Search Results (4,142)

Background/Objectives: The healthcare sector accounts for an estimated 1–5% of the global environmental footprint, and hospitals concentrate a large share of that impact. As the largest professional group, nurses are pivotal to the transition toward green and regenerative hospitals. This review aimed to map the sustainable nursing practices implemented in hospitals internationally and to characterise the contribution of nursing to reducing the environmental footprint of care. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). PubMed, Scopus, ScienceDirect, SpringerLink, SciELO and Dialnet were searched for studies published between 2021 and 2026, complemented by grey and institutional literature, citation searching and hand-searching. Records were screened in duplicate against predefined eligibility criteria, and the evidence was charted into six thematic categories aligned with the review objectives. Results: Twenty-five sources were included—11 reviews and 14 primary studies, most of them descriptive. Waste management emerged as the domain most sensitive to nursing action, whereas energy and water use were only minimally nurse-sensitive. Education, leadership and nurse engagement were the main enablers; insufficient training, resistance to change and limited investment were the recurrent barriers. Only one study explicitly addressed the regenerative transition, from a governance perspective. Conclusions: Embedding sustainable practices in nursing can reduce the environmental impact of hospitals while preserving quality of care. Nurse-sensitive environmental indicators and the integration of sustainability competencies into curricula and hospital governance are key levers for this transition, whose regenerative dimension remains incipient and requires primary, comparative research. Full article

Emotional eating (EE) is associated with poor dietary quality and weight gain among university students. Therefore, exploring innovative, non-invasive interventions to support healthier food-related responses is essential. While multisensory environments show promise in influencing food-related responses, their effectiveness in managing stress-induced EE remains understudied. Grounded in the Environment–Organism–Health (EOH) model, this study used an immersive virtual reality (VR) platform and recruited 49 Chinese university students. After emotional induction tasks, participants were randomly assigned to VR campus environments with varying wall colors and environmental sounds. The study combined physiological indicators and subjective questionnaires to evaluate the effects of color–sound combinations on emotional responses, physiological recovery, food preferences, and food cravings. Food preference was assessed via an image-selection task, and food healthiness was rated by an expert panel (n = 7). A mixed-design analysis of variance (ANOVA) revealed a significant interaction between wall color and sound. Natural sounds were associated with reduced negative emotions and higher healthy food preference scores under several color conditions, while both color and sound influenced food craving. Independent t-tests revealed significant differences according to gender and emotional eating tendency on multiple outcomes. This study suggests that multisensory environments may offer a novel approach to influencing stress-related emotional responses, food preferences, and food cravings. Full article

Coupling-coordination degree (CCD) indices are often interpreted as evidence that urban systems are developing in a mutually supportive manner. The standard formula, however, combines similarity between subsystem scores with their average level. This article evaluates those two components against the following external outcome: municipal environmental services. A city panel for China in 2002–2024 combines broadband and mobile adoption, urban road provision, licensed internet data-center records, and seven indicators of water, gas, drainage, sewage, waste, and urban greening. The preferred sample contains 6094 observations for 275 cities in 28 provinces. We estimate city fixed-effect models with province-by-year fixed effects and report province-clustered, two-way-clustered, and 9999-repetition wild-cluster-bootstrap inference. In the baseline rank-based construction, a one-standard-deviation increase in relative digital-transport alignment is associated with a 0.046-standard-deviation increase in the environmental-service index (wild-bootstrap p = 0.042), whereas the portfolio-level coefficient is 0.191 (p < 0.001). The alignment estimate is concentrated in water and gas access and is not robust to complete-component outcomes, PCA or entropy weighting, logarithmic standardization, road density, or lags beyond one year. Portfolio level remains positive across these tests, although lead and reverse-direction estimates preclude causal interpretation. The findings show why infrastructure scale, relative configuration, and realized service outcomes should be monitored separately. For SDGs 9 and 11, city governments should target documented service bottlenecks and operating capacity rather than maximize a composite coordination score. Full article

This study examines whether China’s Intelligent Manufacturing Pilot Demonstration Program is associated with changes in Huazheng-rated environmental, social, and governance (ESG) performance among listed manufacturing firms. Using a panel of 2581 firms from 2012 to 2022, we exploit the staggered admission of 91 pilot firms. The preferred doubly robust group-time difference-in-differences estimator yields an average treatment effect of 0.881 Huazheng points, positive at the 10% level. The conventional firm and year fixed-effects estimate is 1.801 points and significant at the 1% level; it is retained as a benchmark rather than the main policy estimate. The direction remains positive across alternative fixed effects, lagged controls, a pre-COVID-19 sample, reweighting, matching, stacked estimation, randomization inference, wild-cluster bootstrap inference, and leave-one-out tests. Under the benchmark specification, the environmental and governance ratings rise significantly, whereas the social estimate is imprecise. The organizational regressions are consistent with possible information verification, managerial incentive, and financing channels, and cross-fitted double machine learning provides a functional-form check. Overall, pilot designation is associated with higher Huazheng-rated ESG performance, with evidence consistent with a possible policy effect. Selective designation and the provider-specific outcome preclude stronger claims about independently verified corporate sustainability. Full article

The recovery and reuse of construction materials following disasters has emerged as an important strategy for reducing construction waste, improving resource efficiency, and enhancing the resilience of post-disaster housing reconstruction. However, existing studies remain fragmented across different disaster contexts and material categories, limiting a comprehensive understanding of the engineering, environmental, and institutional factors influencing material recovery decisions. This systematic literature review synthesises current evidence on the recovery and reuse of construction materials in post-disaster housing reconstruction. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of the Scopus and EBSCO databases identified 303 records published between 2015 and 2026, of which 69 studies satisfied the inclusion criteria. Of these, 20 studies directly examined post-disaster contexts, while the remaining 49 addressed broader construction material recovery and reuse and were included as transferable engineering evidence relevant to post-disaster reconstruction. The review synthesised evidence from both disaster-specific studies and broader research on construction material reuse to provide a comprehensive understanding of material recovery practices applicable to post-disaster housing reconstruction. The review demonstrates that both structural and non-structural construction materials have varying potential for recovery and reuse following disasters. The findings further indicate that reuse suitability is governed not only by material type but also by the interactions among disaster characteristics, residual material condition, structural integrity, contamination, durability, regulatory compliance, and intended reuse applications. Recovery pathways were found to depend on condition-based engineering assessment, while successful implementation is further influenced by economic feasibility, institutional capacity, stakeholder coordination, and recovery infrastructure. This review advances existing knowledge by synthesising disaster characteristics, engineering assessment requirements, recovery pathways, and implementation considerations into an evidence-derived condition-based perspective for construction material recovery. The proposed conceptual framework provides an evidence-informed reference for engineers, emergency management agencies, policymakers, local authorities, and construction practitioners seeking to integrate reusable construction materials into resilient and resource-efficient post-disaster housing reconstruction. Full article

Aesthetic perception of rural streetscapes reflects individuals’ cognitive responses to their surroundings and is central to understanding how landscape preferences are formed. Existing studies using Scenic Beauty Estimation (SBE) are constrained by incomplete indicator systems and overreliance on linear approaches. This study proposes an interpretable machine learning framework that integrates multi-source data to examine the nonlinear influences of streetscape features on SBE. Using Sanguan Village, a water-networked settlement in Jiangsu, we developed a 24-indicator system spanning color, spatial, natural, artificial, and cultural dimensions. Based on 523 panoramic images and aesthetic ratings from 1175 respondents, we compared OLS, DT, MLP, SVR, RF, and XGBoost models. The best-performing XGBoost, combined with SHAP analysis, revealed threshold effects and interaction patterns among variables. Green visibility, architectural aesthetics, building visibility, sky visibility, environmental coordination, and water are the top six feature variables most strongly associated with rural streetscape aesthetic perception, and each exhibits threshold effects. The saturation threshold for green visibility is 0.153, and architectural aesthetics can only make a positive contribution when its score exceeds 3.815. The appropriate range for building visibility is below 0.452, while the optimal value for sky visibility is approximately 0.194. We also explored the context-dependence of these threshold effects across urban and rural settings. This study proposes streetscape optimization strategies focusing on screening key factors, controlling their thresholds, and coordinating the allocation of streetscape features. The interpretable analytical framework for rural scenic beauty established in this research can facilitate evidence-based landscape optimization and provide scientific support for sustainable rural development and tourism in this case. Full article

Sustainable poultry nutrition is increasingly challenged by feed variability, environmental constraints, resource competition, and the growing demand for precision feeding strategies. Conventional feed evaluation systems based on proximate analysis, static nutrient tables, and empirical formulation are often insufficient to predict the biological and functional value of modern sustainable feed resources. This critical integrative review examines emerging approaches for evaluating and optimizing sustainable feeds in poultry nutrition through the integration of advanced analytical technologies, biological validation systems, omics sciences, and artificial intelligence (AI). This review was developed as a structured narrative review following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-inspired workflow and organized around an integrated AI–omics–feed evaluation framework. Recent advances in spectroscopy-based analytical techniques, in vitro digestibility systems, microbiomics, metabolomics, nutrigenomics, machine learning, and predictive modeling are discussed in relation to feed characterization, nutrient utilization, host–microbiota interactions, and precision nutrition, with emphasis on the transition from static compositional assessment toward dynamic, system-oriented feed evaluation. Explainability, biological validation, and generalizability of AI-based models across heterogeneous production systems are highlighted as key challenges for practical implementation, alongside emerging frontiers in AI-driven nutritional decision-support. Integrating analytical, biological, molecular, and computational approaches may support adaptive precision nutrition systems capable of improving nutrient efficiency, reducing environmental emissions, and optimizing sustainable poultry production. Full article

The rapid expansion of fiber-reinforced polymers (FRPs) in wind energy, transportation, and aerospace is generating increasing amounts of accompanied waste, making effective valorization essential to a circular economy. This review compares FRP recovery technologies in terms of recovered-fiber quality, operating conditions, post-treatment, environmental impacts, and industrial applicability. Then, it also examines direct reuse, FRP remanufacturing, and reuse in cementitious composites. Quantitative synthesis indicates that high-quality recycled carbon fibers (rCFs) generally retain more than 90% of their original strength, whereas mechanically recovered glass fibers (rGFs) typically retain approximately 70–90%. The preferred pathway depends on the intrinsic value, damage state, morphology, and residual properties. Components with sufficient residual capacity should be directly reused; high-quality fibers are better suited to polymer remanufacturing; and heterogeneous or lower-grade glass-FRP (GFRP) fractions are more compatible with cementitious applications, where mechanically recycled GFRP can provide interfacial bond strengths comparable to conventional engineering macrofibers. Future research should establish quantitative links among recovered material quality, processing, interfacial behavior, and end-use performance, while adopting consistent environmental and economic assessment boundaries. A graded utilization framework is therefore required to support both large-scale and value-added reuse of FRP waste. Full article

Public parks support health, wellbeing, and social inclusion, yet conventional wayfinding approaches remain largely focused on physical mobility and directional navigation, often overlooking the cognitive and sensory experiences of neurodivergent users. This study develops a theory-informed framework for neuroinclusive wayfinding design through an integrative literature review. Using a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-informed protocol, 106 core sources were synthesized from landscape architecture, environmental psychology, public health, and neurodiversity research. A qualitative theory-building approach combining thematic analysis, theoretical mapping, and abductive synthesis identified four themes: Cognitive Navigation and Environmental Legibility, Sensory Processing and Environmental Regulation, Restorative Landscapes and Nature-Based Wellbeing, and Inclusive, Adaptive, and Neurodiverse Design. These themes were subsequently translated into eight environmental challenges and operationalized into eight neuroinclusive wayfinding design criteria organized within four support domains. The resulting framework conceptualizes wayfinding as a cognitive–sensory support system rather than a purely navigational function, linking interdisciplinary evidence to practical landscape architectural design strategies. By integrating fragmented knowledge across multiple disciplines, the study provides a foundation for future validation and the advancement of neuroinclusive public park design. Full article

This study develops an integrated Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making (MCDM) framework to systematically evaluate and rank composite material combinations based on 24 fibers (16 natural and 8 synthetic), 15 matrices (thermosets, thermoplastics, and biopolymers), and 9 fiber volume fractions (30–70%) for UAV inlet applications. Ten evaluation criteria covering technical performance, environmental sustainability, and economic viability were weighted using AHP pairwise comparisons based on Saaty’s 1–9 scale, yielding a consistency ratio of CR = 0.009, which confirms the reliability of the judgments. The TOPSIS analysis identified Carbon (PAN-HM)/Epoxy as the optimal composite material, achieving the highest TOPSIS score of 0.8893. In contrast, Flax/Epoxy emerged as the best natural fiber composite, with a TOPSIS score of 0.2686, indicating a performance gap of approximately 231% in favor of the synthetic composite. Comprehensive sensitivity analysis across four weighting scenarios (Equal, Technical, Environmental, and Economic) confirmed the stability of the reinforcement rankings, with Carbon (PAN-HM) remaining the top synthetic fiber and flax the top natural fiber across all scenarios. The findings contribute to the growing body of knowledge on sustainable aerospace materials and provide practical guidance for UAV designers seeking to optimize material selection for micro-turbojet engine inlet components, supporting the development of more environmentally responsible UAV designs while maintaining the performance requirements for safe and reliable operation. Full article

Tourism is a complex economic activity all over the world shaped by distinct local resources: culture, nature, industrial heritage, urban ambiance, place-based uniqueness, and geographical accessibility. The simultaneous governance and management of economic growth motives, preservation of the cultural heritage base, and respect for nature and ecological quality calls for an evidence-based and multi-faceted policy analysis that seeks to achieve sustainable development among conflicting policy objectives in tourist areas. The present paper seeks to explore the feasibility of a sustainable balance for various heterogeneous cultural heritage areas in Europe (‘urban pilot regions’), with particular attention to sustainable local development characterized by circular economic objectives and an ecological balance strategy based on the principle of stakeholders’ co-creation. The paper addresses the knowledge gap between generic policy aims and site-specific views of actors or visitors. To that end, an extensive survey experiment was administered in the urban regions concerned, in which a wide range of relevant management issues/questions related to environmental preferences and perceptions were posed to stakeholders and visitors. The data were analyzed by means of a novel respondent-oriented multivariate statistical tool, viz. Generalized Q-Analysis, which is suitable for handling big databases with many respondents. The paper shows that the application of Generalized Q-Analysis to common survey data enriches the results from the application of a conventional Q-Analysis. Furthermore, the study also highlights that, based on the views expressed by the surveyed visitors, the tourist areas concerned are quite different from each other in attracting specific classes of visitors. Therefore, functional specialization in the tourist sector seems to be an important anchor point for effective governance of urban tourism, not only in Europe, but also elsewhere in the world. Full article

With the rapid development of E-commerce platforms (EPs) and the accumulation of consumer data, demand information sharing has become increasingly important in green supply chains. However, the interaction between selling mode selection and information-sharing strategies remains insufficiently explored. This study develops a game-theoretic model for a green supply chain consisting of a manufacturer and an EP to examine selling modes and information-sharing decisions under reselling and agency modes. The results indicate that information sharing is highly dependent on information accuracy. Specifically, when information accuracy exceeds a threshold, the manufacturer no longer purchases demand information because the marginal benefit cannot compensate for the acquisition cost. Under the reselling mode, the EP shares information only when information accuracy falls within an intermediate range, whereas under the agency mode, it always chooses to share information due to additional information-sharing revenue. Moreover, the manufacturer prefers the agency mode when the service cost performance factor is below the critical threshold and switches to the reselling mode otherwise. Information sharing consistently improves environmental performance, while the preferred selling mode depends on the service cost performance factor. The extension further shows that introducing an information collection cost narrows the information-sharing region and weakens the EP’s incentive to share information. Full article

China’s pursuit of its dual-carbon targets amid rapid artificial intelligence (AI) development raises an important question: do the environmental implications of AI innovation change as regional innovation advances? Using a balanced panel of 30 Chinese provinces from 2011 to 2024, this study measured patent-based AI innovation intensity using applications identified through a strict AI patent classification. Linear and quadratic models with province and year fixed effects were estimated, and the Lind–Mehlum test was used to assess the shape of the relationship within the observed range. The preferred specification indicates an inverted-U-shaped association: carbon emission intensity initially increases with patent-based AI innovation but declines beyond an interior turning point. The negative quadratic coefficient remains stable when the emissions data source, patent classification, sample period, treatment of outliers, and timing of the AI terms are varied. Supplementary Bartik and copula-control analyses preserve the negative curvature, although their identification limitations preclude a definitive causal interpretation. A Kaya-based exact decomposition shows that the estimated curvature is concentrated in energy intensity rather than the carbonization factor. Human capital strengthens the estimated concavity, while the clearest regional contrast is observed between central and eastern China, with the strongest curvature in the central provinces. These findings suggest that greater AI patenting does not automatically reduce emissions. Its environmental implications depend on the stage of regional innovation and its interaction with energy efficiency and absorptive capacity. Policies promoting AI innovation should therefore be coordinated with cleaner energy supply, efficiency improvements, and human capital investment. More broadly, the study provides a stage-sensitive basis for evaluating the sustainability implications of patent-based AI innovation through measurable changes in carbon emission intensity. Full article

This paper presents a physiological signal-guided uncertainty-management framework that integrates real-time indicators of the operator’s supervisory state into UAV autonomy to improve obstacle avoidance and risk-aware decision-making under uncertain conditions. During UAV supervisory control, multimodal physiological signals, including heart rate variability, blood pressure, electrodermal activity, and other cardiovascular or stress-related measures, are time-synchronized with UAV telemetry, perceived obstacle fields, planner confidence, environmental uncertainty estimates, and operator intervention logs, including waypoint edits, overrides, and replanning commands. These heterogeneous data streams are fused using a Bayesian hierarchical state-space framework to estimate latent supervisory states representing trust miscalibration, risk sensitivity, and situational-awareness degradation. The estimated states are then incorporated into the UAV decision-making stack as bounded uncertainty-management parameters that regulate safety margins, replanning priority, and risk preference without relaxing hard safety constraints. The framework was evaluated in a simulation-based human-in-the-loop study involving 24 operators and 180 UAV obstacle avoidance missions. Performance was assessed using held-out-operator mission success AUROC, proxy-state RMSE, mission success rate, mean minimum obstacle clearance, mission risk index, operator override rate, and replanning latency. The results support the feasibility of using physiological, behavioral, vehicle, and environmental information to adapt UAV supervisory control to uncertainty in both the operating environment and human supervisory readiness. Full article

Shifting dietary patterns is necessary to meet global environmental and health targets. Legumes represent a nutritious and viable protein source. Yet, consumption remains low among consumer groups due to low familiarity and inferior sensory perceptions. Sensory conditioning has proven effective in increasing acceptance of unfamiliar foods and may have applicability in shaping future legume preferences. The study compared Mere-Exposure, Flavor–Flavor and Flavor–Nutrient learning strategies in their effectiveness in increasing consumer preferences for legumes in a Danish adult sample (N = 70). Participants filled out a questionnaire before and after a three-week at-home meal intervention. Fava beans were used as the legume case, and all participants, were exposed to 12 fava bean test stimuli. Participants in the Mere-Exposure group experienced a significant increase in preferences, while the Flavor–Flavor and Flavor–Nutrient groups did not. The Mere-Exposure group also experienced an increase in liking of the appearance and flavor of fava beans. By showcasing the potential of a widely applicable sensory strategy, the study highlights how sensory conditioned learning might support the adoption of more sustainable foods in an adult sample. The results provide insights for health and nutritional personnel, researchers and policymakers seeking to promote sustainable diets. Full article