Search Results (333)

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom. Full article

This study investigates the phylogenetic relationships, genetic diversity, and biogeographic structure of Pipistrellus species in Türkiye using mitochondrial cytochrome b (Cytb) sequences from 156 specimens collected across 26 localities. Our primary aim was to clarify taxonomic boundaries of morphologically cryptic species and elucidate the evolutionary role of Anatolia in the Western Palearctic. Analyses strongly confirmed that molecular data are mandatory for defining taxonomic boundaries. Crucially, all individuals morphologically identified as P. pygmaeus were genetically determined to be P. pipistrellus, highlighting the inadequacy of external traits for cryptic species. We resolved deep intraspecific divergence across the genus. In P. pipistrellus, two major lineages (Eastern and Western) were identified, partially separated by the Anatolian Diagonal. Their co-occurrence in multiple localities confirms Anatolia’s function as a secondary contact zone. Similarly, P. kuhlii populations represent a transition zone where two distinct lineages, one of Asiatic origin (P. k. lepidus) and one Mediterranean-Levantine (P. k. kuhlii), meet. Furthermore, while P. nathusii is largely associated with migratory European lineages; a genetically distinct, potentially resident lineage was revealed in southwestern Anatolia. Divergence time estimations indicate that this diversification was shaped by major climatic events from the Miocene to the Pleistocene. This study demonstrates that Anatolia is more than just a geographic bridge; it is a dynamic center of evolution, functioning critically as both a glacial refugium and a secondary contact zone for Palearctic bat fauna. Full article

Rapid industrialization and urbanization have driven China’s economic growth but also worsened air pollution, posing serious challenges to sustainable development and public health. Balancing economic growth and environmental protection has become essential for achieving the UN Sustainable Development Goal of “Climate Action.” The National Ecological Civilization Construction Demonstration Zone policy, a major institutional innovation for green transition, aims to integrate ecological protection, industrial upgrading, and spatial governance to achieve both economic and environmental goals. Using county-level panel data from 2010 to 2022, this study applies a difference-in-differences (DID) approach, treating the phased establishment of NECCDZs as an exogenous policy shock. It further explores the mediating effects of green innovation capability and land use efficiency. The results show that the NECCDZ policy significantly reduces PM2.5 concentrations in pilot regions, and the findings remain robust under multiple tests. Improvements in green innovation and land use efficiency are identified as key transmission mechanisms, while policy effects vary across city hierarchies, industrial base types, and regions. Overall, the NECCDZ policy demonstrates the effectiveness of institutionalized ecological governance and offers policy insights for developing countries seeking coordinated progress in economic growth and environmental sustainability. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

Urban coastal areas are increasingly vulnerable to compound flooding due to the convergence of extreme rainfall, storm surges, and infrastructure aging, especially in high-density settings. This study proposes and empirically validates a multi-scale strategy for enhancing urban flood resilience in the Macau Peninsula, a densely built coastal city with complex flood exposure patterns. Building on a previously developed network-based resilience assessment framework, the study integrates hydrodynamic simulation and complex network analysis to evaluate the effectiveness of targeted interventions, including segmented storm surge defense barriers, drainage infrastructure upgrades, and spatially optimized low-impact development (LID) measures. The Macau Peninsula was partitioned into multiple shoreline defense zones, each guided by context-specific design principles and functional zoning. Based on our previously developed flood simulation framework covering extreme rainfall, storm surge, and compound events in high-density coastal zones, this study validates resilience strategies that achieve significant reductions in inundation extent, water depth, and recession time. Additionally, the network-based resilience index showed marked improvement in system connectivity and recovery efficiency, particularly under compound hazard conditions. The findings highlight the value of integrating spatial planning, ecological infrastructure, and systemic modeling to inform adaptive flood resilience strategies in compact coastal cities. The framework developed offers transferable insights for other urban regions confronting escalating hydrometeorological risks under climate change. Full article

In the context of rapid urbanization, human activities have profoundly transformed urban thermal environments. However, most existing studies have focused on single cities or relatively uniform climatic contexts, and the long-term dynamics between land surface temperature (LST) and nighttime light (NTL) across urban–rural gradients in diverse climates remain insufficiently explored. This gap limits a systematic understanding of how human activities and thermal environments co-evolve under varying regional conditions. To address this gap, we selected ten representative cities spanning multiple climate zones in China. Using MODIS LST and NTL datasets from 2000 to 2020, we developed an urban–rural gradient analysis framework to systematically assess the spatiotemporal response patterns and coupling mechanisms between LST and NTL. Our findings reveal the following: (1) From 2000 to 2020, NTL exhibited a pronounced upward trend across all climate zones, most notably in the marginal tropical humid region, while LST changes were relatively moderate. (2) LST and NTL displayed power-law distributions along urban–rural transects, marked by steep declines in monocentric cities and gradual transitions in polycentric cities, with sharper thermal gradients in northern and inland areas and more gradual transitions in southern and coastal regions. (3) The long-term increase in NTL was most evident in suburban areas (0.94 nW/cm²/sr/a), surpassing that in urban cores (0.68 nW/cm²/sr/a) and rural zones (0.60 nW/cm²/sr/a), with inland cities (0.84 nW/cm²/sr/a) outpacing their coastal counterparts. Although LST changes were modest, suburban warming (0.16 ± 0.08 °C/a) was over twice that of urban and rural areas. Notably, the synergistic escalation of light and heat was most pronounced in tropical and subtropical cities. (4) Eastern coastal cities exhibited strongly synchronized rises in NTL and LST, whereas cities in the plateau, temperate semi-arid, and mid-temperate arid regions showed clear decoupling. Along urban–rural gradients, NTL–LST correlations generally weakened from urban centers to peripheries, yet coupling coordination peaked in fringe areas (mean = 0.63), underscoring pronounced spatial heterogeneity. This study advances our understanding of the spatiotemporal coupling of urban light and heat under varying climatic and urbanization contexts, offering critical insights into managing urban thermal environments. Full article

Global climate change is expected to alter characteristics of flood events. This study evaluates the rising flood risk and damage potential in the lower Vipava River valley—a transboundary catchment between Slovenia and Italy—under climate scenarios RCP 2.6, 4.5, and 8.5. The area has experienced multiple floods in recent decades, indicating high vulnerability. Using hydraulic modeling for current and future conditions, flood hazard zones were identified and integrated into the KRPAN model to estimate expected annual damage (EAD). The findings show that EAD escalates from €0.97 million under current conditions to €1.97 million under the most extreme scenario. A 20% rise in flood peaks leads to a 1.4-fold increase in damage, while a 40% rise results in losses that are more than double. Buildings show a 2.5-fold increase in EAD, and water infrastructure EAD rises by a factor of 1.9. These results underscore the substantial economic consequences of climate change on flood risk. The study highlights the urgent need to incorporate climate scenarios into flood risk assessments and spatial planning to support adaptive strategies and reduce future damage. These insights are essential for making informed decisions and achieving long-term resilience. Full article

This study examined the trend changes as well as the spatial distribution of average precipitation and the abrupt change characteristics of precipitation in Gannan Prefecture, China, using daily precipitation monitoring data from 1980 to 2021 at eight meteorological stations. Analytical methods employed included the climate change trend rate, anomaly analysis, Innovative Trend Analysis (ITA), ITA-change boxes (ITA-CB), ArcGIS technology, and BEAST Ensemble Algorithm. Long-term average precipitation variability was comprehensively analyzed across multiple temporal scales. Results indicated that over the 42 years, interannual precipitation exhibited a significant increasing trend, with an annual rate of 14.363 mm/decade, and abrupt changes were detected in 1984, 2003, and 2018. The distribution of average precipitation varied substantially from year to year. July was the month with the highest average monthly precipitation, and December was the month with the lowest. Summer precipitation contributed the most to annual totals (51.33%), whereas winter precipitation contributed the least (2.01%). Interdecadal precipitation exhibited a pattern of an initial decrease followed by an increase over the study period. Based on the mean and standard deviation of the series’ first half, which was divided by the ITA method, we established a three-category classification for mean precipitation (low, medium, and high). Annual average and seasonal average precipitation showed non-monotonic variations. While the overall trend of annual average precipitation showed a modest augmentation, the increasing tendencies in the middle-value and high-value categories slowed. In spring, the decreasing trend in high-value categories slowed. In summer, decreasing trends in middle-value categories and overall zones slowed, with an enhanced increasing trend observed in autumn and winter overall. At the spatial scale, the average precipitation across Gannan Prefecture exhibited a decreasing trend from south to north. Higher precipitation was recorded at meteorological stations in the southwest (Maqu), west (Luqu), and south (Diebu), primarily influenced by the interaction between the Qinghai–Tibetan Plateau monsoon and westerly circulation, as well as regional topographic effects. The research findings have significant implications for agricultural and pastoral production planning and sustainable economic development in Gannan Prefecture, China. Full article

Ecosystem services provide the scientific foundation and optimization objectives for constructing ecological security patterns, and their spatial characteristics directly affect planning decisions such as ecological source identification and corridor layout. However, current methods for constructing ecological security patterns rely excessively on static spatial optimization of landscape structure and ecological processes, while overlooking the dynamic variations in ecosystem service values under climate change. Taking Yunnan Province as a case study, this paper calculates ecosystem service values, analyzes their spatiotemporal variations, and based on ecosystem service value hotspots, applies the MSPA model and circuit theory to identify ecological sources, corridors, pinch points, barrier areas, and improvement areas. On this basis, we construct and optimize the ecological security pattern of Yunnan Province and propose ecological protection strategies. The results show that: (1) From 2000 to 2030, ecosystem service values in Yunnan exhibit significant spatiotemporal heterogeneity. From 2000 to 2020, they first declined and then increased, with aquatic ecosystems contributing the most. Under future climate scenarios, ecosystem service values continue to increase, with the greatest growth under the SSP2-4.5 scenario. The spatial pattern is characterized by higher values in the central region and lower values in the eastern and western areas. (2) In 2020, 56 ecological sources were identified; under the SSP1-1.9 scenario, 61 were identified, while 57 were identified under both SSP2-4.5 and SSP5-8.5 scenarios. These sources are mainly distributed in northwestern Yunnan and the Nujiang and Lancang River basins, presenting a “more in the west, fewer in the east” pattern. (3) In 2020, 132 ecological corridors and 74 pinch points were identified. By 2030, under SSP1-1.9, there are 149 corridors and 84 pinch points; under SSP2-4.5, 135 corridors and 55 pinch points; and under SSP5-8.5, 134 corridors and 60 pinch points. (4) By integrating results across multiple scenarios, an ecological security pattern characterized as “three screens, two zones, six corridors, and multiple points” is constructed. Based on regional ecological background characteristics, differentiated strategies for ecological security protection of territorial space are proposed. This study provides a scientific reference for the synergistic optimization of ecosystem services and ecological security patterns under climate change. Full article

This study investigates the potential of bio-based phase change materials (bio-PCMs) to reduce both operational and embodied carbon in Moroccan service buildings. Using EnergyPlus 8.3 simulations and life cycle assessment (LCA), the research evaluates the integration of five bio-PCM types across six Moroccan climate zones. Results show that climate-specific PCMs can lower heating and cooling energy demands by up to 20.3% and 28.0%, respectively, leading to operational CO₂ emission reductions between 17.0% and 24.0%. Bio-PCM Q25 performed best in Coastal, Mediterranean, and Saharan zones, Q23 in Continental and Mountainous areas, and Q29 in hot-arid climates. In parallel, bio-based PCM M27 exhibited an embodied carbon of only 0.08 kgCO₂/kg over 97% lower than conventional PCMs like paraffin or stearic acid. These findings confirm that optimized bio-PCM integration, combined with passive design strategies, offers a robust solution to decarbonize buildings in hot and diverse climates like Morocco. The study provides practical guidelines for material selection and policy direction toward climate-adapted, low-carbon construction. Full article

This study presents a comprehensive latitudinal analysis of air particulate matter (PM) across an 1400 km pollution corridor spanning Chile’s central-southern zone. We systematically analyzed PM_2.5 and PM₁₀ concentrations across eight major urban centers (2014–2015), providing crucial pre-Paris Agreement baseline data for South America’s most extensive air quality monitoring network. Our analysis reveals significant pollution gradients, with Coyhaique ranking one of the world’s most severely polluted cities (95th percentile globally, WHO database) and demonstrating an extreme 86% fine particulate matter ratio that far exceeds international urban standards. Residential wood combustion (RWC) demonstrates systematic correlations with fine PM concentrations (R² > 0.96), suggesting RWC is the dominant pollution driver across multiple climate zones. The documented pollution patterns represent a concerning continental-scale environmental pattern, with 4900–6500 annual premature deaths directly attributable to PM_2.5 exposure-one of the highest per-capita pollution mortality rates in South America. This work provides a methodological framework applicable to mountain-valley pollution systems globally while addressing critical knowledge gaps in regional air quality science. The evidence indicates the need for urgent implementation of comprehensive wood combustion control strategies and positions this research as essential baseline documentation for both national air quality policy and international climate change assessment frameworks. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

Against the backdrop of climate change and the accelerated process of urbanization, the risks of extreme weather and natural disasters that cities are facing are increasing day by day. Based on the framework of the local climate zone (LCZ), this paper studies the spatio-temporal evolution of the urban surface morphology and the heat island effect of Tongren City. Using the comprehensive mapping technology of remote sensing and GIS, combined with the inversion of surface temperature, the distribution of LCZs and the changes in heat island intensity were analyzed. The results show that: (1) The net increase in forest coverage area leads to a decrease in shrub and grassland area, resulting in an ecological deficit. (2) The built-up area expands along transportation routes, and industrial areas encroach upon natural space. (3) The urban heat island pattern has evolved from a single core to multiple cores and eventually becomes fragmented. (4) Among the seasonal dominant driving factors of urban heat islands, the impervious water surface is in summer, the terrain roughness and building height are in winter, and the building density is in spring and autumn. These findings provide feasible insights into mitigating the heat island effect through climate-sensitive urban planning. Full article

Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article