Manufacturing agglomeration and high-quality economic development: Unveiling nonlinear dynamics and spatial spillovers in China

Spatial spillovers in U.S. airport non-aeronautical revenue performance

Spatial spillover effect of green credit on carbon emission intensity in China: The role of high-quality productive forces

Assessing the potential of new-typed infrastructure in decarbonizing the industrial sector: A spatial quantile Durbin model.

Introduction/Main Objectives: Food security is a key concern for all countries, especially Indonesia. Technological development and democratic quality are vital for sustainable food security. This study aims to determine the impact of technology and democracy on food security. Background Problems: The relationship between food security and these two factors remains uncertain. Moreover, the extant literature on the spatial impacts on food security yields results that are inconclusive. Novelty: This study offers a comprehensive depiction of the impact of spatial relationships between variables, with a particular focus on the quality of democracy and technology, on the multidimensionality of food security. Research Methods: A spatial lag model is applied to ascertain the impact of technological and democratic on multidimensional food security using data from 34 provinces in 2022. Finding/Results: The results reveal significant spatial dependence in Indonesia’s food security. Technological development and democratic quality positively and significantly affect food security, while urbanization and food crop land expansion show negative and positive effects, respectively. Spatial spillover accounts for approximately 37%–38% of the total impact of each explanatory variable. These findings suggest that technology adoption, democratic strengthening, and interprovincial collaboration are crucial for improving food security.

Enhancing urban resilience is critical for mitigating disaster risks. This study investigates the relationship between digital infrastructure construction (DIC) and urban resilience using panel data from 283 cities in China (2011–2022). Employing constructed indices for DIC and multi-dimensional urban resilience, we empirically analyze the mechanisms and spatial spillover effects of DIC. The study finds: (1) DIC significantly enhances urban resilience, with this positive effect mediated through improvements in critical public services, technological innovation, and resource allocation efficiency. (2) The resilience-enhancing impact of DIC exhibits significant heterogeneity, varying substantially across regions and industrial structures. (3) DIC generates positive spatial spillovers, boosting urban resilience not only locally but also in neighboring regions. These findings provide robust empirical evidence for leveraging DIC as a strategic tool to strengthen urban resilience against disasters.

This study examines the nexus between climate change and regional economic convergence in Indonesia using panel data from 34 provinces (2011-2024). Employing the Spatial Durbin Model (SDM), the analysis explicitly captures spatial spillovers across regions. Results confirm conditional β-convergence with a relatively slow rate of 1.02% per year. Climate variables are critical: temperature and precipitation both exhibit inverted U-shaped relationships with growth, indicating that moderate levels support development while extremes become detrimental. Spatial spillovers reveal that severe weather shocks in neighbouring provinces significantly hinder convergence in the region of origin. Conversely, investment shows strong direct and indirect positive effects, while population growth slows convergence, and human capital contributes modestly. Overall, the findings highlight that climate dynamics shape not only local growth trajectories but also interregional interdependencies. The study provides new evidence for developing economies and offers policy insights for inclusive, low-carbon, and climate-resilient regional development.

Based on panel data of 281 prefecture-level cities in China from 2006 to 2020, this paper constructs a two-way fixed effects model to explore the impact of dig-ital infrastructure on agricultural total factor productivity (ATFP), and intro-duces a moderated mediation effect model to test the synergistic effect of re-source misallocation and industrial upgrading. The results show that: (1) Digital infrastructure significantly promotes ATFP; (2) Resource misallocation plays a mediating role in this process, and it significantly inhibits ATFP; (3) With in-dustrial structure upgrading, the promoting effect of digital infrastructure on ATFP weakens; (4) Digital infrastructure improves ATFP by suppressing re-source misallocation, and industrial structure upgrading can enhance the inhib-itory effect of digital infrastructure on resource distortion; (5) Spatial effect analysis reveals that digital infrastructure has a positive spatial spillover effect. Based on these findings, this paper puts forward policy suggestions such as in-creasing investment in digital infrastructure, balancing the relationship be-tween industrial structure upgrading and agricultural development, strength-ening policy coordination, and optimizing the regional layout and resource sharing of digital facilities to promote the positive interaction between industri-al structure upgrading and digital infrastructure.

This study explores the regional and temporal effects of e-government development on economic growth in Vietnam. While prior literature recognizes the role of digital governance in enhancing administrative efficiency and institutional performance, limited attention has been paid to spatial spillovers and the evolving nature of these impacts. Employing provincial panel data from 2018–2022 within a spatiotemporal heterogeneity framework, the study identifies pronounced regional variation and a temporal learning process in the benefits of e-government. The findings highlight the importance of institutional capacity and local context. Policy implications call for regionally tailored implementation strategies, targeted support for underperforming provinces, and sustained investment in digital infrastructure and human capital to foster equitable digital development and regional economic growth.

Understanding the pathways through which digital innovation contributes to carbon emission reduction is crucial for designing effective climate policies. Existing studies generally find a negative association between digitalization and carbon emissions, but they often treat this relationship as a “black box” and pay insufficient attention to the distinct local and spatial mechanisms through which digital innovation operates. This paper investigates the impact of digital innovation on city-level carbon emissions in 283 Chinese cities from 2010 to 2020 and decomposes the total effect into a local synergistic effect and a spatial spillover effect using a Spatial Durbin Model. We further conduct an empirical test of the underlying mechanisms, including energy efficiency gains and industrial structure upgrading for the local synergy pathway, and green technology diffusion for the spatial spillover pathway. The results indicate that (1) digital innovation significantly reduces city-level carbon emissions, confirming an overall negative effect; (2) spatial decomposition reveals two simultaneous pathways, with a significant local synergistic effect within cities and a spatial spillover effect to neighboring cities; (3) the mechanism analysis shows that the local synergy is significantly associated with improvements in energy efficiency and industrial upgrading, whereas the spatial spillover is significantly associated with the diffusion of green patents; and (4) the effects are especially pronounced in technology-intensive industries and cities in more advanced regions. These findings imply that carbon reduction driven by digital innovation occurs through both intra-city optimization and inter-city technology diffusion. Therefore, policies should not only motivate cities to strengthen their own digital capacities, but also promote interregional collaboration to enhance positive spillovers and achieve cost-effective and well-coordinated carbon neutrality.

Synergistic governance is essential for addressing transregional environmental pollution challenges in the Yellow River Basin. To explore the effect of synergistic environmental pollution management in the Yellow River Basin, we constructed a four-tier index system—driving force, pressure, synergy, and effectiveness—using 2012–2020 panel data from nine provinces. A comprehensive evaluation was performed using an ecological niche suitability model, and a spatial panel econometric model was applied to identify regional spillover effects of pollution diffusion and synergistic management efforts. From 2012 to 2020, the overall effectiveness of coordinated governance in the Yellow River Basin improved from Level V to Level II. The evolution of governance levels across provinces exhibited noticeable spatial heterogeneity, with upstream areas generally exhibiting higher effectiveness than the middle and lower reaches. Spatial model results revealed a significant spatial spillover effect of cross-provincial pollution control, as indicated by the spatial Durbin model, which showed a significant spatial lag term (p<0.05). This suggests that pollution control inputs in one province enhanced its own governance efficiency and had a positive impact on neighboring provinces. In addition, robustness checks using the Bootstrap method supported the stability of the ecological niche suitability estimates. Multicollinearity diagnostics indicated no serious collinearity (all variance inflation factor values <5), and the key regression coefficients were statistically significant (p<0.01). This study proposes differentiated policy recommendations to enhance coordinated pollution management in the Yellow River Basin, including strengthening cross-regional collaboration mechanisms, establishing unified joint prevention and control assessment indices, increasing environmental inputs and technical support for weaker middle and lower basin regions, and improving ecological compensation and benefit-sharing systems to promote high-quality, sustainable basin development.

In the context of global warming, cities are the core source of carbon emissions, and promoting their low-carbon transformation is the key to China’s implementation of the “double carbon” goal. This paper aims to systematically sort out the frontier achievements of China’s urban low-carbon transformation, and build a comprehensive theoretical analysis framework. At the theoretical level, it traces the evolution of the concepts of decarbonization, LCD and LCT, clarifies the logical relationship, and integrates the theories of ecological modernization and sustainable development to lay the foundation for transformation. At the empirical level, this paper analyzes the suitability of IPCC, IOA and LCA carbon accounting methods and the index evaluation system, identifies that per capita income, energy structure, population size, industrial structure and green technology innovation are the main driving factors affecting carbon emissions, and summarizes the emission reduction effects of low-carbon city pilot, carbon trading market and other policy tools, revealing that the distribution pattern of carbon emissions is high in the East and low in the west, with significant spatial spillover effect, and that individual behavior plays an important role in low-carbon transformation. At the same time, this paper also points out that the current research still faces challenges such as insufficient theoretical localization, lack of micro mechanism mining, incomplete data system and poor policy adaptability, and puts forward corresponding future policy prospects, so as to provide theoretical reference and practical guidance for promoting the development of low-carbon transformation in Chinese cities.

We consider spatial panel data models with genuine unbalancedness arising from the non-presence of some spatial units in certain time periods. General M-estimation methods are proposed for model estimation, which take into account the estimation of the incidental fixed effects parameters and allow for spatiotemporal heteroskedasticity and high-order time-varying spatial effects. Corrected plug-in methods are proposed for standard error estimation. The proposed estimation and inference methods are rigorously studied for their asymptotic properties and finite sample performance. An application to China’s provincial FDI inflows shows that properly accounting for genuine unbalancedness uncovers significant positive spatial spillovers that are masked when the data are artificially treated as balanced.

Abstract This paper examines the territorial determinants of female employment rates using labour market areas (LMAs) – functional units based on commuting patterns – to mitigate the Modifiable Area Unit Problem (MAUP). Drawing on detailed Spanish census microdata and spatial econometrics, we find that male unemployment negatively affects female employment through a discouraged worker effect , whereas higher shares of part‐time jobs, medium household income, and a greater proportion of immigrant women from emerging countries are associated with better female employment outcomes. Certain aspects of local economic structure, particularly a larger service sector, positively influence women’s employment. Comparing spatial and non‐spatial specifications shows only modest gains, consistent with limited cross‐area spillovers when the analysis relies on functionally defined units. Overall, the evidence supports LMAs as appropriate territorial units and highlights the importance of care‐related and income‐based interventions, together with efforts to broaden sectoral opportunities for women and to improve data availability at the functional‐area level.

While spatial autoregressive (SAR) models are increasingly used in population-level psychological studies, researchers often overlook the crucial step of parsing effects into direct, indirect and total impacts, a standard practice in spatial econometrics. In this paper, we demonstrate the necessity of this practice by re-analyzing Gruda et al.'s (2024) U.S. Dark-Triad and health dataset with heteroskedasticity-robust SAR models and full impact decomposition, revealing significant changes. The previously observed direct protective effect of state-level narcissism on hypertension mortality disappeared when accounting for interstate spillovers. Conversely, the association with lower cancer prevalence and depression strengthened. Several health-behaviour findings reversed direction, indicating naïve regressions conflated within- and between-state effects. Machiavellianism and psychopathy coefficients also shifted. These results demonstrate that spatial spillovers can dilute, negate or reverse local effects, cautioning against policy inferences based solely on direct estimates.

Food security is a key pillar of national development, reflecting a region’s ability to sustain food availability, accessibility, utilization, and stability. The Food Security Index (FSI) serves as a crucial measure of this capability. Based on 2023 data, West Sumatra Province achieved the highest FSI score on the island of Sumatra. This study analyzes food security in 19 regencies and cities of West Sumatra from 2019 to 2023 using a Spatial Lag Fixed Effects Model. The research integrates spatial analysis and panel data approaches to identify determinants of the FSI and assess spatial spillover effects between regions. Secondary data were obtained from the Statistics Agency (BPS) and the National Food Agency. The results reveal significant spatial autocorrelation in most years, except 2023. The best-fitting model is the Spatial Lag Fixed Effects Model. Changes in land area, food expenditure, and rice productivity significantly improve FSI, while non-food expenditure and economic growth do not show a positive effect. The findings emphasize the importance of incorporating spatial dependencies in regional food security policies. Moreover, significant spillover effects indicate that improvements in one area can influence neighboring regions. Therefore, inter-regional cooperation and integrated food distribution policies are essential to achieving sustainable food security.

Industrial development is a fundamental driver of socio-economic progress, and industrial structure upgrading plays a vital role in advancing rural revitalization. Based on county-level panel data from Fujian Province from 2017 to 2022, this study employs Ordinary Least Squares (OLS) and spatial econometric models—including the Spatial Lag Model (SLM) and Spatial Error Model (SEM)—to empirically assess the impact of county-level industrial structure upgrading on rural revitalization, as well as its spatial transmission mechanisms. The findings reveal that: (1) an increase in the proportion of secondary and tertiary industries significantly enhances the rural revitalization development index at the 1% level of significance; (2) rural revitalization development exhibits strong spatial dependence and positive spatial spillover effects, indicating a “local club convergence” pattern among neighboring counties; and (3) the SEM outperforms OLS and SLM, suggesting that inter-county disparities in rural revitalization primarily result from spatial heterogeneities such as infrastructure and public service quality. Additionally, factors such as transportation accessibility, social public services, and per capita GDP have significant positive effects, while the impact of fiscal agricultural investment appears limited. This study provides empirical evidence to support coordinated development between industrial upgrading and rural revitalization strategies and offers policy insights for constructing an integrated and regionally synergistic framework for rural development in China.

Climate change and increasingly severe weather pose dual pressures on agriculture: to reduce carbon emissions and to manage climate risk. These pressures challenge the transition to green, low-carbon development. On the basis of panel data from 31 provinces in China from 2003 to 2023—a period selected for data continuity and to capture the implementation of major national agricultural and environmental policies—in this study, an evaluation index system for agricultural green and low-carbon development (GAC) was established. This study aims to analyze the impact of climate change risks (CPRI) on GAC, focusing on the moderating role of agricultural insurance (INS) and spatial spillover effects. Specifically, it seeks to answer the following questions: (1) What is the direction and magnitude of CPRI’s effect on GAC? (2) Can INS mitigate this effect? (3) Does CPRI exhibit spatial spillover effects on GAC? Using data from the NOAA and Chinese statistical yearbooks, by employing a model with two-way fixed effects, moderating effect analysis, and the spatial Durbin model, the mechanisms underlying the spatial spillover effects of CPRI and regional heterogeneity were examined, as well as the moderating function of INS. CPRI was found to significantly inhibit GAC, as extreme weather events triggered short-term decision-making among farmers and constrained investment in green technologies. These events reduced the capacity of the soil to sequester carbon. This inhibitory effect was greater in nonmajor grain-producing regions and in eastern China. INS helped reduce negative impacts by providing effective risk transfer mechanisms. Furthermore, CPRI was found to exert harmful spillover effects across different regions, with greater indirect effects than direct effects. In conclusion, CPRI significantly hinders agricultural green transition, a process moderated by insurance and characterized by spatial spillovers. On the basis of these observations, we recommend several policies, including the development of regionally tailored adaptation strategies, the achievement of innovation in agricultural insurance products, and the establishment of collaborative governance frameworks that span regions to address these challenges.

The quality of the living environment is a critical determinant of residents’ health and urban sustainability. To mitigate the environmental pollution induced by the traditional urbanization model, China launched the New-Type Urbanization Pilot Policy (NTUPP) as a nationwide experimental reform. Yet whether the NTUPP has improved the living environment remains underexplored. Drawing on a balanced panel dataset of 273 prefectures in China from 2011 to 2023, this study applies a staggered difference-in-differences approach to examine the NTUPP’s impact. We find that the NTUPP significantly improves the quality of the living environment, while its spatial spillovers to neighboring prefectures remain limited. Mechanistic analysis suggests that these gains are driven by stronger environmental regulation and increased investment in environmental governance. The policy’s effects are more pronounced in northwestern prefectures, prefectures in the servitization stage and the middle stage of urbanization, and small or medium-sized prefectures.

ABSTRACT Africa faces significant challenges in achieving the Sustainable Development Goals (SDGs), particularly SDG 13 (climate action) and SDG 16 (Peace, Justice, and Strong Institutions), amid widespread corruption, weak governance, and political instability. This study innovatively explores the spatial spillover effects of public sector transparency—proxied by the corruption perceptions index (CPI)—alongside rule of law and political stability, on key sustainable development metrics, including the sustainable development index (SDI), CO 2 emissions, and ecological footprint. By highlighting these interconnections in Africa's SDG context, it addresses a critical gap in understanding how governance reforms can propagate across borders to foster regional progress. Drawing on panel data from 42 African countries spanning 2012 to 2022, the analysis employs spatial econometric models to capture spatial dependencies. Key findings reveal that the rule of law exerts a significant positive direct effect on SDI (0.024% total increase per 1% improvement) but insignificant spillovers. Enhanced public sector transparency (CPI) elevates CO 2 emissions primarily through spillovers, while the rule of law reduces ecological footprint via indirect effects. Income and population growth positively drive SDI with stronger spillovers yet exhibit mixed environmental impacts. These results underscore the interconnected governance‐sustainability nexus in Africa, advocating regional anti‐corruption and institutional reforms to harness positive spillovers for equitable green growth and SDG attainment.