Search Results (162)

Utilizing MODIS LST data from 2003 to 2024, in conjunction with multi-source remote sensing data including DEM, land use, NDVI, and nighttime lights, this study conducts a remote sensing quantitative assessment and spatiotemporal characteristic analysis of the urban heat island (UHI) effect in Yinchuan City. An improved urban-rural dichotomy approach was adopted to select rural background areas, and elevation correction of land surface temperature was performed based on the zonal ordinary least squares (OLS) regression to eliminate systematic errors caused by topographic differences. The results show that: (1) From 2003 to 2024, the overall intensity of the UHI in Yinchuan City showed a slight downward trend, while the UHI area continued to expand, presenting the characteristics of “decreasing intensity and expanding scope”; (2) The UHI exhibited concentrated and contiguous distribution in summer, and the cold island phenomenon was significant in winter, reflecting the typical seasonal contrast between summer and winter; (3) The global Moran’s I value increased from 0.39 to 0.82, indicating a significant enhancement in the spatial agglomeration of the UHI; (4) The standard deviation ellipse analysis revealed that the centroid of the UHI migrated toward the westward as a whole, which was consistent with the main axis of urban construction. The research results reveal the long-term evolution law and spatial pattern characteristics of the UHI effect in Yinchuan City, and provide a scientific reference for ecological planning and thermal environment regulation of cities in arid regions. These findings enhance the understanding of long-term urban thermal environment dynamics and provide important scientific support for sustainable urban planning, climate adaptation, and ecological management in arid regions. The study contributes to the quantitative monitoring of urban environmental sustainability and supports sustainable development goals related to climate action and sustainable cities. Full article

Rapid urbanisation and climate change are intensifying urban heat stress, posing significant challenges for climate-responsive urban planning. Digital and data-driven approaches, including GIS, remote sensing, microclimate simulation, and artificial intelligence (AI), have advanced urban climate analysis; however, their capacity to support human-centred planning remains insufficiently synthesised. This review analyses 78 peer-reviewed studies (2015–2025) to evaluate how digital methods address urban microclimate and outdoor thermal comfort. The reviewed studies are classified into four methodological groups: spatial data analytics, simulation-based models, parametric and optimisation workflows, and AI-driven or hybrid approaches. The results show that the majority of studies rely on proxy indicators, such as land surface temperature and sky view factor, while physiologically based comfort indices (e.g., PET and UTCI) are applied in a limited proportion of studies and remain largely confined to microscale simulations. A persistent scale mismatch is identified between large-scale analytics and pedestrian-level thermal experience, alongside geographic and climatic biases, particularly in hot-arid regions. Unlike previous reviews, this study integrates digital methodologies, urban microclimate processes, and human-centred thermal comfort within a unified framework. The findings provide actionable insights for planners and designers by supporting the integration of thermal comfort into multi-scale, climate-responsive decision-making. Full article

Local governments increasingly adopt climate and energy strategies addressing both mitigation and adaptation objectives, yet these domains are often treated separately, limiting integrated planning. This study develops a structured Climate–Energy Action Library to support more coherent local decision-making. The library was constructed through a systematic review and harmonisation of actions from European Sustainable Energy and Climate Action Plans (SECAPs), international repositories, and related frameworks, resulting in a taxonomy of 171 actions grouped into thematic bundles and policy categories. The methodology enables the identification of potential synergies among measures, and revealing consistent cross-sector interaction patterns. The strongest interaction potential occurs when technical measures are combined with enabling governance actions, including policy instruments, planning frameworks, and capacity-building. Cross-sectoral synergies are evident in building retrofit programmes linked with heat-stress adaptation and in nature-based solutions contributing to mitigation, urban cooling, and ecosystem services. These findings indicate that governance and ecosystem-based measures often enhance the effectiveness of sector-specific interventions. The proposed library provides a practical analytical reference for municipalities, supporting the design and evaluation of integrated climate strategies and helping bridge the persistent separation between mitigation and adaptation in local climate governance. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

Background/Objectives: Hajj is a major annual mass gathering. It requires prolonged walking under conditions of fatigue, heat stress, and crowd density, which increases mobility difficulties and fall risk, particularly among older adults and individuals with chronic diseases. Therefore, rapid operational mobility screening is required to identify risk and plan mobility. To support an operational mobility-classification workflow in a pre-Hajj setting, this study evaluated whether Timed Up and Go (TUG)-based stratification, combined with spatiotemporal gait and plantar pressure measurements, differentiates fall-risk categories. Methods: We conducted a cross-sectional study at a seasonal medical center near Al-Haram in Madinah Al-Munawwarah (21 May–3 June 2025) within the “I Lean On It” screening initiative. Participants completed the TUG and dynamic baropodometric gait assessments. We stratified the risk of falling as low (≤10 s), moderate (10.1–13.5 s), and high (>13.5 s) according to the TUG performance. We performed between-group comparisons using the Kruskal–Wallis test and evaluated the associations using Spearman’s correlation analysis. Results: Participants were classified as having low (n = 103), moderate (n = 24), or high (n = 29) fall risk. TUG performance significantly increased across the fall-risk groups. Significant between-group differences were observed in cadence, half-step length, walking speed, test duration, and functional mobility, whereas plantar pressure magnitude and gait symmetry did not differ significantly. Spearman correlation analysis showed significant negative correlations between TUG time and sex (rs = −0.357), half-step length (rs = −0.617), walking speed (rs = −0.577), and cadence (rs = −0.420). Significant positive correlations were observed with weight-bearing time (right: rs = 0.584; left: rs = 0.461), test duration (rs = 0.376), and number of steps acquired (rs = 0.356) (all p ≤ 0.003). Overall, TUG performance was primarily associated with dynamic gait and functional mobility. Conclusions: Integrated functional mobility and spatiotemporal gait screening significantly differentiate fall risk and provide clinically actionable mobility-support guidance in a mass-gathering pre-Hajj clinical workflow. Full article

Winter cities face the dual pressures of climate change and population aging, urgently requiring a shift from a singular focus on winter protection toward a development model adaptable to both winter and summer conditions. This shift is essential to enhance social resilience and safeguard the health of all age groups. This case study investigates how the thermal environment of life-sustaining streets in winter cities correlates with older adults’ daily activities. Employing Spearman correlation analysis, a heat exposure–pedestrian flow coupling matrix, and a comprehensive risk diagnostic model, the research analyzes the spatiotemporal variation patterns and underlying drivers of the street thermal environment. The key findings are: (1) All 15 surveyed streets exhibited Wet Bulb Globe Temperatures (WBGT) exceeding 28 °C during peak activity hours, with afternoon values (17:00–19:00) up to 2.7 °C higher than morning values. (2) On East Chaoyang Road, despite building shade and a high Visible Green Index (39.68%), the WBGT ranked second highest. This condition is attributed to a critically low average wind speed of 0.69 m/s (significantly below the city’s summer average of 2.67 m/s) and the widespread use of low-albedo asphalt, which collectively trap heat and negate the benefits of shading. (3) Using a dual-dimensional diagnostic framework, four streets were identified as dual-pressure streets with their combination of high elderly pedestrian flow (exceeding 126 persons/h) and high thermal risk (WBGT > 29 °C), marking them as priority intervention units. Based on these findings, the study proposes categorized street retrofit strategies that synergistically integrate climate adaptation and aging-friendliness. This provides an actionable, evidence-based foundation for planning decisions to support the sustainable renewal of winter cities amid climate change and population aging. Full article

Accurate crop yield prediction is paramount for food security amid climate volatility but struggles with complex, nonlinear, and spatially heterogeneous weather–crop interactions. This study develops a novel Spatially Heterogeneous Functional Additive Model (SH-FAM), representing a methodological innovation by uniquely integrating Multivariate Functional Principal Component Analysis (mFPCA) with data-driven climate zoning into a Generalized Additive Model (GAM) framework. The U.S. Midwest was selected as a study area for its pronounced east–west aridity and north–south thermal gradients, forming a natural laboratory for dissecting spatially heterogeneous climate–yield relationships. Unlike traditional models, SH-FAM preserves the continuous temporal structure of weather while allowing nonlinear biological thresholds to vary structurally across distinct agro-climatic zones. Extensive cross-validation shows SH-FAM reduces prediction error by 19% compared to benchmarks and substantially mitigates spatial bias during extreme events like the 2012 drought. We reveal distinct regional sensitivities to Heat and Drought Stress: water-limited western counties face immediate linear yield declines; the high-yielding core exhibits a nonlinear resilience threshold with catastrophic loss beyond a critical tipping point; northern regions show an inverted-U response where moderate warming enhances productivity. These spatially explicit response patterns enable zone-specific adaptation strategies, from drought mitigation in water-limited regions to thermal opportunity exploitation in heat-limited zones, providing actionable guidance for climate-resilient agricultural planning. Full article

Thermal stress in hot–humid urban environments constitutes a persistent sustainability challenge, driven by the interaction of extreme temperatures, high atmospheric moisture, and heat-retaining urban surfaces, which collectively intensify outdoor discomfort and increase cooling-energy demand. Within this context, soft landscape systems have gained recognition as nature-based solutions capable of moderating microclimates and enhancing residential livability; however, their systematic prioritization based on integrated sustainability performance remains insufficiently addressed, particularly in Gulf-region residential developments. This study proposes a sustainability-oriented decision-support framework that evaluates and prioritizes soft landscape strategies for thermal comfort enhancement using the Analytic Hierarchy Process (AHP) as the core analytical method. Expert judgments were elicited and structured across five sustainability-driven criteria—shading effectiveness, evapotranspiration potential, airflow facilitation, aesthetic–psychological comfort, and implementation and maintenance cost—and applied to five soft landscape alternatives. To verify the physical plausibility of the expert-derived prioritization, microclimate simulations were conducted using ENVI-met under extreme summer conditions, representing the hottest day of the year, at key diurnal intervals. The results reveal a clear dominance of shading-based mechanisms, with tree canopy systems emerging as the most effective and sustainable intervention due to their superior radiative control, ecological cooling capacity, and perceptual benefits. Simulation outputs confirm that canopy-driven strategies achieve the most substantial reductions in mean radiant temperature during peak thermal stress, while surface-based interventions provide secondary benefits primarily related to diurnal heat dissipation. At peak thermal stress (14:00), Scenario 2 reduced mean radiant temperature (MRT) from 71.69 °C to 54.23 °C (≈24% reduction) and PMV from 7.33 to 5.70 (≈22% reduction) relative to existing conditions. By integrating expert-based multi-criteria evaluation with simulation-based thermal verification, the study advances a robust and transferable framework for climate-responsive residential landscape planning. The findings reposition soft landscape systems as essential climatic infrastructure, offering actionable guidance for enhancing thermal resilience, reducing cooling-energy dependence, and supporting sustainable residential development in hot–humid regions. Full article

Urban heat and hardscapes increase cooling electricity demand, stressing power grids and disproportionately burdening deprived neighborhoods. While previous studies have documented the cooling benefits of urban tree canopy, most analyses remain at coarse spatial scales and do not isolate the canopy’s marginal effect from built surfaces, limiting their utility for equitable neighborhood-level planning. We introduce a novel neighborhood-scale (census block-group, CBG) model to estimate cooling-season energy demand across Baltimore City and Baltimore County, Maryland. We quantify demand drivers and actionable tree-canopy targets while controlling for built surfaces. Correlation analysis shows demand increases with developed fraction and imperviousness, and decreases with tree canopy and other vegetated or water cover. Using an explainable monotone gradient-boosted tree model (SHAP) with controls for imperviousness and development, we isolate the canopy’s marginal effect. Demand reductions begin once the canopy exceeds ~11% in Baltimore City and ~23% in Baltimore County, with diminishing returns beyond ~18% (City) and ~24% (County). This flattening is strongest in highly impervious CBGs, while low-impervious county areas show renewed reductions at very high canopy (>55–60%), consistent with forest-dominated microclimates. Spatial hotspots cluster in Baltimore City and southern Baltimore County, where low canopy and high hardscapes coincide with elevated demand; 61% of City CBGs fall below the 18% threshold. We translate these findings into priority intervention tiers combining demand, hardscapes, jurisdiction-specific canopy thresholds, and an equity overlay, identifying 21% of City and 1.2% of County CBGs as high-priority targets for greening and energy-relief interventions. Full article

Existing office buildings in Australia contribute to 24% of the nation’s electricity consumption and 10% of greenhouse gas emissions, with energy use projected to rise by 84%. Meeting the 2050 sustainability target and United Nations (UN) 17 Sustainable Development Goals (SDGs) requires improving sustainability within existing office buildings. This systematic review examines net zero energy and climate resilience strategies in these buildings by analysing 74 studies from scholarly literature, government reports, and industry publications. The literature search was conducted across Scopus, Google Scholar, and Web of Science databases, with the final search in early 2025. Studies were selected based on keywords and research parameters. A narrative synthesis identified key technologies, evaluating the integration of net zero principles with climate resilience to enhance energy efficiency through HVAC modifications. Technologies like heat pumps, energy recovery ventilators, thermal energy storage, and phase change materials (PCMs) have been identified as crucial in reducing HVAC energy usage intensity (EUI). Lighting control and plug load management advancements are examined for reducing electricity demand. This review highlights the gap between academic research and practical applications, emphasising the need for comprehensive field studies to provide long-term performance data. Current regulatory frameworks influencing the net zero transition are discussed, with recommendations for policy actions and future research. This study links net zero performance with climate adaptation objectives for existing office buildings and provides recommendations for future research, retrofit planning, and policy development. Full article

Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high computational cost or yield statistically accurate but physically inconsistent results. Urban air quality in mountain–valley cities is strongly shaped by thermal inversions and weak nocturnal ventilation that trap pollutants close to the surface. We present a hybrid physics–machine-learning framework that combines a Navier–Stokes surface-layer model with data-driven post-processing to produce short-term forecasts of wind, temperature, and particulate matter while preserving physical consistency. The approach captures diurnal ventilation patterns and the well-known negative linkage between near-surface wind and particulate loadings during wintertime inversions. Compared with purely statistical baselines, the hybrid system improves short-range forecast skill and maintains interpretability through physically grounded diagnostics. Beyond Almaty, the workflow is transferable to other mountain–valley environments and is directly actionable for early warning, traffic and heating-related emission management, and health-risk communication. By uniting physically meaningful fields with lightweight Machine Learning correction, the method offers a practical bridge between computational fluid dynamics and operational decision support for cities facing recurrent stagnation episodes. Aim: Develop and verify a method for the diagnostics and short-term forecasting of surface circulation and particle concentrations in Almaty (2024), ensuring physical consistency of fields, increased forecast accuracy on 6–24 h horizons, and interpretability of risk factors. Compared to purely statistical baselines (R² ≈ 0.55 for PM forecasts), our hybrid framework achieved a 16% gain in explained variance and reduced RMSE by 25%. This improvement was most evident during winter inversion episodes. Methods: This study introduces a hybrid modeling framework that integrates the Navier–Stokes equations with machine-learning algorithms to diagnose and forecast surface air circulation and particulate matter concentrations. The approach ensures both physical consistency and improved predictive accuracy for short-term horizons (6–24 h). The Navier–Stokes equations in the Boussinesq approximation, the energy equation, and K-closure particulate matter transport were used. The numerical solution is based on the projection method (convection—TVD/QUICK, pressure—Poisson equation). The ML module is gradient boosting and decision trees for meteorological parameters, lags, and diagnostic quantities. The 2024 data are cleaned, normalized, and visualized. Results: The hybrid model reproduces the diurnal cycle of ventilation and concentrations, especially during winter inversions. For 6 h: wind RMSE ≈ 1.2 m/s (R² ≈ 0.71), temperature RMSE ≈ 1.8 °C (R² ≈ 0.78), and particles RMSE ≈ 0.012 mg/m³ (R² ≈ 0.64). Errors are higher for 24 h. A negative relationship between wind and concentration was established: +1 m/s reduces the median by 10–15% during winter nights. Conclusions: The approach can be generalized to other mountain–valley cities beyond Almaty. Combining the physical model and ML correction improves short-term predictive ability and maintains physical consistency. The method is applicable for air quality risk assessment and decision support; further clarification of emissions and consideration of urban canyon geometry are required. The results support early-warning systems, health risk communication, and urban planning. Full article

Vegetation in urban green spaces plays a critical role in mitigating surface heat, yet the magnitude of this effect remains uncertain across scales and measurement methods. This study assesses the cooling performance during the summer of 94 green spaces in three Chilean cities—classified in three types according to their size—combining satellite-derived land surface temperature (LST) data with high-resolution in situ thermal imaging. We performed comparisons of the cooling effects of green spaces and their components (vegetation, impermeable and semi-permeable surfaces). Spearman’s correlation analysis, the Mann-Whitney U test and Kruskal-Wallis and Dunn post hoc were used to evaluate associations and differences. Results demonstrate that vegetation quantity and composition—particularly tree and shrub cover—are key determinants of cooling performance. In situ measurements reveal that green spaces are on average 9.3 °C cooler than their urban surroundings, substantially exceeding differences captured by LST. Additionally, shaded surfaces within green spaces exhibit temperature reductions of 12 °C to 17 °C compared to sun-exposed areas, underscoring the role of vegetation in mitigating surface heat extremes. These findings challenge the sole reliance on remote sensing for urban heat assessments and highlight the value of integrating ground-based observations. This study advances understanding of vegetation’s localized cooling potential in Latin American cities and provides actionable insights for urban climate resilience planning. Full article

Motivated by the policy urgency of China’s dual-carbon goals and the practical obstacle that official input–output (IO) and MRIO tables are sparse and non-consecutive, this study investigates how to generate credible, mechanism-aware provincial–sector forecasts of carbon footprints and embodied transfers for Western China—a region with pronounced structural heterogeneity. We develop a regionalized forecasting pipeline that fuses balance-constrained MRIO completion (RAS–CE) with a Whale-optimized Grey Neural Network (WOA–GNN), bridging the data gap (2007–2017 reconstruction) and delivering 2018–2030 projections at province–sector resolution. The novelty lies in integrating RAS–CE with a meta-heuristic grey learner and layering explainable network analytics—Grey Relational Analysis (GRA) for factor ranking, complex-network measures with QAP regressions for driver identification, and SHAP for post hoc interpretation—so forecasts are not only accurate but also actionable. Empirically, (i) energy mix/intensity and output scale are the dominant amplifiers of footprints, while technology upgrading (process efficiency, electrification) is the most robust mitigator; (ii) a structural sectoral hierarchy persists—S2 (non-metallic minerals) remains clinker/heat-intensive, S3 (general/special equipment) operates as a mid-chain hub, and S6/S7 (electrical machinery/instruments) maintain lower, more controllable intensities as the grid decarbonizes; (iii) by 2030, the embodied carbon network becomes denser and more centralized, with Sichuan–Chongqing–Guizhou–Guangxi forming high-betweenness corridors; and (iv) QAP/SHAP converge on geographic contiguity (D) and economic differentials (E) as the strongest positive drivers (openness Z and technology gaps T secondary; energy-mix differentials F weakly dampening). Policy-wise, the framework points to green-power contracting and trading for hubs, deep retrofits in S2/S3 (low-clinker binders, waste-heat recovery, efficient drives, targeted CCUS), technology diffusion to lagging provinces, and corridor-level governance—demonstrating why the RAS–CE + WOA–GNN coupling is both necessary and impactful for data-constrained regional carbon planning. Full article

This study investigates the influence of urban greenery on microclimate conditions in Novi Sad, a city characterized by a temperate oceanic climate, by integrating high-resolution remote sensing data with in situ measurements from 12 urban climate stations. Sentinel-2 imagery was used to capture vegetation patterns, including tree lines and small green patches, while air temperature data were collected across two climatically contrasting years. Vegetation extent and structural characteristics were quantified using NDVI thresholds (0.6–0.8), capturing variability in vegetation activity and canopy density. Results indicate that high-activity vegetation, particularly dense tree canopies, exerts the strongest cooling effects, significantly influencing air temperatures up to 750 m from measurement sites, whereas total green area alone showed no significant effect. Cooling effects were most pronounced during summer and autumn, with temperature reductions of up to 2 °C in areas dominated by mature trees. Diurnal–nocturnal analyses revealed consistent spatial cooling patterns, while seasonal variability highlighted the role of evergreen and deciduous composition. Findings underscore that urban heat mitigation is driven more by vegetation structure and composition than by green area size, emphasizing the importance of preserving high-canopy trees in urban planning. This multidimensional approach provides actionable insights for optimizing urban greenery to enhance microclimate resilience. Full article