The increasing frequency of extreme heat events poses serious challenges to public health and urban sustainability. Urban expansion is a key driver of extreme heat, yet the distinct mechanisms behind daytime and nighttime heat remain underexplored. This study proposes a multi-scale analytical framework to examine how 2D and 3D urban landscape changes influence extreme heat intensity (EHI), using both macro-scale (Spatial Difference-in-Differences) and finer-scale (Causal Forest) approaches. Two key findings emerge: 1) at the macro scale, urbanization significantly intensifies EHI, demonstrating its detrimental impact on thermal environments; 2) at the finer scale, heterogeneity analysis reveals that the landscape changes of building, impervious surface, cropland, and water bodies affect EHI in varied and localized ways. The results indicate the need for differentiated daytime and nighttime heat mitigation strategies, including enhancing blue-green infrastructure, optimizing urban landscape, and preserving cropland–water spatial balance to improve urban thermal resilience.
{"title":"Discovering the causal mechanism of day-night extreme heat driven by 2D and 3D urban landscape changes: a case study of Wuhan, China","authors":"Yingqiang Zhong , Shaochun Li , Xinmeng Zhou , Xun Liang , Qingfeng Guan","doi":"10.1016/j.scs.2026.107163","DOIUrl":"10.1016/j.scs.2026.107163","url":null,"abstract":"<div><div>The increasing frequency of extreme heat events poses serious challenges to public health and urban sustainability. Urban expansion is a key driver of extreme heat, yet the distinct mechanisms behind daytime and nighttime heat remain underexplored. This study proposes a multi-scale analytical framework to examine how 2D and 3D urban landscape changes influence extreme heat intensity (EHI), using both macro-scale (Spatial Difference-in-Differences) and finer-scale (Causal Forest) approaches. Two key findings emerge: 1) at the macro scale, urbanization significantly intensifies EHI, demonstrating its detrimental impact on thermal environments; 2) at the finer scale, heterogeneity analysis reveals that the landscape changes of building, impervious surface, cropland, and water bodies affect EHI in varied and localized ways. The results indicate the need for differentiated daytime and nighttime heat mitigation strategies, including enhancing blue-green infrastructure, optimizing urban landscape, and preserving cropland–water spatial balance to improve urban thermal resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107163"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-07DOI: 10.1016/j.scs.2026.107133
Zeqian Jin , Yicheng Xiong , Chengcheng Yu , Chen Li , Zexin Jin , Xin Ye
Flood disasters cause substantial casualties and economic losses, particularly in densely populated urban areas worldwide. Understanding public flood evacuation behavior is crucial for enhancing urban resilience and environmental sustainability. This study develops an agent-based modeling (ABM) to simulate evacuation behaviors of self-evacuees during predictable flood events. This model incorporates five submodules: population response, road network, shelter, flood propagation, and visualization. Based on protection motivation theory, we construct a structural equation model to examine the causal relationships among psychological attributes, which are then integrated into agents' characteristics within the ABM. Evacuees are classified as decision-makers and non-decision-makers, with the latter modeled using a Flocking behavior rule to capture their herd behavior. Four scenarios are designed to explore the impacts of different proportions of decision-makers and departure times (pre-disaster and post disaster) on fatality rates and evacuation efficiency. Conducted in Zhengzhou City, China, the model incorporates three evacuation modes (walking, bicycling, and vehicles) and three shelters (residential, commercial, and hotels). The results reveal that pre-disaster vehicle evacuation proves most effective, estimated to save 71,400 lives and extend the evacuation time by approximately 5 h. During post-disaster evacuation, walking evacuees exhibit the lowest fatality rates, indicating that walking should be the immediate emergency option when evacuation is forced after a disaster. A multi-intervention strategy combining pre-disaster evacuation and increasing the number of decision-makers achieves optimal performance, reducing the fatality rate by 20% compared to the baseline. These findings provide valuable insights for policymakers in improving urban flood disaster management and reducing human casualties in similar contexts.
{"title":"Enhancing urban flood resilience through agent-based modeling of evacuation behaviors","authors":"Zeqian Jin , Yicheng Xiong , Chengcheng Yu , Chen Li , Zexin Jin , Xin Ye","doi":"10.1016/j.scs.2026.107133","DOIUrl":"10.1016/j.scs.2026.107133","url":null,"abstract":"<div><div>Flood disasters cause substantial casualties and economic losses, particularly in densely populated urban areas worldwide. Understanding public flood evacuation behavior is crucial for enhancing urban resilience and environmental sustainability. This study develops an agent-based modeling (ABM) to simulate evacuation behaviors of self-evacuees during predictable flood events. This model incorporates five submodules: population response, road network, shelter, flood propagation, and visualization. Based on protection motivation theory, we construct a structural equation model to examine the causal relationships among psychological attributes, which are then integrated into agents' characteristics within the ABM. Evacuees are classified as decision-makers and non-decision-makers, with the latter modeled using a Flocking behavior rule to capture their herd behavior. Four scenarios are designed to explore the impacts of different proportions of decision-makers and departure times (pre-disaster and post disaster) on fatality rates and evacuation efficiency. Conducted in Zhengzhou City, China, the model incorporates three evacuation modes (walking, bicycling, and vehicles) and three shelters (residential, commercial, and hotels). The results reveal that pre-disaster vehicle evacuation proves most effective, estimated to save 71,400 lives and extend the evacuation time by approximately 5 h. During post-disaster evacuation, walking evacuees exhibit the lowest fatality rates, indicating that walking should be the immediate emergency option when evacuation is forced after a disaster. A multi-intervention strategy combining pre-disaster evacuation and increasing the number of decision-makers achieves optimal performance, reducing the fatality rate by 20% compared to the baseline. These findings provide valuable insights for policymakers in improving urban flood disaster management and reducing human casualties in similar contexts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107133"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-13DOI: 10.1016/j.scs.2026.107154
Shengnan Li , Pu Wang , Qi Liu , Ling Liu
While existing works have extensively documented vehicle emission patterns, the carbon footprint of short-distance vehicle trips (SDTs) remains critically understudied. Here, we employ large-scale License Plate Recognition data from Changsha, China to systematically analyze the emission patterns, influential factors and emission reduction potentials of SDTs. Our analysis indicates that SDTs account for 27.31 % of urban vehicle trips, and the associated CO2 emissions exhibit spatial agglomerations at specific urban areas. By leveraging an interpretable machine learning framework, we identify the land use, demographic and socioeconomic characteristics that exhibit a strong correlation with the volume of SDTs. This study emphasizes the potential to mitigate emissions induced by SDTs. It suggests that with the enhancement of public’s environmental awareness and the promotion of new energy vehicles, daily CO2 emissions caused by SDTs could reduce 172 tons, which are equivalent to 1.23 % of the total CO2 emissions of all small vehicles, providing valuable insights for developing sustainable urban transport.
{"title":"Reducing CO2 emissions from short-distance vehicle trips: A pathway to sustainable urban transport","authors":"Shengnan Li , Pu Wang , Qi Liu , Ling Liu","doi":"10.1016/j.scs.2026.107154","DOIUrl":"10.1016/j.scs.2026.107154","url":null,"abstract":"<div><div>While existing works have extensively documented vehicle emission patterns, the carbon footprint of short-distance vehicle trips (SDTs) remains critically understudied. Here, we employ large-scale License Plate Recognition data from Changsha, China to systematically analyze the emission patterns, influential factors and emission reduction potentials of SDTs. Our analysis indicates that SDTs account for 27.31 % of urban vehicle trips, and the associated CO<sub>2</sub> emissions exhibit spatial agglomerations at specific urban areas. By leveraging an interpretable machine learning framework, we identify the land use, demographic and socioeconomic characteristics that exhibit a strong correlation with the volume of SDTs. This study emphasizes the potential to mitigate emissions induced by SDTs. It suggests that with the enhancement of public’s environmental awareness and the promotion of new energy vehicles, daily CO<sub>2</sub> emissions caused by SDTs could reduce 172 tons, which are equivalent to 1.23 % of the total CO<sub>2</sub> emissions of all small vehicles, providing valuable insights for developing sustainable urban transport.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107154"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-14DOI: 10.1016/j.scs.2026.107159
Ke Lu , Jingfang Hu , Tingyu Shang , Yuan Xu
Despite explosive growth of integrated ride-hailing services (IRHS), the impact on long-term behavioral pattern has been little examined. This study intends to investigate travelers’ continuance behavioral intention towards IRHS, using a theoretical framework based on Expectation Confirmation Model (ECM). Moreover, four IRHS-specific feature variables are included, such as compatibility, hassle cost, convenience, and security. Further, this study introduces habit as moderating variable. Moreover, socio-demographic factors are considered as control variables, including gender, age, income, and educational level. With data collected from Nanjing, China, an empirical analysis is conducted using hybrid approach of Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The findings indicate that perceived usefulness, satisfaction, and expectation confirmation are key determinants. Noteworthily, perceived usefulness exhibits as more important than expectation confirmation. Further, it shows that all IRHS-specific features play crucial roles. Specifically, compatibility and hassle cost show stronger influence on expectation confirmation, while convenience and security affect more on perceived usefulness. Habit acts as a moderator within relationships between expectation confirmation and satisfaction, and satisfaction and continuance behavioral intention. Additionally, travelers’ continuance intention is negatively related to age and education level. These findings shed valuable insights for understanding the general pattern of travelers’ behavior, and add practical value for platforms and policymakers.
{"title":"To be integrated or not? Understanding continuance behavioral intention towards integrated ride-hailing services: Empirical evidence from Nanjing, China","authors":"Ke Lu , Jingfang Hu , Tingyu Shang , Yuan Xu","doi":"10.1016/j.scs.2026.107159","DOIUrl":"10.1016/j.scs.2026.107159","url":null,"abstract":"<div><div>Despite explosive growth of integrated ride-hailing services (IRHS), the impact on long-term behavioral pattern has been little examined. This study intends to investigate travelers’ continuance behavioral intention towards IRHS, using a theoretical framework based on Expectation Confirmation Model (ECM). Moreover, four IRHS-specific feature variables are included, such as compatibility, hassle cost, convenience, and security. Further, this study introduces habit as moderating variable. Moreover, socio-demographic factors are considered as control variables, including gender, age, income, and educational level. With data collected from Nanjing, China, an empirical analysis is conducted using hybrid approach of Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The findings indicate that perceived usefulness, satisfaction, and expectation confirmation are key determinants. Noteworthily, perceived usefulness exhibits as more important than expectation confirmation. Further, it shows that all IRHS-specific features play crucial roles. Specifically, compatibility and hassle cost show stronger influence on expectation confirmation, while convenience and security affect more on perceived usefulness. Habit acts as a moderator within relationships between expectation confirmation and satisfaction, and satisfaction and continuance behavioral intention. Additionally, travelers’ continuance intention is negatively related to age and education level. These findings shed valuable insights for understanding the general pattern of travelers’ behavior, and add practical value for platforms and policymakers.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107159"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-09DOI: 10.1016/j.scs.2026.107138
Siqi Lu , Heli Lu , Zhenchuang Wang , Huan Li , Zongran Han , Fang Liu , Changhong Miao , Xiaoye Zhang , Chuanrong Zhang
The rapid development of urbanization has led to the vertical expansion of urban buildings, significantly impacting the potential for solar photovoltaic (PV) utilization. This study simulates the vertical development of urban structures using a machine learning random forest model and evaluates how changes in urban three-dimensional morphology affect the comprehensive benefits of solar PV utilization. The findings indicate that when the average height of a city increases by 12.08%, PV returns can rise by 39.91%, while electricity generation costs can decrease by 11.1%. Further analysis reveals that Class II urban blocks (mid-rise high-density) achieve the highest PV returns, which are 8.27 times greater than those of Class III urban blocks (high-rise low-density). Our research demonstrates that the urban three-dimensional morphology is closely linked to the potential for solar PV utilization. Designing rational urban three-dimensional morphology to maximize solar resource utilization is crucial to achieve Sustainable Development Goal 11 (SDG11) targets for smart sustainable cities.
{"title":"Comprehensive benefits evaluation of the impact of vertical city on solar PV utilization for achieving smart sustainable cities","authors":"Siqi Lu , Heli Lu , Zhenchuang Wang , Huan Li , Zongran Han , Fang Liu , Changhong Miao , Xiaoye Zhang , Chuanrong Zhang","doi":"10.1016/j.scs.2026.107138","DOIUrl":"10.1016/j.scs.2026.107138","url":null,"abstract":"<div><div>The rapid development of urbanization has led to the vertical expansion of urban buildings, significantly impacting the potential for solar photovoltaic (PV) utilization. This study simulates the vertical development of urban structures using a machine learning random forest model and evaluates how changes in urban three-dimensional morphology affect the comprehensive benefits of solar PV utilization. The findings indicate that when the average height of a city increases by 12.08%, PV returns can rise by 39.91%, while electricity generation costs can decrease by 11.1%. Further analysis reveals that Class II urban blocks (mid-rise high-density) achieve the highest PV returns, which are 8.27 times greater than those of Class III urban blocks (high-rise low-density). Our research demonstrates that the urban three-dimensional morphology is closely linked to the potential for solar PV utilization. Designing rational urban three-dimensional morphology to maximize solar resource utilization is crucial to achieve Sustainable Development Goal 11 (SDG11) targets for smart sustainable cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107138"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-09DOI: 10.1016/j.scs.2026.107135
Nicolas Reiminger , Cédric Wemmert , Loïc Maurer , José Vazquez , Xavier Jurado
This study examines how far solar irradiations modify the wind velocity–concentration relationship commonly used in isothermal computational fluid dynamics (CFD) modeling of urban air quality. The main aim is to evaluate the validity under non-isothermal conditions of this widely used relationship and to provide new insights into the influence of solar-induced thermal effects on urban pollutant dispersion. While this relationship enables long-term concentration estimates through extrapolation from a limited set of simulations—thus offering strong operational advantages—its validity under non-isothermal conditions remains untested. Yet, recent regulatory changes and empirical evidence increasingly highlight the limitations of the isothermal assumption, especially in capturing short-term pollutant dynamics influenced by solar-driven thermal effects. To address this gap, a systematic CFD analysis of pollutant dispersion within an idealized 5 × 5 urban building array was conducted. This array was exposed to varying inlet wind velocities and solar irradiance levels, under fixed solar position and thermal boundary conditions. Results reveal that thermally induced flow structures can significantly modify pollutant dispersion patterns, particularly under low wind and high irradiance conditions. However, as mechanical forcing increases, flow fields and resulting pollutant concentration distributions tend to converge, reducing the impact of thermal perturbations. A comparative analysis of simulated pollutant fields and those recalculated using the isothermal wind velocity–concentration relationship shows that the reliability of this approach depends on the balance between thermal and mechanical forcing. Under favorable conditions—i.e., high wind, low solar irradiance—using this relationship remains robust. Conversely, under solar-dominated scenarios, it introduces significant errors.
{"title":"Breaking the isothermal assumption in CFD air quality modeling: Solar irradiance effects on the wind velocity-concentration relationship","authors":"Nicolas Reiminger , Cédric Wemmert , Loïc Maurer , José Vazquez , Xavier Jurado","doi":"10.1016/j.scs.2026.107135","DOIUrl":"10.1016/j.scs.2026.107135","url":null,"abstract":"<div><div>This study examines how far solar irradiations modify the wind velocity–concentration relationship commonly used in isothermal computational fluid dynamics (CFD) modeling of urban air quality. The main aim is to evaluate the validity under non-isothermal conditions of this widely used relationship and to provide new insights into the influence of solar-induced thermal effects on urban pollutant dispersion. While this relationship enables long-term concentration estimates through extrapolation from a limited set of simulations—thus offering strong operational advantages—its validity under non-isothermal conditions remains untested. Yet, recent regulatory changes and empirical evidence increasingly highlight the limitations of the isothermal assumption, especially in capturing short-term pollutant dynamics influenced by solar-driven thermal effects. To address this gap, a systematic CFD analysis of pollutant dispersion within an idealized 5 × 5 urban building array was conducted. This array was exposed to varying inlet wind velocities and solar irradiance levels, under fixed solar position and thermal boundary conditions. Results reveal that thermally induced flow structures can significantly modify pollutant dispersion patterns, particularly under low wind and high irradiance conditions. However, as mechanical forcing increases, flow fields and resulting pollutant concentration distributions tend to converge, reducing the impact of thermal perturbations. A comparative analysis of simulated pollutant fields and those recalculated using the isothermal wind velocity–concentration relationship shows that the reliability of this approach depends on the balance between thermal and mechanical forcing. Under favorable conditions—i.e., high wind, low solar irradiance—using this relationship remains robust. Conversely, under solar-dominated scenarios, it introduces significant errors.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107135"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-14DOI: 10.1016/j.scs.2026.107124
Bo Jiang , Hongtao Lei , Wenhua Li , Kai Xu , Yajie Liu , Tao Zhang
With rising energy demand and advances in energy conversion technologies, expansion planning for existing integrated energy systems is increasingly urgent, which is essential for improving efficiency and supply stability while reducing long-term costs. Additionally, the rising frequency of extreme disasters underscores the necessity of incorporating resilience alongside economic considerations in planning processes. To address these dual requirements of economic performance and resilience, this paper proposes a multi-objective two-stage stochastic programming model. In the first stage (planning stage), the model aims to minimize total costs while maximizing a standardized resilience index (RI) to determine the optimal expansion plan for the integrated energy system. In the second stage (operation stage), the model simulates both normal and fault modes to evaluate operational costs and RI values, feeding the results back to further improve the planning stage. Operational strategies aimed at either economic performance or resilience are developed for the two modes to effectively manage the model’s computational complexity. To efficiently solve the proposed multi-objective model, a diversity-enhanced evolutionary algorithm with a knowledge-guided offspring generation method (DeEA/K) is employed, yielding a uniformly distributed Pareto front. The experimental results demonstrate that the proposed method can achieve high-quality multi-objective expansion planning solutions, and the algorithm exhibits strong performance on mixed-integer optimization problems.
{"title":"Co-optimization of expansion planning and dual-mode operations for regional integrated energy systems considering resilience enhancement under multiple uncertainties","authors":"Bo Jiang , Hongtao Lei , Wenhua Li , Kai Xu , Yajie Liu , Tao Zhang","doi":"10.1016/j.scs.2026.107124","DOIUrl":"10.1016/j.scs.2026.107124","url":null,"abstract":"<div><div>With rising energy demand and advances in energy conversion technologies, expansion planning for existing integrated energy systems is increasingly urgent, which is essential for improving efficiency and supply stability while reducing long-term costs. Additionally, the rising frequency of extreme disasters underscores the necessity of incorporating resilience alongside economic considerations in planning processes. To address these dual requirements of economic performance and resilience, this paper proposes a multi-objective two-stage stochastic programming model. In the first stage (planning stage), the model aims to minimize total costs while maximizing a standardized resilience index (RI) to determine the optimal expansion plan for the integrated energy system. In the second stage (operation stage), the model simulates both normal and fault modes to evaluate operational costs and RI values, feeding the results back to further improve the planning stage. Operational strategies aimed at either economic performance or resilience are developed for the two modes to effectively manage the model’s computational complexity. To efficiently solve the proposed multi-objective model, a diversity-enhanced evolutionary algorithm with a knowledge-guided offspring generation method (DeEA/K) is employed, yielding a uniformly distributed Pareto front. The experimental results demonstrate that the proposed method can achieve high-quality multi-objective expansion planning solutions, and the algorithm exhibits strong performance on mixed-integer optimization problems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107124"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As cities increasingly endure hotter conditions, there is a critical need for reliable metrics that capture the cumulative and perceptual nature of pedestrian heat exposure. This study develops an integrated approach combining high-resolution urban Computational Fluid Dynmics (CFD) simulations with two complementary indices: a cumulative Heat Exposure Index and a Cooling Efficiency Index that quantify the magnitude, duration, and spatial variability of human heat stress. The analysis is applied to a tropical hot-humid neighborhood that includes a park, street trees, and lift-up buildings. Heat exposure is defined as the cumulative thermal load exceeding a specified UTCI (Universal Thermal Climate Index) threshold over time, weighted by the Dynamic Thermal Sensation (DTS) to better represent human perception. Cooling efficiency is calculated as the ratio of heat exposure between a test configuration and a reference scenario. This framework enables evaluation of both local and non-local effects on pedestrian comfort. Results show that unshaded areas can reach daily exposures of 700 °C.h, while shaded zones under trees achieve up to 40% reduction, though localized heating up to 25% may occur downwind of dense canopies. Among individual heat mitigation strategies, larger, densely positioned trees, as in parks, are shown to be the most effective, while trees should be avoided in ventilation corridors. The heat exposure index is also used to assess walkability by calculating cumulative thermal stress along pedestrian routes. The proposed approach establishes a reproducible methodology for quantifying cooling efficiency of heat mitigation strategies and translating thermal data into design-relevant indicators.
{"title":"Heat exposure and cooling efficiency of trees in a tropical hot-humid neighborhood with a park","authors":"Clément Nevers , Jan Carmeliet , Aytaç Kubilay , Dominique Derome","doi":"10.1016/j.scs.2026.107122","DOIUrl":"10.1016/j.scs.2026.107122","url":null,"abstract":"<div><div>As cities increasingly endure hotter conditions, there is a critical need for reliable metrics that capture the cumulative and perceptual nature of pedestrian heat exposure. This study develops an integrated approach combining high-resolution urban Computational Fluid Dynmics (CFD) simulations with two complementary indices: a cumulative Heat Exposure Index and a Cooling Efficiency Index that quantify the magnitude, duration, and spatial variability of human heat stress. The analysis is applied to a tropical hot-humid neighborhood that includes a park, street trees, and lift-up buildings. Heat exposure is defined as the cumulative thermal load exceeding a specified UTCI (Universal Thermal Climate Index) threshold over time, weighted by the Dynamic Thermal Sensation (DTS) to better represent human perception. Cooling efficiency is calculated as the ratio of heat exposure between a test configuration and a reference scenario. This framework enables evaluation of both local and non-local effects on pedestrian comfort. Results show that unshaded areas can reach daily exposures of 700 °C.h, while shaded zones under trees achieve up to 40% reduction, though localized heating up to 25% may occur downwind of dense canopies. Among individual heat mitigation strategies, larger, densely positioned trees, as in parks, are shown to be the most effective, while trees should be avoided in ventilation corridors. The heat exposure index is also used to assess walkability by calculating cumulative thermal stress along pedestrian routes. The proposed approach establishes a reproducible methodology for quantifying cooling efficiency of heat mitigation strategies and translating thermal data into design-relevant indicators.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107122"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.scs.2026.107125
Zhonglin Tang , Yaxin Rao , Min Fu
Urban agglomerations are increasingly facing the compounded challenges of escalating thermal stress, rising energy consumption, and intensifying carbon emissions under climate change and the green transition. This study develops an integrated Thermal-Energy-Carbon (TEC) framework to evaluate the Coupling Coordination Degree (CCD) of cities in the Yangtze River Delta (YRD) from 2000 to 2022, addressing the pressing issues of urban sustainability and green transformation. By combining spatial econometrics, threshold models, and GeoShapley decomposition, this study introduces a comprehensive approach to understanding the complex dynamics of urban systems in the context of climate change. The results reveal that: (1) although CCD has steadily improved, it remains at a moderate level with significant interprovincial disparities, highlighting uneven spatial progress in addressing environmental challenges. (2) Key drivers of coordination, including land urbanization (lnland), R&D investment (lnrd), and patch density (lnPD), significantly enhance CCD, whereas foreign direct investment (lnopen) suppresses coordination and financial development (lnfin) shows a negative local effect. (3) Spillover effects are asymmetric, with lnrd, lnfin, and lnPD generating positive spillovers, while industrial structure (lnind), lnopen, and green patents (lngreen) impose negative externalities. (4) A double-threshold effect of economic development (lngdp) illustrates the stage-dependent influence of R&D investment, following a “strengthening–weakening–restrengthening” dynamic. Additionally, XGBoost + GeoShapley-based contribution decomposition highlights the significant positive impact of lnland and lnrd on CCD in the non-spatial dimension, while unveiling the nonlinear and heterogeneous effects of these factors. This study offers novel methodological insights, integrating thermal, energy, and carbon, and provides guidance for low-carbon urban transformations in response to environmental challenges.
{"title":"Synergistic dynamics of the thermal-energy-carbon nexus in the Yangtze River Delta: Spatiotemporal measurement, mechanisms, and spatial econometric analysis","authors":"Zhonglin Tang , Yaxin Rao , Min Fu","doi":"10.1016/j.scs.2026.107125","DOIUrl":"10.1016/j.scs.2026.107125","url":null,"abstract":"<div><div>Urban agglomerations are increasingly facing the compounded challenges of escalating thermal stress, rising energy consumption, and intensifying carbon emissions under climate change and the green transition. This study develops an integrated Thermal-Energy-Carbon (TEC) framework to evaluate the Coupling Coordination Degree (CCD) of cities in the Yangtze River Delta (YRD) from 2000 to 2022, addressing the pressing issues of urban sustainability and green transformation. By combining spatial econometrics, threshold models, and GeoShapley decomposition, this study introduces a comprehensive approach to understanding the complex dynamics of urban systems in the context of climate change. The results reveal that: (1) although CCD has steadily improved, it remains at a moderate level with significant interprovincial disparities, highlighting uneven spatial progress in addressing environmental challenges. (2) Key drivers of coordination, including land urbanization (lnland), R&D investment (lnrd), and patch density (lnPD), significantly enhance CCD, whereas foreign direct investment (lnopen) suppresses coordination and financial development (lnfin) shows a negative local effect. (3) Spillover effects are asymmetric, with lnrd, lnfin, and lnPD generating positive spillovers, while industrial structure (lnind), lnopen, and green patents (lngreen) impose negative externalities. (4) A double-threshold effect of economic development (lngdp) illustrates the stage-dependent influence of R&D investment, following a “strengthening–weakening–restrengthening” dynamic. Additionally, XGBoost + GeoShapley-based contribution decomposition highlights the significant positive impact of lnland and lnrd on CCD in the non-spatial dimension, while unveiling the nonlinear and heterogeneous effects of these factors. This study offers novel methodological insights, integrating thermal, energy, and carbon, and provides guidance for low-carbon urban transformations in response to environmental challenges.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107125"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.scs.2026.107145
Negar Rahmatollahi , Zhi-Hua Wang , Yihang Wang , Xueli Yang
Exacerbated thermal environment is one of the most critical challenges in urban development, which causes degradation of air quality, environmental health, and ecosystem services. While there are many existing studies of attributing urban heat to various environmental factors, the underlying causal relationship explainable by these contributors remains largely underexplored. In this study, we conducted machine learning (ML) attribution of urban heat (measured by the land surface temperature LST) to two broad categories of contributors, viz. (a) local landscape characteristics (surface albedo, vegetation coverage, building density, and measure of anthropogenic activities) and (b) meteorological conditions (precipitation, humidity, wind, pressure, solar radiation, and soil moisture), using the Phoenix metropolitan, AZ as a testbed. Furthermore, we quantified the underlying causation between these environmental factors and LST using convergent cross mapping (CCM). It was found that solar radiation and vegetation coverage (NDVI) are the two most important determinants, both statistically and causally, of urban thermal environment. We also identified the impact of water content variables (precipitation, humidity, and soil moisture) that is not captured by ML attribution but emerges as causally significant. These findings help to deepen our understanding of the underlying mechanism that regulates the urban heat and its complex interplay with other environmental factors, which, in turn, will be informative to sustainable urban planning practices.
{"title":"Machine learning and causal attribution of urban heat in the Phoenix metropolitan","authors":"Negar Rahmatollahi , Zhi-Hua Wang , Yihang Wang , Xueli Yang","doi":"10.1016/j.scs.2026.107145","DOIUrl":"10.1016/j.scs.2026.107145","url":null,"abstract":"<div><div>Exacerbated thermal environment is one of the most critical challenges in urban development, which causes degradation of air quality, environmental health, and ecosystem services. While there are many existing studies of attributing urban heat to various environmental factors, the underlying causal relationship explainable by these contributors remains largely underexplored. In this study, we conducted machine learning (ML) attribution of urban heat (measured by the land surface temperature LST) to two broad categories of contributors, viz. (a) local landscape characteristics (surface albedo, vegetation coverage, building density, and measure of anthropogenic activities) and (b) meteorological conditions (precipitation, humidity, wind, pressure, solar radiation, and soil moisture), using the Phoenix metropolitan, AZ as a testbed. Furthermore, we quantified the underlying causation between these environmental factors and LST using convergent cross mapping (CCM). It was found that solar radiation and vegetation coverage (NDVI) are the two most important determinants, both statistically and causally, of urban thermal environment. We also identified the impact of water content variables (precipitation, humidity, and soil moisture) that is not captured by ML attribution but emerges as causally significant. These findings help to deepen our understanding of the underlying mechanism that regulates the urban heat and its complex interplay with other environmental factors, which, in turn, will be informative to sustainable urban planning practices.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107145"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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