Search Results (2,529)

This study examines the role of Low and Zero Emission Zones (LEZ/ZEZ) as urban climate-governance instruments in Latin American cities, using Rio de Janeiro as a case study. The objective is to assess the feasibility and institutional readiness for implementing a LEZ/ZEZ in the city’s central area, taking into account its regulatory framework, urban context, and transport- and emissions-related conditions. The methodology adopts an exploratory, qualitative approach based on the ASIF (Activity-Structure-Intensity-Fuel) framework, combined with a systematic review of municipal legislation, climate action plans, emissions inventories, and international best practices. Rather than developing a mathematical or predictive model, the study organizes these policy and institutional elements into a structured decision-support framework and proposes a roadmap to guide phased implementation. The results show that Rio de Janeiro possesses a favorable legal and policy environment for LEZ/ZEZ deployment, particularly through its Climate Action Plan and the legally established District of Low Emissions, while also identifying constraints related to data availability, monitoring capacity, and inter-institutional coordination. The study concludes that the proposed framework provides a practical governance-oriented tool to support low-carbon urban transitions, whose operational effectiveness will depend on future quantitative data collection, transport-demand simulation, and stakeholder engagement to strengthen evidence-based decision-making. Full article

In recent decades, governments have invested in strategic territories focused on knowledge production and application, which are strategic for socioeconomic development, particularly in urban areas. However, conceptual and terminological diversity hinders aspects such as the systematization of the literature, the advance of theoretical conceptualizations, and the formulation of coherent policies, especially in the context of socioenvironmental challenges. In this study, with the aim of consolidating this literature, we have conducted a systematic review with bibliometric and content analysis, examining publications on eight denominations associated with these territories. The literature reveals the existence of an established field; nonetheless, themes and denominations are still dispersed in the corpus. Among 400 authors, 339 published a single article, and only 13 authors have three or more studies in the sample. We identified a core of 11 journals that concentrate 73 of the 214 analyzed texts. We propose the term “knowledge territories” as an umbrella concept. A total of 114 case studies were identified. Governance is the most recurrent dimension (53% of the texts). Topics such as climate change, food production and diffuse effects of territorial occupation are rarely explored, as are the cases analyzed in the context of semi-peripheral and peripheral countries, indicating gaps and opportunities for future research. Full article

Land-use planning in Latin American coastal cities faces the challenge of integrating visions of the future with multiscale approaches amid high socio-environmental pressure. Using a mixed methodology that included documentary and comparative analysis of regulatory and planning instruments, workshops with experts, and evaluation matrices, this article analyzes the prospective and multiscale capabilities of the 2020–2032 Land Use Plan for the district of Santa Marta. This study provides a methodological and applied novelty by integrating, for the first time in this context, a dual analytical framework that simultaneously assesses the quality of the prospective dimension and the degree of multi-scalar articulation in coastal spatial planning. The study area is a strategic coastal territory exposed to environmental, urban, and socio-ecological pressures. The results reveal limitations in integrating future scenarios, polycentric governance, and adaptive coastal management, as well as a weak prospective approach limited to short time horizons, without constructed scenarios or early warning systems. At the same time, there is fragmented multiscale coordination between the local, regional, and national levels. These limitations partly explain the socio-environmental conflicts identified, particularly at the land-sea interface, where there is an apparent disconnect between urban planning and coastal management. On the other hand, significant progress has been made in the biophysical and social characterization of the territory. Our analysis generated specific knowledge for fast-growing intermediate cities, a critical type of coastal settlement, but less studied than large metropolises. The study provides a replicable framework for other seaside towns in the region. The study concludes that overcoming these gaps requires systematically incorporating forward-looking instruments and strengthening multilevel governance mechanisms. To this end, it summarizes lessons learned for more adaptive, resilient territorial planning in coastal contexts. Full article

The Atlantic City–South Pass (ACSP) orogenic gold district, Wind River Mountains, Wyoming, occurs in the Archean South Pass Greenstone Belt primarily within greywackes and igneous rocks metamorphosed to the upper greenschist–lower amphibolite facies. Approximately 10 Mt of gold has been produced from pyrite and arsenopyrite-bearing quartz veins in deformation zones at the brittle–ductile transition. Multiple generations of primary and/or pseudosecondary fluid inclusions in gold-bearing quartz veins include one- and two-phase gaseous CO₂-CH₄ ± N₂ inclusions and two- and three-phase gaseous CO₂-CH₄-H₂O inclusions with rare NaCl daughter minerals. These primary/pseudosecondary inclusions show a broad range of homogenization temperatures (T_h) of 177.2 to 420.0 °C, with salinities of halite-bearing inclusions of >26 wt. % NaCl, with a high concentration of CaCl₂. Secondary aqueous inclusions formed at lower values of T_h (80.9 to 243.4 °C, with one outlier of 301.1 °C). Carbon from graphitic schists associated with gold-quartz veins yields values of δ¹³C = −28.5 to −19.1 per mil, suggesting that the light C isotope compositions of some carbonates (δ¹³C = −11.0 to −1.5 per mil) involved exchange reactions with graphite in the schists. Isotopic compositions of sulfur in sulfides (δ³⁴S = −1.0 to 3.6 per mil), oxygen in vein quartz (δ¹⁸O = 7.36 to 10.38 per mil), and hydrogen in fluid inclusions in vein quartz (δD = −125 to −55 per mil) are permissive of both magmatic-hydrothermal and metamorphic dehydration models for the origin of gold mineralization. However, a potential source of magmatic–hydrothermal fluids, the post-metamorphic Louis Lake granodiorite was unlikely to transport gold in a vapor state to become focused into shear zones as previously proposed. We favor a metamorphic dehydration model in which gold was derived from the South Pass supracrustal sequence and deposited in second-order shear zones that are spatially related to the first-order Roundtop Mountain Deformation Zone. Full article

With accelerating urbanisation, extreme rainfall events have become increasingly frequent, leading to rising urban flooding risks that threaten city operation and infrastructure safety. The rapid expansion of impervious surfaces reduces infiltration capacity and accelerates runoff responses, making cities more vulnerable to short-duration, high-intensity storms. Although the SWMM is widely used for urban stormwater simulation, its application is often constrained by the lack of detailed drainage network data, such as pipe diameters, slopes, and node connectivity. To address this limitation, this study focuses on the main built-up area within the Second Ring Expressway of Chengdu, Sichuan Province, in southwestern China. As a regional core city, Chengdu frequently experiences intense short-duration rainfall during the rainy season, and the coexistence of rapid urbanisation with ageing drainage infrastructure further elevates flood risk. Accordingly, a technical framework of “open road data substitution–automated modelling–SWMM-based assessment” is proposed. Leveraging the spatial correspondence between road layouts and drainage pathways, open road data are used to construct a virtual drainage system. Combined with DEM and land-use data, Python-based automation enables sub-catchment delineation, parameter extraction, and network topology generation, achieving efficient large-scale modelling. Design storms of multiple return periods are generated based on Chengdu’s revised rainfall intensity formula, while socioeconomic indicators such as population density and infrastructure exposure are normalised and weighted using the entropy method to develop a comprehensive flood-risk assessment. Results indicate that the virtual drainage network effectively compensates for missing pipe data at the macro scale, and high-risk zones are mainly concentrated in densely populated and highly urbanised older districts. Overall, the proposed method successfully captures urban flood-risk patterns under data-scarce conditions and provides a practical approach for large-city flood-risk management. Full article

Data scarcity limits robust assessment of urban overheating and its implications for building energy use, especially in complex-terrain cities such as those in mountain environments. In this context, Land Surface Temperature (LST) from thermal remote sensing can be used to map urban hotspots at high spatial resolution. Nevertheless, it does not provide the full set of hourly atmospheric variables required to run building energy simulations aimed at quantifying their impact and defining mitigation measures. Given these premises, this study proposes a methodology combining satellite-derived LST with ground meteorological measurements to assess Urban Heat Island (UHI) patterns and quantify how measured weather data selection affects urban building energy modeling (UBEM) outcomes. After selecting as a case study Bolzano, an Alpine city in Northern Italy, ECOSTRESS LST (2019–2025, May–August) was first processed and quality-screened to (1) compute ΔLST (urban–rural) and (2) identify diurnal and spatial overheating patterns across the building stock. Second, four measured weather datasets—one rural station and three urban stations located in the city core, in the industrial district, and in the urban edge—were used as boundary conditions in an EnergyPlus-based UBEM parametric campaign for 253 residential buildings, covering multiple envelope insulation levels and window-to-wall ratios. Results show strong diurnal asymmetry in surface overheating, with the largest contrasts in the afternoon and prominent industrial hotspots. Ground measurements confirm persistent intra-urban microclimatic differences, and the choice of measured weather dataset causes systematic shifts in simulated cooling demand and thermal comfort. The study highlights the need for weather data selection strategies based on microclimatic context rather than simple proximity, improving representativeness in UBEM applications for Alpine and other heterogeneous urban environments. Full article

Visual cues in urban street environments shape residents’ perceived safety, and these perceptions often differ across social groups. Using Shanghai as a case study, this research focuses on two vulnerable populations: older adults and migrants. In the context of rapid urban transformation and increasingly fine-grained governance, perceived safety not only reflects environmental experience but also relates to whether different social groups can receive equitable perceptual support and access to opportunities for public-space use. We trained a deep learning model and rated perceived safety using over 160,000 street-level images, integrated with demographic census data at the neighborhood level, to systematically examine inequalities in visual environment perception and underlying group-specific mechanisms. However, existing studies have largely relied on small-sample surveys or average-effect analyses, and systematic evidence remains limited that can simultaneously characterize city-scale inequalities in perceived safety, disparities in group exposure, and group-specific mechanisms, while translating findings into actionable guidance for targeted governance. Firstly, we quantified spatial inequality in perceived safety using the Gini coefficient and the Theil T index. Decomposition results indicate that the remaining disparity is primarily associated with between-group differences linked to social structure. Nonparametric tests and multiple linear regression further identified significant interactions between demographic characteristics (the share of older adults and the migrant proportion) and visual environmental features, confirming group-differentiated responses to comparable streetscape conditions. In addition, we developed a priority governance index that combines perceived safety scores with vulnerability indicators to spatially identify neighborhoods requiring targeted interventions. Results suggest relatively low overall spatial inequality in perceived safety at the city scale, while decomposition analyses reveal clear group-structured disparities between central and peripheral areas and between local residents and migrants. Migrants are more frequently concentrated in neighborhoods with lower perceived safety. Priority intervention areas are primarily older-adult communities in central districts and migrant settlements in peripheral areas, characterized by lower perceived safety and higher demographic vulnerability. These findings underscore the need to shift urban renewal from uniform improvements toward differentiated strategies that account for perceptual equity and social identity. Our main contribution is not the development of a new network architecture but the alignment of image-based perception estimates with demographic vulnerability at the neighborhood scale. By combining inequality decomposition with tests of interaction mechanisms, we provide governance-relevant evidence for identifying priority intervention areas and advancing fine-grained renewal decisions oriented toward visual justice. Full article

Intensifying urbanisation in the Arctic, particularly in spatially constrained coastal and island cities, requires reliable information on long-term land-use/land-cover (LULC) change to assess environmental impacts and support urban planning. However, multi-decadal, high-resolution LULC datasets for Arctic cities remain limited. In this study, we quantify LULC change on Tromsøya (Tromsø, Norway) from 1984 to 2024 using a Random Forest classifier applied to multispectral satellite imagery from Landsat and PlanetScope, complemented by LiDAR-derived canopy height models (CHM) and building footprints. We mapped LULC change trajectories and examined how these shifts relate to district-level population redistribution using gridded population data. The integration of a LiDAR-derived CHM was found to substantially improve the accuracy of Landsat-based LULC mapping and to represent the dominant source of classification gains, particularly for spectrally similar urban classes such as residential areas, roads, and other paved surfaces. Landsat augmented with CHM was shown to achieve practical equivalence to PlanetScope when the latter was modelled using spectral features only, supporting the feasibility of scalable and cost-effective long-term monitoring of urbanisation in Arctic cities. Based on the best-performing Landsat configuration, the proportions of artificial and green surfaces were estimated, indicating that approximately 20% of green areas were transformed into artificial classes. Spatially, population growth was concentrated in a small number of districts and broadly coincided with hotspots of green-to-artificial conversion The workflow provides a reproducible basis for long-term, district-scale LULC monitoring in small Arctic cities where data constraints limit the consistent use of high-resolution image. Full article

Land subsidence constitutes a critical hazard to coastal megacities globally, amplifying flood risks and damaging infrastructure. Taking Tianjin—a major port city underlain by compressible sediments and affected by groundwater over-exploitation—as a case study, we address two key research gaps: the absence of a quantitative framework coupling groundwater extraction with construction land expansion, and the inadequate separation of seasonal and long-term subsidence drivers. We developed an integrated remote-sensing-based approach: high-resolution subsidence time series (2016–2023) were derived via Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, validated against leveling measurements (R > 0.885, error < 20 mm). This subsidence dataset was fused with groundwater level records and annual construction land maps. Seasonal-Trend Decomposition using Loess (STL) isolated trend, seasonal, and residual components, which were input into a Random Forest (RF) model to quantify the relative contributions of subsidence drivers. Dynamic Time Warping (DTW) and Cross-Wavelet Transform (CWT) were further employed to characterize temporal patterns and lag effects between subsidence and its drivers. Our results reveal a distinct shifting subsidence pattern: “areal expansion but intensity weakening.” Groundwater control policies mitigated five historical subsidence funnels, reducing areas with severe subsidence from 72.36% to <5%, while the total subsiding area expanded by 1024.74 km², with new zones emerging (e.g., northern Dongli District). The RF model identified the long-term groundwater level trend as the dominant driver (59.5% contribution), followed by residual (23.3%) and seasonal (17.2%) components. Cross-spectral analysis confirmed high coherence between subsidence and long-term groundwater trends; the seasonal component exhibited a dominant resonance period of 12 months and a consistent subsidence response lag of 3–4 months. Construction impacts were conceptualized as a “load accumulation-soil compression-time lag” mechanism, with high-intensity engineering projects inducing significant local subsidence. This study provides a robust quantitative framework for disentangling the complex interactions between subsidence, groundwater, and urban expansion, offering critical insights for evidence-based hazard mitigation and sustainable urban planning in vulnerable coastal environments worldwide. Full article

The complex topography and harsh natural environment of the Loess Plateau in Longzhong have been suffering from an undefined living circle structure, which has hindered rural planning and development. A rural community living circle is a spatial unit centered on meeting the needs of villagers, within which various service facilities are rationally allocated within a specific spatial scope. To refine its spatial patterns, the concept of living circles was introduced to address travel challenges. The extent of these living circles is affected by the accessibility of public service facilities and barriers to travel. Using land use data, DEM, population density, and road networks, this study employed the MCR model, gravity model, and ArcGIS spatial analysis to examine the patterns of rural community living circles. The focus was on analyzing the living circle structure of rural communities on the Loess Plateau in Longzhong, considering both natural and artificial environmental constraints. The results show: (1) Rural community living circles present multi-scale spatial features. The basic living circle covers a 15 min slow-travel area. The central living circle corresponds to village-level needs, accessible within 35 min by both slow and motorized travel. The town living circle covers a 10 km radius, reachable within 60 min by a mix of transport modes. The county living circle, dominated by motorized travel, represents the top tier of public service configuration. (2) Quantitatively, the delineation identified 2753 basic, 444 central, 19 township, and 1 county-level living circles in the Anding District of Dingxi City. The Northern, Eastern, and Southwest Zones suffer from fragmented mountainous landscapes, limiting mobility and accessibility. The Central Zone, however, benefits from a combination of mountainous terrain and river valley plains, offering superior service accessibility. (3) The analysis results based on the MCR model and gravity model aligned more closely with reality, reflecting the scale patterns of rural community living circles. The results of this study can provide theoretical guidance for rural planning, construction, and management in the hilly and gully areas of the Loess Plateau. Full article

Understanding how housing attributes are capitalized into prices is central to addressing urban affordability challenges. Using 2799 second-hand housing transactions from Wenzhou, China, this study examines residential price formation under pronounced spatial and structural heterogeneity. Multiple predictive models are evaluated within a unified 10-fold cross-validation framework. Results indicate that Random Forest delivers the strongest predictive performance, achieving a normalized mean squared error below 0.10 and explaining over 90% of out-of-sample price variation, substantially outperforming hedonic regression, regression trees, bagging, boosting, and support vector models. Permutation-based importance analysis identifies district location, building scale, and floor area as the dominant price determinants, while the influence of renovation quality, transportation access, and educational amenities varies across districts and dwelling types. These findings reveal strong nonlinearities and heterogeneous valuation mechanisms in rapidly urbanizing housing markets. Methodologically, the study demonstrates how interpretable machine learning complements traditional hedonic analysis, while providing policy-relevant insights into housing affordability dynamics in medium-sized Chinese cities. Full article

Sustainability assessment in UNESCO World Heritage city centres often treats spatial configuration, functional accessibility, and heritage governance as separate analytical domains. This study addresses this fragmentation by developing a composite assessment framework to evaluate morphological sustainability in historic urban cores. The Morphological Sustainability Model (MSM) and its numerical expression, the Morphological Sustainability Index (MSI), are applied to the Bursa Khans District for the 2020–2025 period. The model integrates Space Syntax variables (integration, connectivity, choice, and intelligibility), 15-Minute City indicators related to proximity, pedestrian accessibility, active mobility, and inclusivity, and Historic Urban Landscape-based governance evaluations derived from UNESCO-compliant management plans. These components are synthesised into six weighted composite indicators (BKH1–BKH6). Results show that the MSI increases from 0.38 in 2020 to 0.51 in 2025 (+34.2%), indicating a strengthened alignment between spatial configuration, pedestrian-oriented functional performance, and heritage governance capacity. The findings reveal a shift from car-oriented axial dominance toward a more pedestrian-centred spatial structure along the historic bazaar spine. Overall, the study demonstrates that the MSI provides a transferable, decision-support-oriented framework for assessing morphological sustainability in historic urban environments. Full article

With the continuous expansion of high-density urban forms, residents’ opportunities for daily contact with natural environments have been increasingly reduced, making the equity of urban green space allocation a critical challenge for sustainable urban development. Existing studies have largely focused on green space quantity or accessibility at single time points, lacking systematic investigations into the spatiotemporal evolution of green space exposure (GSE) and its equity from the perspective of residents’ actual environmental experiences. GSE refers to the integrated level of residents’ contact with urban green spaces during daily activities across multiple dimensions, including visual exposure, physical accessibility, and spatial distribution, emphasizing the relationship between green space provision and lived environmental experience. Based on this framework, this study takes the central urban area of Hangzhou as the study area and integrates multi-temporal remote sensing imagery with large-scale street view data. A deep learning–based approach is developed to identify green space exposure, combined with spatial statistical methods and equity measurement models to systematically analyze the spatiotemporal patterns and evolution of GSE and its equity from 2013 to 2023. The results show that (1) GSE in Hangzhou increased significantly over the study period, with accessibility exhibiting the most pronounced improvement. However, these improvements were mainly concentrated in peripheral areas, while changes in the urban core remained relatively limited, revealing clear spatial heterogeneity. (2) Although overall GSE equity showed a gradual improvement, pronounced mismatches between low exposure and high demand persisted in densely populated areas, particularly in older urban districts and parts of newly developed residential areas. (3) The spatial patterns and evolutionary trajectories of equity varied significantly across different GSE dimensions. Composite inequity characterized by “low visibility–low accessibility” formed stable clusters within the urban core. This study further explores the mechanisms underlying green space exposure inequity from the perspectives of urban renewal patterns, land-use intensity, and population concentration. By constructing a multi-dimensional and temporally explicit analytical framework for assessing GSE equity, this research provides empirical evidence and decision-making references for refined green space management and inclusive, sustainable urban planning in high-density cities. Full article

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R²) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05. Full article

The decarbonisation of urban heating systems represents a key challenge for the transition towards sustainable cities. This study investigates the field integration of a Medium-Temperature Heat Pump (MTHP) within the Osimo District Heating Network (DHN) in Italy, demonstrating how low-grade return flows (30–50 °C) can be effectively upgraded to supply temperatures of 65–75 °C, in line with 4th-generation district heating requirements. Specifically, 5256 h of MTHP operation within the DHN were analysed to validate the initial design assumptions, develop surrogate performance models, and assess the system’s techno-economic and environmental performance. The results indicate stable and reliable operation, with a weighted average Coefficient of Performance (COP) of 3.96 and a weighted average thermal output of 134.5 kW. From an economic perspective, the system achieves a payback period of approximately six years and a Levelised Cost of Heat (LCOH) of 0.0245 €/kWh. Environmentally, the MTHP enables CO₂ emission reductions of about 120 t compared with conventional gas-fired boilers. Beyond its technical performance, the study highlights the strong replicability of MTHP solutions for small- and medium-scale DHNs across Europe. The proposed approach offers urban utilities a scalable and cost-competitive pathway towards low-carbon heat supply, directly supporting municipal climate strategies and aligning with key EU policy frameworks, including the European Green Deal, REPowerEU, and the “Fit-for-55” package. Full article