main pollutant component, followed by SO2, and NOx in 53 cities. The spatial autocorrelation strengthen of each pollutant in all cities is as the order: BC > PM2.5>CO > PM10> NH3> SO2> NOx. There are significant spatial aggregation effects of emissions of pollutants except NOx. Furthermore, the direct effects of per capita GDP, non-agricultural industries and urban residents' per capita consumption on pollutants emissions are the biggest on the whole. The direct effect of per capita GDP on each pollutant emissions is entirely negative, while that of non-agricultural industries is positive on the whole. The spatial spillover effects of above three factors on pollutants emissions are the largest; the spatial spillover effect of non-agricultural industries is positive, while that of per capita GDP is negative. According to the results, some policy implications are provided to control regional air pollution.

State Higher Education Spending: A Spatial Econometric Perspective

This article uses spatial econometric modeling to estimate the extent to which state higher education spending in three categories (need-based aid spending, non-need-based aid spending, and appropriations to public universities) is affected by the spending decisions of neighboring states. Drawing on the relevant empirical and theoretical literature in public finance and economics, it evaluates three theoretically justified alternatives for defining the connectivity between states: physical proximity, relative population migration flows between states, and common state membership in regional interstate higher education compacts. Our empirical results find statistically significant geographical spatial interdependence in spending for need-based aid and higher education appropriations, but no equivalent effects for non-need-based aid. We discuss our results in the context of the continually evolving role of higher education in state budgets.

Does foreign direct investment affect environmental pollution in China's cities? A spatial econometric perspective

Environmental pollution has aroused extensive concern worldwide. Existing literature on the relationship between foreign direct investment (FDI) and environmental pollution has, however, seldom taken into account spatial effects. Addressing this gap, this paper investigated the spatial agglomeration effects and dynamics at work in FDI and environmental pollution (namely, in waste soot and dust, sulfur dioxide, and wastewater) in 285 Chinese cities during the period 2003–2014, using global and local measures of spatial autocorrelation. Our results showed significant spatial autocorrelation in FDI and environmental pollution levels, both of which demonstrated obvious path dependence characteristics in their geographical distribution. A range of agglomeration regions were observed. The high-value and low-value agglomeration areas of FDI were not fully consistent with those of environmental pollution. This result indicates that higher inflows of FDI did not necessarily lead to greater environmental pollution from a geographic perspective, and vice versa. Spatial panel data models were further adopted to explore the impact of FDI on environmental pollution. The results of a spatial lag model (SLM) and a spatial error model (SEM) revealed that the inflow of FDI had distinct effects on different environmental pollutants, thereby confirming the Pollution Heaven Hypothesis and Pollution Halo Hypothesis. The inflow of FDI was found to have reduced waste soot and dust pollution to a certain extent, while it increased the degree of wastewater and sulfur dioxide pollution. The findings set out in this paper hold significant implications for Chinese environmental pollution protection.

Evaluating impact of air pollution on China’s inbound tourism industry: a spatial econometric approach

ABSTRACTDespite widespread concern about China’s air pollution crisis, very few studies have quantitatively explored its impact on China’s inbound tourism industry. From a spatial econometric perspective, this paper has quantified the direct, indirect and total effects of air pollution on international tourists visiting China, using panel data on 31 Chinese provinces during the period 2001–2013. The results confirm that air pollution has a significant direct negative effect on international tourists visiting China. There exists apparent spatial autocorrelation for provincial-level air pollution in China. Spatial Durbin model shows evidence that air pollution in the neighbouring provinces has a significant negative impact on international tourist arrivals in the local province. The spillover effect is even larger than the direct effect. When air pollution in the neighbouring provinces becomes serious, international tourists are unlikely to travel to the local province. The findings of this study have implications for local governments to strengthen inter-governmental environmental management to invigorate tourism market.

Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective

This paper analyzes the impacts of the built environment (BE) as it relates to the potential job accessibility (PJA) effects of road pricing. The relationships between the BE elements and PJA under a road charging policy are established using a spatial econometric approach, which uses an integrated land use and transportation model (TRANUS model) and a spatial lag model (SLM). With the intent of further analyzing the differences in the PJA effects of road pricing on traffic analysis zones (TAZs) that contain different combinations of BE elements, a quantitative classification method combining factor and cluster analysis is applied. This will quantitatively categorize TAZs inside and outside the tolled areas. In exploring the relationship between changes in PJA and the road pricing policy, we found the spatial autocorrelation coefficient to be negative. This result suggests that we are unable to increase the PJA of all the regions through road pricing, but rather affect a redistribution of PJA between different regions. Results also indicate that the impacts of road charging on PJA are associated with urban BE elements. Moreover, such effects are the common result of specific characteristics of the BE. The higher the number of jobs, the better the public transportation conditions, and the better the street design (high densities of street and intersections), the less the region will be negatively influenced by a road charging policy, and vice versa. To avoid the negative effects of road pricing on PJA prior to the launch of such a policy, cities should improve public transportation networks and enhance the street design of the road pricing policy areas, especially the toll ring periphery area.

How did Japanese exports evolve from 1995 to 2014? A spatial econometric perspective

Spatial econometric techniques can eliminate the bias when spatial effects are neglected. However, so far there is no research on investigating the spatial configuration of Japanese exports and determinants of its exports to sustain its exports competitiveness.Empirical evidence shows that the spatial configuration of Japan’s exports evolved over the past two decades. It exported to countries with higher political stability, higher degree of economic integration with Japan and to countries which have larger outward foreign direct investment from Japan. Also, its exports have positive and significant relationship with countries which have large imports to Japan. Its exports have the negative relationship with the GDP per capita of its export partner countries. These signify that it increased and expanded exports greatly to East-Asia countries, European Union and North America. These findings have great relevance to policymaking for both corporations and governments where trade or regional economic integration are concerned.

The Role of Foreign Direct Investment on Income Convergence in China after Early 1990s from a Spatial Econometric Perspective

AbstractThis paper introduces foreign direct investment (FDI) as an endogenous variable based on a neoclassical model of economic growth and investigates the impact of FDI on income convergence in China using provincial panel data from 1991 to 2007. A spatial model is later exploited to further examine the effect of FDI on convergence in China in light of remarkable and positive spatial correlations among neighboring regions. Results of estimates confirm the role of FDI inflow as a significant driving force to promote conditional convergence in China after the early 1990s. They also confirm that the non‐spatial classical model underestimates the impact of FDI on regional economic growth and also underestimates the speed of convergence.

Effects of knowledge capital on total factor productivity in China: A spatial econometric perspective

The transformation of China into a knowledge based economy is one of the most intensively debated research issues in Economic Geography. The focus of this study is on effects of knowledge capital on manufacturing total factor productivity (TFP) in China through the lens of the regional knowledge capital model (KCM). The objective is to estimate the impact of region-internal and region-external knowledge capital — measured in terms of patents granted by the Chinese patent office — on TFP across Chinese regions. We derive a Spatial Durbin Model (SDM) for empirical testing, using panel data on 29 Chinese regions for the years 1988–2007. The results indeed point to a shift of Chinese productivity growth to a more knowledge based one, statistically confirming the impact of knowledge capital on regional TFP after 1998. Furthermore, this shift is not only based on region-internal knowledge capital, but also on inter-regional knowledge spillovers.

Embeddedness of European Regions in European Union-Funded Research and Development (R&D) Networks: A Spatial Econometric Perspective

Wanzenböck I., Scherngell T. and Lata R. Embeddedness of European regions in European Union-funded research and development (R&D) networks: a spatial econometric perspective, Regional Studies. This study focuses on the embeddedness of regions in research and development (R&D) networks within European Union Framework Programmes by estimating how distinct regional factors affect a region's network positioning. Graph theoretic centrality measures in terms of betweenness and eigenvector centrality are calculated at the organizational level to reflect the relevant network structure before aggregation to the region level. Panel spatial Durbin error models (SDEM) reveal that region-internal knowledge production capacities, a region's level of economic development as well as spatial spillovers are important determinants for a region's positioning in the European Union-funded R&D network, but their significance differs depending on the centrality concept.

Embeddedness of European Regions in EU Funded R&D Networks: A Spatial Econometric Perspective

This study focuses on the embeddedness of regions in R&D networks within the EU Framework Programmes (FPs). Embeddedness denotes a region’s position in terms of graph theoretic centrality measures. We use betweenness centrality as a proxy for the ability to control knowledge flows in networks, and eigenvector centrality to measure intensive participation and connectedness with central hubs. The centrality measures are calculated at the organisational level to reflect relevant network structures, and then aggregated to the region. The objective is to estimate how region-internal and region-external characteristics affect a region’s network embeddedness, using panel spatial Durbin error models (SDEM). The results reveal that R&D expenditures, the strength of the knowledge base and a region’s socio-economic potential enable high network embeddedness in general. More specifically, a region’s importance in the network weighted by its linkages to central hubs (eigenvector centrality) is notably determined by a region’s endowment with human capital, while a diversified technological and economic structure is conducive to accessing and controlling diverse knowledge flows (betweenness centrality) in the European network of R&D collaboration.

Transport Turnover with Spatial Econometric Perspective under the Energy Conservation and Emissions Reduction in China

The method of spatial econometrics model is used to study the space correlation of road turnover (freight and passenger) among the 31 provinces (municipalities and autonomous regions) in China. Since some factors (such as the level of economic development, industrial structure, and population) may impact the road turnover (freight and passenger), these indexes are used as variables to reflect the influence in this model. The data of 31 provinces (municipalities and autonomous regions) in China in 2012 is collected to analyze the method. The results show that when the characteristics of turnover in a region are analyzed, it is necessary to consider the influence of its surrounding areas in space. Different scenarios were established according to different economic growth and degree of emission reduction to analyze the influence of the reduction of road turnover in regions with high spatial cluster effect to the whole country. We also carried out how to use the space effect of road turnover to the energy saving and emission reduction in the transportation industry.

Modeling History Dependence in Network-Behavior Coevolution

Spatial interdependence—the dependence of outcomes in some units on those in others—is substantively and theoretically ubiquitous and central across the social sciences. Spatial association is also omnipresent empirically. However, spatial association may arise from three importantly distinct processes:common exposureof actors to exogenous external and internal stimuli, interdependence of outcomes/behaviors across actors (contagion), and/or the putative outcomes may affect the dimensions along which the clustering occurs (selection). Accurate inference about any of these processes generally requires an empirical strategy that addresses all three well. From a spatial-econometric perspective, this suggests spatiotemporal empirical models with exogenous covariates (common exposure) and spatial lags (contagion), with the spatial weights being endogenous (selection). From a longitudinal network-analytic perspective, the same three processes are identified as potential sources of network effects and network formation. From that perspective, actors' self-selection into networks (by, e.g., behavioralhomophily) and actors' behavior that is contagious through those network connections likewise demands theoretical and empirical models in which networks and behaviorcoevolveover time. This paper begins building such models by, on theoretical side, extending a Markov type-interaction model to allow endogenous tie-formation, and, on empirical side, merging a simplespatial-laglogit model of contagious behavior with a simplep*-logit model of network formation. One interesting consequence of network-behavior coevolution—identically, endogenous patterns of spatial interdependence—emphasized here is how it can produce history-dependent political dynamics, including equilibriumphatand path dependence (Page 2006). The paper concludes with an illustrative application to alliance formation and conflict behavior among the great powers in the first half of the twentieth century.

On the determinants of public infrastructure spending in Chinese cities: A spatial econometric perspective

In the context of fiscal decentralization, we use cross-sectional data of 242 Chinese cities in 2005 to explore the major factors contributing to the decline of public investment. The main finding is that a city government appears to reduce its own infrastructure spending as a response to the rise of infrastructure spending of its neighboring cities, revealing evidence of positive spillover effects of public infrastructure expenditure. This paper contributes to the existing literature by providing a new perspective for understanding the decline in public investment. In addition, this paper sheds some light on the ongoing debate on the nature of government competition in China and has important implications for policy makers in making fiscal arrangements among government tiers in a decentralized economy.

Enterprise and Competitive Advantage in the Australian Context: A Spatial Econometric Perspective

Contemporary understanding of the evolution of the geography of uneven development is dominated by research derived from either the ‘new’ geographical economics or the new regionalism, typically in the context of either the European Union or North America. By way of contrast, we consider local economic performance in the Australian context. Building on Fingleton's work, we employ a spatial econometric modeling methodology to account for the role of both endogenous technological change and export orientation in determining local competitive advantage. The evidence suggests that competitiveness depends on both the indigenous characteristics of a local economy and its exposure to global competition. Entreprise et compétitivité dans le contexte australien: une perspective économétrique spatiale Résumé Les connaissances contemporaines sur l’évolution de la géographie de développements irréguliers sont dominées par la recherche dérivée d'une « nouvelle » économie géographique ou du nouveau régionalisme, généralement dans le contexte de l'Union européenne ou de l'Amérique du Nord. A titre de contraste, nous examinons les performances économiques locales dans le contexte de l'Australie. En nous basant sur Fingleton, nous appliquons une méthodologie de modélisation économétrique spatiale pour interpréter le rôle de variations technologiques endogènes et de l'orientation de l'exportation dans la détermination de la compétitivité locale. Les résultats des recherches indiquent que la compétitivité est tributaire à la fois des caractéristiques indigènes d'une économie locale et de son exposition à la concurrence mondiale. Empresas y la ventaja competitiva en el contexto australiano: una perspectiva econométrica espacial Extracto La comprensión contemporánea de la geografía evolutiva del desarrollo desigual está dominada por investigación derivada de la ‘nueva’ economía geográfica o del nuevo regionalismo, típicamente en el contexto de la Unión Europea o de Norteamérica. A modo de contraste, consideramos el rendimiento económico local dentro del contexto australiano. Basándonos en Fingleton, empleamos una metodología de modelación econométrica espacial para tener en cuenta la función del cambio tecnológico endógeno y la orientación de la exportación a la hora de determinar la ventaja competitiva local. La evidencia sugiere que la competitividad depende de las características indígenas de una economía local y en su exposición a la competencia global.

Sectoral Trends and British Regional Economic Growth – A Spatial Econometric Perspective

Sectoral Trends and British Regional Economic Growth – A Spatial Econometric Perspective

On the stability of the German Beveridge curve: a spatial econometric perspective

In this paper, the framework of the aggregated Beveridge curve is used to investigate the effectiveness of the job matching process using German regional labour market data. For a fixed matching technology, the Beveridge curve postulates a negative relationship between the unemployment rate and the rate of vacancies, which is efficiently estimated using spatial econometric techniques. The eigenfunction decomposition approach suggested by Griffith (2000, 2003) is the workhorse to identify spatial and nonspatial components. As the significance of the spatial pattern might vary over time, inference is conducted on the base of a spatial SUR model. Shifts of the Beveridge curve will affect its position, and time series estimates on this parameter are obtained. In contrast to findings for the US and the UK, the results provide serious indication that the degree of job mismatch has increased over the last decade. Although the outward shift of the Beveridge curve can be explained by structural factors such as the evolution of long term unemployment, it is also affected by business cycle fluctuations. The role of cyclical factors challenges the stability property of the curve. The relationship might be inappropriate to investigate policy measures directed to improve the mismatch, such as labour market reforms.