Search Results (890)

Economic development is multidimensional, yet provincial comparisons in Thailand often rely on individual indicators or broad sustainability indices. This study constructs separate TOPSIS rankings of economic prosperity and distributional outcomes for all 77 Thai provinces. Six prosperity indicators capture economic output, human capital, spatial agglomeration, labor market outcomes, and structural transformation, while four distributional indicators capture income inequality, economic vulnerability, informal employment, and household dependency. The analysis uses 2024 provincial accounts and Labor Force Survey microdata, with comparisons for 2022–2024. Prosperity is concentrated in Bangkok, the surrounding metropolitan region, and the Eastern Seaboard, while Phuket represents a distinct tourism-led pathway. Favorable distributional outcomes are concentrated within the metropolitan–industrial corridor, whereas disadvantage is more prevalent in the North and Northeast. The rankings are associated but conceptually distinct, and the prosperity ranking differs meaningfully from one based solely on GPP per capita. The prosperity ranking is highly stable over time, while the distributional ranking exhibits substantial stability after harmonizing income coverage. Both rankings are robust to alternative normalization procedures, weighting schemes, and aggregation methods. Spatial statistics confirm clustering. The findings demonstrate the value of separating economic prosperity from distributional disadvantage when benchmarking provincial development and assessing urban–regional disparities. Full article

Background/Objectives: Many Medicare beneficiaries often face substantial financial strain due to healthcare costs. This study examined out-of-pocket expenditures for healthcare services by demographics among Medicare beneficiaries in 2019 and 2021. Methods: A repeated cross-sectional study analyzed the 2019 and 2021 Medicare Current Beneficiary Survey Cost Files of beneficiaries aged ≥65 years (2019, n = 6895; 2021, n = 6094). Survey-weighted generalized linear models with a log link and gamma distribution with a three-way interaction of sex (male, female), age (65–74, ≥75), and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and Other), adjusted for covariates, and to 2021 USD, were conducted to estimate per capita annual out-of-pocket expenditures for healthcare services, excluding premiums. Results: Estimated average per capita annual out-of-pocket expenditures for healthcare services were $2511 in 2019 and $2641 in 2021. Differences in out-of-pocket expenditures were observed among various demographic groups in 2019 and 2021. Non-Hispanic Blacks had lower out-of-pocket expenditures than non-Hispanic Whites in both years (in 2021, $2139 [1761–2517] vs. $2730 [2580–2879]). Beneficiaries aged 65–74 years who were non-Hispanic Black had lower out-of-pocket expenditures than non-Hispanic Whites in the same age group for both years. A significant proportion of non-Hispanic Blacks had lower incomes (in 2021, income < $25,000, 46.1% vs. 17.0%) and more comorbidities (in 2021, comorbidities ≥ 4, 55.6% vs. 44.7%), compared to non-Hispanic Whites in both years. Similar differences in incomes and comorbidities were observed among those aged 65–74 years of both groups. Conclusions: Medicare beneficiaries face substantial out-of-pocket burdens for healthcare services, with persistent demographic disparities. Understanding contributing factors and implementing effective policies/practices are essential to reduce/mitigate high out-of-pocket expenditures for beneficiaries. Full article

In the context of digital rural development, social commerce has become a low-threshold channel through which smallholders can connect directly with consumers. Yet limited evidence explains how acquaintance-based social networks shape the income effects of social commerce in fresh agricultural-product marketing. Using survey data from 724 peach-farming households in Pinggu District, Beijing, in 2024, this study examines the effect of WeChat-based direct marketing on farmers’ per capita net income from peach production. Benchmark regression, propensity score matching, and an instrumental variable approach are used to address selection bias and potential endogeneity. The results show that WeChat-based direct marketing significantly increases farmers’ income, with the fully controlled benchmark estimate indicating an increase of 0.287 log points in per capita net income. This finding remains stable after replacing the dependent variable, addressing extreme values, and excluding special samples. Mechanism analysis indicates that the income effect operates mainly through higher average selling prices. Heterogeneity analysis further shows that the effect is stronger among households with higher social expenditure, suggesting that acquaintance-based embeddedness helps transform social relations into market value. Going beyond prior studies that focus mainly on participation, sales performance, or average income effects, this study identifies the selling-price transmission mechanism and shows that acquaintance-network embeddedness amplifies the income gains from WeChat-based direct marketing. Full article

This paper estimates end-use demand elasticities for European Union residential energy consumption and applies them to projections of household energy demand up to 2030 and 2050. Using a 2013–2023 panel for the EU27, we estimate country fixed-effects demand equations with Driscoll-Kraay standard errors for total household energy and five Eurostat end-use categories: space heating, space cooling, water heating, cooking, and lighting and appliances. The explanatory variables are heating and cooling degree days, real household electricity and gas prices, and real GDP per capita. The estimated elasticities are applied to six scenarios that combine two climate trajectories with three energy-price pathways, calibrated to international policy scenarios. These pathways are price approximations of the corresponding IEA scenarios; technology, electrification, and fuel-mix transformations are not modeled. We also examine stylized Renovation Wave sensitivities that impose reductions of 20%, 40%, and 60% in projected space-heating energy demand. The results show that EU27 residential energy demand is projected to fall by 2% to 12% by 2050 across the six scenarios, mainly because of reductions in heating demand, which dominate increases in cooling demand in absolute energy terms. Under the central scenario, total demand falls by 4.0%. Renovation sensitivities imply substantially larger reductions, ranging from 17% to 40%. The findings highlight the importance of building-envelope improvements, cooling-related adaptation, and end-use heterogeneity in long-run residential energy-demand policy. The paper contributes a harmonized end-use projection framework that links climate exposure, household energy prices, income, and building-envelope efficiency within a single empirical model of EU residential energy demand. Full article

This study examined the spatial carbon emission effects of new quality productive forces (NQPs) using provincial panel data from China during 2012–2021. A dynamic spatial Durbin model with instrumental variable generalized method of moments estimation was used to address endogeneity in assessing direct and spillover effects on carbon emission intensity. Results show that the direct effect (0.0072) and spatial spillover ($-0.0100$) are statistically insignificant, with no identifiable emission reduction during the sample period. Technology diffusion exhibits a J-curve left-sided feature with a short-term total effect of 0.0553, as general technology diffusion accompanies energy-intensive capacity expansion without crossing the turning point. The spatial environmental Kuznets curve reveals a positive intensification effect: neighboring per-capita gross domestic product increases local carbon emission intensity, with marginal effects rising from 0.084 to 0.168. Heterogeneity analysis shows that green-technology-oriented NQPs exhibit the most promising emission-reduction potential, while digital infrastructure generates carbon rebound. These findings challenge the presumptions that local income growth automatically reduces carbon and that technology diffusion naturally leads to reduced emissions. Cross-regional carbon compensation mechanisms, green technology market standards, and extended technology transformation evaluation periods are recommended. Full article

The rapid expansion of information and communication technology (ICT) alongside sustained economic growth poses a critical policy challenge for hydrocarbon-based economies seeking to reconcile digital development with environmental sustainability. This study examines both the short- and long-term relationships between per capita CO₂ emissions, GDP per capita, and the adoption of ICT in Saudi Arabia, using annual time-series data for 1995–2024. Methodologically, we employ the ARDL bounds test, an error correction model (ECM), and Granger causality analysis to test the EKC hypothesis. Additionally, we use the Zivot–Andrews test to detect structural breaks, accounting for these through dummy variables. Long-run cointegration among the three variables is validated by the bounds test results. GDP is the dominant long-run driver of internet-based ICT adoption, while CO₂ emissions and GDP are strongly cointegrated through the 2010 structural break and the EKC mechanism. No direct causal link is found between ICT and CO₂ emissions in either direction. The long-run validity of the EKC hypothesis is confirmed, with an estimated income turning point of approximately USD 26,021 per capita, a level reached during the latter part of the sample period. These findings suggest that Saudi Arabia’s internet adoption has yet to generate an independent decarbonization dividend, underscoring the need for policies that couple ICT investment with clean energy deployment under Vision 2030. Full article

This study analyses the Environmentally Sustainable Human Development Index (ESHDI) across 42 municipalities in Puebla, Mexico, which share the same predominant biome, in 2010 and 2020. The objective was to compare four methods for constructing synthetic indices—Geometric Mean (GM), Principal Component Analysis (PCA), and two variants of Data Envelopment Analysis (DEA-CCR and DEA-SBM)—to identify the approaches most consistent with the ESHDI’s multidimensional nature. Considering the dimensions of Health, Education, Income, and per capita CO₂ emissions, the robustness of the classifications was evaluated using Kendall’s W, Spearman’s rho, and Kendall’s tau coefficients. The results reveal statistical convergence among the methods, as well as relevant structural differences in the weighting schemes and the generated rankings. While MG, ACP, and DEA-SBM assigned relatively balanced weights to the dimensions and produced similar rankings, DEA-CCR concentrated weights on the best-performing dimensions of each municipality, allowing a single dimension to exert a disproportionate influence on the aggregate index and compromise its multidimensional balance. Among the methods evaluated, DEA-SBM stood out for its ability to combine conceptual consistency with benchmarking capacity. In addition to measuring environmentally sustainable human development, the method enables the identification of reference municipalities, the establishment of realistic performance targets, and the diagnosis of specific inefficiencies in each dimension, thereby supporting the formulation of more effective and territorially contextualised public policies. The evidence reinforces the theoretical and empirical relevance of incorporating environmental sustainability into human development metrics and demonstrates that methodological choices in aggregation significantly influence the evaluation of territorial performance. Full article

Background/Objectives: Abnormal blood pressure (BP) in youth is strongly linked to genetic predisposition, particularly involving lipid metabolism genes. However, little is known about whether such polymorphisms affect ambulatory BP (ABP) parameters and whether other factors modify the associations in youth populations. Methods: A total of 510 medical students from a university in Changsha were included. Multivariable linear regression and logistic regression models were used to analyze the association between sterol regulatory element-binding protein cleavage-activating protein (SCAP) gene polymorphisms and ABP parameters among youth. Results: After adjusting for age, sex, BMI, ethnicity, monthly household income per capita, salt intake habits, fruit and vegetable intake frequency, smoking, drinking, history of hypertension and waist circumference, carriers of the A allele of rs76558868 had higher daytime systolic blood pressure (SBP) than G allele carriers (β = 1.55, SE = 0.77, p = 0.046, corrected p = 0.092). A significant interaction between rs12487736 and sex on 24 h SBP (p_interaction = 0.023) and daytime SBP (p_interaction = 0.048) levels was found. In males, the CC genotype carriers had elevated 24 h SBP (β = 3.53, SE = 1.36, p = 0.010, corrected p = 0.020) and daytime SBP (β = 3.18, SE = 1.53, p = 0.040, corrected p = 0.080) levels compared to TC/TT genotype carriers, whereas no significant association was found in females. In addition, we also noted an interaction between rs76558868 and sex on 24 h SBP (p_interaction = 0.016), nighttime SBP (p_interaction = 0.027) and nighttime diastolic blood pressure (DBP) (p_interaction = 0.025) levels; the A allele carriers had significantly higher 24 h SBP (β = 2.02, SE = 0.84, p = 0.018, corrected p = 0.036), nighttime SBP (β = 2.64, SE = 0.98, p = 0.008, corrected p = 0.016) and DBP levels (β = 1.65, SE = 0.63, p = 0.009, corrected p = 0.018) compared to G allele carriers among males, but not in females. Conclusions: An interaction between SCAP gene polymorphisms and sex was observed for ABP parameters among college students. These findings provide insights for future targeted early hypertension prevention and personalized health management strategies. Full article

Cities in the Majority World face a widening climate investment gap that is often attributed to the absence of suitable financing instruments. Green bonds promise to mobilise private capital for low-carbon urban infrastructure, yet they have diffused unevenly, leaving the economies with the greatest needs at the market’s margins. This study asks whether macroeconomic constraints—the cost of finance, monetary instability, and public indebtedness—systematically shape green bond issuance across emerging and developing economies. We assemble an original panel of 24 such economies over 2015–2024 (240 country-year observations) and estimate pooled ordinary least squares (OLS), random-effects, two-way fixed-effects, Tobit, and probit models with robust standard errors. The public debt-to-GDP ratio is positively associated with issuance in most specifications, though the strength of this relationship varies across estimators and it is not statistically significant in the preferred two-way fixed-effects model; the renewable energy share is consistently positive, while consumer price inflation shows no significant suppressive effect. A probit model of the extensive margin shows that public debt, the renewable energy share, and income per capita raise the probability of issuing among the economies for which the data permit estimation. The four lower-income Sub-Saharan economies in the sample fall outside this estimation owing to missing data, yet record no issuance whatsoever over the decade—a descriptive pattern consistent with the structural barriers the model identifies. The findings challenge the assumption that monetary stabilisation is a precondition for climate finance, pointing instead to capital-market depth and subnational fiscal capacity as the more binding constraints. Full article

Decoupling economic growth from carbon emissions is central to the sustainable development of rapidly urbanizing economies, and urban agglomerations are the pivotal spatial units for delivering this transition under China’s dual-carbon goals, yet systematic cross-agglomeration comparisons that could inform differentiated sustainability policy remain scarce. Using panel data for 107 prefecture-level cities in five agglomerations—the Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH), Pearl River Delta (PRD), Chengdu–Chongqing (CY), and the middle reaches of the Yangtze River (MRYR)—across five benchmark years spanning 2005–2023, we combined a two-way fixed-effects environmental Kuznets curve (EKC) model, the Tapio decoupling model, and cross-sectional quadrant analysis to examine the growth–emission relationship in shape, decoupling dynamics, and spatial structure. All five agglomerations traced an inverted-U trajectory, with turning-point per capita gross domestic product (GDP) rising in the order CY < PRD < BTH < MRYR < YRD. Once fixed effects and structural controls were added, most quadratic terms became insignificant and reversed sign after the secondary-industry share and carbon intensity entered; only the PRD and BTH retained a significant nonlinear form. The net income effect is therefore largely monotonic, with the inverted U carried by industrial upgrading and energy-efficiency gains. Tapio decoupling followed a non-monotonic “improve-then-regress” path, with expansive negative decoupling re-emerging across all agglomerations during 2020–2023. Spatially, high-value clustering persisted in the YRD, weakened in the BTH after 2020, and concentrated on single cores in Chengdu and Wuhan. We accordingly propose sustainability-oriented low-carbon pathways differentiated jointly by agglomeration and quadrant. By showing that decoupling is stage-dependent and reversible rather than an automatic by-product of income growth, our findings indicate that durable progress toward regional sustainability hinges on structural transformation and coordinated governance tailored to each agglomeration’s stage of development. Full article

Marine macroplastics (MMP) generation is rising faster than existing management capacity, posing major ecological and governance challenges, particularly in developing coastal regions. This study applied bibliometric, social network, and S-curve analyses to evaluate 4870 publications on MMP pollution indexed in Scopus SCI and SSCI databases from 2001 to 2025 to achieve a systematic research evaluation, thematic focus, and future research direction. The results demonstrate that publication outputs have increased sharply from 20 in 2001 to 905 in 2025, with increasing focus on developing and optimizing recycling methods. Leading countries, including China, the U.S.A., and the U.K., with 577, 395, and 339 publications, respectively, reflect potential research activities and international collaboration. Keyword analysis identified marine pollution (475), macroplastic (frequency = 460), recycling technologies (frequency = 435), plastic recycling (403), and rivers (frequency = 326) as dominant research themes. Rivers remain the primary pathway for transporting macroplastics to marine environments, with the 20 largest rivers contributing the majority of global inputs, while smaller rivers in rapidly industrializing regions are becoming increasingly important. Per capita MMP generation was generally higher in high-income countries, including the U.S.A., Italy, Spain, France (0.5–0.75 kg/person), while lower in China and India (0.4–0.5 kg/person). S-curve analysis shows that physical and chemical recycling technologies, particularly open loop and pyrolysis, have reached a mature stage of development, whereas biological recycling remains an emerging research area with considerable future potential. Scenario analysis further suggests that targeted intervention strategies could reduce single-use plastic from 18.3 to 0.79 MMt by 2060, achieving up to 90% mitigation. Overall, findings demonstrate the urgent need for combined monitoring, technological innovations, and policy action to control marine plastic pollution to protect the marine ecosystem. Full article

Sustainable energy transition is vital for the achievement of economic sustainability, environmental sustainability, and social inclusion. Hence, this paper explores the effect of ICTs, institutions, and per capita GDP on sustainable energy transition in the E7 economies between 2000 and 2024 in favor of causality and cointegration tests appropriate to the characteristics of the dataset. The causality test points out the existence of a one-way Granger causal relation from ICTs, institutions, and per capita GDP to sustainable energy transition. The estimated cointegration coefficients refer to a positive long-term association of ICTs and per capita GDP on sustainable energy transition in most of the E7 countries. Further, institutional quality positively impacts sustainable energy transition in China, India, and Indonesia in the long term, while it negatively impacts sustainable energy transition in Mexico. Our outcomes highlight the significant roles of ICTs, institutions, and per capita income for sustainable energy transition. Full article

Long-term forest landscape change in mountainous rural areas reflects interactions among land-use pressure, forest structure and human activities. This study had two primary objectives: (i) to quantify 53 years of change in forest extent, stand structure and landscape configuration in the Kümbet Planning Unit in the Eastern Black Sea Region of Türkiye and (ii) to evaluate associations of annual registered forest crime counts with election-year status and broader-scale population and real per capita income indicators while treating the crime records as contextual indicators of recorded pressure rather than as causal measures of landscape change. Forest stand-type maps from 1971, 2013 and 2024 were analyzed using LULC transition matrices and landscape metrics. Annual registered forest crime records for 1981–2025 were examined in relation to election-year status, the provincial population and real per capita income using correlation analysis and negative binomial regression, with linearly interpolated annual forest area included as an exposure offset. Total forest area declined from 6747.8 to 6298.1 ha, whereas agricultural land increased from 637.3 to 2177.7 ha. Degraded forest and open areas decreased, and 44.3% of the study area changed land-cover type. Landscape fragmentation increased between 1971 and 2013, followed by partial spatial consolidation between 2013 and 2024. Election-year status and real per capita income were not significantly associated with annual registered forest crime counts. Within the 1981–2025 study period, the provincial population showed a positive temporal association with registered forest crime counts. Each increase of 10,000 persons corresponded to an approximately 7.5% increase in the expected registered forest crime rate, but this relationship should not be interpreted as a direct local demographic effect. Registered forest crime density did not differ significantly among management-related periods. The temporal correspondence between higher crime density and stronger fragmentation was descriptive and did not establish causality. These findings demonstrate that forest conservation and landscape-restoration planning should integrate forest extent, stand structure, land-cover transitions, landscape configuration and appropriately scaled administrative indicators of recorded forest-related pressure. Full article

Demographic change, economic growth, and carbon dioxide emissions are strongly interconnected in countries facing long-term sustainability challenges. Colombia is undergoing a demographic transition characterized by changes in population size, age structure, and progressive aging, while also seeking to sustain economic development and reduce environmental pressure. This study develops a calibrated system dynamics model that integrates three interacting modules: demographic structure, economic growth, and CO₂ emissions. The demographic module represents the population through three age groups: young population aged 0–14, working-age population aged 15–64, and older population aged 65 and above. The economic module links demographic structure with GDP per capita growth, while the environmental module represents CO₂ emissions through a compact Kaya/STIRPAT-type formulation based on population, income, and carbon intensity. The model was calibrated using historical data for Colombia over the period 2000–2023 and projected to 2050 under five alternative economic and decarbonization scenarios. The demographic module reproduced the total population with a mean absolute percentage error of 1.05% and an $R^2$ of 0.977. The economic module achieved a MAPE of 3.06% and an $R^2$ of 0.950 for GDP per capita, while the CO₂ module obtained a MAPE of 7.20% and an $R^2$ of 0.741. Scenario results indicate that, under the assumed decarbonization rates, stronger economic growth increases emissions unless accompanied by sufficiently deep carbon-intensity reductions. The proposed framework provides a transparent and policy-oriented tool to evaluate sustainable development pathways in Colombia, while the environmental results should be interpreted as conditional scenario outcomes rather than as a fully identified causal decomposition of emissions drivers. Full article

This paper examines the long-run relationship between subjective well-being and digital transformation in the six Gulf Cooperation Council (GCC) countries—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over the period 2011–2025 using a balanced country-year panel dataset. Subjective well-being is measured by the national average Cantril Ladder score from the Gallup World Poll as reported in the World Happiness Report. Explanatory variables include a binary AI Readiness Period Indicator (AI) distinguishing the pre-AI-readiness phase (2011–2018, AI = 0) from the post-AI-readiness phase (2019–2025, AI = 1), anchored by the Oxford Insights Government AI Readiness Index, Internet penetration from the International Telecommunication Union (ITU), and real GDP per capita. After accounting for cross-sectional dependence and non-stationarity, the analysis employs a panel autoregressive distributed lag (ARDL) framework estimated via the Pooled Mean Group (PMG) approach. The results indicate the existence of a stable long-run cointegrating relationship among the variables. The baseline PMG estimates suggest positive long-run associations between GDP per capita and the AI Readiness Period Indicator with subjective well-being, and a negative association between Internet penetration and well-being in a high-connectivity regional context. Short-run effects are generally weak, while the error-correction term confirms adjustment toward the long-run equilibrium. Robustness checks based on alternative estimators confirm the positive long-run effect of income, while the estimated effects of the AI Readiness Period Indicator and Internet penetration show sensitivity in sign and significance across specifications and should therefore be interpreted as indicative rather than definitive. Overall, the findings suggest that digital transformation is not a homogeneous driver of subjective well-being. Instead, the AI Readiness Period Indicator and Internet penetration operate through distinct mechanisms, with potentially different welfare implications in highly connected rentier-state economies. Full article