Indonesia's maritime position exhibits marked potential to advance a blue economy that promotes sustainable growth, social well-being and conserves marine ecosystem. Although quantitative blue economy studies are well developed internationally, similar data-driven approaches remain limited in the Indonesian context, particularly at district/city level. This study aims to construct a district-level blue economy index (BEI) and examine its relationship with regional economic performance across Sumatra using the multiscale geographically and temporally weighted regression (MGTWR) approach. The BEI integrates environmental, social and economic dimensions using 38 indicators across 154 districts and cities from 2019 to 2022. Variable selection is conducted using the least absolute shrinkage and selection operator (LASSO) method to alleviate multicollinearity. Results reveal notable spatial and temporal disparities: Medan City consistently exhibits the highest BEI, whereas Sawahlunto City records the lowest. The socialand environmental dimensions show strong associations with economic outcomes, highlighting the importance of human capital, fisheries infrastructure and waste management in promoting inclusive blue growth. These findings provide empirical evidence for spatially adaptive policy interventions to strengthen Indonesia's district-level blue economy performance.
Previous studies overlooked the impact of green growth (GRE) on load capacity factors (LCF). Additionally, environmental modelers, especially in the Centraland Eastern European (CEE) countries, did not account for LCF perspectives. Notably, LCF provides valuable insights into the unique dynamics of the natural environment's demand and supply sides. This study explores the asymmetric association between these indicators using quantile-on-quantile regression (QQR) and spectral Granger causality approaches. The empirical findings highlight that GRE exerts a negative impact on LCF in the low and middle quantiles of LCF, whereas it has a positive effect in the higher quantiles of LCF across all GRE quantiles in the CEE countries. In addition, there are causal linkages between GRE and LCF over the range of time frames such as the short, middle and long runs. The paper concludes with comprehensive policy recommendations aimed at improving environmental quality.
Some of the literature on catchment areas offers valuable insights into spatial units defined by the various types and functions of urban centres. In addition, a range of functional urban region delineation methodologies contribute significantly to understanding citizens' commuting habits. However, while the former often lacks a complex, multifunctional perspective, the latter typically focuses on labour market areas linked to global megacities. As a result, smaller centres fulfilling a wide range of everyday functions are frequently omitted from regional maps, and they remain hidden in the catchment area of larger cities. This research aims to address these gaps by proposing a method for identifying urban centres on the basis of multidimensional selection criteria. This approach enables the general mapping of their multifunctional catchment areas and their wider functional urban zones. This analysis extends the range of statistics commonly used: in addition to labour market commuting, it includes commuting for general and vocational education, as well as accounting for the accessibility of everyday commercial centres and frequently used urban-level public services. Owing to differing spatial organizing principles, this research highlights that everyday centres take on varied structural forms-justifying a multidimensional mapping approach that is in addition to traditional labour market commuting analyses. Although this study is grounded in the Hungarian context, its multidimensional approach to mapping functional urban catchment areas offers valuable insights and a flexible framework for international research. By integrating a wide range of everyday spatial behaviour indicators, this analysis not only deepens our understanding of urban networks but also provides urban planning and regional development professionals with a robust tool for identifying gaps in public service provision. In doing so, it can help increase access to essential services, particularly for residents in rural areas far from major urban centres, which are typically the focus of the literature.
This study offers new insights into the impact temperature shocks on international trade activity in Vietnam using tax revenue as a proxy. Temperature shocks reveal deviations climate variables from the historical norms relation to human activities, enabling us to examine the way people adapt to climate change. It is likely a superior proxy compared conventional approaches that either create temperature bins or standardise measurements. The research findings indicate that while the temporary effect of temperature shocks significantly reduces tax revenue from international trade, the permanent effect is nearly six times as great as the temporary effect. The results also reveal that the shock is more pronounced in areas with prominent foreign direct investment companies, and less in regions with more favourable temperature conditions, indicating heterogeneous effects. The increased energy costs associated with higher temperatures in manufacturing processes potentially leading to longterm vulnerability may explain the findings. Accordingly, we propose several policy implications for fostering sustainable growth Vietnam amidst rising temperatures.
A key problem today is reducing greenhouse gas (GHG) emissions and environmental damage without hampering economic growth. This study aims to analyse the main determinants of GHG in the European Union (EU) countries for the period 2004-2022 in view of the Paris Agreement and the Green Deal objectives. This analysis aims to examine the validity of the environmental Kuznets curve (EKC) in EU member states. We aim to explore if any increase in foreign direct investment (FDI) and the share of the service sector shows significantly effective carbon dioxide (CO2) emissions. The results show if economic growth (real gross domestic product [GDP] per capita), final energy consumption per capita, and the share of fossil fuels have a significant positive effect on GHG emissions. Reducing fossil fuels and increasing energy efficiency can lower emissions. The significant impact of capital leakage on CO2 emissions cannot be clearly determined from the panel analysis. Growth in the services sector's share of GDP contributes to emission reductions. Fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), fixed effect, random effect panel models, cointegration models, k-means cluster analysis and comparative analysis were used for the analysis. The models suggest a possible turning point in the EKC; however, due to the complexity of the phenomenon, the hypothesis cannot be clearly confirmed for all 27 member states.
This study examines Indonesia's Village Fund policy. Introduced in 2015, it has been implemented across rural villages (desa) throughout the country. The policy's primary goal is to foster rural development. This study evaluates its effects using high-quality night-time light data, (VIIRS) as an innovative proxy for village-level economic activity from 2013 to 2021. It employs inverse probability weighting difference-in-differences (IPW-DID) to estimate the policy's impact and address selection bias. The results show that, after weighting, the treated and untreated groups are comparable, indicated by a parallel trend before treatment, and the estimates are robust. Moreover, the policy's impact turned positive two years after implementation because 80% of village funds were allocated to infrastructure in the first two years. The heterogeneous effects are significant for villages within 5 km of the city centre. The policy also has a substantial impact on landlocked villages and those in flat areas.
The European Union's (EU) waste policy relies on the waste hierarchy which sets waste prevention as the top priority for causing the least environmental burden in waste management. Practice shows that waste prevention has not got priority in the implementation of waste policies, nor has its monitoring. Because of its completely distinctive characteristics from other forms of waste management - in motivation, key players, due authorities, the legal environment, and in supporting activities - it is difficult to set up defined policy goals and the conceptual framework for monitoring. The systematic review of the waste prevention monitoring literature is presented. The paper attempts to give a panorama of the EU legislation and the waste prevention programmes of EU member states as a top-down approach, including the settlement level aspects and interests in the context to approximate local and EU-level measurement needs. It presents regression analysis of the available official statistics of Eurostat and municipal waste generation. The outcome of the research is a comprehensive proposal for EU-level waste prevention indicators which could also track progress at the local level.
This study analyses the persistence of regional inequalities in productivity in Mexico during the period 2010-2023 through a multiscalar structural approach. By integrating nonlinear functional models, interpretable machine learning, and spatial econometric validation, this study identifies the economic growth trajectories of federal entities and the structural factors underlying them. Using a double-well potential model, states are classified into convergence, stagnation, or divergence regimes, capturing their long-term dynamic behaviour. Subsequently, the CatBoost algorithm with SHapley Additive exPlanations decomposetion is applied to estimate the marginal importance of variables such as digital connectivity, government efficiency, and labour productivity. Finally, a spatial fixedeffects model and territorial cluster analysis reinforce the consistency of the findings, highlighting the institutional and spatial dimensions of inequalities. Results confirm that regional growth in Mexico responds to persistent structural configurations, implying the need to design differentiated and territorially sensitive policies.
Regional competitiveness significantly influences the quality and accessibility of public service delivery. Research indicates that competition plays a pivotal role in shaping accessibility indicators, particularly spatial accessibility, which are critical for sectors such as transportation, urban planning and healthcare (Douthit et al. 2015, Guagliardo 2004, OECD 2021). The spatial distribution of public service facilities affects the quality of life of residents, and different degrees of agglomeration and accessibility are observed between different categories of facilities (McGrail et al. 2017). Moreover, the spatial impact of the availability of public services on house prices highlights the potential for greater clustering effects of public services (Diao et al. 2017). The availability of public service infrastructure also serves as an informative signal for citizens to assess political performance, influences political trust and potentially contributes to geographically polarised perceptions of quality (Christensen-L ae greid 2005). In this paper, the author undertakes a multivariate statistical analysis of the indicators mentioned above related to public services to investigate the impact of the qualitative characteristics of the indicators included in the analysis on the variation of population and demographic characteristics of municipalities. The results show strong positive correlations between the availability of educational, health and cultural public services and population retention. Areas with higher business incomes and well-developed public services tend to experience lower rates of out-migration. These findings underscore the importance of integrating economic and social development strategies to enhance rural demographic stability.
This study evaluates the impact of free health insurance targeting the poor on individuals' and households' healthcare utilisation and out-of-pocket expenditures in Vietnam. Using data from the 2018 Vietnam Household Living Standards Survey and the propensity score matching method to assess the causal effects of health insurance for the poor on health-related outcomes. While we demonstrate that free insurance increases outpatient and inpatient visits, the estimation results are heterogeneous. Specifically, health insurance for the poor benefits individuals living near the poverty line-typically poor Kinh people or poor individuals residing in urban areas. We find no evidence of its impact on the poorest, who are usually members of ethnic minority groups. The concentration of hospitals in major cities and urban areas may explain why the poorest do not benefit. Hence, in addition to providing free health insurance, policymakers should expand healthcare facilities in rural and remote areas and offer other forms of support to poor patients, such as covering travel and accommodation costs.
Competitiveness is a central concept in European Union (EU) policy discourse; however, recent reports on the single market and competitiveness frequently underes-timate the pivotal role of foreign direct investment (FDI) and trade. This study addresses this gap by analysing EU-level FDI and trade trends between 2005 and 2023, incorporating comparative insights from the United States and China, as well as intra-EU dynamics. The analysis reveals a complex and uneven landscape: FDI flows are highly concentrated, with some member states primarily acting as intermediaries, thereby distorting resource allocation. In terms of trade, the European Union faces rising competitive pressure from China, persistent internal trade imbalances, and significant dependence on foreign value added. Although the EU performs strongly in services and medium-tech exports, it lags in high-tech and primary products. The study argues that addressing these challenges necessitates a differentiated and targeted EU competitiveness strategy aimed at strengthening strategic autonomy within an evolving global economic environment.
This study aims to examine the applicability of the Poincar & eacute; plot to economic convergence and demonstrate that the new instability indicator proposed in the study complements the assessment of convergence at average growth rates. We investigate the convergence of NUTS 2 regions over the last decade by applying a novel approach. Using Poincar & eacute; plots in the assessment of regional convergence, the analysis goes beyond the speed of GDP growth to include its stability. Cluster analysis is also used to identify groups of regions with similar growth patterns, adding to the empirical illustration. The results reveal that there is convergence, with less prosperous regions generally experiencing higher growth. Further, growth in richer regions is less stable, although it is not statistically significant. The results also reveal that convergence is complex and that its analysis requires a multidimensional approach. The proposed measure of instability (based on the Poincar & eacute; plot and related methodology) and the cluster analysis based on growth dynamics can help policymakers to support more stable convergence. The conclusions nuance our knowledge of regional convergence, as obtained using the methodology already employed in previous studies.
This study demonstrates the mosaic nature of European regions in terms of the usage and adoption of artificial intelligence (AI) technology. A dataset of EU27 NUTS 2 regions is compiled and analysed using multilayer perceptron neural networks to produce original research results that contribute to the evolving discourse on regional AI. The findings indicate that considerable regional differences exist in AI-technology usage across European regions. Mapping results enhance understanding of the sharp performance disparities between and within countries. The study presents a clear centripetal development with the capital regions, metropolitan areas and Northern European regions generally outperforming the rest. It reveals a largely continuous corridor along the 'Aarhus-Bologna Axis' and the 'German Pentagon' area characterised by substantial capacity to use and adopt AI technology.
This study introduces a methodology that may be used to perform a head count of an ethnic group and collect detailed ethnodemographic data. Due to the lack of ethnicity questions in censuses or in the absence of a census, this is a real gap in the knowledge internationally. This study focuses on Ukraine, presenting a new methodology for estimating the basic demographics of ethnic Hungarians. The research was based on a large-scale questionnaire survey conducted in 2017, covering all municipalities in Western Ukraine, which hosts a significant Hungarian minority. According to our model, in 2017, the number of Hungarians in Transcarpathia most likely ranged between 125,000 and 135,000. Two crucial methodological issues challenge the validity of data: the varying interpretations of temporary and permanent migration may lead to significant differences in the population size surveyed, while the situational self-identification of individuals, mostly of outlier categories such as the Hungarian-speaking Roma, may increase the uncertainty of results. Nevertheless, we argue that in the context where traditional censuses are being replaced by censuses that do not collect ethnic data, our model might fill the gap; however, its applicability to other minorities depends on the studied group's particular structural characteristics.
Income inequality continues to pose a critical challenge in developed economies, with ongoing discussions regarding its association with economic growth. Clarifying whether increased in gross domestic product (GDP) per capita balances income distribution or reinforce existing disparities is essential for shaping effective economic and social policies. Herein, the connection between economic growth and income inequality notably varies between the United States and the United Kingdom in 1990-2021, reflecting their distinct institutional arrangements and policy approaches. Applying the autoregressive distributed lag model, the analysis investigates the long-term relationships between GDP per capita and income inequality. The F-bounds test statistic exceeds the critical upper bound for both countries, confirming a strong cointegration relationship. Reportedly, a 1% rise in GDP per capita in the United States corresponds to a 0.026% increase in income inequality, whereas a similar rise is associated with a 0.138% decrease in income inequality in the United Kingdom. These divergent patterns highlight the influence of national policy design, social welfare systems, and income redistribution mechanisms on the distributional outcomes of economic growth, suggesting the need for context-specific approaches to promote equitable development.
Adult learning and upskilling (AL) strategies and outcomes are deeply influenced by the institutional context, which varies across regions in terms of coordination, regulation, specialization, informality, and knowledge intensity. This study examines how regional varieties of market economies moderate the impact of AL on labour market outcomes, focusing on non-formal education and online self-learning. Unlike previous research, this study uses social capital and perceived respect as additional outcome measures. To capture regional variations in market economies, we apply factor and cluster analyses based on the regional varieties of capitalism theory. Panel regressions with fixed effects and hybrid models are used to examine within-and between-cluster effects of AL on individual labour market outcomes. The research focuses on the case of 82 Russian regions from 2013 to 2023, utilizing Federal State Statistics Service (Rosstat) data and a panel sample from the Russian Longitudinal Monitoring Survey. The findings identify four key factors shaping regional varieties of market economies: knowledge intensity, social inequality, labour market informality, and resource dependence. Five types of regional variations of Russia's state-led market economy are identified. The study reveals age-and gender-related heterogeneity in the impact of AL on within-individual labour market outcomes, and robust, positive outcomes across individuals, resulting in wage premiums up to 30%. Differences in wages, job satisfaction, trust, and perceived respect across regions can largely be explained by underlying institutional characteristics. Knowledge-intensive regions tend to have more liberal, competitive labour markets, whereas resource-dependent regions exhibit more coordinated environments. Both types of regions positively impact wages across individuals. Moderation analysis indicates that coordinated environments in resourcedependent regions enhance the returns to non-formal AL, yielding better wage outcomes when local employers provide education. In contrast, knowledge-intensive regions exhibit a significant negative interaction between self-learning and job satisfaction, suggesting that the benefits of AL may be context-dependent.
This study examines the interplay between public debt and economic growth in BRICS nations (Brazil, Russia, India, China, South Africa), focusing on the relatively unexplored quantile-frequency domain, which integrates quantile regression with frequency analysis. Using data spanning from 1994 to 2022, the analysis employs wavelet quantile correlation (WQC) and quantile-on-quantile regression (QQR) techniques. These advanced methodologies facilitate a nuanced understanding of the public debt (DEBT)-economic growth (GDP) relationship across various quantiles and time horizons. The results suggest that public debt has a negative influence on economic growth in the short and medium term, while a positive association emerges over the long term. Furthermore, the findings highlight an asymmetric effect of public debt on economic growth across different quantiles within the region. These insights offer valuable guidance for policymakers, providing tailored recommendations for debt management in BRICS countries by addressing both the beneficial and adverse effects of public debt on economic performance.
In this study we classify the EU27 countries based on their Covid-19 national preparedness (human development index, hospital bed availability), health effects (excess mortality, reproduction rate), economic consequences of the crisis (GDP changes), and protective measures (stringency index, vaccination rates) during the coronavirus crisis and apply hierarchical clustering with Ward linkage to form comparable groups. Six clusters have emerged, four reflecting geographical patterns or institutionalized groups, such as the Baltic states, the Mediterranean countries, and the Visegrad Group 4 (V4), while Bulgaria constitutes a separate cluster. Central Europe is divided into Western and Eastern clusters. Additionally, the study examines the European Commission's State Aid Temporary Framework program using public data and reveals notable patterns in terms of economic support measures. This study is novel as it uses a multidimensional framework to assess Covid-19's impact and investigates both the early phase of the crisis and its later stages (January 2020-April 2022). One limitation is using averaged data, which smooths short-term fluctuations and wavespecific insights. Another limitation is the focus on European Commission-funded aid with the exclusion of national aid schemes. Our findings provide insight for policymakers and researchers on regional differences in pandemic response, economic impacts, and EU financial aid distribution.
Building upon the traditional concept of the "Blue Banana" as Europe's economic core area, this study examines the multifaceted impacts and underlying causes of regional competitiveness disparities across Europe. In particular, it delves into Central and Eastern Europe (CEE), along with the Mediterranean territories of the European Union (EU), analysing the ongoing economic and urban transformations within these areas while also assessing similar patterns in Western and Northern Europe. Leveraging the Nomenclature of Territorial Units for Statistics 2 (NUTS 2) regional breakdown and data sourced from the EU's latest regional competitiveness index (RCI) report, this study conducts a comprehensive analysis of spatial concentrations within the EU. The findings suggest that although some regions of the Blue Banana maintain their relative dominance in regional competitiveness, significant recent developments in other European regions indicate that the concept may be losing relevance. In particular, the CEE regions-especially their capital regions-converged toward the standards set by the Blue Banana. The results of the spatial regression model on the RCI in 2022 substantiate the conclusion that the Blue Banana is no longer a significant territorial concentration. Moreover, the results of the spatial regression model on the changes in RCI scores between 2016 and 2022 indicate that the less affluent CEE and Mediterranean regions have greater development potential than the wealthier western and northern regions, further challenging the traditional notion of the Blue Banana.
