This study analyses the impact of environmental policy stringency, hydropower consumption and oil consumption on the sustainable development index for 20 EU countries from 1996 to 2020. By incorporating a measure of strict environmental policies, this research aims to fill a critique gap in literature, becoming the first study to examine spillover effects of sustainable development among EU countries. The results demonstrate that sustainable development spills over among these countries, indicating a positive regional impact. Environmental policy stringency and hydropower consumption directly, indirectly and positively affect sustainable development, whereas GDP per capita and oil consumption exert negative effects. Relevant policy implications are discussed based on these findings.
We propose a fundamental modification of the relatedness density index which considers the region's whole technological structure, including linkages between technologies in which the region is specialised and those in which it is not. When carefully disentangled, relatedness to technologies in which the region is not ostensibly specialised is shown to boost diversification into new technologies, over and above the positive role of relatedness to technologies where a local specialisation does exist. This phenomenon is more pronounced in high-income regions, and provides the crucial insight that these more varied and complex local linkages help high productivity regions be so productive.
Artificial intelligence is used in many workplaces today, yet the initial adoption process of this new general-purpose technology remains underexplored. This paper examines whether a head start in AI adoption determines the regional variation in adoption rates over time. Using AI-related job vacancies as a proxy for firm-level adoption, we study AI adoption across Dutch municipalities between 2012-2020. This period is marked by the introduction of several AI techniques and offers a quasi-experimental setting to study adoption of a new technology in firms across regional labour markets. Findings indicate that AI adoption is concentrated in urban areas and is positively associated with early regional AI adoption, with no evidence of spatial convergence over time, suggesting that path dependency in AI diffusion produces structural regional inequalities.
This paper examines how violence impacts migration flows and the strength of migration networks across Mexico's 2454 municipalities. Using a novel network algorithm and census data from 2005 to 2020, we detect structural changes in domestic and international migration beyond what net flows reveal. To identify causal effects, homicide rates are instrumented using variation in fuel prices and municipal distance to fuel pipelines, capturing exogenous shocks from large-scale fuel theft. Rising violence led to 1.12 million additional domestic emigrants, 50,200 fewer returnees from the United States, stronger emigration networks and reduced highway traffic linking violent areas to the rest of the country.
Measuring vulnerability accurately is essential for strengthening resilience and supporting disaster risk reduction. While international frameworks such as the INFORM Risk Index and Risk Data Hub (RDH) Vulnerability Index provide valuable benchmarks, they often overlook local socio-economic and environmental specificities. This study advances vulnerability assessment by introducing a Regional Risk Index that integrates hazard, exposure and vulnerability through principal component analysis (PCA). By deriving data-driven weights, PCA reduces subjectivity and improves robustness compared to equal-weighted approaches. The index innovates conceptually by incorporating new variables that capture underexplored dimensions of resilience, including education quality (school abandonment), business vitality, accessibility to green spaces and public transport, and environmental stressors (tree loss, carbon emissions, air pollution). These additions extend existing methodologies by combining social, economic and ecological factors with hazard intensity and recurrence. Applied to Romania's 42 NUTS 3 (nomenclature of territorial units for statistics, level 3) regions, the index reveals higher vulnerability in the northeast and southeast, and stronger resilience in areas such as Timis, Ilfov and Tulcea. Robustness tests confirm the stability of results under alternative normalisation and weighting schemes. Overall, the framework offers a transferable tool for disaster risk analysis, contributing both conceptually and methodologically to vulnerability measurement and providing policymakers with more nuanced evidence for resilience planning.
This paper examines how the gender composition of national parliaments affects foreign aid allocation in African countries. The findings indicate that female representation in parliament positively correlates with increasing aid directed mainly toward social sectors, which women legislators typically prioritise. Additionally, the study explores the role of spatial interdependence between neighbouring countries, revealing that the distribution of foreign aid is influenced not only by a country's parliamentary gender composition but also by gender representation in the parliaments of neighbouring countries. These results emphasise the importance of promoting gender parity in political institutions to achieve more effective and targeted aid allocation.
This study provides firm-level evidence of the adoption of advanced digital technologies (DTs) in Greater Manchester. Examining the drivers and impact of six technologies, namely AI, Big Data, Cloud Computing, 3D-printing, IoT and Robotics. Using random forest models, a predictive machine learning technique, the findings suggest that a disaggregated approach is necessary to analyse and understand DT adoption. This paper argues that while firms' contextual factors, which include size, age, and turnover, are important for successful digital transformation, firms must combine the adoption of DTs with relevant digital skills to allow companies to turn technological investments into productivity gains.
This study investigates how financial development influences green economic efficiency within a spatial framework. We construct a green economic efficiency index using a spatio-temporal differentiation-based comprehensive evaluation method, and measure financial development through a super-efficiency slacks-based model (SBM) incorporating multiple indicators across the banking, insurance, securities and public finance sectors. Employing a spatial Durbin model (SDM), we examine both the direct effects and spatial spillovers of financial development across Chinese provinces from 2006 to 2020. Our findings indicate that financial development significantly enhances local green economic efficiency while also generating positive spillover effects in neighbouring regions. Further analyses reveal heterogeneity in these effects across regional divisions, financial sub-industries and levels of marketisation. These results offer valuable insights for designing regionally tailored financial and environmental policies to promote sustainable economic growth.
China's population growth rate has been consistently below the replacement level. Urbanisation is a key structural factor influencing changes in the fertility rate, so it is particularly urgent to clarify its mechanism of action. Existing studies often rely on conventional measures of urbanisation, which may not fully capture its spatial-economic intensity, and seldom examine its nonlinear dynamics or spatial spillover effects on fertility. To address these gaps, this study develops a spatio-temporal consistent and high-resolution urbanisation index using DMSP/OLS nighttime light data. Employing provincial panel data from 2004 to 2019 and applying both panel and spatial econometric models, this paper assesses the impact of urbanisation on fertility and its mechanisms. The results reveal an 'inverted S-shaped' relationship between urbanisation and the fertility rate. Specifically, a one-unit increase in the urbanisation index is associated with an average decline in the birth rate of approximately 0.033 percentage points. While the 'two-child' policy raised the fertility rate by about 0.007 percentage points, its effect is negatively moderated by urbanisation, each unit increase in urbanisation weakens the policy's boost by roughly 0.004 percentage points. Spatial analysis further indicates that urbanisation's suppressive effect spills over across regions, leading to an additional decrease of about 0.022 percentage points in neighbouring areas through spatial linkages. Additionally, factors such as female labour force participation, educational attainment, and social endowment insurance exhibit significant negative effects on fertility, whereas housing prices show a positive association. This study proposes to implement a regional collaborative policy system, transcending the single encouragement of childbirth and shifting towards a people-centred urbanisation path, emphasising balanced spatial development, and establishing a full life-cycle support system, thereby alleviating the constraint effect of urbanisation on the fertility level.
This study examines how objective and subjective measures of air quality affect housing rental prices in Medell & iacute;n, Colombia. Building on Rosen's hedonic framework, we incorporate an interaction between PM2.5 (particulate matter smaller than 2.5 micrometres) concentrations and residents' perceptions of air quality to assess their joint influence on rents. Controlling for spatial dependence, the results show that subjective perceptions significantly amplify the negative effect of objective pollution on rental prices. We also document substantial spatial heterogeneity across communes. These findings highlight the importance of considering both measured pollution and perceived environmental quality when evaluating housing market responses to air pollution.
The aim of this article is twofold. First, it describes the methodology used to obtain a comprehensive dataset of inter-regional trade flows of goods in Europe, covering 2010-2018, 297 NUTS-2 (nomenclature of territorial units for statistics, level 2) regions for the EU27, the UK, Lichtenstein, Iceland, Norway and Switzerland, five transportation modes, 14 types of goods, and two units (tons and euros). The dataset is harmonised with the main country-to-country flows datasets. Second, drawing from the most recent developments in trade theory and the gravity equation, we identify and measure the 'unobservable trade barriers' (UTBs) still extant in the European Single Market (ESM). Employing recently developed structural gravity equations, we obtain new results on the deterrent effects of trade integration and administrative borders.
Data limitations have restricted the understanding of how mobility varies across space, despite its importance for analysing spatial interactions and place-based policies. I geo-code over 21 million quarterly Visa and Mastercard origin-destination transactions (2019Q1-2023Q3) to estimate postcode-level distance decay gradients for 2118 districts via maximum likelihood estimation. Mobility varies markedly along socio-economic ('intrinsic') and built-environment ('extrinsic') dimensions. The shortest mobility horizons occur in the most deprived urban areas, which complete half of their spending within 9.8 min - over three times faster than prosperous peripheral-urban areas. Spatio-temporal patterns reveal widening mobility inequalities and differential resilience to COVID-19.
