Search Results (815)

This study examines the relationship between income and municipal waste generation within the Waste Kuznets Curve (WKC) framework, with a focus on selected disaggregated household waste fractions (paper and cardboard, glass, bulky waste, and biowaste). The aim is to assess whether increases in earnings per capita are associated with non-linear waste dynamics once spatial interactions and local socio-demographic characteristics are taken into account. The study employs a spatial panel dataset for 378 Polish counties over the period 2017–2024. Fixed-effects panel models, supplemented with random-effects panel models with Mundlak’s approach, are estimated alongside spatial panel specifications. Control variables include population ageing, urbanisation, and tourism, while spatial effects are decomposed into direct and indirect impacts. The results indicate that, in non-spatial models, an inverted U-shaped relationship between earnings and waste generation is observed for most waste fractions. However, once spatial dependence is explicitly incorporated, income effects weaken. In contrast, demographic structure—the share of retirement-age population—emerges as a robust and spatially persistent determinant of waste generation. Urbanisation and tourism exert only a limited influence across waste fractions. The paper advances WKC research by using spatial econometric methods and disaggregated waste fractions at the county level. The evidence suggests that conclusions about income-driven waste decoupling are sensitive to spatial dependence, emphasising the need for locally tailored waste management strategies. Full article

Adult obesity rates have risen steadily across the United States over the past decade, with more than 40% of adults affected. Persistent geographic and demographic disparities exist in obesity prevalence across the nation. While prior research has examined individual or environmental associated factors of obesity, limited studies have addressed both physical activity and food environments across the nation using multilevel approaches. This cross-sectional ecological study (2014–2024) used a two-level random intercept model to assess the association between county- and state-level factors and adult obesity prevalence across over 3000 U.S. counties nested within 51 states. County-level associated factors included food insecurity, poverty, unemployment, median household income, limited access to stores, and the density of various food outlets (grocery stores, convenience stores, supercenters, fast-food restaurants, Supplemental Nutrition Assistance Program (SNAP)-authorized retailers, and farmers’ markets), along with access to recreational facilities. State-level factors included SNAP benefits per capita and the presence of soda and chip taxes. Variables were group-mean- or grand-mean-centered to distinguish within- and between-state effects. Results showed that food insecurity, poverty, unemployment, limited access to stores, and a higher density of fast-food and convenience stores were positively associated with adult obesity prevalence. While higher recreational facility access, supercenter availability, median household income, SNAP benefits per capita were associated with lower adult obesity prevalence, these associations varied in strength across counties and states. These results emphasize the need for place-based strategies that address both the physical activity and food environment in shaping obesity disparities Full article

Food waste remains a significant challenge in the European Union, reflecting structural differences across economic sectors and member states. This study examines how macroeconomic conditions relate to sectoral food waste using harmonized Eurostat data for the EU-27, covering five stages of the food chain and three economic indicators: GDP (Gross Domestic Product) per capita, adjusted gross disposable income per capita, and the Harmonized Index of Consumer Prices for food. The research design integrates factor analysis, structural equation modeling, and hierarchical clustering. Results show that income-related variables have a positive, statistically significant effect on overall food waste, particularly in manufacturing and distribution. In contrast, food prices show a negative, statistically non-significant relationship with waste generation. Cluster analysis identifies two statistically distinct country groups; however, substantial internal heterogeneity indicates that these clusters reflect structural economic configurations rather than typological or behavioral categories. The findings suggest that macroeconomic factors partially explain cross-country differences in food waste and support the need for context-sensitive, sector-specific policy interventions. Full article

Given global efforts to promote sustainable energy transitions, this study investigates how the deployment of renewable energy technologies (RETs) relates to multidimensional societal welfare and provides empirical evidence on these linkages in Lithuania. The purpose of the study is to provide an integrated, Lithuania-specific assessment of how economic, social, and environmental determinants associated with RET deployment are related to multiple dimensions of societal welfare. Drawing on scientific literature, an integrated indicator framework is developed that links the economic, social, and environmental determinants of renewable energy technology (RET) deployment to six societal welfare dimensions, as defined by the Lithuanian Quality of Life Index. Using official Lithuanian statistics for 2020–2024, a standardized panel dataset is constructed and Pearson correlation analysis and multiple linear regression are applied using aggregated determinant categories, with model assumptions verified using the Breusch–Pagan and Durbin–Watson tests. Correlation results show very strong positive links between RET intensity indicators and key economic welfare measures (for example, wages, GDP per capita, foreign direct investment, disposable income), with absolute correlation coefficients typically between 0.90 and 0.99 (p < 0.05), and strong negative correlations between air-pollution indicators and GDP, income, FDI, and education (correlation coefficients between −0.96 and −0.90; p < 0.05). The results indicate that RET-related economic determinants have a statistically significant positive effect on the societal welfare dimensions of material living conditions; entrepreneurship/business competitiveness; and public infrastructure, living-environment quality/safety. Social factors also significantly support the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. In the retained regression models, explanatory power is very high (R² between 0.91 and 0.999), with positive and statistically significant coefficients for the economic determinant (regression coefficients between 0.43 and 0.96; p < 0.05) and negative, statistically significant coefficients for the environmental determinant in the entrepreneurship and public-infrastructure dimensions (regression coefficients between −1.13 and −1.51; p < 0.05). Environmental determinants are associated with lower air pollution but show negative effects on the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. Overall, the findings suggest that RET deployment is an important correlate of the economic aspects of societal welfare, while environmental and social dimensions display more complex, domain-specific impacts. Full article

Background/Objectives: Japan introduced a certification system for Advanced Practice Nursing Workforce (APNW) in 1996. The Japanese Nursing Association formally certified two types of the APNW: Certified Nurses (CNs) and Certified Nurse Specialists (CNSs). Little is known about the geographic distribution of CNs and CNSs. Methods: We conducted an ecological panel analysis using prefecture-level data from 1996 to 2022. To assess the degree of inequality of CN and CNS among prefectures, we calculated the Gini overall coefficients, as well as those by categories of CN and CNS, number of hospitals, number of hospital doctors, and hospital nurses. Using data available from 2000 to 2017, we examined factors associated with CN and CNS density through fixed-effects panel data analyses of log-transformed overall and category-specific densities. Results: During the study period, the number of CNs and CNSs consistently increased, and geographic disparities in their distribution decreased until around 2010. After 2010, however, geographic disparities in prefectures with persistently low CN and CNS densities persisted without significant change. For overall CN and CNS density, significant associations were observed with population aging, per capita income, hospital density, hospital doctor density, hospital nurse density, and study year, whereas hospital nurse wages showed a positive but not statistically significant association. When stratified by clinical category, the directions of associations for several regional factors varied; however, hospital nurse density and hospital nurse wages tended to be positively associated with CN and CNS density in most categories. Conclusions: This study highlighted the need for targeted strategies to increase CN and CNS numbers specifically in prefectures with persistently low densities, tailored to each clinical category. Full article

This paper examines how logistics performance conditions the relationship between trade openness, economic growth and per capita CO₂ emissions in Mediterranean economies. Using an unbalanced panel of 20 countries over the period 2007–2022, we combine static fixed-effects, dynamic panel generalized method of moments (GMM) estimators and Method-of-Moments Quantile Regression (MM-QR). CO₂ emissions per capita, the World Bank Logistics Performance Index (LPI), trade openness and GDP per capita are drawn from World Bank databases, and interaction terms between LPI and both income and trade openness are constructed to capture conditional effects. The results from fixed-effects and system GMM estimations show that logistics performance exerts a robust and statistically significant negative effect on emissions, whereas GDP per capita is a positive driver and trade openness tends to reduce emissions when logistics capacity is sufficiently strong. Negative and significant interaction terms between LPI and both income and openness indicate that logistics efficiency amplifies the environmental benefits of trade and growth. Quantile regressions reveal that these patterns are most pronounced in high-emission countries, where improvements in logistics performance and its interaction with trade and income generate larger marginal reductions in CO₂ emissions. Overall, the findings highlight the central role of logistics modernization and green trade facilitation in reconciling trade-led growth with decarbonization in the Mediterranean Basin. From a policy perspective, the evidence suggests that prioritizing green logistics and trade facilitation—particularly in high-emission Mediterranean economies—can yield the largest marginal reductions in CO₂ emissions. Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

In a worldwide context marked by increasing attention to financial literacy as a factor of financial inclusion, Morocco take part of this dynamic, seeking to improve the financial skills of its population. This article does not measure financial literacy directly but aims to explore the structural conditions that enable its diffusion in Morocco, using macroeconomic indicators such as income, employability, and education, along with financial infrastructure. Adopting a mixed methodology, this study combines both qualitative and quantitative analysis of the national context, including an overview of public policies, socioeconomic characteristics, and financial literacy initiatives, with a quantitative analysis based on an Autoregressive Distributed Lag (ARDL) econometric model. Bank branch density is employed as an indirect proxy for financial infrastructure, reflecting access to formal financial services in the absence of time-series literacy data. The results show that gross national income (GNI) per capita, the labor forces, and elementary school enrolment rates influence banking density, though without producing statistically significant effects in the long term. In the short term, only GNI has a temporary but not very robust impact. These results highlight the limitations of macroeconomic indicators alone in explaining financial literacy diffusion and underscore the potential role of structural factors such as digital innovation, governance, or inclusion of youth and female indicators. Full article

Electric vehicles (EVs) are widely promoted as a key strategy for reducing carbon dioxide (CO₂) emissions and advancing sustainable development. However, the real-world benefits of EV adoption may vary across countries with different income levels and energy systems. This study investigates the relationship between EV adoption and CO₂ emissions per capita, as well as overall sustainable development performance (SDG Index), across 50 countries from 2010 to 2023. Using panel quantile regression, we find that EV adoption is significantly associated with reduced CO₂ emissions particularly in the high-emitting countries in high-income countries (interaction coefficient at the 90th quantile = −0.24, p < 0.05) but positively associated with emissions in lower- and middle-income countries at lower quantiles of the emissions distribution. Similarly, while EV adoption correlates positively with the SDG Index in high-income countries, it shows negative effects at the median and several quantiles. These findings challenge the “zero-emission” assumption and demonstrate that the climate and development benefits of EV diffusion are context-dependent and unevenly distributed, highlighting the need for policies that link electrification to renewable energy deployment, infrastructure development, and equitable access. Full article

Over the past decades, the tourism sector has grown into one of the rapidly expanding sectors in the global economy, becoming an important source of income generation and distribution. Even though tourism development is associated with economic growth and increased employment, its impact on income inequality remains ambiguous and depends on economic, institutional and social conditions. The aim of this study is to assess the impact of international tourism receipts on income inequality in Lithuania, Latvia and Estonia in the period of 2004–2024. This study employed a comparative analysis of scientific literature and a multiple regression model based on macroeconomic indicators. The results showed that international tourism receipts did not have a statistically significant impact on income inequality in any of the Baltic countries. However, the robust model analysis confirmed and strengthened the main model results: international tourism in Latvia reduced income inequality but increased it in Estonia. In Lithuania, the impact remained insignificant. Foreign direct investment in Lithuania and GDP per capita in Latvia were statistically significant in explaining income inequality. The findings highlight that the determinants of inequality vary across the Baltic States. Full article

Air pollution episodes caused by particulate matter (PM) persist in and around Kraków even after the city’s ban on solid fuels. We examine how household wealth and the ongoing replacement of old heat sources with modern, energy-efficient units affect these emissions. Years of hourly data from a network of low-cost sensors for neighboring municipalities are combined with the Poland building emissions register specifying the number and type of heating devices and municipal personal income tax records. Two distinct emission patterns emerge. Episodes of elevated concentrations near houses with old hand-loaded stoves follow pronounced behavioral cycles tied to residents return home hours and the nightly sleep cycle, whereas elsewhere the pattern is smoother—consistent with modern heating sources or with advection from dispersed upwind sources. Municipalities that recorded per capita income growth also showed declines in average PM concentrations, suggesting that rising incomes accelerate the transition to cleaner, more efficient heating. Our findings suggest that economic development is linked to the shift towards cleaner and more efficient energy, and that providing targeted support for low-income households should not be overlooked in completing the transition. Full article

This article examines labor market dynamics in Bulgaria, Italy, and the United Kingdom by integrating demographic pressures, wage and labor cost adjustment, redistribution mechanisms, inequality outcomes, and digital readiness into a single comparative framework. This study first applies hierarchical clustering to a harmonized EU country panel for 2017–2024, using GDP per capita in PPS, average annual wage, and unemployment rate to position the three countries within the European convergence space and income–labor cost groupings. The results show that Bulgaria belongs to a low-income, fast-converging group, with nominal wages and hourly labor costs more than doubling, strong real-wage growth from a low base, and an improving price level index. At the same time, unemployment fell to below the EU average, yet income inequality remains persistently high. Italy represents a high-income but slow-growing labor market, in which real wages have declined, and labor costs per hour remain above the EU mean with a significant non-wage component. Unemployment remains relatively elevated, indicating divergence in workers’ purchasing power despite high income levels. The UK has labor costs in the mature high-income range, low unemployment, and the lowest tax wedge for low-wage workers, but with relatively high and volatile inequality. This study shows that wage dynamics, labor cost composition, and tax–benefit structures jointly mediate the translation of macroeconomic performance into household outcomes, generating distinct policy trade-offs across the three labor market configurations. Digital indicators further suggest that income level is not a sufficient predictor of digital engagement and that the observed aggregate labor market trends do not indicate a sharp employment contraction contemporaneous with the diffusion of technical innovations, such as generative AI. Full article

This paper examines the impact of industrial energy efficiency on household energy poverty in the twenty-seven Member States of the European Union for the period 2003–2023. Although the literature has widely discussed energy efficiency as an enabler of decarbonisation and economic performance, its direct link to energy poverty at the macro level has rarely been analysed, let alone with respect to structural changes in industry. Filling this gap, this paper evaluates whether reductions in industrial energy intensity result in reduced energy poverty, understood as the share of households unable to maintain adequate indoor thermal comfort. Empirical analysis relies on a balanced panel dataset and uses fixed-effects regression models to take into account unobserved country-specific and time-specific heterogeneity. In addition, potential endogeneity between industrial energy intensity and labour productivity is addressed by the instrumental variable approach using two-stage least squares. The main models also include key macroeconomic and social control variables: real GDP per capita, social benefit expenditure, electricity prices for households, and unit labour costs. The results yield a robust and statistically significant positive link between industrial energy intensity and energy poverty, suggesting that efficiency improvements in industry make a quantifiable difference in household energy deprivation. This effect even increases in strength after the correction for endogeneity, thereby corroborating the causal relevance of productivity-driven efficiency gains. The findings also show substantial heterogeneity between EU Member States, indicating that national structural features will determine baseline levels of energy poverty. However, no strong evidence is found for an indirect price-mediated transmission mechanism or for moderation effects bound to income levels or social expenditure. This study provides sound empirical evidence that industrial energy efficiency is an important but structurally conditioned lever to alleviate energy poverty in the European Union. The results emphasise the integration of industrial efficiency policies with social and institutional frameworks while designing strategies for a just and inclusive energy transition. Full article

Anti-Poverty Programmes (APPs) are closely linked to rural livelihoods, yet comparative evidence on how participants and non-participants differ in livelihood-capital composition and income-generation patterns remains limited in ecologically fragile pastoral regions. This study draws on a cross-sectional household survey conducted in Northern Tibet in July 2020, covering 696 households—including 225 APP participants and 471 non-participants. Using the Sustainable Livelihoods Framework and the entropy weight method, we construct multidimensional livelihood-capital indices (human, social, natural, physical, and financial capital) and compare the two groups. We further apply Ordinary Least Squares (OLS) regressions to examine factors associated with per capita net income. The results reveal substantial heterogeneity in livelihood capital and income across both groups. APP participants exhibit higher human-capital scores, largely driven by a higher share of skills training, whereas they show disadvantages in physical and financial capital relative to non-participants. Natural capital shows no statistically significant difference between the two groups under the local grassland contracting regime. Significant differences are observed and identified in certain dimensions of social capital. Regression results suggest that income is positively associated with skills training, contracted grassland endowment, and fixed assets, with skills training showing the strongest association. For participants, herd size and labour capacity are not statistically significant correlates of income; for non-participants, larger herds and greater labour capacity are associated with lower income. Taken together, the findings indicate that APP participation is associated with stronger capability-related capital (notably training) alongside persistent constraints in productive assets and financial capacity. Policy implications include improving the relevance and quality of training, strengthening cooperative governance and market linkages, and designing complementary packages that connect skills, inclusive finance, and productive asset accumulation. Given the cross-sectional design and administratively targeted certification of programme participation, the results should be interpreted as context-specific associations rather than strict causal effects. Full article

Objectives: To analyze the prevalence and influencing factors of overweight and obesity among left-behind children (LBC) under 6 years old in China, and to provide a reference basis for their early prevention and control. Methods: The data were derived from the National Nutrition and Health Survey among children and lactating mothers (2016–2017). A total of 19,229 left-behind children under 6 years old in China were included in this study. The results were post-stratification weighted and adjusted using data from the Sixth National Population Census released by the National Bureau of Statistics of China in 2010. The Rao–Scott chi-square test with sampling design-weighted correction was used to test for statistical differences, and multivariate unconditional logistic regression analysis was conducted to explore influencing factors. Results: The prevalence of overweight and obesity among LBC under 6 years old in China were 6.68% and 2.22%, respectively. The overweight rate and obesity rate of boys were higher than those of girls (7.96% vs. 5.15%, 2.77% vs. 1.56%). Both the overweight rate and obesity rate showed a “U”-shaped trend with increasing age (p < 0.0001). LBC with migrant fathers had the highest overweight rate and obesity rate. Logistic regression analysis indicated that being male, being in infancy or preschool age, residing in eastern China, having a migrant father, and higher annual per capita household income were risk factors for overweight and obesity. Conclusions: Left-behind children under 6 years old in China are at risk of overweight and obesity. Among LBC under 6 years old in China, the issues of overweight and obesity are relatively prominent in boys, as well as those in infancy and preschool age. Additionally, LBC with fathers who migrate for work have relatively higher overweight/obesity rates. It is essential to pay attention to the problems of overweight and obesity among LBC under 6 years old in China, strengthen the monitoring of their growth and development, and incorporate the improvement of overweight and obesity in LBC into national nutrition improvement policies at all levels. Full article