Spatial Weight Matrices Research Articles

Dynamic evolutionary characteristics and influence mechanisms of carbon emission intensity in counties of the Yangtze River Delta, China.

Clarifying the intrinsic mechanism of county carbon emission intensity (CEI) is essential for guiding the realization of a low-carbon economy as well as for the strategic goals of carbon peaking and carbon neutrality. However, at present, scholars mostly focus on provincial and city scales, with the identification of influencing factors and spatial effect mechanisms of CEI rarely included in the analysis framework. Herein, with the help of three spatial weight matrices, the spatial autocorrelation, the "F + S" influence factor identification method, and the spatial panel econometric model were used to analyze the evolutionary paths and influencing factors of CEI for 209 counties in the Yangtze River Delta (YRD) from 2007 to 2020. The results show that (1) the CEI of the YRD decreased from 1.998t/104 RMB to 0.858t/104 RMB. Furthermore, the spatial pattern was low in the southeast and high in the northwest, with high-value areas concentrated in municipal districts and resource-based counties. (2) Moran's I spatial autocorrelation index indicated significant spatial clustering of county CEI. (3) Financial science and technology expenditure, industrial structure, share of urban built-up land, and the urban-rural income gap affected the change in CEI and its spatial effect, whereas total imports and exports had a significant negative effect on local CEI. Therefore, to achieve China's "double carbon" goal, it is necessary to consider the five development concepts as the core, strengthen inter-county exchanges and collaboration, as well as promote collaborative management of the ecological environment.

Linkage‐ and structure‐based technological proximity and interregional spillovers of innovation growth

AbstractThis paper explores the roles of different types of technological proximity on the interregional spillovers of innovation growth. Using a panel dataset containing Chinese province patents and R&D inputs from 2002 to 2019, this paper estimates a knowledge production function with the spatial weight matrices constructed based on the linkage‐based and structure‐based technological proximity. We find that the two types of technological proximity are significantly different and their contributions to interregional innovation spillovers differ. The structure‐based proximity shows a lower influence on spillovers and its effect in the process of interregional innovation spillovers is affected by the regional development level. This paper originates to clarify two conceptualizations of technological proximity, compare their influences on innovation spillovers, and explore their regional differences.

Testing the correct specification of a system of spatial dependence models for stock returns

This paper provides two specification tests for the system of spatial autoregressive model of order m. We derive the theoretical limit distributions and show in a detailed Monte Carlo simulation study that the tests result in reasonable sized testing procedures with large power. In the empirical application, we analyze Euro Stoxx 50 returns in two different time spans, looking for insights how well models with different specifications of the spatial weighting matrices (local, country, industry and country-industry specific dependencies including interaction effects) fit to the data. The analyzes also demonstrate the ability of the tests to detect inaccurate Value-at-Risk forecasts.

How does green fiscal expenditure promote green total factor energy efficiency? — Evidence from Chinese 254 cities

This paper explores how green fiscal expenditure affects Green Total Factor Energy Efficiency (GTFEE) from both non-spatial and spatial perspectives based on selected panel data of 254 cities in China. The results of the non-spatial panel model indicate that green fiscal expenditure has a significant positive impact on GTFEE. A comparative study of different econometric models shows that the main parts of the results are largely consistent, which indicates that the conclusions are robust. The spatial autocorrelation findings indicate a statistically significant presence of positive spatial spillover effects in the GTFEE of Chinese cities. Spatial Durbin Model (SDM) and Dynamic Spatial Durbin Model (DSDM) indicate that green fiscal expenditure has a similarly significant positive effect on GTFEE, which will increase over time. This paper considers different spatial weight matrices and changing measures to measure GTFEE, and the conclusions remain robust. Furthermore, industrial structure upgrading, and digital economy are vital in strengthening the relationship between local governments' green fiscal expenditure and GTFEE. According to heterogeneity analysis, green fiscal expenditure has a more significant effect on the GTFEE in mature resource-based cities and non-resource-based cities.

Spatial analysis for psychologists: How to use individual-level data for research at the geographically aggregated level.

Psychologists have become increasingly interested in the geographical organization of psychological phenomena. Such studies typically seek to identify geographical variation in psychological characteristics and examine the causes and consequences of that variation. Geo-psychological research offers unique advantages, such as a wide variety of easily obtainable behavioral outcomes. However, studies at the geographically aggregate level also come with unique challenges that require psychologists to work with unfamiliar data formats, sources, measures, and statistical problems. The present article aims to present psychologists with a methodological roadmap that equips them with basic analytical techniques for geographical analysis. Across five sections, we provide a step-by-step tutorial and walk readers through a full geo-psychological research project. We provide guidance for (a) choosing an appropriate geographical level and aggregating individual data, (b) spatializing data and mapping geographical distributions, (c) creating and managing spatial weights matrices, (d) assessing geographical clustering and identifying distributional patterns, and (e) regressing spatial data using spatial regression models. Throughout the tutorial, we alternate between explanatory sections that feature in-depth background information and hands-on sections that use real data to demonstrate the practical implementation of each step in R. The full R code and all data used in this demonstration are available from the OSF project page accompanying this article. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Systemic Risk Spillover Effects among China’s Financial Institutions: Evidence from the Spatial Econometric Model

This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.

Industrial agglomeration, environmental protection, and high-quality economic development: evidence from cities along the Yellow River Basin of China

China’s economy has entered a new phase of development, but ecological and environmental issues continue to hinder the quality of economic development. Meanwhile, the externalities of industrial agglomeration are seen as essential for promoting high-quality economic development. This article proposes a theoretical framework to examine the relationship between industrial agglomeration, environmental protection, and high-quality economic development. Using cities along the Yellow River Basin as the research object, a spatial econometric model is employed to verify the hypothesis. Ultimately, the conclusions are as follows. First, there is a significant U-shaped curve relationship between industrial agglomeration and high-quality economic development, as well as between environmental protection and a high-quality economy. Industrial agglomeration plays a significant role in promoting environmental protection. Second, high-quality economic development exhibits spatial spillover effects under both spatial weight matrices, while industrial agglomeration and environmental protection only show spatial spillover effects based on geographical distance spatial weight matrices. Finally, the article suggests that cities should accelerate the degree of industrial agglomeration to achieve the dual effect of promoting environmental protection and high-quality economic development. Additionally, governments should pay special attention to inter-regional synergistic mechanisms when fostering ecological protection and high-quality economic development.

Financial agglomeration and regional green innovation efficiency from the perspective of spatial spillover

Starting from the theoretical mechanism of the impact of financial agglomeration on regional green innovation efficiency, and using panel data from 30 provinces in China from 2009 to 2017, this study calculates a financial agglomeration index and green innovation efficiency applying principal component analysis and the slacks-based measure (SBM) of super-efficiency. The study then constructs static and dynamic spatial Durbin models using two different spatial weight matrices to empirically test the spatial spillover effect of financial agglomeration on regional green innovation efficiency. The results reveal a significant positive spatial correlation between China's inter-provincial green innovation efficiency and financial agglomeration. Both static and dynamic models demonstrate that financial agglomeration has a significant spatial spillover effect on the green innovation efficiency of adjacent provinces, but the static model overestimates the impact of spatial spillover. In addition, human capital level, technology market activity, and environmental regulation all have significant positive impacts on the green innovation efficiency of provinces and adjacent provinces.

The impact of entrepreneurship of farmers on agriculture and rural economic growth: Innovation-driven perspective

This research delves into the underlying impacts of farmers' innovative entrepreneurship on agricultural and rural economic development in China, adopting a dynamic and spatio-temporal perspective. The study utilizes panel data encompassing 30 provinces (cities and autonomous regions) from 2015 to 2020, with a systematic consideration of diversified spatial weight matrices. The empirical findings reveal that the spatial distribution of rural innovative entrepreneurship demonstrates a characteristic of low-low agglomeration, accompanied by evident positive spatial spillover effects and radiation-driving effects, especially in regions with similar urbanization levels. Additionally, the study identifies heterogeneous effects across regions with different grain production patterns and household income levels. Ultimately, the research underscores the significance of deeply integrating farmers' innovation and entrepreneurship and provides empirical evidence to support the necessity of adopting differentiated and specialized incentive measures for rural entrepreneurship amid the context of the new economic normal.

Beyond distance: The spatial relationships of European regional economic growth

We analyze the spatial relationships of economic output dynamics in European regions from 2000 to 2019 using dynamic spatial autoregressive growth models. In contrast to previous studies that rely on exogenous spatial weight matrices based solely on geographical proximity, we use a novel Bayesian approach to fully estimate the spatial weight matrix. Our results show that economic and sectoral characteristics (e.g. sectoral production structure, education, etc.) significantly influence the degree of regional interdependence. The approach thus allows to study the complex dynamics of regional economic development beyond mere distance.

Spatial nonlinear effects of urban vitality under the constraints of development intensity and functional diversity

Urban vitality is the comprehensive characterization of the activities and processes in the city, which is influenced not only by environmental factors but also by the vitality of surrounding areas. Thus, the spatial effect of urban vitality can be understood under the constraint of the built environment. Taking Xiamen Island as a case, multisource big data are used to measure urban vitality, development intensity, and functional diversity. Spatial weight matrices with different distances are constructed. Through the spatial autoregressive threshold model, the threshold values of development intensity and functional diversity are estimated, and the spatial nonlinear effects of urban vitality based on different spatial weight matrices are analyzed. Results show that: (1) There is a significant spatial spillover effect of urban vitality. The spatial spillover effect is influenced by the built environment conditions and shows the characteristics of nonlinear growth with the change in development intensity and functional diversity; (2) the spatial spillover effect of urban vitality is significantly enhanced with the expansion of influence distance; (3) The development intensity has double thresholds. With the improvement of development intensity, the spatial spillover effect of urban vitality becomes stronger and more significant. The functional diversity has a significant single threshold value. The discussion of functional diversity should be carried out on the premise of density. The conclusion provides a theoretical basis for the optimization of urban vitality and built environment.

The Spatial Autoregressive Panel Data Model with Spatial Moving Average Errors

This paper advocates the wider use of the spatial autoregressive (AR) panel data model with spatial moving average (MA) errors, individual and time effects, and different spatial weight matrices for each spatial lag. We demonstrate the practical relevance of this model, derive and investigate the asymptotic properties of a simple quasi maximum likelihood within estimator when is large and is finite, and provide an empirical example explaining military expenditures in 144 countries over the period 1993–2007.

Sensitivity analysis of Bayesian nonparametric spatial crash frequency models

This study focused on the sensitivity analysis of Bayesian nonparametric spatial models which combine the strengths of spatially structured random effects and the Dirichlet mixture to account for the unobserved heterogeneity of crash counts. Various evaluation criteria were employed to compare the performance of models with varying spatial weight matrices and precision parameters (alpha). The results demonstrate that there exists strong correlation among the posterior number of clusters, alpha, the fraction of variation explained by the spatial random effect, and different evaluation criteria. Even though the increased upper bound value of alpha does not necessarily lead to the enhanced model performance, the models with the full flexibility to choose the desirable amount of clustering tend to perform better than those with limited flexibility due to smaller allowable mass components. Compared with the precision parameter, no obvious trend is illustrated for the different evaluation criteria along the varying spatial weight matrices.

Green finance, the low-carbon energy transition, and environmental pollution: evidence from China.

With 2008-2020 China's provincial panel data as the research object, we use the entropy weight method to compute green finance and pollution, and we empirically study the scope of the spillover effects and threshold impacts of green finance on environmental pollution using the spatial Durbin model and threshold regression model. The findings are as follows: first, under different spatial weight matrices, green finance has a spatial spillover effect on pollution. And the effect boundary is approximately 500km. Second, with the low-carbon energy transition, technological progress, and green finance as threshold variables, there are a single threshold, single threshold, and double threshold, respectively, that have significant threshold effects. Third, the spillover and threshold effects of green finance on pollution differ by region. The indirect effect is negative in the eastern region and positive in the central and western regions. There is a single threshold effect of low-carbon energy transition and green finance in the western region, a single threshold effect of green finance in the central region, and a single threshold effect of technological progress in the eastern region. On this basis, we put forward. specific policy recommendations.

Spatial spillovers and world energy intensity convergence

This paper studies the influence of spatial spillovers on energy intensity growth and convergence across 153 countries from 1999 to 2018. The inclusion of spatially lagged variables allows the study of the influence of trade, technological diffusion and policy mimicry on these two processes. Contrary to previous literature, we consider different spatial specifications and weight matrices to test whether spatial spillovers are exogenous or endogenous, as well as their range of influence. Our study finds the spatial lag of X model (SLX), with local spatial weight matrices, fits better to the data. Convergence is found for the world as a whole, conditional to the spatial distribution of countries and several long-term economic characteristics. In this sense, clusters of nearby countries present higher rates of convergence. Besides, domestic capital accumulation, total factor productivity growth and renewable energy consumption significantly determine the characteristics of clubs of convergence. Furthermore, spatial spillovers associated to capital accumulation, population growth and renewable energy consumption also contribute to the definition of these clubs.

Comparison of Queen Contiguity and Customized Weighting Matrices on Spatial Regression to Identify Factors Impacting Poverty in East Java

Poverty is crucial problem that negative impact on all sectors, including economic, social, and cultural development in East Java Province. Poverty can also increase unemployment, crime, trigger social disasters and hinder progress East Java province. One efforts overcome problem of poverty in East Java province is detect factors that influence. This effort can be done through statistical modeling to determine factors that influence poverty in East Java province. statistical model that can identify factors that influence poverty and explain relationship between region and surrounding area is spatial regression analysis. In spatial regression analysis, spatial weighting matrix is needed to determine spatial influences between regions where one region influences neighboring regions. spatial weighting matrices that is often used is queen contiguity, and according to Anselin (1988:20), this spatial weighting also considers initial information, purpose of case studied, and theory underlying the research. This weighting uses social and economic variables case under study, namely customized weighting matrix. Based on results of this study, shows that best spatial regression and spatial weighting models are General Spatial Model (GSM) with customized weighting because customized weighting produces better estimation results than SAR, SEM and GSM models with queen contiguity weighting in district and city poverty modeling in East Java province with Akaike Infomation Criterion (AIC) value of 188.77 and detemination coefficient (R2) of 84.95%. School's Expected Time, Life Expectancy Score, and Employment Participation Rate are factors that will have substantial impact on percentage of population living in poverty East Java's districts and cities in 2021.

Global Value Chains, Digital Economy, and Upgrading of China’s Manufacturing Industry

This study utilized panel data from 31 provinces in China from 2006 to 2020 to investigate the impact of the digital economy on the upgrading of the manufacturing industry’s global value chain. Two types of spatial weighting matrices were used to construct SAR, SEM, SAC, and SDM models. The results revealed that technological innovation plays a direct mediating role in the upgrading of the manufacturing industry, and the global value chain has a positive regulatory effect on the relationship between the digital economy and the manufacturing industry’s upgrading. Under the economic distance spatial weighting matrix, the spatial spillover effect of the digital economy on the manufacturing industry’s global value chain is not significant, whereas, under the geographic distance spatial weighting matrix, the digital economy has a positive and significant spatial spillover effect. The SDM model showed the best explanatory effect. This implies that geographic spatial dependence has a significant impact on the upgrading of the manufacturing industry’s industrial structure, and it is positively influenced by nearby provinces. Understanding the impact mechanism and spatial spillover effects of the digital economy on the manufacturing industry’s upgrading can help promote efficient, fair, and balanced regional development. It can also aid in constructing a new domestic and international “dual circulation” development pattern that evolves with the global manufacturing value chain, sharing the dividends of the digital economy’s impact on the global value chain’s development.

Analysis of the effect of heterogeneous human capital on new urbanization in China

The impact of human capital on the process of urbanization is very important. This research aims to examine the effect of heterogeneous human capital on the process of new urbanization and divides human capital into basic human capital, skill-based human capital, and knowledge-based human capital. The 2005–2019 provincial spatial panel data model analyzes the impact of heterogeneous human capital and spatial spillover effects on the process of new urbanization based on distinct spatial weight matrices. The results indicate that the three types of human capital have obvious spatial correlations with the evolution of new urbanization. Basic and skill-based human capital have a significant positive influence on boosting the process of new urbanization, while the influence of knowledge-based human capital is not obvious, and less effect on surrounding cities. In terms of regional heterogeneity, the promotion effect of human capital in the east is higher than that in the middle and west. According to the research, this paper suggests that the government should guarantee the social welfare of basic human capital and skilled human capital, because they are an important force in the construction of urbanization, and weaken the impact of academic qualifications on the introduction of urban talents.

So close, no matter how far: A spatial analysis of CO2 emissions considering geographic and economic distances

AbstractIn recent years, the effects of climate change have become a topic of growing interest in the literature. Many works claimed the importance of spillover effects while studying CO2 emissions. Most part of them considers these indirect effects from a geographical perspective. The reduction of transportation costs makes other factors more important. Thus, the main aim of this paper is to analyse the existence of spatial dependence, considering geographical and economic proximity and comparing both measures. Empirically, we make use of the World Input–Output database with a worldwide focus from 2000 to 2014. Based on an environmentally extended multiregional input–output model, we estimate the CO2 emissions embodied in the domestic production and international trade between countries. To analyse the dependence from both perspectives, we carry out a spatial econometric analysis and make use of two different spatial weight matrices. The results offer a new approach on this field, highlighting the importance of the spillover effects to explain the CO2 emissions of the local country, showing that economic proximity is even more important than geographical one.

Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text

Using the annual report text information of Chinese listed banks from 2007 to 2020, this paper constructs multiple kinds of spatial weight matrices from the perspective of business similarity to identify the systemic risk spillover channels. Furthermore, the Bayesian posterior probability methodology proposed by Debarsy and LeSage (2018) is employed to assess the relative importance of each spillover channel. Besides, we discuss the risk spillovers of specific factors. The empirical results show that the loan type, loan region, investment industry, and income structure are all effective risk spillover channels, and the loan type channel is of the utmost importance. And also the spillover effects of bank-specific factors are identified and continuous. Our results are validated by robust analysis.