City and Environment Interactions最新文献

{"title":"How urban surface and morphological features shape air and surface temperatures disparities: a spatiotemporal investigation in Hong Kong","authors":"Songyang Li,&nbsp;Man Sing Wong,&nbsp;Majid Nazeer,&nbsp;Jinxin Yang,&nbsp;Pak-Wai Chan","doi":"10.1016/j.cacint.2026.100453","DOIUrl":"10.1016/j.cacint.2026.100453","url":null,"abstract":"<div><div>Near-surface air temperature (Ta) is a vital indicator for urban heat island (UHI) research, but continuous high-resolution Ta data remain scarce. Land surface temperature (LST) is frequently used as a Ta alternative, though spatiotemporal distinctions are rarely evaluated. We investigated spatiotemporal heterogeneity, nonlinear correlations, and influential factors of time-aligned Ta and LST in Hong Kong during summers and winters (2018–2023). Integrating meteorological station data and satellite imagery, a machine learning (ML) framework and SHapley Additive exPlanations (SHAP) analysis evaluated the contributions of nineteen meteorological, urban morphological, topographic, landscape, and anthropogenic factors. Both temperature metrics show heterogeneity under day/night and seasonal contexts. Aligned Ta-LST pairs were positively correlated, but fitting outcomes varied across local climate zones, limiting LST-to-Ta linear translation accuracy, especially in summer. ML models showed acceptable accuracy for Ta (RMSE: 0.53–1.14 °C) and LST (RMSE: 1.08–2.70 °C) estimation. Temporal–spatial SHAP decomposition found that coarse-resolution reanalysis variables had nearly all predictive power in temporal signals rather than in spatial gradients. Target-demeaned attribution isolated spatial drivers: building height was a leading contributor to nighttime Ta; impervious surface fraction influenced daytime LST. SHAP analysis reflected several findings: daytime shading suppresses surface heating, while nighttime radiative trapping within deep canyons sustains elevated Ta. Leave-one-station-out validation confirmed spatial generalizability of the models. These results suggest that UHI mitigation is most effective when strategies target each metric separately. Reducing LST alone may do little for ambient heat stress if the 3D morphological and aerodynamic processes that control urban air temperature are left unaddressed.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100453"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban airborne allergenic pollen shows highly local patterns correlated with public green spaces","authors":"Nemi Dorst,&nbsp;Maarten Hogeweij,&nbsp;Aukje Clercx,&nbsp;Dominique de Boer,&nbsp;Ilse Thijssen,&nbsp;Leon van Leeuwen,&nbsp;Martijn van de Velde,&nbsp;Arjen G.C.L. Speksnijder,&nbsp;Gijs M. Gerrits,&nbsp;Laurens Sparrius,&nbsp;Letty A. de Weger,&nbsp;Kevin Raaphorst,&nbsp;Marcel Polling,&nbsp;Barbara Gravendeel","doi":"10.1016/j.cacint.2026.100434","DOIUrl":"10.1016/j.cacint.2026.100434","url":null,"abstract":"<div><div>Ongoing climate change is increasing global temperatures, with cities experiencing amplified warming due to the urban heat island effect. Urban trees, forbs, and grasses are widely planted to mitigate heating. However, many plant species produce allergenic pollen, causing hay fever symptoms that can discourage people from using green spaces. This avoidance may increase heat exposure and reduce the physical and mental benefits of urban greenery. Despite this, optimal urban green design that minimizes allergy risks remains unclear, partly due to limited knowledge of local airborne pollen dispersion in urban heat islands. To address this gap, we investigated whether (1) local vegetation influences urban pollen concentrations and composition, (2) urban trees, forbs, or grasses contribute most to allergenic pollen loads, and (3) public or private urban spaces are the main source of airborne allergenic pollen. We conducted spatial and temporal street-level pollen monitoring at 10 sites across two neighborhoods in Leiden, the Netherlands, over 12 months. Samples were analyzed using DNA metabarcoding and microscopy. We identified 366 plant species across 105 families. Results show that public green spaces are the primary source of allergenic pollen in cities. Grasses dominated pollen loads in summer, whereas trees contributed most in winter, spring, and autumn. Notably, allergenic tree pollen was detected well beyond peak flowering periods. These findings highlight the importance for critically selecting urban tree species and the use of more non-allergenic forbs and less grasses when designing healthy urban green infrastructure.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100434"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation and uncertainty analysis of anthropogenic heat in Beijing under clear-sky late-morning conditions based on surface energy balance method","authors":"Ruotong Wang,&nbsp;Jianjun He,&nbsp;Yarong Li,&nbsp;Sunling Gong","doi":"10.1016/j.cacint.2026.100455","DOIUrl":"10.1016/j.cacint.2026.100455","url":null,"abstract":"<div><div>Numerous approaches have been developed to estimate anthropogenic heat. Selecting a method that requires relatively little data and has broad applicability, while systematically evaluating its associated uncertainties, is crucial for improving anthropogenic heat estimates and the accuracy of numerical simulations of urban meteorological fields. Based on the Remote Sensing Surface Energy Balance (RS-SEB) model with MODIS (MODerate-resolution Imaging Spectroradiometer) remote sensing products and meteorological station observation data, combined with refined Local Climate Zone (LCZ) classification map, this study quantitatively estimates anthropogenic heat in Beijing, and analyzes its uncertainty. Clear-sky days in October 2018 were selected as the study period to minimize interference to anthropogenic heat from seasonal energy consumption associated with winter heating or summer air conditioning. The results show that building density and form have significant impacts on anthropogenic heat. The average anthropogenic heat in Beijing around the MODIS overpass time near 11:00 AM in October was 139 W·m⁻², with values in the 10 building types (LCZ 1–10) ranging from 60.69 to 192.32 W·m⁻². Areas characterized by compact buildings exhibit lower anthropogenic heat than those with open building types, while lightweight low-rise, large lowrise and sparsely built areas generally display higher anthropogenic heat fluxes. This pattern may be attributed to the greater heat storage capacity of high- and mid-rise buildings and the delayed release of stored heat. Uncertainty analysis of the input variables revealed that land surface temperature and wind speed are the key factors affecting anthropogenic heat estimation. Their coefficients of variation of different LCZ anthropogenic heat range from 0.094 to 0.606 and from 0.263 to 0.652, respectively, with corresponding absolute errors exceeding 50 W·m⁻². In contrast, uncertainties in surface albedo, downward shortwave radiation, air temperature, and water vapor pressure had relatively minor effects on anthropogenic heat estimation. These findings provide useful insights into the estimation of anthropogenic heat emissions at broader urban scales.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100455"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Crafting change: developing behaviour change communication intervention package for improved household waste segregation in urban India” [City Environ. Interact. 31 (2026) 100428]","authors":"Rachna Soni,&nbsp;Tanwi Trushna,&nbsp;Madhanraj Kalyanasundaram,&nbsp;Surya Singh,&nbsp;Kavya Krishnan,&nbsp;Manju Yadav,&nbsp;Deepmala Vishwakarma,&nbsp;Vivek Parashar,&nbsp;Yogesh Sabde,&nbsp;Ashish Pathak,&nbsp;Manju Purohit,&nbsp;Cecilia Stålsby Lundborg,&nbsp;Kamran Rousta,&nbsp;Salla Atkins,&nbsp;Krushna Chandra Sahoo,&nbsp;Vishal Diwan","doi":"10.1016/j.cacint.2026.100432","DOIUrl":"10.1016/j.cacint.2026.100432","url":null,"abstract":"","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100432"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure–lag-dependent bias in estimating PM2.5 health risks: Evidence from linear and nonlinear distributed lag models","authors":"Abhishek Dutta,&nbsp;Pasicha Chaikaew","doi":"10.1016/j.cacint.2026.100462","DOIUrl":"10.1016/j.cacint.2026.100462","url":null,"abstract":"<div><div>While associations between PM_2.5 exposure and respiratory morbidity are well documented, the temporal dynamics and non-linear nature of these effects across pollution levels remain poorly understood. This knowledge gap limits the development of timely and evidence-based mitigation strategies. This study applied the distributed lag model <strong>(</strong>DLM) and distributed lag non-linear model (DLNM) within a generalised additive model (GAM) framework to assess the relationship between short-term PM_2.5 exposure and respiratory morbidity at four pollution levels in Nakhon Ratchasima, Thailand, during 2019–2021. PM_2.5 exposure was positively associated with respiratory morbidity, with risks increasing in magnitude and persistence at higher pollution levels. At low PM_2.5 concentrations, DLM and DLNM produced comparable instantaneous and peak risk estimates. However, substantial divergence emerged at higher pollution levels. At 75th and 95th percentiles of PM_2.5 exposure, DLNM estimated higher risks (RR = 1.23; 95% CI: 1.17,1.28) and (RR = 1.21; 95% CI: 1.17,1.25), respectively than DLM (RR = 1.10; 95% CI: 1.07,1.14) and (RR = 1.15; 95% CI: 1.11,1.19), respectively. Likewise, the maximum risks estimated by DLNM were greater (RR = 1.39; 95% CI: 1.33,1.46) at lag 2; (RR = 1.34; 95% CI: 1.28,1.40) at lag 3 than DLM (RR = 1.19; 95% CI: 1.16,1.22) at lag 2 and (RR = 1.20; 95% CI: 1.15,1.25) at lag1, respectively. At the 95th percentile exposure level, DLNM projected a substantially higher social burden (86.76 million THB) than DLM (47.23 million THB) arose from the differences in estimated risks. These findings highlight the importance of appropriate model specification for accurate epidemiological inference and policy development.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100462"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ontology-driven digital-twin SDSS integrating machine-learning travel-time forecasting for explainable and sustainability-aware urban routing: a corridor-scale study in Tehran","authors":"Zahra Rezaei,&nbsp;Alireza Sharifi","doi":"10.1016/j.cacint.2026.100441","DOIUrl":"10.1016/j.cacint.2026.100441","url":null,"abstract":"<div><div>This study proposes a Digital-Twin-enabled Spatial Decision Support System (DT-SDSS) for explainable and sustainability-aware urban traffic routing under dynamic and uncertain conditions. The framework integrates spatiotemporal data harmonization, machine-learning-based travel-time prediction, ontology-driven semantic reasoning, and controlled candidate-route evaluation within a closed analytical workflow. It is evaluated through a corridor-scale case study in western Tehran, characterized by heterogeneous traffic conditions and recurrent congestion. Historical observations collected from 3 April to 5 July 2024 produced 8439 causal ten-minute-ahead forecasting samples, which were partitioned chronologically into 6596 model-development samples, a 97-sample temporal gap, and an untouched 1746-sample final holdout. Seven forecasting approaches were evaluated under common temporal boundaries: persistence, historical time-slot mean, Random Forest Regression, a deterministic random-feature approximation to radial-basis-function ε-Support Vector Regression, a deterministic second-order XGBoost-style gradient-boosted regression-tree approximation, Long Short-Term Memory, and Bidirectional Long Short-Term Memory. Random Forest Regression achieved the lowest final-holdout error, with an MAE of 0.606 min, an RMSE of 1.493 min, a MAPE of 2.410%, and an <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.959. The approximate XGBoost-style model achieved an MAE of 0.957 min, while the LSTM and BiLSTM models achieved MAEs of 1.546 and 1.565 min, respectively. The SDSS interprets predicted traffic states together with weather, incidents, temporal context, road characteristics, and policy constraints to produce traceable route evaluations. Commercial navigation services were excluded from quantitative benchmarking; instead, the routing component was evaluated against Dijkstra using the same scenario-adjusted candidate-union graph. Fuel and CO₂ values are reported as controlled scenario estimates derived from an explicitly documented speed-dependent fuel function rather than as locally calibrated fleet measurements. The results demonstrate the feasibility of integrating reproducible predictive analytics, semantic reasoning, and sustainability-aware route evaluation within a local corridor-scale research prototype.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100441"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable GeoAI for smart urbanism in data-scarce environments: Spatial assessment of cardiometabolic resilience in heat-vulnerable cities","authors":"Manuel Medrano-Blanco,&nbsp;Andrea González Martínez,&nbsp;Juan Sánchez Campos,&nbsp;Olatz Grijalba,&nbsp;Ester Higueras García,&nbsp;Emilia Román López,&nbsp;Matthias Rosenthal","doi":"10.1016/j.cacint.2026.100451","DOIUrl":"10.1016/j.cacint.2026.100451","url":null,"abstract":"<div><div>Advancing anticipatory smart urbanism in data-scarce environments requires interpretable methodologies capable of translating limited urban data into actionable insights. This study proposes a GeoAI-driven framework for the spatial modelling of cardiometabolic resilience in heat-vulnerable cities, integrating unsupervised machine learning with knowledge-based reasoning to decode the urban exposome. Applied to San José, Costa Rica, the framework reveals pronounced spatial inequalities, identifying a North-South polarisation driven by a “geography of double exclusion,” where tropical thermal stress (characterised by land surface temperatures exceeding 36 °C, high humidity, and socio-environmental vulnerability) intensifies cardiometabolic risk. Moving beyond conventional descriptive analytics, the approach establishes an evidence-based, anticipatory decision-support system that operates effectively under data scarcity. By constructing a foundational layer of mathematical infrastructure, the methodology combines Fuzzy-AHP logic, K-means clustering, and Principal Component Analysis (PCA) to integrate heterogeneous indicators, reduce dimensionality, and identify critical urban hotspots. This hybrid GeoAI architecture enables the anticipation of risk patterns and supports evidence-based urban interventions. The results demonstrate how diagnostic spatial modelling can inform optimal sensor placement strategies (AI Readiness), guide targeted mitigation policies, and enhance resilience planning in resource-constrained settings. The proposed framework positions GeoAI as a strategic enabler of equitable and sustainable smart city transitions, particularly in the Global South, where data limitations and climate vulnerability intersect most acutely.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100451"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148776483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unpacking spatial-digital coupling: nonlinear morphological association ranges and spatially heterogeneous built-environment relationships in Beijing","authors":"Zuozheng Shi,&nbsp;Senyang Yao,&nbsp;Jin Rui,&nbsp;Changran Li,&nbsp;Wen Ouyang","doi":"10.1016/j.cacint.2026.100438","DOIUrl":"10.1016/j.cacint.2026.100438","url":null,"abstract":"<div><div>Digital infrastructure usage in historic urban cores is shaped by a fine-grained interaction among commercial service quality, spatial permeability, heritage agglomeration, and environmental comfort. Using anonymized operator-side cellular records from Beijing's Capital Core Functional Area, this study constructs Digital Infrastructure Usage Intensity (DIUI) as a grid-level indicator that combines passive ambient population presence with active foreground cellular traffic. An interpretable modelling framework integrating XGBoost, SHAP, and geographically weighted regression is then used to identify dominant correlates, nonlinear association ranges, and spatially varying local relationships. The results indicate that commercial service quality and block openness are the most influential predictors, but their associations with DIUI are conditional rather than monotonic: commercial upgrading is associated with higher DIUI only when the surrounding morphology provides sufficient permeability and visibility, whereas overly enclosed or over-saturated settings weaken this relationship. Heritage concentration shows an inverted-U association with DIUI, suggesting that moderate agglomeration may support digital activity more effectively than excessive clustering. Spatially, transitional historic neighborhoods show stronger associations with commercial improvement than already saturated core areas, while street-level greenery appears to amplify digital activity in permeable settings. Overall, the results support a context-specific interpretation of spatial-digital coupling and caution against translating model-derived ranges into universal planning prescriptions without local validation.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100438"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial patterns of ecosystem carbon sequestration under different land use scenarios in Shandong Province and its implications for territorial spatial optimization","authors":"Feili Wei,&nbsp;Yongqiang Ren,&nbsp;Tianqi Yang,&nbsp;Hong Jiang,&nbsp;Dahai Liu,&nbsp;Guoliang Yun","doi":"10.1016/j.cacint.2026.100439","DOIUrl":"10.1016/j.cacint.2026.100439","url":null,"abstract":"<div><div>In the context of global climate warming, optimizing land spatial patterns to enhance ecosystem carbon sequestration offers a cost-effective and immediate strategy for carbon absorption. This approach not only mitigates the pressure of emission reductions but also secures a critical window for industrial decarbonization. While many studies optimize land spatial patterns by simulating land use scenarios and forecasting land demand, few integrate ecological red lines, control zones, and restricted areas into their models. Additionally, existing studies often fail to balance food security, ecological protection, and economic development. To address these gaps, it is essential to explore a broader range of potential land use scenarios to define the upper and lower limits of land use changes, facilitating multi-objective optimization analysis. This study takes Shandong Province as a case study, employing the PLUS and InVEST models to simulate five scenarios: Current Continuation (CC), Food Security (FS), Ecological Protection (EP), Urban Expansion (UE), and Comprehensive Optimization (CO). The objective is to analyze spatial patterns and spatiotemporal distribution characteristics of carbon sequestration under different scenarios by 2030. Key findings are as follows: (1) Ecologically sensitive and vulnerable areas in Shandong are primarily concentrated in the central, northeastern, and eastern mountainous and hilly regions. (2) Compared to 2020, land use structures exhibit significant changes across all five scenarios in 2030, with a notable expansion of construction land, particularly in the UE scenario. Carbon stocks increase under all scenarios, but the CO scenario maximizes carbon sequestration potential, achieving the optimal balance between food security, ecological protection, and economic development. (3) Future land use planning in Shandong should be guided by the spatial patterns derived from the CO scenario, with a strong emphasis on preserving ecologically sensitive and vulnerable regions in the central and northeastern mountainous areas. These findings provide theoretical support for land use management in Shandong Province and offer valuable insights for achieving national carbon peak and carbon neutrality goals.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100439"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of university-driven urban transformation: a Benefit Transfer approach in the city of Turin","authors":"Sebastiano Barbieri,&nbsp;Marta Bottero,&nbsp;Caterina Caprioli","doi":"10.1016/j.cacint.2026.100431","DOIUrl":"10.1016/j.cacint.2026.100431","url":null,"abstract":"<div><div>Brownfield regeneration is a debated topic in post-industrial cities, where major structural changes and disused areas can be used to improve urban quality, reconnect fragmented districts, and host new public functions. In this context, universities act as driving forces behind urban transformation, increasing the number of students in cities and the demand for cultural services, study spaces, student housing, and new urban hubs. However, there is limited quantitative data supporting the impact of university-led regeneration projects on the market. To fill this gap, the study proposes the application of the Benefit Transfer, grounded on the Hedonic Price method and Geographic Weighted Regression, to obtain the necessary information to transfer the real estate benefits associated with university-led urban regeneration projects. The methodology is tested using a real regeneration project in Turin (Italy), analyzing the impact on the surrounding residential properties. For this purpose, four university-led transformations that have already taken place in Turin were analyzed using real estate listings. The result shows positive effects in three out of four cases, generating a positive effect on price with respect to their proximity to the transformation, even though the outcome of the Geographic Weighted Regression suggests that this effect is spatially heterogeneous. This study contributes to the literature by extending the Benefit Transfer approach to university-led urban regeneration projects. No study has ever used the Benefit Transfer Method to predict the impact of a future university-led transformation. Additionally, the research provides valuable information to build strategies for sustainable planning of post-industrial cities.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"31 ","pages":"Article 100431"},"PeriodicalIF":5.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148636072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}