Search Results (387)

This study examines the agricultural energy transition in 25 European Union Member States over 2005–2024, focusing on energy intensity, fossil-fuel dependence, and carbon intensity. It integrates multi-regional Kaya–LMDI decomposition, Tapio decoupling analysis, and σ- and conditional β-convergence models to identify the components associated with changes in direct energy-related agricultural CO₂ emissions, assess their relationship with real agricultural gross value added, and determine whether national performance gaps are narrowing. The results show that declining energy intensity and fossil dependence were the main emissions-reducing components, while changes in country structure and carbon intensity partly offset these gains. Within the EU-25 analytical sample, the baseline comparison indicates a shift from recessive decoupling before 2020 to strong decoupling over 2020–2024. Sensitivity tests confirm the robustness of post-2020 strong decoupling, although the characterization of the pre-2020 period is sensitive to sample composition. Conditional β-convergence is observed for all three indicators, whereas σ-convergence is found only for energy intensity; fossil dependence and carbon intensity continue to exhibit substantial cross-country dispersion. Bias-corrected estimates preserve the direction of conditional catch-up but indicate slower adjustment, particularly for fossil dependence. The study’s novelty lies in jointly evaluating emissions decomposition, decoupling, and distributional change while distinguishing relative catch-up towards country-specific trajectories from convergence towards a common EU level. The findings identify energy efficiency, fossil-energy substitution, and differentiated national conditions as relevant areas for CAP-related monitoring and policy consideration. The conclusions concern specifically the energy-related component of agricultural decarbonization. Full article

Background/Objectives: Different organizations have established fluid intake recommendations for adolescents; however, studies assessing adherence typically rely on a single reference system, limiting comparisons across populations. This study assessed beverage consumption patterns and beverage-derived energy intake among Polish adolescents and examined how adherence estimates differed when recommendations issued by the European Food Safety Authority (EFSA), the Institute of Medicine (IOM), and the Polish National Institute of Public Health—National Institute of Hygiene (NIH) were applied. Methods: A cross-sectional study was conducted among 1644 secondary school students aged 14–18 years. Beverage consumption (mL/day) and beverage-derived energy intake (kcal/day) were assessed using the validated Polish Beverage Intake Questionnaire (BEVQ-PL). Adherence was defined as achieving at least 80% of the age- and sex-specific Adequate Intake (AI) from beverages, assuming that beverages provide approximately 80% of total daily water intake. Results: Water had the highest median daily intake (960 mL/day). Males consumed significantly more water than females (1080 vs. 720 mL/day; p = 0.037), whereas females consumed significantly more reduced-fat milk (2%), skim milk, plant-based and flavored milk beverages, 100% vegetable juice, and coffee with milk and sugar. Adherence differed according to the recommendation system, reaching 65.2% (EFSA), 50.6% (IOM), and 65.8% (NIH). Female sex was independently associated with higher adherence across all three reference systems, whereas older age was associated with lower adherence only according to the NIH criteria and overweight/obesity only according to the IOM criteria. Conclusions: Estimated adherence to fluid intake recommendations varied substantially according to the reference system applied. These findings highlight the importance of explicitly reporting the selected reference criteria when assessing and comparing the adequacy of beverage-derived fluid intake among adolescents. Full article

Various explanatory factors for perceived happiness have been identified in cross-country comparisons, but their relative importance and causal relationships remain unknown. Associations and causality were studied between 17 societal, economic, and religious variables contributing to happiness scores on the World Happiness Report (WHR) in 147 countries. In a correlation analysis, the strongest association with national happiness scores was observed for the median gross domestic product (GDP)/capita (r squared 0.69), followed by the mean GDP/capita (0.65), tertiary education (0.42), corruption (−0.41), democracy index (0.35), religious non-affiliation (0.31), and consanguinity (−0.25). Bayesian statistical analysis indicated strong causal cascades originating from democracy (a positive effect) and, to a lesser degree, corruption (a negative effect) during the years 1945–1985 to subsequent national happiness levels, with a delay of several decades. The results were confirmed to have good reproducibility with Bootstrap resampling, indicating robust findings. Although the median GDP/capita had the strongest association with happiness in the cross-sectional analysis, it was not identified as a primary causal factor in the Bayesian analysis, in which high levels of democracy and low levels of corruption were entangled with median GDP/capita as apparent prerequisites for perceived happiness. Our results suggest that the median GDP should be used as an indicator of the economic prosperity of citizens instead of the currently established mean GDP when studying the links between economy, democracy, and well-being across countries. Full article

Background/Objectives: Burnout is an important occupational health concern among orthopedic and traumatology physicians. This study aimed to evaluate the individual dimensions of burnout and identify their demographic and occupational correlates among orthopedic and traumatology physicians practicing in Türkiye. Methods: This cross-sectional study used an anonymous online survey distributed through the Turkish Society of Orthopaedics and Traumatology electronic mailing group between October 2024 and June 2026. Burnout was assessed using the Turkish adaptation of the 22-item Maslach Burnout Inventory, comprising the Emotional Exhaustion (EE), Depersonalization (DP), and Personal Accomplishment (PA) subscales. Unadjusted group comparisons, Spearman correlation analyses, and separate multivariable linear regression models with HC3 heteroscedasticity-consistent standard errors were performed. Results: A total of 320 complete questionnaires were analyzed. The mean EE, DP, and PA scores were 20.17 ± 6.83, 7.40 ± 3.58, and 20.37 ± 4.19, respectively. The subscale scores were analyzed as continuous dimensions rather than classified using low-, moderate-, or high-burnout thresholds. In unadjusted comparisons, residents had higher EE and DP scores and lower PA scores than specialists. After adjustment, resident status remained associated with higher DP (B = 2.14, p = 0.010) and lower PA (B = −1.92, p = 0.018). Seeing more than 120 patients per day was associated with higher EE (B = 4.22, p = 0.023) and DP (B = 2.49, p = 0.018), while working more than 100 h per week was associated with higher EE (B = 4.47, p = 0.009). Individual predictor effect sizes and the explanatory power of the models were generally modest. Conclusions: Daily patient volume, weekly working hours, and professional status showed distinct associations with the individual dimensions of burnout. These findings suggest that workload organization and support for resident physicians warrant consideration. However, because of the cross-sectional design and voluntary non-probability sampling, the findings should not be interpreted as causal or nationally representative. Full article

Sustainable supply chains carry a cost premium that can make their demand base fragile when economic shocks tighten household budgets, because the more expensive environmental, social, and governance (ESG)-aligned product is often among the first that consumers forgo. We propose extending resilience thinking to the downstream, demand-side node of the supply chain: we frame premium-tolerant ESG consumption as a conceptual indicator of demand-side resilience—an absorptive buffer that may help keep sustainable supply chains viable through disturbance—and draw on signaling theory to model how it is formed. Using a nationally structured, quota-controlled survey of 3000 Korean consumers collected by the Korea Consumer Agency, we estimate a moderated-mediation structural equation model in which ESG signal trust is associated with resilient sustainable consumption through perceived ESG legitimacy, conditioned by income and perceived greenwashing. Because the data are cross-sectional, we report associations rather than causal effects. Trust was positively associated with perceived legitimacy, which in turn was associated with resilient sustainable consumption, so that legitimacy is the proximal correlate linking trust to resilience. Income moderated this conversion only modestly—the sustainability–resilience trade-off is statistically present but small in magnitude—while perceived greenwashing did not attenuate it, and its unexpected positive coefficient proved unstable across specifications and is not interpreted. A multi-group comparison indicated that the pattern of associations differs with the perceived credibility of the ESG signal environment: when third-party signals were perceived as credible, the trust–legitimacy–consumption route was more pronounced, whereas when they were not, trust was associated with consumption more directly. Consumers also expected ESG far more of large firms than of micro-enterprises. No supply-chain-level outcome was measured; the cross-level link is advanced as a proposition for future work. The study reframes supply chain resilience as partly demand-side, potentially sustained by credible, independently verified ESG signals rather than by firms’ own disclosure. Full article

Sustainability education has produced four major traditions, Environmental Education, Climate Education, Education for Sustainable Development, and Climate Justice Education, that have developed largely in parallel. This fragmentation prevents cross-tradition comparison and leaves the most transformative dimensions of the field systematically underdeveloped. This article proposes a four-dimensional Taxonomy of Sustainability Education Design, organized around Educational Approach (D1), Cognitive Depth (D2), Disciplinary Integration (D3), and Real-World Connection (D4), for diagnosing dimensional gaps across educational programs and traditions. The taxonomy was constructed inductively from a critical literature review and interpreted through Edgar Morin’s philosophy of complex thought, whose seven principles explain why dimensional underdevelopment is paradigmatic rather than merely programmatic. The framework is illustrated through two contrasting cases: the UNESCO Associated Schools Network and the Brazilian National Environmental Education program. The mapping suggests a structural asymmetry: the dimensions most consistently underdeveloped, D1 Criticality and Advocacy, D3.5 Community and Indigenous Knowledge, and D4 Systemic and Political, are those most closely associated with transformative and epistemically plural education. The normative and policy implications of these gaps are examined in relation to the geopolitics of epistemological authority, climate-displaced communities, the historical production of colonial vulnerability, and the risk of epistemicide at algorithmic scale in AI-mediated education. Full article

Green urban renewal requires coordination among climate adaptation, water management, biodiversity conservation, and spatial planning within coherent policy frameworks. This study presents a structured analytical framework for evaluating how nature-based solutions (NbS) and natural water retention measures (NWRM) are represented in local and national policy documents relevant to green urban renewal, using Zagreb, Croatia, as a case study. The analysis combined qualitative content analysis, semi-quantitative assessment, and comparative policy-coherence analysis of strategic, planning, and legislative documents. Recent strategic documents showed the broadest documentary coverage of climate adaptation, biodiversity conservation, sustainable water management, and green infrastructure, whereas legislative and statutory planning documents contained more function-specific provisions. Economic feasibility, adaptive management, and several implementation-related provisions were less consistently represented. Cross-level comparison further identified stronger alignment between several recent Zagreb strategic documents and national climate and water policy documents. The study contributes a structured adaptation of IUCN-derived criteria for cross-level policy-document analysis; its applicability in other legal and urban contexts requires comparative testing. Full article

Data imbalance is common in multi-environment trials (METs) and complicates genotype and environment evaluations. To address this, linear mixed models (LMMs) and factor-analytic linear mixed models (FA-LMMs) were employed to estimate best linear unbiased predictions (BLUPs) for genotype effects. The highly unbalanced wheat grain yield data from the 2021–2025 national regional trials in the Northern Winter Wheat Region (NWWR) served as our case study. These BLUP values were then integrated into the genotype plus genotype-by-environment interaction (GGE) biplot framework to construct BLUP-GGE biplots. Subsequently, these biplots were systematically compared in terms of genotype evaluation, environment evaluation, and mega-environment delineation. Based on Akaike Information Criterion (AIC) and cross-validation, FA2 was chosen as the representative FA-LMM for comparison with LMM. For genotype evaluation, the two biplots produced highly consistent rankings. Based on the FA-LMM output, the top five genotypes were BH5318, BH2605, BH7868, JD39, and ZM8729. For mega-environment delineation, both approaches consistently identified one major and one minor mega-environment, with eight and three environments, respectively. The FA-LMM-based biplot provided clearer separation between them. For environment evaluation, the two biplots showed highly significant correlations for discriminating ability, representativeness, and desirability index. Luannan ranked first, followed by Zunhua, Baodi, Gu’an, Changping, and Shunyi, Xushui and Wuqing were intermediate, while Taiyuan, Tunliu, and Qixian performed relatively poorly. In summary, both methods are effective and reliable for analyzing highly unbalanced MET data, and show high consistency in genotype and environment evaluation. However, the FA-LMM-based BLUP-GGE biplot provides superior goodness-of-fit and more precise mega-environment delineation. Full article

AI-driven personalization has become a defining feature of digital advertising, yet whether it drives purchase intention directly or depends on other underlying mechanisms remains insufficiently understood, particularly in under-studied emerging markets. This study investigates how perceived relevance, usefulness, and privacy concerns shape perceived trust and purchase intention in response to AI-driven personalized advertising across Lebanon and Türkiye, two digitally distinct markets. Drawing on the Stimulus Organism Response model, Technology Acceptance Model, Privacy Calculus Theory, and Trust in Automation Theory, a quantitative cross-national design was employed, using a structured questionnaire administered to 802 social media users (394 Lebanon; 408 Türkiye), with data analyzed through partial least squares structural equation modeling and permutation-based multigroup analysis following partial measurement invariance. Results confirm perceived personalization has no direct effect on purchase intention, operating solely through relevance, usefulness, and privacy concerns; relevance and usefulness build trust while privacy concerns erode it, and trust, relevance, and usefulness each independently drive purchase intention. This study advances an asymmetric mediation account of AI advertising, positioning trust as the conduit through which privacy risk reaches behavior and, for managers, the lever converting personalization into purchase. Future research could examine these dynamics longitudinally or across other digitally emerging markets. Full article

Long-term exposure to ambient fine particulate matter (PM_2.5) is the leading environmental risk factor for premature mortality worldwide, yet comprehensive province-level evidence quantifying its health burden across Türkiye remains limited. This study investigated the spatial relationship between long-term PM_2.5 exposure and all-cause attributable mortality across all 81 Turkish provinces in 2022 using province-level annual mean PM_2.5 concentrations and World Health Organisation (WHO) AirQ+ estimates of PM_2.5-attributable deaths among adults aged ≥30 years, assuming a counterfactual concentration of 5 µg/m³. The association between PM_2.5 exposure and mortality was evaluated using Pearson and Spearman correlation analyses, ordinary least squares (OLS) regression, a log–log elasticity model, and population-weighted regional and exposure-quartile comparisons, while national temporal indicators for 2010–2023 were reported solely as supplementary context for the primary single-year 2022 cross-sectional analysis. The population-weighted annual mean PM_2.5 concentration was 27.0 µg/m³, exceeding the WHO Air Quality Guideline by a factor of 5.4, and all 81 provinces exceeded the recommended threshold. The bivariate OLS model accounted for 41% of the between-province variation in attributable mortality rates (OLS slope = 3.23 additional deaths per 100,000 population for each 1 µg/m³ increase in PM_2.5; 95% CI: 2.37–4.10; R² = 0.41; p < 0.001), while the log–log elasticity model indicated that a 1% increase in PM_2.5 concentration was associated with a 0.80% increase in the attributable mortality rate (95% CI: 0.65–0.95). The attributable fraction of natural-cause mortality increased progressively from 8.8% in the lowest exposure quartile to 24.6% in the highest. Nationwide, an estimated 68,440 premature deaths, representing 14.2% of all natural-cause deaths among adults aged ≥30 years, were attributable to PM_2.5 exposure. These findings quantify a steep, spatially graded PM_2.5-attributable mortality burden across Türkiye. As the attributable estimates derive from the WHO AirQ+ concentration–response function, the gradient describes the magnitude and spatial distribution of the modelled burden rather than an independently estimated exposure–response relationship, and on that basis the results support the adoption of WHO-aligned air-quality standards and accelerated decarbonization strategies to reduce the national health burden attributable to ambient air pollution. Full article

Sustainable forest governance requires fiscal instruments responsive to community preferences for ecosystem services, yet compensation standards are rarely checked against those preferences. China’s ecological public welfare forest program, which covers 120 million hectares, sets graded provincial compensation rates across internally differentiated communities. Using contingent valuation data from 315 households in Lin’an District, Zhejiang Province (2024), this study compares compensation with community-expressed preferences and asks whether that comparison holds across social groups. The aggregate community willingness to pay (WTP) is CNY 41.07 million per year, with a mean household WTP of CNY 210, and the actual provincial compensation of CNY 41.34 million falls within its confidence interval. Because both quantities carry estimation uncertainty, this interval overlap is not a formal test of equivalence and does not establish that the two are equal. The aggregate correspondence also masks substantial heterogeneity. Income and education generate WTP differentials exceeding 30 percent, and rural residents report valuations 16.6 percent below those of urban residents. This pattern is associated with economic capacity rather than institutional trust or environmental awareness. Lin’an is a most-likely case for such correspondence, since its urban–rural income ratio (1.54:1) lies well below the national average (2.31:1). Preference heterogeneity is therefore plausibly more pronounced elsewhere, although cross-regional replication would be required to confirm this. Graded fiscal instruments for forest resource governance, even where broadly consistent with aggregate preferences, warrant differentiation to sustain community support for conservation across heterogeneous populations. Full article

Ground-level ozone (${\mathrm{O}}_3$) is a secondary photochemical pollutant whose formation under intense Mediterranean solar radiation makes Greece prone to elevated concentrations, with well-documented respiratory and cardiovascular health effects. Forecasting of ozone in Greece has nonetheless remained limited: prior work has been largely statistical, focused on one or two sites in Athens, and restricted to a single horizon, and no study has compared machine learning forecasts across the urban-to-rural gradient of the national monitoring network. This study addresses that gap with, to our knowledge, the first multi-site comparison of machine learning ozone forecasts spanning the full urban-to-rural typology gradient of a Greek monitoring network, introducing a typology × model accuracy cross-matrix that links site character directly to forecasting performance and offers concrete, horizon-specific operational guidance for early warning deployment. We develop a single, reproducible pipeline and compare three model families, Support Vector Regression (SVR), Random Forest (RF) and gradient boosting (GBM), against a naïve persistence baseline, for next-hour and next-day-maximum ${\mathrm{O}}_3$ prediction at eight urban, suburban and rural stations of the National Air Pollution Monitoring Network (2021–2024). All predictors are derived from the ozone series itself (lagged, rolling and cyclical calendar features); models were tuned and evaluated using a strictly chronological 70/15/15 train/validation/test split per station, with hyperparameters selected on the validation partition and the test partition evaluated only once to prevent temporal leakage. At the next-hour horizon, all models outperformed persistence at every site, with the single exception of SVR at the rural THR site; gradient boosting was best or joint-best (test $R^2$ from 0.83 at the heavily titrated central-urban site to 0.94 at a rural site), and a Wilcoxon signed-rank test on paired per-sample errors confirmed that GBM significantly outperformed Random Forest at all seven evaluated stations ($p<0.05$, and $p<0.001$ at six of them). The next-day-maximum task was substantially harder ($R^2$ between 0.41 and 0.79), and the ranking reversed, with SVR being the best model almost everywhere. Crucially, forecast accuracy declined systematically from rural and suburban toward central-urban sites: the same nitric oxide titration that suppresses urban ozone is also associated with degraded predictability. These results, summarised in the typology × model accuracy cross-matrix, indicate that an operational early warning system for Greece should adopt horizon-specific models, gradient boosting for next-hour nowcasting and SVR for day-ahead maxima, with explicitly higher uncertainty in the urban core, and can be deployed on the network’s real-time (IoT) data stream using ozone observations alone. Full article

A set of “stylized facts” about state-backed influence operations now circulates across journalism, policy, and the peer-reviewed literature: that they are monolithic troll armies; that they win by weaponizing moral-emotional language; that they manufacture their own virality; that they learn and optimize against feedback; and that they have become indistinguishable from ordinary users. Most rest on single-campaign studies, uncontrolled comparisons, and large-sample significance reported without baselines or multiple-comparison control, and are rarely re-tested. We assemble complete, government-attributed archives of seven state campaigns (25,076,853 tweets from 9071 accounts) with a matched organic-user baseline for five of them, and re-test all five claims under one protocol: pre-registration, Benjamini–Hochberg false-discovery control, permutation nulls, a future-reception placebo, and a takedown-snapshot decomposition. Scoped to these campaigns, every claim weakens or reverses: the operations are narratively segregated, thinly staffed production desks, not a unified army; an organic moral-contagion law fails to replicate in any of them, and a meaningless placebo predicts engagement as well; internal amplification supplies only $0.10$–$5.31\%$ of top-percentile reach; the remainder is captured from an external audience whose composition—genuine organic uptake versus coordination the archive cannot see—is structurally unobservable in takedown data, a limit we state as part of the finding; behavior is scripted, with rare apparent feedback mean-reverting toward baseline; and, while their per-account language has drifted off the 2016 “troll” fingerprint, they still coordinate 7–$70\times$ more tightly than matched real users—a regularity a frozen re-test reproduces, with the language-drift and segregation patterns, across twelve further country-groups. The five verdicts do not carry equal evidentiary weight: three rest on matched-baseline contrasts, one on a placebo-gated temporal design, and one (the moral-emotional claim) on a placebo-anchored test alone, a hierarchy this paper makes explicit. The corrected picture is coherent: an industrial content factory siloed in production but coordinated in execution, whose reach it does not internally manufacture. The detectable signature has migrated from language to coordination: per-account content fingerprints age out, while cross-account coordination remains the durable, cross-national marker. Because these operations run scripts rather than optimize against feedback, an adversary that genuinely optimized—now feasible with large language models—would look measurably different from the operations studied here: a forward warning, not a present finding. The recurring lesson is methodological: on corpora of confirmed manipulation, baseline-free significance reconstructs the analyst’s expectations, and platform and policy decisions rest on those beliefs. Full article

Extreme wind speed events pose a significant threat to structural safety and are an important consideration in the design of the growing renewable energy sectors. This study models extreme wind speeds in Namibia by applying Extreme Value Theory (EVT) to quality-controlled daily maximum wind speed records from six meteorological stations, spanning 13–21 years per station and covering the 2003–2024 period. A two-rule quality-control procedure, combining a regional plausibility limit with an isolated-spike test supported by cross-station coherence checks, identified and removed 70 spurious automatic weather station records (0.24% of observations) prior to analysis. The Generalized Pareto Distribution (GDP) was fitted to declustered threshold exceedances using the Peaks-Over-Threshold method, with 95% confidence intervals for return levels obtained by profile likelihood. Model comparison based on AIC, BIC, and the negative log-likelihood indicated that the Generalized Pareto Distribution (GPD) provided a better description of the extreme wind speed tails. The results reveal a clear coastal-inland contrast; the coastal station Lüderitz experiences the strongest and most frequent extreme wind events, with a 100-year return level of 49.4 m/s (GPD; 95% profile-likelihood interval 44.9–70.3 m/s) and evidence of a bounded upper tail, while inland stations exhibit more moderate extremes with 100-year return levels 36–45 m/s. A seasonal analysis reveals stronger winter extremes at coastal Walvis Bay, while inland stations experience summer convective peaks. The study provides the first systematic station-level EVT analysis of observed wind extremes for Namibia, offering essential quantitative input for wind-sensitive infrastructure design, renewable energy project siting, and national climate adaptation planning. Full article

Coordinated regional development plays an increasingly important role in promoting low-carbon transitions across China’s urban agglomerations. This study examines the effect of the Beijing–Tianjin–Hebei Coordinated Development Strategy, elevated to major national strategic status in 2014, on urban carbon emissions. Using an unbalanced panel of 294 prefecture-level and higher-level cities from 2010 to 2023, we employ a two-way fixed-effects difference-in-differences model to evaluate the strategy’s overall carbon-mitigation effect, potential transmission pathways, and spatial heterogeneity. The baseline DID coefficient is −0.052, corresponding to a 5.0% decline in city-level total CO₂ emissions in the 13 treated cities relative to the national comparison group after policy implementation. Within the same comparison framework, further analysis documents significant positive post-policy differentials in both the industrial-structure-upgrading index and green invention-patent applications; the industrial-structure-upgrading results are consistent with a potential emissions-reduction pathway. The heterogeneity analysis indicates a statistically significant relative carbon-mitigation effect for the prefecture-level cities of Hebei compared with the national comparison group, with a larger relative effect in Beijing and Tianjin. This spatial pattern is consistent with differences in industrial structure, innovation capacity, and environmental governance capacity across cities. The findings provide city-level quasi-experimental evidence that coordinated regional development can promote urban carbon mitigation and support differentiated carbon governance, coordinated industrial upgrading, and cross-regional low-carbon policy design. Full article