Spatial Econometrics Research Articles

Research on the impact of the digital economy on haze pollution: evidence from prefecture-level cities in China

Lately, the ongoing issue of haze pollution in multiple cities across China has had a profound impact on the residents’ wellbeing and overall health. The urgent necessity to address haze pollution is undeniable. Meanwhile, the rapidly evolving digital economy has sprung up as a pivotal driver for China’s economic growth, providing innovative approach to tackle haze pollution. This research explores the impact of the digital economy on haze pollution through utilizing the big data pilot zone as a quasi-natural experiment. Panel data from 280 prefecture-level cities in China is adopted, covering the period from 2011 through 2020. This analysis incorporates various models, namely, difference-in-differences model (DID), mediation effect model, and difference-in-differences spatial Durbin model. The findings highlight the substantial contributions of digital economy in alleviating haze pollution. Furthermore, these findings hold true even after a series of rigorous robustness checks. The constraint influence of the digital economy on haze pollution is particularly prominent in metropolitan areas, cities with elevated administrative levels, primary environmental protection cities, as well as economically developed cities. Financial development and technological innovation serve as important mechanisms through which the digital economy suppresses haze pollution. After accounting for spatial factors, the digital economy exhibits consequential spillover effects, leading to substantial inhibition of haze pollution in surrounding cities as well as locally. The novel aspects of this paper are as follows: Firstly, it regards the establishment of big data experimental zones as the exogenous policy shock of digital economy and employs a multi period DID model to evaluate the impact of the digital economy on haze pollution. Secondly, it explores the transmission pathways through which the digital economy affects haze pollution from the perspectives of financial development and technological innovation. Thirdly, it investigates the heterogeneous characteristics of the impact of the digital economy on haze pollution. Fourthly, it examines the spatial spillover effects of the digital economy on haze pollution from the perspective of spatial econometrics.

Study on the Promotional Effect and Mechanism of New Quality Productive Forces on Green Development

The new quality productive forces have the potential to spur both the green transformation of the industrial structure and innovative advances in green technology, which will further strengthen the foundation for sustainable growth. This study analyzes panel data from 30 provinces between 2012 and 2022 to build an evaluation system for new quality productive forces and green development at the provincial level. The entropy weight TOPSIS approach is used to assign weights to each indicator. Methods including fixed effects, mediation effects, and spatial econometrics are used to examine the contribution of new quality productive forces to green development and its mediation mechanism. The study finds that: (1) New quality productive forces significantly promote green development, and the conclusion still holds after a robustness test using the instrumental variables method and excluding municipalities. (2) The new quality productive forces contribute significantly to green development by improving technology and optimizing industrial structure. (3) The new quality productive forces not only directly enhance the green development level of the region, but also positively influence the green development level of the neighboring regions through the spatial spillover effect. (4) The eastern and central regions are more affected by new productivity in terms of green development. Based on these, efforts should be made to develop new quality productive forces, increase technological research and investment, and promote the development of industrial structure to be more environmentally friendly and efficient to promote green development.

Examining the evolution trajectory of construction and demolition waste in China: An empirical analysis based on spatial metrology and the environmental Kuznets curve

Curbing the increasing construction and demolition waste (C&DW) is critical for achieving China's goals of cleaner production and environmental sustainability. This paper based on the Environmental Kuznets Curve (EKC) and combining spatial econometrics with the GM (1,1) model, predicts key time points that will change the growth trend of C&DW and explores measures to reduce production. The results indicate that: (1)As of 2022, more than 87% of China's regional C&DW is unevenly distributed and agglomeration exists. More than 50% of the regions exhibit “high-high” agglomeration, which can lead to an inevitable inter-regional transfer of C&DW, but the central region has formed a “segregation zone”. (2) The evolution trajectory of C&DW shows a “growth-decline-regrowth” pattern, with points of change for regrowth occurring when GDP per capita reaches 142,914.24. Construction enterprises and consumption levels contribute significantly to C&DW, while the opposite is true for education, market competition, and transportation. (3) More than 95% of regions will experience C&DW regrowth during 2036–2040. Economic development is no longer considered to play a leading role in C&DW reduction measures. Even if the economic growth rate is increased by 10%, 30%, or even 50%, C&DW will enter the era of full-scale regrowth only three years earlier on average. This paper aims to shift the current focus of C&DW forecasting from solely on model innovation to accurately calculating the timing of future C&DW regrowth by verifying the EKC hypothesis application. This approach fills the gap in the quantitative analysis of long-term C&DW forecasting and provides new empirical cases for the theory of environmental economics. Furthermore, it addresses potential obstacles for decision-makers in achieving “zero waste cities”, thereby promoting global cleaner production and environmental sustainability.

Decoding Socio-Economic Demographic Trends: The Power of Spatial Econometrics and Geographic Analysis

Greece is experiencing a steady population decline caused by the declining migratory and natural balance. This research investigates the spatial impact of socio-economic and demographic factors on the natural population balance in Greece for the spatial zoning of municipal administrative units. Using geographically weighted regression (GWR) on data from the 2011 Greek census, the research explores the local impacts of factors like housing repair permits, vacant housing, employment rates, population inflows, distance from regional centers, aging, gender ratios, and education levels. An initial ordinary least squares (OLS) regression was conducted, revealing significant spatial variation and emphasizing the necessity of spatial econometric methods. The GWR model proved to be more effective in accounting for the variance in the data, removing spatial autocorrelation and revealing high local variation. Results show the high negative impact of the aging index in Western Greece and the Ionian islands, the counterintuitive positive effect of the gender ratio in urban areas, and the positive influence of population inflows in high-migration regions like Northern Greece and Crete. The results of this study underline the need to utilize spatial econometric methods for a precise and detailed understanding of demographic trends and provide valuable insights for localized strategies to address demographic challenges.

Transmission of economic growth between neighboring countries in Europe

ABSTRACT This study investigates the impact of economic growth of a country on the growth of neighbouring countries closely tied through trade and foreign direct investment in Europe. Empirical results show that a country’s growth has a significantly positive impact on the growth of its neighbouring countries. However, the impact is transitionary without affecting the steady-state growth of a country significantly. This study suggests that countries included in spatial econometrics should be limited to the countries with meaningful relationship. Spatial effect could be misspecified and overstated if it is included as steady-state growth factor in a growth model.

The MAUP and the effect of industrial diversity on Brazilian regional economic stability

The correlation between industrial diversity and regional economic stability is theoretically expected to be positive, suggesting that a region with greater industrial diversification will be less affected by exogenous shocks, resulting in less economic instability. Historically, however, empirical studies have produced mixed results, often finding a lack of significance in the correlation. Among the possible reasons that can lead to such divergence, this study focuses on the Modifiable Areal Unit Problem (MAUP), highlighting the importance of changes in geographic scale on the results due to spatial interference of data. To conduct this study, spatial econometrics and data from the RAIS and Demographic Census are used to collect information from Brazilian municipalities, micro-regions, and meso-regions between 2010 and 2019. The results show spatial influence at all geographic levels, as well as variation in the magnitude, significance and direction of the correlation depending on the scale used, confirming the hypothesis of MAUP in the study. Furthermore, the municipal level was the only one that presented results more consistent with the theory in the Brazilian case.

Research on the impact of manufacturing servitization on environmental pollution: spatial econometric analysis of sulfur dioxide based on 284 prefecture-level cities

Using China’s regional input–output table, the paper constructs indicators of manufacturing servitization, matches manufacturing servitization at the regional level with city data, and uses spatial econometrics to empirically analyze the impact of manufacturing servitization on urban sulfur dioxide (SO2) emissions within the classical Environmental Kuznets Curve (EKC) framework. The results show that manufacturing servitization can reduce SO2 emissions. Producer servitization and consumptive services can both significantly reduce industrial SO2 emissions. Transportation and warehousing servitization, information servitization, leasing, and commercial servitization, technology research and development servitization significantly reduce SO2emissions; technology research and development servitization, in particular, have the largest influence coefficient, while the reduction effect of servitization in the wholesale and retail and finance sectors is not significant. The study also found that servitization reduced the SO2 emissions through technological innovation and industrial structure upgrading.

Spatial Econometrics in Agriculture: Modelling Spatial Dependencies in Data

Spatial econometrics in agriculture focuses on modelling spatial dependencies in data, recognizing that agricultural outcomes are often influenced by geographic proximity and spatial interactions. This approach acknowledges that agricultural phenomena, such as crop yields, pest outbreaks, and soil quality, can exhibit spatial patterns that traditional econometric models may overlook. By incorporating spatial elements into econometric analysis, researchers can better understand how neighbouring regions or locations influence each other's agricultural outcomes. This is crucial for policymakers and farmers seeking to optimize resource allocation, manage environmental impacts, and enhance productivity in agriculture. Spatial econometrics provides a robust framework to uncover hidden relationships and spatial interactions within agricultural data, thereby supporting informed decision-making and sustainable agricultural practices in a spatially interconnected world.

A Model to Analyze Industrial Clusters to Measure Land Use Efficiency in China

An understanding of how land use efficiency and industrial clusters interact helps one to make informed decisions that balance economic benefits with sustainable urban development. The emergence of industrial clusters is a result of market behavior, while the determination of administrative boundaries is a result of government behavior. When these two are not consistent, it can lead to distortions in the allocation of land resources. However, current research on industrial development and land use efficiency is based on agglomeration within administrative regions rather than on industrial clusters. This study addresses this gap by identifying industrial clusters based on the spatial distribution of enterprises and analyzing their impact on land use efficiency. This study uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify industrial clusters, the convex hull algorithm to study their morphology, and spatial econometrics to measure the relationship between land use efficiency and the scale of industrial clusters. The results indicate the following: (1) the density of manufacturing industry (MI) clusters is significantly higher than that of information technology industry (ITI) clusters, and larger industrial clusters tend to be more circular in shape; (2) there is a positive correlation between the scale of industrial clusters and land use efficiency, and industrial clusters with varying levels of land use efficiency are interspersed throughout; (3) significant differences exist between the boundaries of industrial clusters and administrative regions, which could lead to biases when analyzing land use efficiency based on administrative regions. This study provides theoretical support for government policies on improving land use efficiency in China.

Production factors, regional economic development and agriculture in brazil: A spatial econometrics approach

Agriculture has a great effect on the development process of a region and production land, labor, and technology are determinant factors in its dynamism. This work carries out a spatial analysis for each production factor of Brazilian municipalities in 2006 and 2017 and intends to verify if there are patterns of association and agglomerations. This work is applied research of quantitative and exploratory nature. The Exploratory Analysis of Spatial Data (ESDA) was used to perform spatial analysis for each production factor in Brazilian municipalities in 2006 and 2017 and to check the patterns of association and agglomerations. The main agricultural activities and practice are related to exports as proposed by North. The formation of clusters verified both in the use of the factors of land, labor and technology, and in the value of agricultural production has a polarized role in the Brazilian space, as described by Perroux. This polarization and the formation of clusters expanded at certain points over time, indicating the formation of new links with neighboring municipalities (a la Hirschman). The spatial association pattern was significant in all analyzed variables, with emphasis on the non-physical technology factor, presenting the highest spatial autocorrelation coefficient in both periods analyzed.

Investigation of the Relationship between Women Indicators and Socio-Economic Development Index using Spatial Econometrics Models

Investigation of the Relationship between Women Indicators and Socio-Economic Development Index using Spatial Econometrics Models

Research on the spatio-temporal evolution and impact of China’s industrial green development from the perspective of digital economy--based on analysis of 279 cities in China

Empowering industrial green development with the digital economy represents a crucial approach to advancing the green transformation of China’s economy and achieving high-quality economic development. Based on panel data from 279 cities in China from 2011 to 2021, this article uses the spatial econometrics model and threshold effect model to explore the spatial effect and nonlinearity of digital economy (DE) on industrial green development (IGD). The results show that, both DE and IGD exhibit fluctuating upward trends in China. There exists stronger spatial linkage effect in the eastern coastal areas, but this effect is weaker in the central and western regions. Furthermore, DE has significantly promoted IGD, and the conclusion remains robust after a series of endogeneity and robustness tests. Additionally, the promotion of the DE for IGD is characterized by regional spillovers and non-linear dynamics. The research results can serve as a theoretical support and decision-making basis for China to enhance industrial green development, promote the construction of an ecological civilization, and implement sustainable development strategies.

Predicting cycling flows in cities without cycling data

Cycling is a potential tool to mitigate many of the problems faced by urban populations today. Encouraging the use of bicycles as a legitimate mobility tool, however, demands adequate knowledge of current mobility patterns, such as locations of trip generation and attraction. Unfortunately, cities usually do not gather enough data to adequately understand cycling demand. We propose models based on spatial econometrics and gradient boosted regression trees which can be trained with data from cities with mature cycling cultures and then applied to cities still in their cycling infancy to supply city officials with a better estimate of potential future OD matrices. We perform a case study in the Boston Metropolitan Area and show results comparing both types of models.

European funds and the dynamics of economic growth among EU regions: A spatial modelling approach

Contemporary development policy concentrates predominantly on reducing noticeable economic differences in a spatial system, and an important role in this respect is played by EU Cohesion Policy. Owing to the considerable scale of financial exposure of Cohesion Policy, the assessment of effectiveness of the implemented measures and their greater reliance on evidence are of major significance. Despite numerous attempts to empirically verify the effects of EU funds spending, the problem remains unresolved, and the results of recent studies lead frequently to ambiguous conclusions. The article aims to verify the β-convergence process in EU regions in the years 2007–2015 allowing for the impact of the received EU funds and the spatial effects determining economic growth. In the research, use was made of a covergence approach consisting in the regression modelling of per capita GDP growth. Spatial econometrics methods were applied, by adding variables determining spatial interactions that can influence the economic growth rate to the specification of the estimated models. The estimated econometric models show that in the years 2007–2015 EU funds positively affected economic growth. At the same time, the process of reducing economic disparities between EU regions was observed. Moreover, the existence of spatial effects for a dependent variable was confirmed. The results also show that the value of the EU funds received in the surrounding area generally did not translate into the dynamics of growth in a given location. The research presented is one of the few in which spatial interaction was verified by using weights matrices based on contiguity, distance, flows and affiliation.

Evaluating cropping intensity in registered and unregistered farms: Evidence from Kenya

Sustainable agricultural intensification strategies are crucial in increasing Africa's agricultural productivity. Globally, literature shows strong linkages between land tenure security and sustainable agriculture. However, in Sub-Saharan Africa (SSA), findings remain inconsistent. Studies attribute this inconsistency to the use of cost-benefit and yield methodologies that are limited in capturing the nuance of subsistence farming and spatial and temporal distribution. Thus, this study seeks to fill this gap using Tharaka Nithi County in Kenya by employing remote-sensing and spatial econometrics to evaluate this relationship. The study uses the Normalised Difference Vegetation Index (NDVI) as the dependent variable and a proxy for cropping intensity, which similar studies have not used. NDVI datasets covered seven years (2014–2020) acquired from Copernicus Global Land Service. Explanatory factors include land registration status, topography and climatic characteristics. The study utilises a three-step analysis, which includes exploratory regression for model selection, global regression using Ordinary Least Squares (OLS), and localised regression using Geographically Weighted Regression (GWR). Spatial autocorrelation tests on OLS residuals using Moran's Index indicated spatial heterogeneity; hence, the study ran GWR. GWR performed better with an adjusted R2 value of 0.941, compared to OLS with an R2 value of 0.807. Mapping the GWR model illustrated that NDVI is lowest in unregistered land in the semi-arid zone of the County (0.012608) and highest in registered areas (0.883172). A hot-spot analysis using the Gettis-Ord Gi* Statistic validated the findings. The test showed cold spots in unregistered land in the semi-arid zone and hot spots in registered land, which was significant at 99 % confidence. The findings shows that the relationship between land registration and cropping intensity is highly significant, particularly in arid and semi-arid lands (ASALs), an indicator that land tenure security promotes sustainable agriculture in Kenya. The results enrich policy discussions aimed at land reforms and sustainable agriculture. The study recommends fast-tracking land registration prioritising ASALs, implementing the national land information system, and modernisation of land registries in Kenya to improve public access to land information. The study also recommends similar studies on various agroecological zones to supplement the findings, mainly where subsistence farming is dominant. The findings will add a critical layer of information in decision-making for sustainable agriculture and land reform policies.

Public debt, investment and economic growth dynamics: Do geographical proximity and spatial spillover effects matter?

This study employed an innovative spatial econometrics approach to analyze the spillover effects of foreign public debt and foreign direct investment (FDI) on regional economic growth dynamics of Eastern Africa. Specifically, we utilize Spatial Durbin Fixed Effect (SDM-FE) Model, drawing on balanced panel data from the World Development Indicators (WDI) for the period 1992–2019. This study contributes by utilizing a spatial weight matrix and investigating its interactions with economic growth and various other macro-regional factors, thereby unveiling the presence of spatial spillover effects and the impact of geographical proximity of countries within the region. Our results indicate that foreign public debt, FDI, gross fixed capital formation, human capital development, inflation, and government expenditure, significantly have spatial spillover effects on regional economic growth. Additionally, the results underscore the presence of regional growth divergence and provide further evidence of substantial regional spatial dependence in economic growth. Based on these outcomes, it is evident that foreign public debt has a negative impact on economic growth, which is consistent with the debt overhang theory and the crowding-out hypothesis. Moreover, the foreign public debt and regional growth policies should be devised considering the geographical interconnectedness between countries in the region, as the research output has revealed the impacts of growth and debt spillovers among the countries studied. Policies that prioritize these objectives would promote long-term regional economic growth and prevent the effects of excessive foreign public debt by creating a favorable macroeconomic climate.

Do government-guided funds "guide" the transregional mobility of capital? Evidence from cross-city equity investments

The study tries to evaluate and explain the influence of government-guided funds (GGFs) on the transregional mobility of equity capital. We employ spatial econometrics to investigate the spatial effects of GGFs on the mobility of equity capital. Using data on cross-city equity investments of A share listed companies in 335 Chinese cities from 2007 to 2021, we first develop a novel measure of Chinese capital mobility. We find a V-shaped correlation between GGFs and the flow of equity capital. Notably, reducing market segmentation and enhancing native identity are two important mechanisms by which GGFs facilitate the influx of equity capital into local regions. Also, GGFs show a significant spatial spillover effect on neighboring cities. The results still hold after a series of robustness tests and heterogeneity tests.

European Structural and Investment Funds (ESIFs) and Regional Development across the European Union (EU)

This scoping review synthesizes the evidence from eleven key studies to assess the impact of European Structural and Investment Funds (ESIFs) on regional development across the European Union (EU), focusing on fund efficiency, regional disparities and convergence, governance quality, economic freedom, and fund management. A systematic search was conducted across multiple databases to identify the relevant literature published up to 2023. Eleven studies were selected based on the date published and their focus on ESIFs’ role in regional development, employing a range of methodological approaches including Data Envelopment Analysis (DEA), spatial econometrics, and multivariate analyses. The thematic analysis identified four main categories: Methodological Approaches in Evaluating Fund Efficiency, Regional Disparities and Convergence, The Interconnection between Governance Quality, Economic Freedom, and the Efficiency of Structural Fund Management, and The Absorption Capacity and Fund Management. The review highlights the importance of sophisticated analytical tools in evaluating fund efficiency, with DEA and spatial econometrics providing critical insights into fund management efficiency. Studies underscored the nuanced efficacy of ESIFs in reducing regional disparities, albeit pointing to the need for more targeted fund allocation. Governance quality and economic freedom emerged as pivotal factors enhancing fund management efficiency, suggesting the potential of governance reforms in optimizing ESIF allocation and utilization. Challenges related to fund absorption and management were illuminated, advocating for enhanced institutional management capabilities and the development of innovative performance indicators. The findings of this scoping review contribute to a deeper understanding of the complexities surrounding ESIFs’ impact on regional development within the EU. They underscore the critical importance of governance quality, economic freedom, methodological rigor, and strategic fund allocation in enhancing the effectiveness of ESIFs. The review calls for tailored policy interventions and the integration of national and European funding strategies to maximize the impact of these programs on regional development and SME support. Future research should continue to refine these methodological approaches and explore the causal effects of funding, to enhance our understanding of ESIFs’ efficiency in promoting regional development and convergence within the European Union.

How public environmental appeals affect the collaborative governance in pollution and carbon reduction: Evidence from spatial effects across Chinese cities

China has always adhered to the strategy of sustainable development. It is prevalent the public want a good living environment, which requires local governments and businesses to enhance their environmental governance capabilities. Using the panel data from Chinese cities from 2012 to 2019 and econometrics models, we examine the impact mechanisms of public environmental appeals (PEA) on efficiency of collaborative governance in pollution reduction and carbon mitigation (GPC). Results indicate that there is a positive spatial clustering of GPC across cities, with high-high clustering is notably concentrated in the southern regions of China and low-low clustering is prevalent in the northern regions. Spatial econometrics model results reveal that the stronger PEA, the higher GPC. The result of mechanism analysis shows the mediation of environmentally friendly technological innovation is crucial. Subsequent inquiry uncovers that the digital economy positively moderates the impact of PEA on GPC. The Belt and Road policy region exhibits heightened sensitivity to PEA, thereby enhancing the positive impact of PEA on GPC.

NiReMS: A regional model at household level combining spatial econometrics with dynamic microsimulation

ABSTRACT The heterogeneous spatial and individual impacts of the Great Recession, Brexit and COVID-19 have generated an important challenge for macroeconomic and regional/spatial modellers to consider greater integration of their approaches. Focusing on agent heterogeneity at the ITL 1 level in the UK, we propose the National Institute Regional Modelling System (NiReMS) – a synthesis of dynamic microsimulation with a spatial regional macroeconometric model. The model gives regional macro projections while allowing for household level inference. To showcase the model, we explore the impact of discontinuing the uplift in Universal Credit (UC) before the end of the pandemic and show that it led to more households consuming less. Importantly, the proposed framework highlights the unequal distributional impact across regions of the UK.