Search Results (11,677)

Unplanned urban expansion accompanied by a decline in green infrastructure poses significant challenges for sustainable land-use planning in semi-arid, water-constrained secondary cities. Quetta, Pakistan, exemplifies these challenges due to rapid population growth, ecological degradation, water scarcity, and the absence of an updated master plan. This study develops a GIS-based spatial decision-support framework to evaluate residential development and green infrastructure priorities and to identify areas of conflict, synergy, and balanced planning opportunities. Sentinel-2A imagery acquired in May 2023 was used to generate a land-use/land-cover map, while residential and green-infrastructure suitability factors were standardized using fuzzy membership functions and integrated through an AHP Weighted Linear Combination approach. The resulting Residential Suitability Index (RSI) and Green-Infrastructure Suitability Index (GSI) were normalized and combined through a rule-based Residential–Green Infrastructure Trade-off Index (RGTI). Unlike conventional suitability assessments that evaluate development and ecological priorities independently, the proposed framework explicitly identifies zones of residential dominance, ecological dominance, and shared planning potential. Five planning-priority categories were delineated, comprising Very High Green Infrastructure Priority, Moderate Green Infrastructure Priority, Shared Zone, Moderate Residential Expansion Priority, and Very High Residential Expansion Priority. A spatial consistency assessment demonstrated that the identified planning zones correspond closely with existing land-use patterns and available land resources, supporting the plausibility of the proposed framework. The results provide a practical basis for delineating ecological conservation areas, residential development zones, and integrated planning zones capable of balancing urban growth and environmental sustainability. The framework offers a transparent and transferable approach for supporting land-allocation decisions in arid, data-scarce, and rapidly urbanizing cities facing competing development and ecological pressures. Full article

This study examines the moderating role of environmental connectedness in the relationship between environmental attitude and environmental protection intention among Generation Z polytechnic students in Singapore. A cross-sectional survey across five polytechnics yielded 709 valid responses, and partial least squares structural equation modeling was used to assess both direct and moderating effects. Findings confirm that environmental attitude significantly and positively influences environmental protection intention. However, environmental connectedness did not moderate this relationship, suggesting that in dense urban environments, cognitive attitudes may have a stronger impact than affective ties to nature. This study offers novel insights by extending the Theory of Planned Behaviour to include emotional constructs within an Asian urban setting, highlighting how limited nature exposure may weaken the role of affective factors. While the cross-sectional design and convenience sampling limit causal inference and generalisability, the findings align with national trends, as government agencies and educational institutions have intensified efforts to cultivate environmental awareness through green curricula and co-curricular programmes. These findings indicate that interventions such as curriculum redesign and gamification aimed at shaping environmental attitudes may be more effective than strategies relying solely on emotional engagement with nature, thereby supporting Singapore’s climate goals and fostering responsible urban citizenship. Full article

Coastal cities are highly sensitive and vulnerable to climate change risks. A scientifically grounded assessment of urban climate change risk and its driving mechanisms is essential for strengthening urban climate adaptation capacity and supporting sustainable development. Taking Ningbo as the study area, this paper constructs a risk assessment system comprising five categories of extreme climate indicators, namely heat, rainstorm, drought, humidity, and strong wind, based on the China Surface Climate Normals Dataset for 1981–2010 and meteorological observations from the National Centers for Environmental Information (NCEI) for 2015–2024. Using 30 m resolution land-use data for 2023, three land-use sensitivity indicators are extracted: the proportion of built-up land, the proportion of green space and forest land, and the proportion of water area. The CRITIC objective weighting method is then applied to construct an integrated climate change risk index and identify the spatial pattern of climate change risk in Ningbo. On this basis, the high-risk area identification performance of Logistic Regression, Random Forest, and XGBoost is compared. The optimal XGBoost model is selected and combined with the SHAP method to systematically reveal the direction, relative importance, and nonlinear threshold relationships through which land-use characteristics affect the formation of high-risk areas. The results show that urban climate change risk in Ningbo exhibits a pronounced spatial differentiation pattern, with higher risk in the northeastern coastal and central–eastern areas and lower risk in the western and southwestern areas. Insufficient green space and forest land buffering is the most important factor affecting the formation of high-risk areas. All three land-use variables have clear nonlinear thresholds. The critical turning points for identifying high-risk areas are 20.0% built-up land, 2.0% green space and forest land, and whether there is a water body or not. Significant interaction effects are observed among land-use variables, among which the interaction between built-up land and green space/forest land is the most prominent. These findings provide methodological support and empirical evidence for climate change risk assessment and climate-adaptive spatial planning regulation in coastal cities. Full article

This study proposes a BIM-integrated Eco-Digital framework for enhancing predictive maintenance and sustainability assessment in educational buildings, supporting their transition towards Digital Twin readiness. Existing facilities often rely on reactive maintenance practices, fragmented data systems, and limited integration of sustainability indicators, which hinder efficient lifecycle management and environmental performance. To address these challenges, the research develops a data-driven methodology that combines condition assessment (CA), Markov-based deterioration prediction (DP), and sustainability metrics within an interoperable BIM–facility management (FM) environment. The framework is validated through a real educational building case study, where a hierarchical asset structure and relative weighting system are established to prioritize maintenance actions based on both functional importance and condition state. The Markov model is employed to predict component deterioration under uncertainty, enabling proactive maintenance planning without reliance on real-time IoT data. Sustainability is incorporated through energy consumption analysis and lifecycle performance indicators, linking maintenance decisions to environmental impacts. Results demonstrate improved maintenance prioritization, enhanced predictive capability, and better integration of sustainability considerations within facility management workflows. The proposed framework mainly contributes to providing a practical, scalable approach for transforming conventional buildings into data-driven, sustainable assets, offering a viable pathway toward Digital Twin-enabled environments, particularly in contexts with limited digital infrastructure. Full article

Chronic obstructive pulmonary disease (COPD) remains a major cause of respiratory morbidity and healthcare utilization, creating challenges for healthcare planning, resource allocation, and early intervention. This study aimed to develop a forecasting framework for quarterly age- adjusted COPD outpatient visit rates across 762 regions in Pennsylvania from 2019 to 2023, using multi-source data including PHC4 outpatient records, satellite-derived environmental pollutant variables, and meteorological variables. To compare models that capture nonlinear relationships with those that explicitly model temporal dependencies, several machine learning models and a deep learning long short-term memory (LSTM) model, designed to learn sequential patterns and lagged temporal effects, were evaluated. The seasonal naïve baseline achieved $R^2=0.570$, classical machine learning models achieved $R^2=0.58$–$0.62$, and the LSTM model achieved $R^2=0.705$ with lower prediction error. Lagged COPD activity was the strongest predictor, while environmental, meteorological, and geographic variables provided additional predictive information. These findings highlight the value of integrating multi-source environmental data with sequence-based modeling for forecasting regional COPD activity and suggest that environmental and meteorological variables can provide additional predictive information beyond historical COPD activity alone. Full article

As the world focuses more on the Sustainable Development Goals, there is a growing need to assess applied environmental improvement actions across regions. In this research, we suggest that policymakers prioritize the Circular Economy (CE) and Eco-Innovation (EI) to enhance decision making in response to environmental sustainability challenges. Our research offers a groundbreaking framework that integrates CE and EI concepts, providing an innovative lens for monitoring and comparing improvements in environmental sustainability. This approach not only identifies provinces excelling in environmental sustainability strategies but also sheds light on the ones going through demanding situations in regions like uneven improvement and environmental aid control. Considering the provinces of China as a macro-case study, the proposed framework precisely integrates nudge theory with the simultaneous assessment of criteria and options (SECA) methodology, enriched by professional insights and a comprehensive literature review. The findings show that Guangdong, Jiangsu, and Shandong are the most environmentally sustainable provinces for growth. Other provinces and areas that struggle with inconsistent growth and ecological aid management due to difficult circumstances include Tibet, Qinghai, and Hainan. Our findings make appreciable contributions to the discourse on environmental sustainability. Full article

This study assesses the capabilities of UAV-based Earth observation for analyzing marginalized communities, using Roma settlements in southeastern Slovakia as a case study. Marginalized populations are often underrepresented in official spatial datasets, resulting in a limited understanding of their living conditions, infrastructure needs, and environmental risks. To address this gap, we propose a multi-scalar, UAV-based observational approach that bridges the limitations of coarse satellite imagery and logistically constrained ground surveys. High-resolution RGB and thermal imagery were acquired across three settlements with varying spatial characteristics and processed using photogrammetric workflows to generate detailed orthophotos and spatial products. The results demonstrate that UAV data with centimeter-level spatial resolution enable precise mapping of settlement morphology, infrastructure, waste distribution, and thermal inequalities. Furthermore, UAV observations enable change detection and environmental risk assessment at scales that are not achievable with conventional remote sensing. However, the study also highlights critical operational and ethical challenges, including regulatory constraints, privacy concerns, and the need for community engagement. By integrating technical evaluation with socially sensitive research practices, this work proposes a methodological framework for responsible UAV deployment in marginalized contexts. The findings underscore the potential of UAV-based observation to improve spatial visibility and support evidence-based planning while emphasizing the importance of ethical implementation. Full article

A transition to sustainable development and circular economy (CE) principles requires an interdisciplinary understanding of industry-specific technological characteristics and a strategic rethinking of priorities. Given the oil and gas industry’s critical role in socio-economic development, tightening environmental regulations, growing resource consumption, waste generation, and environmental impacts, the aim of this research is to identify industry-specific directions for implementing CE principles and to assess their contribution to achieving the Sustainable Development Goals (SDGs). A multi-stage methodology was applied, including an expert evaluation for the identification of key directions for CE implementation; a literature review of related technological practices; subsequent analysis of linkages between each of 12 identified directions and CE principles within the “10R” circularity framework; and qualitative analysis and systematization of their contribution and potential impact on relevant SDGs. Based on these findings, foundational management principles for the oil and gas industry, aligned with sustainability priorities and the circular transition, were formulated. The results obtained provide a methodological basis for strategic management and corporate planning that integrates CE principles and SDGs, which can be further developed to obtain quantitative instruments for assessing the compliance of the companies with CE principles and can be used to improve the sustainability reporting procedure in the oil and gas enterprises. The findings can also be applied to support the modernization of industry standards and justify government support measures. Full article

Background/Objectives: School feeding is a fundamental component of public policies aimed at promoting health, improving educational outcomes, reducing inequalities, and guaranteeing the human right to adequate food. This study aimed to evaluate the nutritional quality of school meals offered to public school students in a federal unit of Brazil, quantify the environmental impacts using carbon and water footprints, and simulate potential reductions through a strict vegetarian menu. Methods: This cross-sectional, descriptive study analyzed 130 daily menus (390 meals) from full-time public schools in the Federal District of Brazil in 2024. Nutritional quality was assessed based on energy, nutrients, food groups, degree of processing, and food origin. Carbon and water footprints were estimated using literature-based indicators. A nutritionally adequate strict vegetarian menu was then developed and compared with the observed menus. Results: The current menus presented good overall nutritional quality, with high food diversity and predominance of fresh or minimally processed foods. Most nutritional parameters met the recommended levels; however, protein and saturated fat exceeded the recommended limits. Animal-based foods accounted for most of the carbon and water footprints. The simulated strict vegetarian menu demonstrated significantly lower environmental impacts while maintaining nutritional adequacy. Conclusions: These findings highlight the importance of integrating nutritional and environmental strategies, such as a weekly “Meatless Monday” initiative alongside food and nutrition education, to improve student health outcomes and reduce the environmental burden of public school meals. Incorporating environmental sustainability criteria into school meal planning and public food procurement may advance nutritional quality, resource efficiency, and climate goals, positioning school feeding programs as strategic instruments for sustainable development. Full article

The Cascade Reservoir System (CRS) in the upper Yellow River delivers integrated benefits (flood control, water supply, hydro-power generation, and ecological regulation), but it also alters the natural runoff regime and exerts non-negligible impacts on the regional eco-environment. However, the long-term trajectory of reservoir-cascade effects on land use/land cover (LULC) in alpine basins has not yet been systematically quantified. Here, we focused on the Hualong-Xunhua reach and delineated two impact domains—the Reservoir Influence Zone (RIZ, enclosed by the first-order mountain ridge lines closest to the river channel representing direct hydrological impacts) and the Local Microclimate Influence Zone (LMIZ, spanning from the first-order ridges to the outer watershed boundary representing indirect climatic impacts)—to investigate the spatiotemporal dynamics of LULC associated with reservoir development. Results show that, from 1985 to 2023 in the CRS area, cropland and shrub-land decreased by 89.56 km² (−16.09%) and 9.41% (−9.41%), respectively, whereas forest and grassland increased by 79.92 km² (+14.36%) and 7.74% (+7.74%). Within the RIZ, cropland declined by 29.49 km² (−20.14%), while water bodies increased markedly by 32.19 km² (+22%); forest cover also expanded by 9.09 km² (+6.21%). In the LMIZ, forest and grassland exhibited pronounced increases of 70.83 km² (+17.27%) and 39.37 km² (+9.60%), respectively. Correlation analysis indicates that GDP and air temperature are strongly and positively correlated with forest, water bodies, and impervious surfaces (Pearson’s r > 0.9), whereas cropland shows significant negative correlations with GDP, forest, and grassland (Pearson’s r < −0.8). Overall, the distinct spatiotemporal contrasts between the RIZ and LMIZ, coupled with the temporal alignment of cropland-to-forest transitions post-2000, suggest that reservoir-cascade construction and the Grain-for-Green Project are associated with these major LULC transitions, serving as contributing factors, while temperature rise and GDP growth represented the background environmental and socioeconomic context. These findings provide data support and a conceptual basis for long-term monitoring and assessment of eco-environmental responses to reservoir cascade development, and offer scientific evidence particularly relevant to reservoir planning and management in high-altitude cold regions. Full article

Major incidents, such as large fires and floods, often trigger a complex series of environmental and health risk assessments involving multiple institutions and authorities. They can also lead to long-lasting concerns from members of the local communities about the possible impact on their health and environment. Here, we use the example of a large fire that occurred in 2000 at a chemical storage site in Sandhurst, Gloucestershire, England, to contrast evidence from official reports and scientific studies with the expectations of members of the local community. We reflect on how the handling of such an incident has evolved over the last two decades and how public involvement can be further improved in the future. Firstly, we present the results of a 20-year follow-up (2001–2020) retrospective small-area ecological epidemiological study with rates of overall cancer incidence, all-cause mortality and hospital admissions for respiratory disease in the exposed area compared with rates in the South West region of England. We also studied the ten years preceding the fire (1991–2000). Secondly, we discuss the limitations of these findings to alleviate the concerns of the local community. Finally, we use this case study to make recommendations about how to better manage this balance between scientific evidence and public concerns for future incidents. In line with earlier reports, the 20-year follow-up study did not identify any major increase in relative risks for cancer or mortality. Although an increase in admissions for respiratory disease was identified, this was also observed before the fire, suggesting that this may be due to other local factors. Despite these findings, members of the local community still believe that some local cases of cancer were caused by the fire. As rigorous as a long-term epidemiological study can be, it cannot determine causality between an incident and individual cases for a given health outcome, nor can it make up for the absence of data not—or only partly—collected after the incident (e.g., individual-level health register). This case study further highlights that engaging with local communities and managing their expectations and planning potential long-term follow-up studies immediately after a major incident should be carefully considered. Full article

Climate change is reshaping species distributions worldwide, while landscape patterns can further influence habitat suitability and species persistence. Fireflies are important environmental indicators, given their high sensitivity to environmental disturbances. However, the effects of climate change and landscape structure on their future survival remain unclear. Here, we used the MaxEnt model to examine the current and predict the future potentially suitable habitats of Aquatica leii, an endemic aquatic firefly in China, under the SSP126, SSP245, and SSP585 climate scenarios. We also examined the spatial associations between potentially suitable habitats and landscape patterns using bivariate spatial autocorrelation analyses. The results showed that soil clay content, soil pH, annual mean temperature, slope, and distance to roads were the main factors influencing habitat suitability. Potentially suitable habitats were concentrated in western Zhejiang Province and are projected to expand under future climate scenarios. Potentially suitable habitats showed significant positive spatial associations with the number of patches, landscape shape index, Shannon’s diversity index, and Shannon’s evenness index. Associations with the largest patch index varied among scenarios, and were generally strengthened under future climate change. Overall, heterogeneous landscapes with diverse and complex habitat structures play an important role in supporting A. leii and should be considered in future conservation planning. Full article

The Cau river basin in northern Vietnam is experiencing increasing pressures on water resources due to rapid urbanization, industrial development, agricultural expansion, and inadequate wastewater management. Understanding the interactions between surface water, groundwater, and water quality is essential for developing effective and sustainable water management strategies. This study developed and applied a coupled MIKE SHE–MIKE 11 framework to simulate surface–groundwater connectivity and its influence on water quality dynamics in the Cau river basin. Hydrometeorological and water quality datasets collected during 2023–2024 were used to calibrate and test the integrated model at key monitoring locations, including Cha, Phuc Loc Phuong, and Dap Cau stations. The hydrological component demonstrated satisfactory performance, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.55 to 0.79 for water level simulations, indicating a reliable representation of surface and subsurface flow processes. Simulated river–aquifer exchange fluxes revealed pronounced spatial variability across the basin. Upstream reaches predominantly functioned as groundwater recharge zones, whereas the middle and downstream sections exhibited dynamic bidirectional exchanges governed by river stage fluctuations, hydraulic gradients, and local hydrogeological conditions. Water quality simulations for BOD₅, COD, NH₄⁺, total nitrogen (TN), and total phosphorus (TP) showed good agreement with observations, with calibration and testing errors generally remaining below 25%. Incorporating surface–groundwater interactions improved the representation of pollutant transport, residence time, and nutrient accumulation processes compared with conventional river-only simulations. The results demonstrate that river–aquifer connectivity plays a critical role in regulating both hydrological processes and water quality conditions in the basin. The coupled modeling framework provides a robust scientific basis for identifying critical interaction zones, assessing pollution risks, optimizing monitoring programs, and supporting integrated water resource planning. By explicitly linking hydrological connectivity with water quality dynamics, the proposed framework serves as a practical decision-support tool for sustainable water resource management in the Cau river basin and other river–aquifer systems facing increasing environmental pressures and progressive water quality degradation. Full article

The transition from traditional siloed to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart city as an urban cognitive Cyber–Physical System (CPS) across environmental, economic, and social sustainability pillars, metrics and their trade-offs. A methodology based on Deep Reinforcement Learning (DRL), specifically adopting Deep Q-Networks (DQNs), is proposed to represent and assess sustainability pillar dependencies and their interplay. A case study on Low-Power Wide-Area Network planning, deployment and management in a Sicilian municipality has been developed to demonstrate the effectiveness of the proposed approach in dealing with the dynamics and non-linear dependencies of the sustainability pillars. Full article

Facade thermal defect diagnosis is a critical prerequisite for energy-efficiency retrofitting of old residential buildings. However, conventional infrared thermography is easily affected by environmental conditions and occupant behavior, making it difficult to distinguish persistent thermal defects from transient anomalies. To address this challenge, this study proposes an integrated diagnostic framework for old residential buildings in Wuhan, China, combining unmanned aerial vehicle (UAV) infrared thermography, multi-temporal data acquisition, 3D flight-path planning, thermal anomaly recognition, facade spatial mapping, and temporal screening. Field experiments were conducted to determine key acquisition parameters, including sensor preheating time, imaging distance, and acquisition timing. Thermal anomalies were identified through image-processing techniques and mapped onto facade representations derived from 3D models. Repeated observations across different times and days were then used to evaluate anomaly recurrence and spatial stability. The results show that preheating the sensor for at least 10 min, maintaining a UAV-to-facade distance of 8–10 m, and acquiring data around 17:00 provide more reliable thermal images. Multi-temporal screening effectively reduces false positives caused by temporary disturbances, while persistent anomalies associated with window–wall joints, floor slabs, wall surfaces, and moisture-related areas can be identified more robustly. The proposed framework provides a practical workflow for facade thermal defect diagnosis and retrofit-oriented decision support. Full article