Sustainable Cities and Society最新文献_第10页

Scenario-based capacity optimization of multi-type energy storage in integrated energy systems using a flexible interaction model 基于柔性交互模型的综合能源系统多类型储能容量场景优化

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2025-12-30 DOI: 10.1016/j.scs.2025.107076

Xin-Yu Ren , Zhi-Hua Wang , Liu Zhang

As Integrated Energy Systems (IES) evolve toward multi-energy synergy and high operational efficiency, determining the optimal allocation of multiple energy storage technologies has become a key challenge in system planning. This study investigates the capacity optimization of cooling, heating, and electrical energy storage systems across multiple operational scenarios. A unified modeling framework and scenario-based optimization approach are developed to address this problem. Eight representative configurations are analyzed, including systems without storage, single-type storage, dual-storage combinations, and fully integrated storage systems. The proposed flexible interaction optimization model simultaneously considers supply–demand coupling, storage constraints, and multi-objective criteria related to economy, energy efficiency, and environmental performance. Simulation results reveal that coordinated thermal–cold and thermal–cold–electric storage configurations significantly enhance overall system performance. Under grid-connected conditions, wind turbines (WT), solar thermal (ST), and gas boilers contribute minimally, while under off-grid conditions, WT, ST, and electrical energy storage play important roles in maintaining system stability. Compared with a fixed-order dispatch model, the proposed approach improves economic, energy efficiency, and environmental performance by at least 27.83 %, 1.09 %, and 1.09 % in off-grid electro-thermal scenarios, and by 15.95 %, 8.14 %, and 4.57 % under grid-connected conditions. These findings confirm the effectiveness of the proposed model in achieving flexible coordination and enhancing the overall performance of IES.

随着综合能源系统（IES）向多能协同和高运行效率的方向发展，确定多种储能技术的最佳配置已成为系统规划中的关键挑战。本研究探讨了制冷、供暖和电能存储系统在多种操作场景下的容量优化。为了解决这一问题，开发了统一的建模框架和基于场景的优化方法。分析了8种典型配置，包括无存储系统、单存储系统、双存储组合系统和全集成存储系统。提出的柔性交互优化模型同时考虑了供需耦合、存储约束以及与经济、能源效率和环境绩效相关的多目标标准。仿真结果表明，协调的热冷和冷电存储配置显著提高了系统的整体性能。在并网条件下，风力涡轮机（WT）、太阳能热（ST）和燃气锅炉的贡献最小，而在离网条件下，WT、ST和电能存储在维持系统稳定方面发挥重要作用。与固定订单调度模型相比，该方法在离网电热工况下的经济性、能效和环境绩效分别提高27.83%、1.09%和1.09%，在并网工况下分别提高15.95%、8.14%和4.57%。这些发现证实了所提出的模型在实现灵活协调和提高IES整体绩效方面的有效性。

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引用次数: 0

Integrating ensemble machine learning and SAR-based geospatial modelling for inclusive and equitable urban flood resilience 集成集成机器学习和基于sar的地理空间建模：包容和公平的城市洪水抵御能力

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.scs.2026.107158

Javed Mallick , Hoang Thi Hang , Alok Das , Sayanti Poddar , Chander Kumar Singh

Urban flooding has become a major challenge in fast-growing cities worldwide, disrupting mobility, damaging infrastructure, and increasing social vulnerability. In Bengaluru, where rapid urban expansion and inadequate drainage exacerbate monsoon floods, this study develops an integrated and data-driven framework to map flood susceptibility and exposure between 2020 and 2025. The main objective is to generate a scientifically validated, high-resolution, and explainable flood-risk assessment model combining radar-based detection, machine learning, and social-infrastructure exposure analytics. Multi-temporal Sentinel-1 dual-polarisation (VV and VH) SAR data were used to detect flood events through backscatter change analysis, ensuring accurate mapping even under cloudy conditions. An ensemble of five algorithms-Random Forest, Histogram-based Gradient Boosting, XGBoost, Quadratic Discriminant Analysis, and Gaussian Naïve Bayes-was trained and optimized to capture complex spatial relationships between floods and controlling parameters. Explainable AI (SHAP, permutation, ablation) to identify key drivers for transparent planning. Probability calibration and reliability curves to check and correct forecast bias infrastructure and demographic overlays to quantify who and what is exposed. Results show that the ensemble delivered AUC = 0.985 with accuracy 78–83%, precision 0.78–0.83, recall 0.79–0.83, and F1 ≈ 0.80 across six independent flood events; calibration improved reliability with well-aligned predicted vs observed probabilities. Spatially, 32.63 km² were mapped as Very High and 61.73 km² as High susceptibility, with stable hotspots in Electronic City, Bommanahalli, Mahadevapura, and Bellandur-Varthur. Road exposure was dominated by local streets (62,402 segments) and tertiary roads (3505 segments), indicating neighbourhood-scale disruption; female and schedule caste (SC) populations showed disproportional exposure in central/eastern wards, while schedule tribe (ST) exposure was lower but non-negligible on the periphery. This analysis enable ward-wise prioritisation of drain upgrades, permeable retrofits, safe-route planning, and targeted protection of at-risk groups, aligning with the Sendai Framework and sustainable development goals (SDG)-11/13.

城市洪水已成为全球快速发展城市面临的主要挑战，它扰乱了交通，破坏了基础设施，并增加了社会脆弱性。在班加罗尔，快速的城市扩张和排水不足加剧了季风洪水，本研究开发了一个综合的数据驱动框架，以绘制2020年至2025年期间的洪水易感性和暴露程度。主要目标是将基于雷达的探测、机器学习和社会基础设施暴露分析相结合，生成一个经过科学验证的、高分辨率的、可解释的洪水风险评估模型。多时相Sentinel-1双极化（VV和VH） SAR数据用于通过后向散射变化分析来检测洪水事件，确保即使在多云条件下也能准确测绘。随机森林、基于直方图的梯度增强、XGBoost、二次判别分析和高斯Naïve贝叶斯五种算法的集合进行了训练和优化，以捕获洪水与控制参数之间的复杂空间关系。可解释的人工智能（SHAP，排列，消融），以确定透明规划的关键驱动因素。概率校准和可靠性曲线，以检查和纠正预测偏差基础设施和人口覆盖，以量化暴露的人员和内容。结果表明：6个独立洪水事件的AUC = 0.985，准确度78 ~ 83%，精密度0.78 ~ 0.83，召回率0.79 ~ 0.83,F1≈0.80；校准提高了可靠性，预测概率与观测概率对齐良好。在空间上，32.63 km²为非常高敏感区，61.73 km²为高敏感区，热点稳定分布在Electronic City、Bommanahalli、Mahadevapura和Bellandur-Varthur。道路暴露主要是本地街道（62,402段）和三级道路（3505段），表明社区规模的破坏；女性和时间表种姓（SC）人群在中部/东部地区的暴露程度不成比例，而时间表部落（ST）人群在外围地区的暴露程度较低，但不可忽略。该分析能够根据仙台框架和可持续发展目标(SDG)-11/13，对下水道升级、透水改造、安全路线规划和有针对性地保护风险群体进行定向优先排序。

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引用次数: 0

Forecasting heat-related impacts with multivariate multi-step time series models using advanced deep learning 利用先进的深度学习方法预测与热相关的多步时间序列模型

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.scs.2026.107142

Jung-Ching Kan , Marlon Vieira Passos , Georgia Destouni , Karina Barquet , Carla S.S. Ferreira , Zahra Kalantari

Record-breaking temperatures and frequent heatwaves have been experienced worldwide in recent years. Heatwaves pose an escalating threat to public health and heat-related impact forecasting is critical to implementing suitable mitigation strategies. Deep learning (DL) models, notably Long Short-Term Memory (LSTM), have been widely applied for heat-related impact forecasting. However, the emergence of state-of-the-art forecasting DL architectures such as Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) provides a novel solution for long-term heat-related impact forecasting. This study develops, evaluates, and compares multiple time series forecasting models—including advanced DL architectures (N-BEATS, N-HiTS, LSTM), a classical statistical model (ARIMA), and a Naïve seasonal baseline—to predict heat-related morbidity across 21 Swedish counties using data from 2008 to 2023. Both local (individually trained) and global (cross-learning across counties) modeling strategies were explored, incorporating exogenous variables (Heatwave Index and number of people with respiratory disease), and comparing recursive and Multi-Input-Multi-Output (MIMO) forecasting output strategies. Results indicate that the local N-BEATS model achieves superior predictive performance, particularly when both exogenous variables are included. MIMO generally yields a better performance by mitigating error propagation over extended forecasting horizons. Moreover, individually trained N-BEATS models outperform cross-learning global N-BEATS, underscoring the importance of localized adaptation plans. These findings highlight the potential utility of multivariate N-BEATS for more accurate heatwave impact forecasting. This study can complement and support early warning frameworks by integrating the developed impact forecast model with existing hazard models, thereby enabling more proactive public health interventions and improving community resilience to heatwaves.

近年来，全球经历了破纪录的气温和频繁的热浪。热浪对公众健康构成日益严重的威胁，与热浪有关的影响预测对于实施适当的缓解战略至关重要。深度学习（DL）模型，特别是长短期记忆（LSTM）模型，已广泛应用于热影响预测。然而，最先进的预测深度学习架构的出现，如可解释时间序列预测的神经基础扩展分析（N-BEATS），为长期热相关影响预测提供了一种新的解决方案。本研究开发、评估和比较了多个时间序列预测模型，包括先进的深度学习架构（N-BEATS、N-HiTS、LSTM）、经典统计模型（ARIMA）和Naïve季节性基线，利用2008年至2023年的数据预测瑞典21个县的热相关发病率。结合外生变量（热浪指数和呼吸系统疾病患者人数），研究了本地（单独训练）和全局（跨县交叉学习）建模策略，并比较了递归和多输入多输出（MIMO）预测输出策略。结果表明，局部N-BEATS模型具有优越的预测性能，特别是当两个外生变量都包括在内时。MIMO通常通过在扩展的预测范围内减少误差传播而产生更好的性能。此外，单独训练的N-BEATS模型优于交叉学习的全局N-BEATS，强调了本地化适应计划的重要性。这些发现突出了多元N-BEATS在更准确的热浪影响预测方面的潜在效用。通过将开发的影响预测模型与现有的危害模型相结合，本研究可以补充和支持预警框架，从而实现更积极主动的公共卫生干预，提高社区对热浪的抵御能力。

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引用次数: 0

Community-based solar-powered and open-air cooling shelter for urban heat mitigation 以社区为基础的太阳能和露天降温避难所，用于城市降温

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-11 DOI: 10.1016/j.scs.2026.107153

Ji Yoon Bae , Eric Teitelbaum , Sara F. Jacoby , Dorit Aviv

The increasing frequency of heatwaves and the Urban Heat Island (UHI) effect pose growing public health risks, particularly for urban communities with limited access to cooling infrastructure. Conventional strategies—such as air-conditioned cooling centers—present challenges related to energy consumption, resilience, and equitable access. In response, we developed and tested a novel, open-air cooling shelter that can be installed as public infrastructure such as bus stops, designed to mitigate heat stress through solar-powered radiant and conductive cooling systems. Constructed in partnership with a community organization in a heat-vulnerable Philadelphia neighborhood, the shelter integrates a shading canopy, radiant cooling panels, and a conductive cooling bench, all operated by a fully off-grid renewable energy source. To examine its impact, we conducted thermal comfort surveys with community members as well as physiological and environmental measurements. Results showed that the shelter reduced occupants’ thermal stress by 35–45% compared to unshaded outdoor conditions using the Index of Thermal Stress (ITS), and subjective survey responses corroborated this improvement. Concurrently, energy monitoring validated the system’s self-sufficiency; solar energy generation surpassed the cooling demand by 40%. The combination of scalable technology and integrated local engagement, as modeled in this study, offers a replicable strategy for sustainable and inclusive urban heat mitigation.

热浪日益频繁和城市热岛效应造成越来越大的公共卫生风险，特别是对于使用制冷基础设施有限的城市社区。传统的战略，如空调冷却中心，在能源消耗、弹性和公平获取方面存在挑战。作为回应，我们开发并测试了一种新型的露天冷却罩，它可以安装在公共基础设施中，如公交车站，通过太阳能辐射和传导冷却系统来减轻热应力。该项目是与费城一个易热社区的社区组织合作建造的，它集成了遮阳篷、辐射冷却板和导电冷却工作台，所有这些都是由一个完全离网的可再生能源运行的。为了研究其影响，我们对社区成员进行了热舒适调查，并进行了生理和环境测量。使用热应力指数（ITS）的结果表明，与无遮蔽的室外条件相比，遮阳棚使居住者的热应力降低了35-45%，主观调查结果证实了这一改善。同时，能源监测验证了系统的自给自足；太阳能发电量比制冷需求高出40%。可扩展的技术和综合的地方参与的结合，在本研究中建模，为可持续和包容性的城市热缓解提供了可复制的策略。

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引用次数: 0

A supply-flow-demand network framework for unraveling the compounded impacts of future climate and functional space on urban water-soil ecosystem services 揭示未来气候和功能空间对城市水土生态系统服务复合影响的供需网络框架

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-10 DOI: 10.1016/j.scs.2026.107144

Yu Wang , Junfei Chen , Tonghui Ding , Yufei Han , Xiaodong Yan , Jiayuan Guo , Qingqing Ban , Ting Cheng

The intensification of global climate change and human activities has exacerbated the supply-demand imbalance of urban water-soil ecosystem services (WSES). However, the compounded impacts of climate change and human-induced functional space transformation remain unclear. It is necessary to examine how dual pressures restructure the production, transfer and consumption of future WSES. Therefore, this study proposed an integrated supply-flow-demand framework to simulate the spatiotemporal WSES evolution under future composite scenarios. Through cross-scenario comparative analysis, the proposed framework examined the independent contributions and interactive effects of climate change (CC) and functional space change (FSC) on WSES supply-demand, and employed network analysis to elucidate the structural properties of WSES flow networks. Taking 69 cities in the Yellow River Basin as a representative case, CC contributed over 65.28% to the internal supply-demand variations, while FSC’s influence strengthened, doubling for water yield and rising about 6.5-fold for soil retention, emerging as a key regulator. CC-FSC interactions exhibited antagonistic effects across 65.75% of the basin due to geographical heterogeneity. Moreover, extreme climate increases inter-urban WSES flows, raising water yield by 4.40–5.06 billion m³ and soil retention by 0.83–0.85 billion tons, partially alleviates internal supply-demand imbalances. The biophysical differences drive contrasting WSES network responses: water yield flow network become centralized and fragile, while soil retention flows gain redundancy. From the perspectives of internal supply-demand balance and external flow networks, this study highlights the critical role of ecosystem service flows in mitigating supply-demand imbalances across urban agglomerations, providing a scientific basis for precision spatial planning and climate-adaptive strategies.

全球气候变化和人类活动的加剧加剧了城市水土生态系统服务的供需失衡。然而，气候变化和人类活动引起的功能空间转换的复合影响尚不清楚。有必要研究双重压力如何重组未来WSES的生产、转移和消费。因此，本研究提出了一个综合的供给-流动-需求框架来模拟未来复合情景下WSES的时空演变。通过跨情景对比分析，探讨了气候变化（CC）和功能空间变化（FSC）对WSES供需的独立贡献和交互作用，并利用网络分析阐明了WSES流量网络的结构特征。以黄河流域69个城市为例，CC对内部供需变化的贡献超过65.28%，而FSC的影响增强，对出水量的影响增加了一倍，对土壤保持的影响增加了约6.5倍，成为关键的调节因素。由于地理异质性，CC-FSC相互作用在65.75%的流域呈现拮抗效应。此外，极端气候增加了城市间WSES流量，使出水量增加4.4 ~ 50.6亿m³，土壤保有量增加8.3 ~ 8.5亿吨，部分缓解了城市内部供需失衡。生物物理差异驱动了不同的WSES网络响应：产水流网络变得集中和脆弱，而土壤保持流变得冗余。从内部供需平衡和外部流量网络的角度，强调了生态系统服务流量在缓解城市群供需失衡中的关键作用，为精准空间规划和气候适应策略提供了科学依据。

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引用次数: 0

The compounding effects of pollution reduction and low-carbon policy synergies on urban economic resilience and environmental performance 污染减排和低碳政策协同效应对城市经济弹性和环境绩效的复合效应

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.scs.2026.107162

Xi Wang , Hua Shang , Xiaofei Lv , Sai Yuan

The synergistic governance effects of multi-objective environmental regulations are crucial for achieving coordinated economic and ecological development. Nevertheless, existing investigations have primarily focused on their singular effects on either the environment or the economy, while their synergistic governance effects require further validation. Utilizing urban data from China spanning 2007 to 2023, we construct a policy synergies variable for pollution reduction and low-carbon (PPCR) based on the Key Air Quality Control Zone Policy (KACP) and the Low-Carbon City Pilot Policy (LCCP). Then, we employ the Difference-in-Differences (DID) model to investigate the composite effects of PPCR on urban economic resilience (EOR) and environmental performance (PCR). The findings indicate that PPCR significantly enhances both EOR and PCR, with this conclusion demonstrating robustness. Meanwhile, the mechanism analysis reveals that the primary channels fostering EOR are the high-skilled talent siphoning and labor productivity-driven effects. Energy structure optimization and circular economy initiatives serve as significant pathways for PPCR to enhance PCR. Furthermore, the enabling role of PPCR is even stronger in cities with non-resource-based economies and strong public-oriented environmental regulations (PER).

多目标环境规制的协同治理效应是实现经济与生态协调发展的关键。然而，现有的调查主要集中在它们对环境或经济的单一影响上，而它们的协同治理效应需要进一步验证。本文利用2007 - 2023年中国城市数据，基于重点空气质量控制区政策（KACP）和低碳城市试点政策（LCCP），构建了污染减排与低碳（PPCR）政策协同变量。在此基础上，采用差分模型研究了PPCR对城市经济弹性（EOR）和环境绩效（PCR）的综合影响。结果表明，PPCR显著提高了EOR和PCR，这一结论具有稳健性。同时，机制分析表明，提高采收率的主要渠道是高技能人才的吸纳和劳动生产率的驱动效应。能源结构优化和循环经济倡议是PPCR增强PCR的重要途径。此外，在非资源型经济和强有力的公共导向环境法规（PER）的城市，PPCR的促进作用甚至更强。

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引用次数: 0

Extreme wind events at the pedestrian level of an actual urban morphology: Statistical correlation and space-time evolution 一个实际城市形态中行人层面的极端风事件：统计相关性和时空演化

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.scs.2026.107121

Wei Wang, Yezhan Li, Naoki Ikegaya

Extreme wind events at the pedestrian level in urban areas pose significant safety risks. While most previous studies have emphasized the statistical (“static”) aspect, the parameters that best characterize extreme values remain unclear, and few have explored their spatiotemporal (“dynamic”) evolution. To address these gaps, this study investigates both aspects using large-eddy simulation (LES) within a realistic urban morphology. The analysis integrates statistical correlations with spatiotemporal evolution to provide a comprehensive understanding of extreme winds in complex urban environments. Correlation results show that skewness has the strongest relationship with gust and peak factors, underscoring its potential for improving statistical models. Higher-order moments (up to the sixth) are mainly positively correlated with gust and peak factors, but their weak or moderate association with exceedance wind speed suggests that extreme events are mainly governed by the combined effects of mean flow and standard deviation, rather than gust or peak factors alone. Turbulence measures such as integral time scale, and peak frequency exhibit only marginal links to extremes. The spatiotemporal evolution is examined through conditional space–time proper orthogonal decomposition (CST-POD). Although the first CST-POD mode aligns well with conditional averages, over 75% of the energy resides in higher modes. Reconstructing weaker events requires multiple modes, particularly in sheltered regions, whereas dominant low-order modes near tall buildings allow accurate reconstruction with fewer modes. This study advances understanding of extreme winds by integrating statistical and dynamical perspectives, offering insights for improved urban wind risk assessment.

城市地区行人层面的极端大风事件带来了重大的安全风险。虽然大多数先前的研究都强调统计（“静态”）方面，但最能表征极端值的参数仍然不清楚，很少有人探索它们的时空（“动态”）演变。为了解决这些差距，本研究在现实的城市形态中使用大涡模拟（LES）来研究这两个方面。该分析将统计相关性与时空演变相结合，提供了对复杂城市环境中极端风的全面理解。相关结果表明，偏度与阵风和峰值因子的关系最强，强调了其改进统计模型的潜力。高阶矩（至6阶矩）主要与阵风和峰值因子呈正相关，但与超速风速的相关性较弱或中等，表明极端事件主要受平均流量和标准差的综合影响，而不是单独受阵风或峰值因子的影响。湍流测量，如积分时间尺度和峰值频率，只显示出与极端情况的边际联系。通过条件时空固有正交分解（CST-POD）来考察时空演化。尽管第一个CST-POD模态与条件平均模态吻合得很好，但超过75%的能量存在于更高模态。重建较弱的事件需要多种模式，特别是在有遮蔽的地区，而高层建筑附近的主要低阶模式允许用更少的模式进行精确的重建。本研究通过整合统计和动力学的观点，促进了对极端风的理解，为改进城市风风险评估提供了见解。

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引用次数: 0

Unsupervised clustering approach to residential typo-morphologies across multiple cities for urban heat vulnerability assessment 城市热脆弱性评价中多城市住宅类型形态的无监督聚类方法

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-01 DOI: 10.1016/j.scs.2025.107107

Maha Habib , Doruntina Zendeli , Marjolein van Esch , Wim J. Timmermans , Maarten van Ham

Residential environments are central to addressing urban heat stress for vulnerable populations and are prime target areas for implementing climate adaptation strategies. The reliance on urban heat island (UHI) intensity mapping alone has been argued to provide limited guidance for adaptation efforts, whereas linking heat patterns to the built environment characteristics through frameworks such as Local Climate Zones (LCZ) provides actionable insights for developing neighborhood cooling strategies. However, the widely used LCZ maps have a few limitations, such as misrepresenting variation within types because they cannot account for sub-classes beyond the standardized framework. This paper presents an unsupervised clustering approach to identify residential typo-morphologies across 99 Dutch cities, enhancing their relevance for urban heat vulnerability assessments. The analysis reveals that five morphological and canopy parameters (FSI, GSI, OSR, H_avg, and FVC) selected from 17 parameters are sufficient to identify nine distinct residential typo-morphologies relatable to LCZs within 100 m × 100 m grid cells. The evaluations demonstrate that our approach detects underrepresented LCZ types and reveals new sub-classes absent from standard LCZ classifications. Key findings include detection of high-density areas (LCZ 4₂) reflecting recent urban densification with one of the highest UHI_max next to LCZ 2 (4.2–4.9 K), and vegetation-differentiated variants within sparse and low-rise categories LCZ 9_D and LCZ 6_D, distinguished by distinctive UHI_max (0.5–0.7 K) higher compared to their reference base types. Notably, tree coverage remains low across low-rise and compact typo-morphologies, revealing substantial opportunities for greening interventions. This data-driven refinement preserves LCZ's global comparability while considering local specificity, providing improved frameworks to inform targeted climate adaptation strategies in residential environments.

居住环境是解决弱势群体城市热应激问题的核心，也是实施气候适应战略的主要目标领域。有人认为，仅依赖城市热岛（UHI）强度地图对适应工作提供的指导有限，而通过局部气候带（LCZ）等框架将热模式与建筑环境特征联系起来，为制定社区降温策略提供了可行的见解。然而，广泛使用的LCZ映射有一些限制，比如类型中的变化会被错误地表示，因为它们不能解释标准化框架之外的子类。本文提出了一种无监督聚类方法来识别99个荷兰城市的住宅类型形态，增强其与城市热脆弱性评估的相关性。分析表明，从17个参数中选择的5个形态学和冠层参数（FSI、GSI、OSR、Havg和FVC）足以识别出100 m × 100 m网格单元内与lcz相关的9种不同的居住类型形态。评估表明，我们的方法检测到代表性不足的LCZ类型，并揭示了标准LCZ分类中缺失的新子类。主要发现包括高密度区（LCZ 42）的检测，反映了最近的城市密度，其UHImax最高，仅次于LCZ 2 (4.2-4.9 K)，以及稀疏和低层分类中LCZ 9D和LCZ 6D的植被分化变异，其UHImax （0.5-0.7 K）明显高于参考基准类型。值得注意的是，低层和紧凑类型形态的树木覆盖率仍然很低，这揭示了绿化干预的大量机会。这种数据驱动的改进保留了LCZ的全球可比性，同时考虑了当地的特殊性，为住宅环境中有针对性的气候适应策略提供了改进的框架。

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引用次数: 0

A novel index for evaluating the effects of urban morphology and solar geometry on shadow performance using pixel-based detection from high-resolution satellite imagery 基于高分辨率卫星图像的基于像素的检测，用于评估城市形态和太阳几何形状对阴影性能影响的新指数

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.scs.2026.107123

Soundesse Guettala , Belkacem Marir , Abdelhakime Hanafi , Hamed Laouz

In fast-growing semi-arid cities, shadows reduce outdoor solar exposure, yet the ability of residential urban block morphology to generate effective shading across seasons remains underexplored. This study develops a framework for evaluating mesoscale shadow distribution using a standardized Shadow Efficiency (SE) index. Thirty urban blocks across six typologies were analyzed using 15 morphological parameters, and high-resolution imagery was processed with a Python-based shadow detection algorithm at 11:00 AM, followed by multi-level statistical analysis. Results show that SE is shaped by the adaptability of built form to seasonal solar dynamics. Seasonal SE varied by 6.69 %, reaching a maximum of 37.75 % in winter, and typology explained up to 17.44 % of the variation, with closed I-shaped blocks achieving the highest SE and courtyard forms the most stable performance. Density-related parameters (Building density, aspect ratio, and floor area ratio) had significant positive correlations with SE, while openness indicators (Unbuilt area, Land area, and Sky view factor) had significant negative correlations; height- and spacing-related metrics showed context- and season-dependent effects. ART-ANOVA confirmed typology (η² = 0.41) as the dominant modulator, followed by season (η² = 0.29) and their interaction (η² = 0.17). These findings highlight the importance of morphology-based shading strategies for enhancing thermal resilience in hot, dry urban environments.

在快速发展的半干旱城市中，阴影减少了室外阳光照射，但住宅城市街区形态在不同季节产生有效遮阳的能力仍未得到充分探索。本研究开发了一个使用标准化阴影效率（SE）指数评估中尺度阴影分布的框架。利用15个形态学参数对6种类型的30个城市街区进行分析，并在上午11点使用基于python的阴影检测算法对高分辨率图像进行处理，然后进行多层次统计分析。结果表明，东南方向是由建筑形式对季节太阳动力学的适应性形成的。季节SE的变化幅度为6.69%，冬季最大为37.75%，类型对SE变化的贡献率高达17.44%，其中封闭的i型块体SE最高，院落形态SE表现最稳定。密度相关参数（建筑密度、宽高比、容积率）与SE呈显著正相关，开放度指标（未建面积、用地面积、天景因子）呈显著负相关；与高度和间距相关的指标显示了与环境和季节相关的影响。ART-ANOVA证实类型（η²= 0.41）是主要调节因子，其次是季节（η²= 0.29）及其相互作用（η²= 0.17）。这些发现强调了在炎热干燥的城市环境中，基于形态的遮阳策略对于增强热恢复能力的重要性。

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引用次数: 0

Multi-Hazard assessment methods for planning: A review and potential future directions 规划中的多灾害评估方法：综述和潜在的未来方向

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.scs.2026.107136

Joungwon Kwon , Siyu Yu , Matthew Malecha , Philip Berke

Communities increasingly face multiple hazards simultaneously. However, connections between multiple hazards and urban planning processes remain underexplored. To address this gap, we reviewed the existing literature on multi-hazard assessment in urban planning. We began with 1473 articles and used topic modeling, a technique within Natural Language Processing, to refine the selection to 414 articles that were most aligned with our research focus. After manually reviewing these abstracts, we conducted a systematic review of 64 articles on multi-hazard research. Our findings reveal the predominant types of hazards assessed (e.g., flooding, wildfire), common research methods, the focus on policy—acknowledged in 67% of the articles but rarely accompanied by detailed policy analysis—and various social dimensions, including social vulnerability. We highlight the importance of considering social dimensions in multi-hazard assessments and the need for effective policy translation. To bridge the research-policy gap and improve community resilience, the study proposes a Plan Integration for Resilience Scorecard™ (PIRS™) for Multi-hazards methodology to identify conflicts and gaps and guide coordinated actions to strengthen resilience against multiple hazards. Connecting multi-hazard assessment and modeling with urban planning and policy is necessary to more effectively translate knowledge into practice and strengthen community resilience.

社区越来越多地同时面临多种灾害。然而，多种灾害与城市规划过程之间的联系仍未得到充分探讨。为了解决这一差距，我们回顾了城市规划中多灾害评估的现有文献。我们从1473篇文章开始，使用主题建模（自然语言处理中的一种技术）将选择细化到414篇最符合我们研究重点的文章。在人工审查这些摘要后，我们对64篇关于多危害研究的文章进行了系统的综述。我们的研究结果揭示了评估的主要危害类型（例如，洪水、野火）、常见的研究方法、对政策的关注（67%的文章承认这一点，但很少伴随详细的政策分析）以及各种社会维度，包括社会脆弱性。我们强调在多灾害评估中考虑社会层面的重要性以及有效政策转化的必要性。为了弥合研究与政策之间的差距，提高社区的复原力，本研究提出了一种针对多种灾害的复原力计分卡计划整合（PIRS™）方法，以识别冲突和差距，并指导采取协调一致的行动，加强对多种灾害的复原力。将多灾种评估和建模与城市规划和政策相结合是必要的，这样才能更有效地将知识转化为实践，增强社区抵御能力。

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引用次数: 0