Search Results (1,797)

Announcements of major transport infrastructure can reorganize land markets long before construction begins, as expectations are capitalized into prices and building decisions—with direct implications for sustainable territorial planning. This study examines the real estate response to the 2024 announcement of the Torquemada airport project in the Valparaíso Region, Chile. We assemble a high-resolution microterritorial panel at the block–semester–land-use level, integrating three Chilean administrative registers: the SII cadastre (over 100 million construction lines across 16 semestral snapshots, 2018–2025), the F2890 conveyance records (1.49 million geolocated transactions), and the daily Unidad de Fomento series. We estimate a multi-outcome spatial difference-in-differences design, complemented by an event study, land-use heterogeneity analysis, local indicators of spatial association, placebo tests, spatial-weight sensitivity analysis, and the heterogeneity-robust Callaway–Sant’Anna estimator. We find a robust increase in new-parcel construction in the zone of influence—identified by an annual event study against never-treated controls whose pre-announcement coefficients are small and trendless, in sharp contrast to the uniformly positive pre-trends of the expansion and aggregate-stock series—together with selective appreciation of non-residential uses and no detectable effect on housing value. The expansion and aggregate-stock components are not separately identified: their pre-announcement trends are strongly non-parallel, so the corresponding fixed-effects coefficients are read as design-conditional associations. The evidence supports an activation of the extensive margin (new-parcel building) rather than a recomposition away from densification. We read the evidence as the anticipatory footprint of the announcement rather than a point causal effect. Detecting this footprint before construction enables anticipatory value capture and sprawl-containment policy while the planning window remains open. Full article

The evaluation of stents in coronary computed tomography angiography (CCTA) is still a major topic in cardiovascular imaging. Using Photon-Counting Detector CT (PCCT) may improve the assessment of coronary stents and make on-site CT-FFR analysis feasible for ruling out in-stent restenosis (ISR). In this study, patients with previous coronary stent implantation who underwent CCTA using PCCT and subsequent invasive catheter angiography (ICA) were included. Stent characteristics such as location and length were reported. CT-FFR measurements were taken 1.8 cm before and after the stent, with a value of ≤0.80 defined as hemodynamically significant under respecting the diagnostic accuracy drop in the gray zone between 0.76 and 0.80. Delta CT-FFR with a cut-off value of ≥0.06, indicating hemodynamic significance, was determined. Any ISR and interventional treatment during the following ICA was recorded. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated for post-stent CT-FFR and Delta CT-FFR in detecting ISR. Patients were followed up to evaluate the rate of major adverse cardiovascular events (MACE) 6 months after CCTA. A total of 19 patients (5 female, 14 male, median age 69 years) were enrolled in this study. In most cases, coronary stents were located in the proximal LAD with a median stent length of 70.2 mm. Pathological CT-FFR < 0.76 distal to the stent was detected in 6 cases (31.6%), while pathological Delta CT-FFR ≥ 0.06 occurred in 14 cases (73.7%). ICA was performed in three of these patients, with ISR confirmed in two cases. These findings yield sensitivity and NPV of 100% for both post-stent CT-FFR and Delta CT-FFR for excluding ISR with a superior specificity (76.5% vs. 29.4%) and overall diagnostic accuracy (78.9% vs. 36.8%) for post-stent CT-FFR. Two patients reported a myocardial infarction in follow-up; however, neither of them was located in the territory of the stented coronary artery. This study outlines the feasibility of on-site CT-FFR analysis using PCCT in excluding ISR in coronary stents with a high diagnostic accuracy. These findings highlight the need to extend the benefits of CT-FFR analysis for non-invasive assessment of possible ISR regarding personalized risk stratification and therapy planning. Full article

Global warming is driving large-scale shifts in the climatically suitable habitats of many species. However, climate-only species distribution assessments may overestimate the spatial availability of future suitable habitats when dynamic land-use change is not considered. To assess potential spatial overlaps between climate-driven habitat suitability shifts and human land-use patterns, this study focuses on Quercus L. as a widely distributed keystone forest taxon in China. The genus-level assessment was designed to identify broad-scale habitat–land-use conflict patterns under multiple climate pathways and territorial spatial planning scenarios, rather than to predict species-specific distribution responses. We developed a soft-coupled framework integrating the Maximum Entropy (MaxEnt) model and the Patch-generating Land-Use Simulation (PLUS) model, and applied the Habitat–Land-Use Conflict Index (HLCI) as a categorical spatial overlay framework to classify potential overlaps between projected suitable habitats and future land-use categories across 16 exploratory scenario combinations integrating Shared Socioeconomic Pathway (SSP)-based climate projections and land-use/land-cover (LULC) scenarios for the 2040s at the grid scale. The results indicate that: (1) climate warming may reshape Quercus habitat suitability, characterized by northward/westward expansion and southward contraction in some low-latitude regions; (2) future land-use patterns may reduce the spatial availability of projected suitable habitats by increasing their overlap with built-up land and cultivated land. Under high-emission scenarios, potential newly suitable habitats overlapped with built-up land by up to 5.90 × 10⁴ km² and with cultivated land by up to 36.42 × 10⁴ km²; and (3) the Ecological Protection scenario showed lower overlap with non-ecological land-use categories and a larger area of potentially realizable habitat expansion. This study provides a scenario-based spatial assessment of where future Quercus habitat suitability may overlap with human land-use patterns, offering broad-scale support for adaptive forest conservation and territorial spatial planning. Full article

Land use and land cover (LULC) changes are major drivers of environmental transformation in sensitive regions, such as the Marajó Archipelago in the Brazilian Amazon. This study assessed accumulated anthropization of LULC in the Immediate Geographic Region of Soure–Salvaterra, Eastern Amazon, between the reference years 1985 and 2023, using MapBiomas data and spatial statistical techniques. Bivariate Local Moran’s I (LISA) was applied to evaluate intertemporal spatial associations between areas classified as natural in 1985 and anthropized in 2023. In this approach, High–Low indicates natural areas associated with low anthropization in 2023, whereas High–High indicates areas where natural cover in 1985 was spatially associated with higher anthropization in 2023. The results indicated a strong predominance of High–Low, with values above 94% in all municipalities and up to 99.86% in Santa Cruz do Arari. In contrast, High–High had localized concentrations in Salvaterra (3.53%), Cachoeira do Arari (1.28%), and Soure (1.16%), especially in coastal zones and inland sectors. Low–Low, associated with lower anthropogenic pressure or possible signs of natural regeneration, was extremely low (≤0.0004%). These findings indicate that LISA is useful for identifying local LULC patterns and supporting environmental assessment and territorial planning in tropical regions. Full article

Terraced vineyard landscapes represent complex cultural systems shaped by long-term interactions between geomorphology, agriculture and rural heritage. This study investigates the terraced viticultural landscape of Castiglione di Sicilia, located on the northern slope of Mount Etna (Sicily, Italy), through an integrated GIS-based framework combining Landscape Character Assessment (LCA), rural micro-structure mapping, visibility analysis and slow mobility assessment. Attention was given to dry-stone walls and traditional rural micro-structures, recognized as key components of the historical landscape identity of Etna. The methodology integrated geomorphological variables, vineyard distribution, dry-stone wall density and slope suitability within a Rural Micro-Structure Intensity Framework (RMSI) developed to identify areas characterized by stronger continuity of terraced rural landscapes. A sensitivity analysis was performed to evaluate the robustness of the weighting scheme. Slow mobility corridors were subsequently analyzed according to their relationship with landscape continuity and scenic visibility. The results reveal a strong spatial association between vineyards, dry-stone wall systems and historically structured terraced landscapes. Visibility analysis highlighted that highly visible areas do not fully correspond to the highest RMSI values, reflecting the geomorphological complexity of the volcanic environment. The corridor providing the best balance between scenic visibility, rural heritage continuity and route accessibility emerged as the most suitable option for landscape-oriented planning. The proposed framework supports landscape-sensitive planning and sustainable territorial valorization in Mediterranean terraced agricultural systems. Full article

Landslides are a significant natural hazard in regions with complex topographic, geological, and climatic conditions, where they can constrain sustainable territorial development and threaten infrastructure, land use, and environmental safety. This study aims to assess and map landslide susceptibility in Southern Primorye in order to support hazard-informed territorial planning and risk reduction. The analysis integrates vegetation, precipitation, geological, and topographic predictors with documented landslide occurrence data. A presence-only landslide susceptibility modeling approach was applied using the OneClassSVM algorithm with a radial basis function kernel. The results show that the highest susceptibility is associated with lower slope segments and coastal landforms composed of loose unconsolidated deposits and partly covered by sparse woodland. Surface runoff, subsurface flow, lithological conditions, and precipitation patterns were identified as the principal factors contributing to slope instability, while field observations confirmed that anthropogenic slope cutting related to road infrastructure may act as an additional local trigger. The model demonstrated moderate but acceptable predictive performance and allowed the delineation of areas with elevated landslide susceptibility. The resulting susceptibility map provides a regional-scale basis for more sustainable land-use planning, infrastructure placement, and landslide risk mitigation in Southern Primorye and in other regions with comparable environmental conditions. Full article

Biodiversity and landscape heterogeneity are key components of agroecosystem functioning because they support ecosystem services and strengthen the capacity of agricultural systems to undertake sustainable agroecological transitions. This study assesses the landscape structure of the municipality of Ruvo di Puglia, within the Bio-Distretto delle Lame, to evaluate its potential to support such a transition. Bio-districts are territories in which farmers, local authorities, citizens, and other stakeholders collaborate to manage natural and agricultural resources sustainably, often with a strong connection to organic farming. The research combines freely available Sentinel-2 imagery with UAV-based ground truthing to update land-use/land-cover information and to derive landscape indicators. A systematic sampling scheme was designed in QGIS, and UAV flights over 14 areas were used to generate training and validation vectors. Two classification strategies were tested on 2024 Sentinel-2 data: a supervised pixel-based approach and an unsupervised multi-temporal object-based approach (GEOBIA). The best-performing map was obtained from the supervised classification of July NDVI data, with an overall accuracy of 91.76%. In respect to the 2018 official land-cover dataset indicates a decrease in agricultural land (−490.91 ha), a reduction in arable crops (−1216.43 ha), and an increase in permanent crops (+725.52 ha), suggesting a shift toward specialization. At the same time, natural and semi-natural areas increased, improving the landscape potential for ecological functions. However, the high fragmentation detected by the landscape metrics (average patch size approximately 0.25 ha) may limit habitat continuity and species stability. The results should therefore be interpreted as an assessment of landscape structure and potential biodiversity support, rather than as a direct measurement of biological diversity. Strengthening ecotones, hedgerows and semi-natural linear elements with native species would further improve landscape resilience and support agroecological planning. Full article

This exploratory study examines the heterogeneous spatial evolution of cities in a fragmented non-metropolitan region of Southern Brazil and develops an original functional-symbolic typological framework that integrates functional performance and symbolic production in the classification of cities. Grounded in the theoretical contributions of Lefebvre, Santos, and Corrêa, the framework was designed by the authors to simultaneously incorporate economic, territorial, cultural, and identity-related dimensions that are typically analysed separately in conventional urban typologies. The research adopts a qualitative and inductive approach to analyse secondary data from municipalities in the state of Santa Catarina. Rather than treating urbanisation as a homogeneous process, the study conceptualises urban typologies as analytical devices capable of revealing differentiated urban trajectories, uneven capacities of territorial articulation, and distinct modes of governance in non-metropolitan contexts. The findings show that cities with similar demographic scales perform diverse social, cultural, and economic roles shaped by historically and symbolically produced spatial relations. Five urban typologies were identified: Multifunctional Metropolises, Industrial Regional Capitals, Agroindustrial Cities, Cultural Tourist Cities, and Local Centres of Basic Function. The results demonstrate that urban centrality in non-metropolitan regions is not determined solely by economic performance or demographic scale, but also by symbolic attributes such as cultural heritage, territorial identities, festivals, and religious functions. By integrating material and symbolic dimensions within a single analytical structure, the proposed framework advances the understanding of fragmented urban systems, contributes to contemporary debates on non-metropolitan urbanisation and territorial governance, and offers a transferable approach for the analysis of urban diversity beyond the Brazilian context. The findings also provide practical implications for regional planning and public policy by highlighting the role of symbolic production in shaping territorial organisation and regional influence. Full article

Land-use change poses a growing threat to ecological security, yet existing regional assessments often rely on a single ecological indicator and lack direct linkage to territorial spatial planning. This study develops an integrated ESV–ERI framework coupled with quadrant zoning to provide spatially explicit, planning-compatible guidance for ecological protection in tropical island regions. Taking Hainan, China, as a case study, this research draws on land-use data from 1994 to 2024, applying ESV and ERI assessments coupled with Z-score standardization to examine their spatiotemporal evolution characteristics. The results indicate the following: (1) forest land persistently dominated land use (>63%), while construction land expanded by 197.68% and forest land and grassland decreased by 9.36% and 93.78%, respectively. (2) ESV showed a downward trend, decreasing by 14.683 billion yuan, with forest land accounting for over 83% of total ESV. Spatially, ESV exhibited a “high inland, low coast” pattern, with high-value zones across inland water bodies and central nature reserves and low-value zones in coastal urban agglomerations. (3) ERI increased from 0.0371 to 0.0539, with low-risk zones in the middle mountains and high-risk zones around the island. (4) Based on the dual dimensions of ESV and ERI, the entire island was delineated into four ecological zones. These findings provide scientific decision support for territorial spatial planning and differentiated ecological protection in tropical island regions. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

Over the past fifty years, urban and rural spaces have been reshaped by global sustainability policies, digital innovation, and emerging socio-ecological needs. This article investigates the convergence of agro-urban planning strategies, Smart City infrastructures, and adaptive governance models, proposing an integrated agro-urban paradigm for sustainable territorial transformation. Drawing on a literature review and comparative analysis of international case studies—including Toronto, Milan, and Woven City—the research develops a triadic interpretative framework based on worldview, program, and faith. The study identifies AgroCities as systems centered on food sovereignty and ecological resilience, Smart Cities as efficiency-driven digital ecosystems, and Adaptive Cities as flexible, human-centered responses to complexity. Findings suggest that integrating food systems, technological innovation, and participatory governance enhances urban resilience and sustainability across scales. The article concludes by advocating for multi-scalar planning tools, cross-sectoral policies, and civic engagement to support the transition toward inclusive and regenerative cities. This framework offers a theoretical and operational contribution to reimagining urban planning in line with the principles of Smart Land and adaptive urbanism. Full article

The Milano Cortina 2026 Winter Olympic Games represent a significant departure from traditional mega-event models due to their markedly polycentric territorial structure. This study investigates the sustainability of this “decentralized” model by analyzing the environmental impact of mobility flows across a vast geographic area. Adopting a methodological approach, the research integrates historical attendance data from previous Winter Games with official projections and travel time simulations to model the event’s carbon footprint. Specifically, the framework quantifies gas emissions by categorizing mobility flows into external international travel and internal inter-cluster transit. The analysis highlights a significant discrepancy between the stated sustainability objectives and the actual implementation of the infrastructural plan. Findings reveal that the total carbon debt is heavily driven by international travel, yet the localized impact on Alpine clusters remains critical due to a persistent reliance on road infrastructure over rail systems. The results suggest a “paradox of decentralized sustainability”, where the benefits of reusing existing sporting venues are offset by the environmental costs of connecting geographically fragmented sites. We conclude that without a robust and efficient public transport network, territorial dispersion acts as a catalyst for widespread anthropogenic pressure on fragile mountain ecosystems, challenging the long-term ecological legacy of the event. By empirically exposing these dynamics, this study offers a novel evaluative framework for assessing the true sustainability of distributed governance in future mega-events. Full article

By the end of 2024, global photovoltaic (PV) capacity exceeded 2.2 TW, shifting planning from feasibility demonstration toward site–technology co-selection under energy, technical, economic, environmental, territorial, and socio-regulatory constraints. The existing multicriteria literature treats site and technology selection as independent problems under an implicit infinite-grid assumption, which is untenable in markets such as Chile and Peru. This study develops and validates an integrated Delphi–AHP framework with six criteria and eighteen subcriteria calibrated by twenty-eight experts from six Latin American countries. The framework underwent Delphi binary validation, AHP consistency control ($C{R}_{\mathrm{agg}}$ between 0.0013 and 0.0247; discard rate 2.6%), geometric-mean aggregation, deterministic sensitivity analysis, Monte Carlo simulation (10,000 iterations), rank-reversal testing, and nonparametric subgroup analysis. The dominant pair $\{I_2,E{c}_2\}$, consisting of grid hosting capacity and LCOE, appears as Top-2 in 84.77% of Monte Carlo iterations and is preserved across 15 of 16 leave-one-out scenarios. Grid hosting capacity surpasses useful solar resource by a factor of 3.41. A demonstrative application to 18 site–technology alternatives confirms the ranking, with an objective-weighting benchmark (entropy, CRITIC) yielding concordant results (Spearman $\rho \ge 0.89$). The findings formalize a shift in the PV planning bottleneck from solar resource to grid capacity. Full article

The Ningxia Yellow River Urban Agglomeration, located in the ecologically fragile arid and semi-arid zone of the upper Yellow River, serves as a critical spatial carrier for maintaining the ecological security of the Yellow River Basin and supporting the regional economy and population agglomeration in Ningxia. Driven by rapid urbanization, intensified human–land conflicts have induced widespread ecological degradation and unbalanced water–soil resource allocation across the region. Based on land use data from 2010, 2015, 2020 and 2023, we applied the land use transition matrix, land use dynamic degree and standard deviational ellipse to characterize the spatiotemporal patterns of spatial expansion of the Ningxia Yellow River Urban Agglomeration over the past decade. The Patch-generating Land Use Simulation (PLUS) model was further employed to predict the land use demand and spatial distribution of the study area under diverse scenarios in 2035. The research results reveal three key findings. First, grassland, cropland and unused land constitute the dominant land use types across the study region, jointly occupying more than 90% of the total territorial area. Over the past decade, regional land use has undergone noticeable changes: grassland area has continuously declined, cropland and built-up land have sustained steady expansion, and water areas have experienced a mild reduction. Land use conversions mainly occur among grassland, cropland and built-up land. Second, driving factors vary substantially in their spatial contributions to the expansion of different land use types. The spatial growth of cropland and built-up land is comprehensively shaped by terrain conditions, economic development and transportation location superiority. In comparison, the distribution and dynamic changes in forestland, grassland and water areas are predominantly restricted by natural elements, including precipitation, temperature and soil characteristics. Third, multi-scenario simulation results verify that differentiated territorial spatial planning and regulatory policies profoundly affect the evolutionary trajectory of regional territorial patterns. The natural development scenario experienced the most intensive expansion of built-up land, with a newly increased area of 181.11 km². The ecological protection scenario can effectively curb the loss of ecological land and minimize the shrinkage of grassland resources. The cropland protection scenario is conducive to stabilizing cropland scale to the greatest extent and restraining the disorderly sprawl of urban land. The sustainable development scenario realizes coordinated and balanced changes in all land use types and delivers mutually beneficial progress between regional ecological conservation and socioeconomic development. Full article

Digital infrastructure expansion in Europe raises a spatial planning problem: early-stage screening needs to compare regional conditions while also checking whether rankings remain stable when decision priorities change. This study evaluates 24 European Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) regions for geo-distributed data center development. The 2022 decision matrix uses five Eurostat criteria: information and communications technology (ICT) specialists’ share in employment, average hourly labor cost, renewable electricity share, non-household electricity price and population density. Four criteria are national intensive proxies assigned to the selected NUTS-2 regions, while population density is directly observed at the NUTS-2 level. After a log₁₀ transformation of population density and min–max normalization, we compare the weighted sum model (WSM), TOPSIS and VIKOR across four weighting scenarios. We then apply a random-weighting audit based on Stochastic Multicriteria Acceptability Analysis (SMAA) principles, using 10,000 Dirichlet weight draws, followed by a local Dirichlet sensitivity analysis around the Balanced profile. Results show that the most stable high-performing profiles are not limited to the established FLAP-D market reference. Latvija (LV00), Stockholm (SE11), Helsinki-Uusimaa (FI1B), Eesti (EE00) and Área Metropolitana de Lisboa (PT17) form the main high-performing set across stochastic rank metrics, while several mature Western metropolitan regions remain more sensitive to cost and territorial-pressure criteria. The study provides a reproducible spatial decision support framework for macro-regional screening rather than micro-siting. Full article