Search Results (10,264)

This study developed an integrated Geographical Information System (GIS) based Multi-Criteria Decision Analysis (GIS-MCDA) framework based on hydrological marginality, soil suitability, land cover marginality, slope, social marginality, and contaminated land indicators to identify and classify marginal and underutilized lands in Leon County, Florida, using the analytic hierarchy process (AHP) and weighted overlay analysis. The AHP results assigned the highest weights to hydrological conditions (36%) and soil suitability (24%), indicating their dominant influence on land marginality. The final marginality model classified the study area as low marginality/highly suitable (24.19%), slightly marginal (39.17%), moderately marginal (2.48%), high marginality (30.66%), and very high marginality and underutilization potential (3.5%). According to sensitivity analysis, hydrology, soil, and social emphasis models showed results that were relatively stable compared to the AHP-weighted baseline model. However, an equal-weight model yielded a lower estimate. An exploratory 2035 urban-expansion scenario was developed using a standardized 2-mile (3.2 km) buffer around existing developed areas. Under this scenario, the combined area classified as high and very high marginality increased from approximately 152,771 acres (34.14%) under baseline conditions to approximately 176,534 acres (39.45%), representing a net increase of about 23,763 acres. These findings show that combining environmental and socioeconomic indicators can improve the identification of marginal and underutilized land and support sustainable land-use planning, restoration targeting, and future agricultural or bioenergy suitability assessments. Full article

This PRISMA-guided scoping review examines the use of waste materials in civil engineering as a sustainable approach to reducing environmental impacts, conserving natural resources, and supporting circular economy principles. The rapid growth of urbanization, industrialization, mining, and agricultural activities generates large amounts of waste materials, including fly ash, ground granulated blast-furnace slag, bauxite residue, mining tailings, waste rock, acid-mine drainage sludge, waste plastics, post-consumer vulcanized rubber, recycled construction materials, and agricultural ashes. The disposal of these materials often creates serious environmental and land-use problems, making their reuse increasingly important. In this context, civil engineering is one of the most promising sectors for large-scale waste valorization because of its high material demand and its ability to use different waste streams into practical applications such as concrete and cementitious systems, pavement and asphalt engineering, geotechnical works, and other infrastructure sectors. This review critically evaluates the global availability, material characteristics, engineering applications, environmental and economic benefits, recent advances, and key challenges related to major industrial, mining, agricultural, polymeric, and construction-derived wastes. Although significant progress has been made in this field, wider implementation is still limited by variations in material properties, technical and environmental challenges, economic constraints, and limited field validation of long-term performance. By bringing together current knowledge from different waste streams and civil engineering sectors, this review highlights important research gaps and future directions to support more sustainable, resilient, and resource-efficient infrastructure development. The effective use of waste materials in civil engineering can play an important role in reducing carbon emissions, improving resource efficiency, and supporting global sustainability. Full article

Rapid urban expansion and agricultural development have substantially altered land use/land cover (LULC), surface thermal regimes, and ecosystem conditions in semi-arid cities. This study investigates the spatiotemporal dynamics of LULC and evaluates their associations with land surface temperature (LST), vegetation health, soil moisture, and drought conditions in Ghazni City, Afghanistan, between 2013 and 2023 using a Google Earth Engine (GEE)-based framework. Landsat 8 OLI/TIRS imagery was classified using a Random Forest (RF) algorithm, while the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and LST were derived to evaluate environmental responses. To provide a comprehensive evaluation of urban–climate interactions, monthly Landsat-derived LST time series were analyzed and compared to MODIS and ERA5 datasets through a multi-source consistency assessment framework. The RF classification achieved overall accuracies of 95.56% (2013) and 97.77% (2023), with Kappa coefficients of 0.89 and 0.94, respectively. Results revealed a substantial expansion of built-up areas (5.4%) and vegetation/agricultural land (7.2%), accompanied by a decline in bare land. Urban and barren surfaces consistently exhibited higher LST values, whereas vegetated areas demonstrated a pronounced cooling effect. NDVI and SAVI analyses indicated improving vegetation conditions and soil moisture status over the study period. LST exhibited strong seasonal variability, with summer maxima reaching 49.74 °C and winter minima declining to −8.39 °C. Comparisons among the Landsat, MODIS, and ERA5 datasets demonstrated strong agreement, with a high correlation between Landsat- and MODIS-derived LST (R = 0.84), supporting the reliability of the Landsat-derived LST estimates. Generally, the findings demonstrate the critical role of vegetation in moderating surface temperatures and enhancing urban climate resilience, providing scientific evidence for sustainable land use planning and climate adaptation strategies in semi-arid cities. Full article

The Brazilian Amazon is a contested frontier, shaped by destructive and conservationist forces. Most forest clearing is driven by agro-extractivism, an agrarian pathway based on raw commodity production. The socio-biodiversity economy (SBE) has emerged in response, widely regarded as a transformative agrarian pathway capable of reconciling environmental conservation and rural livelihoods. However, recent research suggests that, as socio-biodiversity products scale up, agro-extractivist dynamics can be reproduced within the SBE. We examine this tension in the cocoa frontier of the Transamazon, where cocoa is institutionally promoted as an SBE alternative. We conduct an exploratory, mixed-methods study combining a geospatial analysis of land use and land cover (LULC) change (2020–2025, random forest classification) with a qualitative analysis drawing on participatory mapping and 87 semi-structured interviews with farmers, cooperatives, buyers, and institutional actors. Using a gradient framework, we read LULC transitions and farmers’ land use practices along an agro-extractivism–SBE continuum. The cocoa frontier emerges as a hybrid geography. The landscape is predominantly stable but internally reorganizing: anthropogenic forest declines while full-sun monoculture expands over pasture, with intensification concentrated in peri-urban areas and restoration in remote ones. Land use practices form five recurring configurations, two firmly anchored at the socio-biodiversity or agro-extractivist poles and three whose alignment with the SBE depends on access to markets, knowledge, and institutions. We argue that the frontier contestation unfolds not only between distinct economies but within the cocoa economy itself, and we identify the policy areas relevant to sustaining SBE-oriented practices in the Transamazon. More broadly, this study suggests that the classification of Amazonian forest-based economies as inherent alternatives to agro-extractivism should be treated as an empirical question rather than an assumption. Full article

Understanding vegetation resilience gradient effects across hierarchical administrative borders is critical for cross-jurisdictional land management yet remains underexplored. This study integrates an AR(1) model with XGBoost-SHAP analysis to assess resilience and its drivers across provincial, municipal, and county borders in the Beijing–Tianjin–Hebei (BTH) region using NDVI data (2000–2025). The results show that (1) vegetation resilience across hierarchical border belts experienced an initial decline followed by recovery. A notable shift in resilience optimality was observed from city borders in 2005 to provincial borders by 2020. Gradient effects were stronger in northern city borders. The stable-fluctuating type dominated, followed by peak and valley types. (2) Precipitation, tree age, and altitude suppress gradient formation. Conversely, cross-boundary economic disparity, nighttime light differences, and land use composition tend to be associated with amplified gradient effects in a hierarchical manner: cropland and built-up expansion intensify gradients at provincial borders but reduce them at city borders, suggesting a potential role of governance coordination. (3) Based on these findings, a tiered governance framework is suggested—northern high-gradient provincial and city borders are proposed as priority areas for intensive interventions, while county borders (where gradient effects are weakest) may benefit from moderate, low-intensity interventions focused on maintaining land use continuity. At the provincial level, stronger inter-provincial coordination—through cross-boundary ecological compensation and harmonized environmental standards—may help mitigate gradient effects. Collectively, this study provides insights for cross-jurisdictional land governance in the BTH region and other analogous urban agglomerations. Full article

Urban noise complaints reflect not only perceived acoustic disturbance, but also complaint behaviour shaped by thermal conditions and the built environment. Using Sanya, China, as a case study, this study integrated Landsat-derived land surface temperature data with the spatial distribution of noise complaints to examine how thermal environment and urban contextual factors jointly influence complaint patterns. An interpretable modeling framework combining eXtreme Gradient Boosting (XGBoost) and Multiscale Geographically Weighted Regression (MGWR) was employed to assess the associations of urban heat island intensity (UHI), road density, point of interest (POI) count, and population density on complaint occurrence and intensity, while also generating spatial predictions of complaint distribution. The results revealed a weak but statistically significant spatial association between the thermal environment and noise complaints, with urban heat island intensity showing nonlinear and spatially heterogeneous associations with complaint counts. POI count emerged as the strongest global predictor, while road density, population density, and thermal environment exhibited substantial spatial heterogeneity in their associations with complaint patterns. The integrated model outperformed both individual models alone, achieving the highest prediction accuracy. Overall, the findings suggest that urban noise complaint patterns reflect not only perceived acoustic disturbance, but also context-dependent social perception and reporting behaviour, providing empirical support for more place-sensitive and people-centered urban noise governance. Full article

The presence of near-surface cavities poses a significant geohazard due to potential ground subsidence and structural collapse. To mitigate threats to urban stability, this study presents an integrated geophysical framework to locate and characterize abandoned mining galleries and exploitation voids near Linares (Jaén, Spain). The approach combines four complementary techniques: electrical resistivity tomography (ERT), ground-penetrating radar (GPR), frequency-domain electromagnetics (FDEM), and microgravity. The resulting multi-physics responses were cross-referenced with visible surface subsidence features and archival mine plans. Air-filled galleries and shafts generated highly pronounced high-resistivity anomalies. Shallow voids detected at depths of 2–5 m were undocumented in 19th-century mining maps, suggesting older historical origins, whereas deeper ERT profiles and structural disturbance trends (up to 30 m) correlated well with historical records. Within this framework, FDEM provided high-resolution lateral mapping, GPR excelled at resolving ultra-shallow structural boundaries, and ERT characterized deep gallery networks. Crucially, microgravity mitigated inversion non-uniqueness by directly confirming physical mass deficits over the anomalies. This integrated workflow overcomes individual resolution limits, offering a practical tool for land-use planning and early geohazard risk assessment in collapse-susceptible areas. Full article

With the rapid development of remote sensing technology, high-resolution satellite imagery has been increasingly applied to land resource monitoring, urban planning, and environmental assessment. Automatically assigning semantic labels to remote sensing image patches remains a fundamental challenge due to pronounced intra-class variation and high inter-class visual similarity. To address the trade-off between model capacity and limited labeled data, this paper proposes a remote sensing image scene classification framework based on an improved EfficientNetV2-S architecture. The proposed model integrates a Squeeze-and-Excitation (SE) channel attention module between the final 1 × 1 expansion convolution and the Global Average Pooling layer, where it functions as a late-stage channel gating mechanism that adaptively recalibrates channel-wise responses, though its accuracy benefit is seed-sensitive rather than consistently reproducible at the current dataset scale. A two-stage optimization strategy was evaluated, comprising a fully unfrozen supervised baseline followed by a pseudo-label semi-supervised fine-tuning stage utilizing a strict confidence threshold ($\tau =0.90$). Evaluated on a 10-class subset of the public NWPU-RESISC45 benchmark, the purely supervised SE-EfficientNetV2-S delivers 98.71% independent test accuracy, matching or exceeding the much larger ResNet50 (98.50%, 24.1 M parameters) despite using only 20.4 M parameters. Multi-seed variance analysis further reveals that semi-supervised fine-tuning yields a small test-set improvement for the No-SE configuration that is consistent in sign across all three seeds (+0.46 pp mean) but not statistically significant at this sample size, and an even smaller, likewise non-significant gain for the SE-augmented model (+0.08 pp), suggesting that channel gating moderates pseudo-label effectiveness in small-data regimes. Full article

Improving green land use efficiency is an important approach to reconciling urban development with resource and environmental constraints. Using panel data from 280 cities at the prefecture level and above in China from 2011 to 2024, this study employs a dynamic spatial Durbin model to examine the effects of the digital economy on green land use efficiency, focusing on temporal dynamics, spatial spillover effects, distance-decay boundaries, moderating mechanisms, and urban heterogeneity. The results show that the digital economy significantly improves local green land use efficiency. Its spatial externalities exhibit a pronounced dynamic transition from the spatial siphoning effect in the short term to the green spillover effect in the long term. The short-term spatial siphoning effect is mainly concentrated within approximately 200 km, whereas the long-term green spillover effect extends to approximately 250 km, indicating a clear distance-decay pattern. Green technological innovation and industrial upgrading mitigate short-term spatial siphoning and strengthen the long-term green spillover effect, whereas factor misallocation intensifies short-term spatial siphoning and weakens the long-term green spillover effect. The green-enabling effect of the digital economy is stronger in cities in eastern China, resource-based cities, high-tier cities, and cities with stricter environmental regulation. These findings underscore the importance of coordinating digital economy development with cross-regional green land governance, while strengthening regional absorptive capacity and improving factor allocation efficiency. Full article

Accelerated urbanisation and associated land-use conversion are reshaping the composition and functions of terrestrial ecosystems globally. In Southeast Asia, ecological change is increasingly mediated not only by demographic urbanisation but also by urban expansion, peri-urban development, and the conversion of agricultural, coastal, and forest land into built-up surfaces. This study integrates multi-source geographical information from 2014 to 2024 to examine 351 provincial-level units in 11 Southeast Asian nations. To describe the spatial-material dimension of urbanisation and ecological conditions, two indices were created: the Composite Nighttime Light Index (CNLI), used as a proxy for urban expansion and built-up development intensity, and the Improved Remote Sensing Ecological Index (IRSEI), which is tailored to tropical coastal locations. The development of human-environment interactions was measured using the Coupling Coordination Degree (CCD) model. Pathways of synergy and trade-off were found using an incremental four-quadrant framework, and nonlinear causes of spatial differentiation were investigated using Spearman correlation and the Optimal Parameter-based Geographical Detector (OPGD). Uncertainty was addressed through data-quality masking, annual compositing, consistent index-construction rules, and cautious interpretation of CCD and driver results as relative provincial-scale patterns. The regional mean CCD rose from 0.250 to 0.314 during the decade, showing a slow improvement; nevertheless, most places still have low to moderate levels of coordination. There is clear pathway divergence, with 38.7% of locations enduring trade-offs where built-up development happens at the price of ecological quality and 58.4% of regions seeing synergistic improvement. The coupling pattern is primarily driven by built-up area expansion, with multiple factors jointly producing strong nonlinear enhancement effects. Climate conditions and forest disturbance further strengthen these effects. This study extends beyond single-country analyses by situating remote-sensing coupling results within land-use transition, peri-urbanisation, urban–rural linkage, and regional-governance perspectives. It provides quantitative evidence to support differentiated policy strategies in rapidly urbanising places. Full article

This study examines the urban–rural interface of Loja, Ecuador, as a differentiated socio-ecological system shaped by environmental structure, the persistence of productive land, reliance on infrastructure, and terrain-adaptive settlement patterns. An integrated territorial taxonomy was developed using a four-phase methodological framework that combined morphological analysis, socio-territorial characterization, adaptive tectonic interpretation, and multivariate synthesis. Ten representative polygons located within Loja’s Andean periphery were evaluated using indicators of Biotic Porosity (BP), Degree of Hybridity (DH), Infrastructure Dependency (ID), and Alimentary Autonomy Extent (AAE), complemented by spatial analysis, field verification, and exploratory correlation assessment. The results revealed strong positive associations between socio-territorial hybridity and alimentary autonomy (ρ = 0.92), and strong negative associations between hybridity and infrastructure dependency (ρ = −0.88). A moderate positive relationship was identified between biotic porosity and hybridity (ρ = 0.67). The analysis also identified three adaptive tectonic typologies—Terracing, Cantilever, and Platform Urbanization—and a territorial taxonomy composed of Resilient, Adaptive, and Consolidating Edges. These findings demonstrate that the Andean urban periphery operates as a heterogeneous continuum rather than a homogeneous expansion zone. Although the statistical relationships should be interpreted as exploratory due to the limited sample size, the framework provides a useful basis for comparative research and resilience-oriented planning in intermediate mountain cities undergoing rapid territorial transformation. Full article

Long-term changes in land-use and water quality degradation are key pressures on riverine ecosystems, yet their effects on the functional structure of fish assemblages remain insufficiently quantified. We conducted a decadal assessment of fish assemblages in four rivers experiencing rapid land-use transformation in Guangxi, China—the Zuojiang (ZY), Youjiang (YJ), Yujiang (YuJ), and Guijiang (GJ) rivers. Species-level functional traits describing body shape, vertical habitat position, feeding guild, trophic level, and maximum total length were used to calculate multiple functional diversity indices related to environmental variables using redundancy analysis and random forest models. Over the decade from 2013 to 2023, impervious surface and bare land increased significantly, while forest, shrubland, and grassland declined, coinciding with marked deterioration in water quality, including higher chlorophyll-a and lower dissolved oxygen concentrations (p < 0.05). Fish abundance and species richness decreased, and formerly dominant benthic cyprinids and loaches were replaced by tolerant and invasive taxa such as Oreochromis niloticus and Tilapia zillii. NMDS and PERMANOVA indicated a significant shift in community composition between 2013 and 2023 (R² = 0.14, p < 0.001), with fish assemblages shifting toward small-bodied, pelagic, omnivorous, and laterally compressed species. Functional richness declined, whereas functional evenness and functional dispersion increased, indicating functional simplification and homogenization. Multivariate and machine learning models consistently identified increasing impervious cover and declining dissolved oxygen as the strongest predictors of these changes. Our results demonstrate that land-use intensification and water quality deterioration are associated with reduced functional integrity in Guangxi rivers and highlight the value of trait-based indicators for monitoring and managing biodiversity in rapidly urbanizing catchments. Full article

Sustainable urbanization requires reconciling rapid land development with ecological security, a challenge particularly acute in mountainous megacities. While land-use/cover change (LUCC) substantially reshapes regional carbon storage, conventional whole-region assessments often mask critical spatiotemporal dynamics and subregional heterogeneity. Taking Chongqing, China, as a representative case, this study integrates the Future Land-Use Simulation (FLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to decode historical carbon-storage dynamics (2000–2020) and simulate future spatial trajectories (2035) under four multi-scenario land-use pathways: Integrated Development Priority Scenario (IDPS), Ecological Conservation Priority Scenario (ECPS), Farmland Conservation Priority Scenario (FCPS), and Economic Priority Scenario (EPS). Results show a net decline of 8.49 × 10⁶ t in total carbon storage during 2000–2020, primarily driven by the often-overlooked degradation of high-carbon-density grasslands alongside construction-land expansion. Scenario simulations reveal that the ECPS is the only pathway achieving a net carbon-storage increase (+4.26 × 10⁶ t), although the resulting land-use pattern was jointly shaped by ecological protection constraints, land suitability, and scenario-specific land-demand allocation. Crucially, subregional analysis highlights distinct spatial roles: the Northeastern Urban Agglomeration (NUA) emerges as the core for ecological restoration, the Main Urban Area (MUA) remains highly sensitive to development-driven carbon loss, and the Southeastern Urban Agglomeration (SUA) acts as a buffer requiring a delicate development balance. By linking coupled spatial modeling with subregional constraints, this framework advocates for a shift from “one-size-fits-all” land management to precise spatial governance, providing a scalable scientific reference for carbon-oriented sustainable planning in mountainous megacities worldwide. Full article

Enhancing green total-factor energy efficiency (GTFEE) is a critical pathway for promoting the green transformation of economic and social development. However, the potential influence of the construction and development of urban functional networks (UFNs) on this process has received limited scholarly attention. Using panel data from 148 cities across nine major Chinese urban agglomerations from 2010 to 2023, this study finds the following: First, the development of UFNs at the urban agglomeration scale significantly enhances GTFEE. Second, UFNs improve GTFEE through two distinct mechanisms: “government action” and “market efficiency.” The “government action” mechanism primarily operates by promoting market integration, reducing land resource misallocation, and curbing urban sprawl. The “market efficiency” mechanism functions by enhancing factor mobility, optimizing the allocation of production factors, and accelerating the diffusion of green technological innovations. Third, the effect of UFNs on GTFEE varies according to local development conditions, infrastructure connectivity, and policy continuity and coordination. The enhancement effect is more pronounced in cities located near coastlines, those connected to other cities within the agglomeration by high-speed rail, and cities with longer-serving municipal Party secretaries. Fourth, urban agglomerations characterized by polycentric spatial structures and stronger agglomeration externalities are better able to leverage the functional node effects of their constituent cities, thereby further enhancing GTFEE. Full article

Land-use change in arid urban agglomerations reshapes landscape ecological risk (LER) and ecosystem service value (ESV), yet few studies jointly diagnose their current coupling state, temporal trajectory, and future scenario response. Focusing on the urban agglomeration on the northern slope of the Tianshan Mountains, we integrated land-use data for 2000, 2010, and 2020 with LER assessment, ESV estimation, static–dynamic coupling zoning, and intPLUS-based 2030 scenario simulation. From 2000 to 2020, cropland and construction land expanded by 46.43% and 107.84%, whereas forest and water bodies declined by 49.76% and 47.64%. LER was dominated by low- and medium-low-risk classes, although medium-high-risk areas expanded locally. Total ESV declined from 512.16 to 429.97 billion CNY, with continued contraction of medium-high- and high-ESV zones. In 2020, potential enhancement zones (PEZs) dominated the integrated management pattern, accounting for 43.64% of the study area. By 2030, static zoning changed only slightly, whereas dynamic and integrated zoning showed clear scenario sensitivity: PEZs accounted for 40.55% under the natural development scenario, while priority governance zones reached 49.06% and 49.26% under economic development and ecological protection scenarios. By treating LER as risk pressure and ESV as service-supply capacity, this framework links static risk–service matching, dynamic trajectory diagnosis, and future scenario response, thereby supporting ecological zoning in arid urban agglomerations. Full article