Search Results (987)

Artificial Intelligence (AI) literacy and competency in pre-service teacher education refer to a programme-level implementation that enables teachers to work with AI systems effectively, critically, and ethically across university coursework, school placements, and early-career practice. This includes not only capability, but also professional enactment, where teachers apply AI-related knowledge in context-sensitive and pedagogically grounded ways. AI literacy refers to a shared knowledge base for understanding how AI systems generate outputs, how to evaluate and verify AI-supported information, and how to reason about task–tool fit in relation to fairness, privacy, transparency, accountability, academic integrity, equity, and environmental sustainability. AI competency refers to the application of this literacy in routine professional tasks, such as designing and justifying AI-informed teaching, learning, and assessment, protecting students’ and school data, documenting decisions, and revising AI-supported materials after checking for reliability, transparency, accountability, and equity. Together, literacy and competency extend beyond personal use of AI by preparing future teachers to support students’ creative, critical, and ethical engagement with AI, while keeping classroom practice aligned with educational goals, objectives, and values. Full article

Generative Artificial Intelligence (GenAI) has rapidly spread worldwide, driving structural changes and redefining approaches to knowledge. This trend has introduced significant challenges, particularly within higher education, where its adoption and acceptance are crucial for pedagogical transformation. However, the increasing integration of GenAI also raises pressing questions related to sustainability, encompassing both its environmental impact (e.g., energy consumption and carbon footprint of AI models) and social and ethical implications (e.g., responsible use, equity, and digital inclusion). This study investigates the factors influencing the adoption and acceptance of GenAI among higher education students, considering these sustainability dimensions. Using an adapted version of the UTAUT2 (Unified Theory of Acceptance and Use of Technology) model, the research analysed data from 229 students, collected in 2025, employing the Partial Least Squares method. By integrating the sustainability perspective, this work seeks to offer an understanding of the challenges and opportunities that GenAI presents for a more equitable and ecologically conscious educational future. The study demonstrates that habit and performance expectancy are the primary drivers of GenAI adoption among students, suggesting that its integration into higher education should prioritize functional value and ethical habit-building over social or hedonic factors. Full article

Climate-related economic losses across Europe have evolved from isolated environmental shocks to persistent, structurally embedded fiscal risks, posing a direct challenge to the long-term fiscal sustainability of European states. This study presents an empirical framework for diagnosing and quantifying this transformation across 38 European countries between 1980 and 2023. Combining regime-switching time-series models with a two-part panel design, we identify temporal shifts and spatial asymmetries in loss exposure. Our findings reveal the emergence of a high-loss regime from the early 2000s, alongside a widening inequality in national vulnerability, with countries such as France, Germany, Italy, and Spain bearing a disproportionate burden. This concentration raises critical questions about the sustainability and equity of current EU risk-sharing frameworks. The two-part model further disaggregates the probability of experiencing losses from their conditional magnitude, enabling country-level estimates of expected annual losses. These results highlight the limitations of current fiscal instruments, which remain reactive and fail to align with the spatial and temporal dynamics of climate risk. We argue for a shift from climate loss management to climate loss governance, underpinned by predictive analytics, differentiated policy tools, and a reorientation of EU fiscal solidarity mechanisms. By quantifying, measuring, and spatially disaggregating climate-related fiscal exposure, this study contributes directly to the sustainability agenda: it demonstrates that climate losses are no longer exogenous disruptions but endogenous features of the European economic landscape that must be integrated into sustainable development planning, fiscal governance, and EU-level adaptation policy. Full article

This study analyzes how sustainability practices and digitalization jointly influence the financial performance of European industrial firms, emphasizing differences between Western and Eastern Europe. The empirical analysis relies on a large multi-country panel dataset and employs fixed effects regression models with robust standard errors to account for unobserved firm-specific heterogeneity and common time shocks. Environmental sustainability is captured by the environmental component of ESG scores, digitalization is measured by digital investment intensity, and financial performance is proxied by return on equity (ROE). The findings indicate that stronger environmental practices are positively associated with profitability across the full sample. Digital investment intensity also has a positive and statistically significant effect on ROE. Importantly, the interaction term between environmental performance and digitalization is positive and significant for Western European firms but not for the full sample, suggesting that the relationship between environmental practices and financial performance may vary with the level of digital investment under specific regional conditions. However, the results reveal substantial regional heterogeneity. The positive effects of environmental practices, digitalization, and their interaction are primarily driven by firms in Western Europe, whereas the relationships are weaker and statistically insignificant in Eastern Europe. These findings underline the complementary role of digital transformation and the importance of institutional and technological readiness. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

The generative mechanisms underlying multifractal scaling in complex systems remain a fundamental unsolved problem, limiting our ability to distinguish healthy from pathological dynamics, predict system failures, or understand how scale-invariant organization emerges across vastly different physical domains. We resolve this challenge by introducing threshold sensitivity analysis—an extension of Chhabra–Jensen’s direct method—as a framework that classifies cascade types by examining how scaling reliability varies across moment orders q. Different q values systematically probe weak fluctuations (negative q) versus strong fluctuations (positive q), and the coefficient of determination ($r^2$) of partition function regressions quantifies scaling reliability at each q. Analyzing $r^2\left(q\right)$ patterns in 280 cardiac recordings (healthy controls through fatal heart failure), 200 financial time series (global equity markets and currencies, 2000–2025), and 80 climate stations (tropical to continental zones, 2000–2025), we discover a universal diagnostic signature: symmetric expansion of valid scaling behavior under relaxed $r^2$ thresholds, spanning both weak and strong fluctuations. This threshold sensitivity fingerprint—predicted by synthetic cascade simulations but never before validated empirically—uniquely identifies additomultiplicative cascades, hybrid processes that randomly alternate between additive stabilization and multiplicative amplification. Critically, this symmetric signature persists universally across domains: cardiac dynamics maintain consistent patterns across health and disease states, financial markets show varying robustness across asset classes (currencies more variable than US equities) while preserving a hybrid structure, and climate systems exhibit geographical variations (subtropical/continental stronger than tropical) without altering fundamental cascade type. These findings suggest that additomultiplicative organization is a unifying feature of complex adaptive systems, offering a resolution to decades of debate between additive and multiplicative models. The $r^2\left(q\right)$ profiling provides a mechanistic diagnostic capable of detecting early dysfunction, assessing system resilience, and revealing how environmental constraints shape—but do not determine—the fundamental principles governing multifractal complexity. Full article

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV₁)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article

The Bees Quarter episode in Amish Tripathi’s Ram Chandra Series rearticulates dharma by relocating it from a transcendent cosmic mandate to a framework enacted through spatial and procedural ethics. Traditionally understood as a sustaining principle of moral and social order, dharma in Tripathi’s narrative is reconfigured through the spatial reorganization of Mithila, where environmental vulnerability and infrastructural design shape the conditions of ethical governance. Interpreting this transformation through the framework of just sustainabilities, the article argues that the episode reconfigures dharma not as a transcendent principle but as a practice grounded in resource equity, institutional responsibility, and the consistent application of law. The crisis surrounding the Battle of the Bees Quarter and Ram’s subsequent self-exile further dramatizes a dharmic dilemma between sovereign authority and procedural justice, foregrounding tensions between power and legitimacy. Read through this lens, Tripathi’s retelling situates dharma within contemporary debates on sustainability and justice, reframing it as a normative response to ecological precarity and institutional fragility. Full article

Urban blue-green spaces (UBGS) are crucial nature-based solutions for enhancing urban resilience and improving public health. This study examined the experiential relationships linking BGS use to human well-being among users of five urban parks in Delhi, India. Using an integrated experience-centered framework, we collected in-situ survey data (n = 411) to profile usage patterns, assess environmental quality, and quantify restorative outcomes grounded in Attention Restoration Theory (ART) and Stress Reduction Theory (SRT). Advanced analytical techniques, including ordinal logistic regression and interpretable machine learning (SHAP), were used to identify the key factors associated with user satisfaction. The results revealed that for these respondents, BGS appeared to function as an essential neighbourhood, with over 40% visiting three or more times per week. Although visual attractiveness was rated positively, deficits in noise buffering and amenities indicated a gap between aesthetic and functional qualities. Restorative benefits, including emotional calmness, mood refreshment, and fatigue recovery, were consistently reported among respondents. Analyses showed that embodied experiences, particularly post-visit relaxation and physical comfort, were more strongly associated with user satisfaction. SHAP interpretation highlighted seating adequacy, routine use, and thermal comfort as prominent contributors, suggesting somatic relief may be particularly salient. This study provides exploratory evidence from a Global South megacity and context-sensitive insights into how restorative processes operate under high-density urban conditions. The findings show that routine accessibility, basic amenities, and thermal comfort are central to the everyday functioning of blue-green spaces as urban infrastructure, underscoring the need for experience-responsive and equity-oriented urban greening policies in high-density cities. Full article

In China’s stock-based renewal agenda, many old residential communities display pronounced intergenerational overlap, in which grandparental childcare becomes a dominant pattern of outdoor-space use. Against the backdrop of age-structure shifts, population ageing, and persistently low fertility, community-level outdoor-space supply, distributive equity, and environmental adaptability have become key concerns in renewal practice. Yet, practitioners still lack a rankable, low-cost, and implementable evaluation-to-decision workflow. Using Xingshe Community in Dalian, China as an empirical case, this study establishes and tests an integrated “NLP–AHP–GBDT” assessment framework. Guided by policy discourse and planning theory, over 50 semi-structured interviews were processed via NLP-based semantic analysis and keyword mining to derive a two-tier indicator set (criterion and indicator layers). Seven specialists then applied the analytic hierarchy process to elicit indicator weights, and a resident survey was administered to generate weighted performance scores for diagnosing deficiencies. In the feedback-validation stage, we adopted both a qualitative Framework Method and a quantitative GBDT approach, first using the Framework Method to conduct feedback validation based on community residents’ open-ended evaluations. Subsequently, gradient boosting decision trees were used for supervised verification with renewal-scenario data, providing empirical backing for the weighting scheme and the resulting priority order for interventions. The findings suggest that outdoor spaces are broadly serviceable but fall short in intergenerational friendliness, reflecting a structural misalignment between intergenerational activity patterns and spatial provision. Based on the validated priorities, the study proposes modular, incremental micro-renewal measures focusing on safety and emergency accessibility, environmental comfort and caregiving–recreation coupling, and place identity with community organizational mobilization. Full article

This study examined the influence of Environmental, Social, and Governance factors on the financial performance of companies listed on the Saudi Stock Exchange (Tadawul). Employing a panel data approach, the analysis covers 450 firm observations collected annually during the period 2018–2023. Financial performance is measured using Return on Assets (ROA) and Return on Equity (ROE), while ESG disclosure scores are disaggregated into their three constituent pillars. Firm size, revenue per share, and leverage are incorporated as control variables. The fixed effects regression results reveal that social factors demonstrate statistically significant positive relationships with both ROA and ROE, supporting the stakeholder theory-based perspective that strong social practices enhance operational efficiency and investor confidence. Conversely, environmental and governance factors exhibit no significant association with either financial performance metric within the study period. Leverage shows a significant negative relationship with ROA but not with ROE, while revenue per share consistently demonstrates strong positive associations with both performance measures. These findings contribute to the limited literature on ESG–performance linkages in Gulf Cooperation Council markets and offer important implications for corporate managers, investors, and policymakers seeking to advance sustainability objectives within the framework of Saudi Vision 2030. Full article

Despite concern over air pollution, fine-scale spatial and demographic disparities in exposure remain largely unquantified in Australian cities due to sparse monitoring and coarse models. In Greater Melbourne, this gap limits neighbourhood-level assessment of PM_2.5 exposure and associated environmental inequalities. To address this gap, we integrated 6-month averaged PM_2.5 observations (October 2023 to March 2024) from 5 regulatory monitoring stations and 13 low-cost sensors (LCSs) to develop a land use regression (LUR) model estimating concentrations at a 100 m resolution. These estimates were used to calculate population-weighted PM_2.5 exposure (PWE) at the mesh block level across Melbourne. To examine factors associated with spatial heterogeneity in PWE, we applied a hybrid modeling framework combining Spatially Explicit Random Forest (Spatial-RF) and Geographically Weighted Regression (GWR), incorporating physical, built-environment, and socio-demographic variables from the Synthesized Multi-Dimensional Environmental Exposure Database (SEED). The Spatial-RF model initially exhibited an R² of 0.56. After multicollinearity diagnostics using the Variance Inflation Factor (VIF), three key explanatory variables were selected for GWR modeling: the Normalized Difference Vegetation Index (NDVI), the Index of Education and Occupation (IEO), and the proportion of culturally and linguistically diverse populations (CALDP). The developed GWR model achieved higher model performance (R² = 0.65) than Spatial-RF and global Ordinary Least Squares (OLS) regression (R² = 0.38), revealing strong spatial non-stationarity. Results show that PWE generally ranged from 5 to 7 µg/m³, exceeding the 2021 WHO air quality guideline, with hotspots in the urban core and along major transport corridors. Elevated exposure occurred in both socioeconomically disadvantaged areas and residents in urban centers with higher socio-economic status, reflecting complex, spatially contingent exposure inequalities. These findings support fine-scale, equity-oriented air quality management. Full article

Against the backdrop of rapid development in megacities, urban park green spaces serve as essential public resources whose accessibility and equity directly affect residents’ quality of life and broader social justice. This study addresses the imbalance between the spatial distribution of green space resources and the socio-demographic characteristics of different population groups in megacities. It takes the six central districts of Beijing as the study area and integrates data from 457 urban parks. The research applies the Gaussian two-step floating catchment area (G2SFCA) method and bivariate spatial autocorrelation analysis (Moran’s I) to systematically evaluate the equity of urban park green space provision across multiple social dimensions, including economic status, educational attainment, and vulnerable groups. The results indicate that urban park green spaces in Beijing’s six central districts exhibit a pronounced central and northern advantage, with significant deficits in southern and peripheral areas. High accessibility and greater per capita green space are concentrated in core and high-housing-price districts, overlapping with high-income and highly educated populations. In contrast, vulnerable groups and migrant workers are more likely to reside in green-space-deficient areas, facing a structural “high population density–low green space provision” disadvantage, reflecting clear social inequities. In addition, inequity is more pronounced at the walking scale than at the cycling scale. The study reveals a dual mismatch in green space provision across both spatial and social dimensions within a megacity context. The findings suggest that future urban planning should shift from quantitative expansion to the optimization of existing green space resources. Planning strategies should prioritize vulnerable groups and adopt a people-oriented approach. Policymakers should allocate greater support to southern and peripheral areas, increase the provision of pocket parks, and improve slow-mobility systems. These measures can more precisely safeguard equitable access to green space for disadvantaged populations and promote the realization of spatial justice. Full article

Background: Quilombola communities, Afro-descendant Brazilian rural settlements with collectivistic culture, have suffered historical invasions and non-legalization of their territories, exposure to environmental degradation/hazards, and educational and health care deprivation by the government. Global climate changes have increased sea levels and the occurrence of floods. This study presents original empirical findings from ongoing qualitative fieldwork in Quilombola communities in Southern Brazil that were severely affected by the 2024 floods, focusing on post-disaster quality of life, health impacts, and community coping strategies. These dimensions remain underexamined in public health and environmental justice research. Methods: Guided by interdisciplinary frameworks of environmental racism, intersectionality, and critical disaster studies, flooding is analyzed not as a natural hazard, but as a socially produced risk shaped by racialized territorial exclusion, historical marginalization, and chronic governance failures. Data were generated by household testimonies, community observations, and assessments of governmental disaster responses. Results: Fragmented disaster management, unequal access to infrastructure, and limited participatory governance mechanisms intensified vulnerability, constrained adaptive capacity, and exacerbated health inequities among Quilombola populations. Despite these constraints, communities demonstrated strong resilience grounded in traditional knowledge, local solidarity networks, and collective agency. Conclusions: The study underscores the urgent need for equity-centered environmental governance and inclusive disaster risk reduction strategies to address healthy aging inequities. Full article

Electric vehicle charging stations (EVCSs) are a critical infrastructure in urban areas. However, because they depend on power grids and digital networks, they are prone to disruptions from grid failures, extreme weather, and cyber threats. Ensuring resilience is therefore essential to minimise service disruptions and ensure reliable transportation in urban areas. While interest in EVCS resilience is growing, current studies are dispersed across technical, environmental, and spatial domains, limiting a consolidated understanding of how resilience is conceptualised and assessed in urban areas. Despite this growing body of research, no prior systematic review has comprehensively synthesised resilience-specific evidence for EVCSs in urban areas. Thus, the objective of the study was to systematically synthesise empirical research on resilient EVCSs in urban areas to identify key factors influencing resilience and how resilience is assessed. A systematic literature review was conducted on 52 empirical articles from Web of Science and Scopus published between 2015 and 2025, following the PRISMA protocol. The review revealed an increasing trend in publications over time, with research geographically concentrated in Asia, the United States of America, and Europe. Results also showed that the resilience of EVCSs in urban areas is influenced by context-related factors (such as location, environment, and governance) and system-related factors (such as operational, technical, and financial), with location and technical issues being the most studied. The resilience of EVCSs is mainly assessed through accessibility, capacity, availability, and vulnerability, using tools such as indices, curves, scenarios, and optimisation models. However, gaps remain in governance, environment, modular design, predictive maintenance, social aspects, and developing economies. Future research should focus on integrating governance and equity into EVCS planning and developing modular, renewable-powered charging systems supported by smart technologies to enhance resilience in urban areas, particularly in developing economies. This review proposes a Factors-Dimensions Implementation framework that operationalises established resilience concepts by linking context- and system-related factors to measurable resilience dimensions of EVCSs in urban areas. Full article