Sustainable Cities and Society最新文献

Interactive scheduling for climate-neutral sustainable cities via carbon capture-enabled virtual energy communities considering digital-social welfare and joint certificate trading 考虑到数字社会福利和联合证书交易，通过支持碳捕获的虚拟能源社区为气候中性可持续城市提供交互式调度

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-05 DOI: 10.1016/j.scs.2026.107208

Peyman Zare , Hossein Shayeghi , SeyedJalal SeyedShenava , Babak Mohamadi

In pursuit of co-creating future climate-neutral and sustainable cities, this study proposes a welfare-aware, interactive, and synergistic framework for scheduling and economic energy dispatch optimization in renewables-rich virtual energy communities, enhanced by carbon capture, utilization, and storage technologies. The model integrates carbon capture systems and x-to-x technologies into a near-zero carbon footprint-conscious operational structure, while dynamically participating in dual eco-friendly market mechanisms: carbon emission trading and green certificate trading. A six-layer architecture is developed to systematically integrate technical, economic, environmental, and welfare dimensions. The optimization is formulated as a dual-objective problem, maximizing profit and minimizing CO₂ emissions, and transformed into a single-objective formulation using an entropy-based weighting approach. Four scenario-based case studies evaluate performance under diverse carbon policies. Results demonstrate an over 29% reduction in CO₂ emissions and a >9% profit improvement under optimized dispatch, confirming robustness across fluctuating market conditions and parameter uncertainties. Moreover, a digital-social welfare assessment layer is incorporated to enhance energy equity, digital inclusion, and citizen engagement. The proposed framework provides a practical, multidimensional decision-support tool for sustainable energy planning, aligning with the long-term visions of climate-neutral and socially inclusive sustainable cities as envisioned by society stakeholders.

为了共同创造未来的气候中和和可持续城市，本研究提出了一个福利意识、互动和协同的框架，用于在富含可再生能源的虚拟能源社区中进行调度和经济能源调度优化，并通过碳捕获、利用和储存技术得到增强。该模型将碳捕获系统和x-to-x技术整合到接近零碳足迹的运营结构中，同时动态参与双重环保市场机制：碳排放交易和绿色证书交易。开发了一个六层架构，以系统地整合技术、经济、环境和福利维度。优化是一个双目标问题，即利润最大化和二氧化碳排放量最小化，并通过基于熵的加权方法转化为单目标问题。四个基于场景的案例研究评估了不同碳政策下的绩效。结果表明，在优化调度下，二氧化碳排放量减少了29%以上，利润提高了9%，证实了在波动的市场条件和参数不确定性下的稳健性。此外，还纳入了数字社会福利评估层，以增强能源公平、数字包容和公民参与。拟议的框架为可持续能源规划提供了一个实用的、多维度的决策支持工具，符合社会利益相关者对气候中和和社会包容性可持续城市的长期愿景。

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引用次数: 0

Rising heat, rising sirens: Spatiotemporal disparities and socio-spatial drivers of heat-related illness exposure risk in Japan 高温上升，警报上升：日本热相关疾病暴露风险的时空差异和社会空间驱动因素

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-04 DOI: 10.1016/j.scs.2026.107210

Jie Chen , Zhenglun Yang , Yuxiao Jiang , Haosen Yang , Linchuan Yang

With the intensification of climate change, heat-related health risks have emerged as a critical challenge to sustainable urban development. This study investigates the spatiotemporal dynamics and correlates of heat-related illness exposure risk across Japan over the period 2003–2020. Municipality-level heat-related illness risk was estimated using heat-related ambulance transport records alongside multi-source environmental and socio-demographic datasets, with spatial mapping subsequently applied to identify temporal trends and the spatial shift of high-risk zones. Machine learning models were then employed to evaluate the nonlinear and interactive effects of urbanization and social vulnerability on long-term risk trajectories. The key findings are as follows: (1) High-risk areas and exposed populations have expanded outward from major metropolitan regions; (2) Approximately one-quarter of municipalities experienced a significant increase in heat-related illness exposure risk; (3) Marked regional and urban inequalities are evident in both risk levels and temporal trends; and (4) Pronounced nonlinear relationships and interactive effects exist between urbanization, social vulnerability, and temporal trends in exposure risk. This study advances our understanding of the dynamic evolutionary characteristics underpinning heat-related health risks and elucidates their coupling mechanisms with urbanization and demographic attributes, thereby providing empirical support for the formulation of targeted, precise, and sustainable urban planning and heat adaptation strategies.

随着气候变化的加剧，与热有关的健康风险已成为城市可持续发展面临的重大挑战。本研究调查了2003-2020年日本各地热相关疾病暴露风险的时空动态及其相关因素。利用与热相关的救护车运输记录以及多源环境和社会人口数据集，估计了市级与热相关的疾病风险，随后应用空间制图来确定高危区的时间趋势和空间转移。然后采用机器学习模型来评估城市化和社会脆弱性对长期风险轨迹的非线性和交互影响。主要发现如下：(1)高发地区和暴露人群从大城市向外扩展；(2)大约四分之一的城市经历了与热有关的疾病暴露风险的显著增加；(3)在风险水平和时间趋势上存在明显的区域和城市不平等；(4)城市化、社会脆弱性和暴露风险时间趋势之间存在显著的非线性关系和交互效应。本研究进一步揭示了热相关健康风险的动态演化特征，并阐明了其与城市化和人口属性的耦合机制，从而为制定有针对性、精准和可持续的城市规划和热适应策略提供了经验支持。

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引用次数: 0

Evolutionary trends of urban three-dimension expansion: A case study in 36 Chinese metropolitan areas 城市三维扩张的演化趋势——以中国36个大都市区为例

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-05 DOI: 10.1016/j.scs.2026.107207

Yukan Jin , Tingting He , Zhaopeng Lu , Fashuai Li , Yuwei Chen

As urban spatial growth continues to shift from outward expansion to vertical expansion, traditional research on two-dimensional urban expansion is insufficient to describe the actual growth process of urban space. Studying urban three-dimensional spatial expansion is essential for understanding the laws of urban development and supporting urban sustainable development.This study uses building volume, population grid, night-time light, and land use data to characterize the evolutionary characteristics of three-dimensional spatial expansion in China's 36 metropolitan areas from the research perspective of "scale-pattern-rationality-source". The results show that: (1)The three-dimensional space of cities in China's metropolitan areas has been continuously expanding, but the growth has shown a slowing trend, and the differences between metropolitan areas have been narrowing. In terms of expansion speed (FES), it has been decreasing from the coastal areas to the inland regions; in terms of expansion intensity (FEI), high values are distributed in the western region, the Middle Yangtze River Delta, and the Yangtze River Delta. (2) Edge expansion dominates Chinese metropolitan areas, accounting for 59.29 % of the growth in floor area. Infilling expansion accounts for a relatively higher proportion of growth in the eastern region. (3) The three-dimensional spatial expansion of cities in China's metropolitan areas is generally faster than population growth but lags behind economic development. (4) Cultivated land has borne the main part of the three-dimensional spatial expansion of cities in China's metropolitan areas, followed by bare land.

随着城市空间增长不断从向外扩张转向垂直扩张，传统的二维城市扩张研究不足以描述城市空间的实际增长过程。研究城市三维空间扩展对于认识城市发展规律，支持城市可持续发展具有重要意义。基于“规模-格局-合理性-源头”的研究视角，利用建筑体量、人口网格、夜间光照和土地利用等数据，对中国36个大都市区三维空间扩展演化特征进行了分析。结果表明：(1)中国都市圈城市三维空间持续扩张，但增速呈现放缓趋势，都市圈间差异不断缩小；从扩张速度（FES）来看，从沿海地区向内陆地区呈下降趋势；在扩张强度（FEI）上，西部地区、长江三角洲中部和长江三角洲地区分布较高。(2)中国都市圈以边缘扩张为主，占建筑面积增长的59.29%。在东部地区，填充扩张占增长的比例相对较高。③中国都市圈城市三维空间扩张总体上快于人口增长，但滞后于经济发展。(4)中国都市圈城市三维空间扩张的主体是耕地，其次是裸地。

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引用次数: 0

Revealing the impact of multi-scale innovation networks on urban carbon emission intensity: Evidence from Chinese cities 多尺度创新网络对城市碳排放强度的影响：来自中国城市的证据

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-10 DOI: 10.1016/j.scs.2026.107223

Qixuan Wang , Haoran Li , Hangying Su , Luqiao Yang , Mi Diao

Understanding how intercity innovation networks foster low-carbon urban development is critical for effective climate policy design. This study examines the effects of multi-scale innovation networks (MSIN), at the national, provincial and local levels, on urban carbon emission intensity (UCEI) across 284 Chinese cities from 2008 to 2022. By combining LightGBM–SHAP interpretation with double machine learning (DML), this study provides both explanatory and causal insights. The LightGBM–SHAP analysis indicates that, after accounting for natural environmental, socioeconomic, and policy-related covariates, MSIN collectively explain 10.75 % of the modeled variation in UCEI, with innovation linkages to top-tier cities at the national scale emerging as the most influential factor and exhibiting diminishing marginal returns. Heterogeneity analysis further indicates that MSIN play a more prominent role in urban cluster cities, economically developed cities, and non-resource-based cities, compared to their counterparts. The DML models assess the causal effect of intercity innovation connection across all network scales on UCEI. The results reveal significant emission-reduction effects of MSIN, with the strongest impact observed for national-level connections. Specifically, a 10 % increase in national-level connection is estimated to marginally reduce UCEI by approximately 2.2 kg of CO2 per 10,000 RMB of real GDP, underscoring its substantial contribution to low-carbon urban transition. Mechanism analysis further identifies two significant mediating channels: enhanced green innovation output and increased enterprise innovation vitality, through which national- and provincial-level innovation connections reduce UCEI. This study contributes by integrating ML-based causal inference with multi-scale spatial network framework, highlighting the importance of strengthening top-tier innovation linkages at the national level and adopting differentiated, network-oriented policy strategies to support urban decarbonization and green development.

了解城际创新网络如何促进低碳城市发展对于有效的气候政策设计至关重要。本文研究了2008 - 2022年中国284个城市的多尺度创新网络（MSIN）对城市碳排放强度（UCEI）的影响，包括国家、省和地方层面。通过将LightGBM-SHAP解释与双机器学习（DML）相结合，本研究提供了解释性和因果性的见解。LightGBM-SHAP分析表明，在考虑自然环境、社会经济和政策相关协变量后，MSIN共同解释了10.75%的UCEI模型变化，其中与全国一线城市的创新联系成为最具影响力的因素，并表现出边际收益递减的特征。异质性分析进一步表明，与城市群城市、经济发达城市和非资源型城市相比，城市信息化的作用更为突出。DML模型评估了跨网络尺度的城际创新连接对UCEI的因果效应。结果表明，MSIN具有显著的减排效果，其中对国家级连接的影响最大。具体而言，据估计，每10000元人民币实际GDP中，10%的国家级互联互通将使UCEI减少约2.2公斤的二氧化碳排放，这凸显了其对低碳城市转型的巨大贡献。机制分析进一步发现绿色创新产出的增加和企业创新活力的增加两个显著中介渠道，国家和省级创新联系通过这两个渠道降低了UCEI。本研究将基于机器学习的因果推理与多尺度空间网络框架相结合，强调了在国家层面加强顶层创新联系，采取差异化、网络导向的政策策略支持城市脱碳和绿色发展的重要性。

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引用次数: 0

Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning 通过地理空间可解释的机器学习对灾害恢复能力和公平性的上下文敏感分析

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-02 DOI: 10.1016/j.scs.2026.107203

Yirong Ding , Lu Zhang , Yang Zhang

Recurrent hurricanes pose significant challenges for disaster resilience and equitable recovery, yet there is limited research focusing on examining equity’s role in resilience across multiple, temporally distinct disasters. To address this gap, our study analyzes Florida communities impacted sequentially by Hurricanes Irma (2017) and Ian (2022), using FEMA Individual Assistance declarations to delineate the overlapping disaster zones. Leveraging geo-information embedded explainable machine learning, which integrates spatially explicit relationships into machine learning frameworks, we explored the interplay between disaster equity and disaster resilience. Specifically, we examined how (1) place-based equity, measured by building code standards, hazard exposure, and building conditions, and (2) capacity-based equity, measured by socioeconomic and demographic factors, influence three critical dimensions of resilience: disaster impact containment, resource mobilization, and recovery capability. Additionally, we assessed how these relationships and the relative influence of equity determinants change between the two hurricane events. This research contributes to the body of knowledge on community resilience by providing new insights into the dynamics of resilience-equity interactions across communities experiencing recurrent disasters. The findings offer actionable guidance for designing context-sensitive disaster management strategies for disaster-prone communities.

经常性飓风对灾害恢复能力和公平恢复构成了重大挑战，但关注公平在多重、时间上不同的灾害恢复能力中的作用的研究有限。为了解决这一差距，我们的研究分析了依次受到飓风Irma（2017年）和Ian（2022年）影响的佛罗里达州社区，使用FEMA个人援助声明来划定重叠的灾区。利用嵌入的可解释机器学习的地理信息，将空间明确的关系集成到机器学习框架中，我们探索了灾害公平与灾害复原力之间的相互作用。具体而言，我们研究了(1)基于地点的公平（通过建筑规范标准、灾害暴露和建筑条件衡量）和(2)基于能力的公平（通过社会经济和人口因素衡量）如何影响复原力的三个关键维度：灾害影响控制、资源动员和恢复能力。此外，我们评估了这些关系和公平决定因素的相对影响如何在两次飓风事件之间发生变化。本研究通过提供对经历周期性灾害的社区间弹性-公平互动动态的新见解，为社区弹性知识体系做出了贡献。研究结果为为易受灾社区设计情境敏感的灾害管理战略提供了可操作的指导。

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引用次数: 0

Sensor configuration optimization for source term estimation of time-varying emissions 时变排放源项估计的传感器配置优化

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-01-28 DOI: 10.1016/j.scs.2026.107186

Yiping Lin , Hong Huang , Jing Wang , Xiaole Zhang

When a hazardous gas leakage accident occurs, accurate source term estimation is essential for timely emergency response. However, the release rate of source is usually time-varying, making the estimation extremely challenging. This paper employs a sensor configuration optimization method to improve the performance of source term estimation for time-varying sources. The method integrates an objective function based on the gradient of the adjoint concentration field with a Genetic Algorithm to identify the most sensitive sensor location combinations. The result shows that the proposed optimum configuration significantly improves the accuracy of source location estimation, compared with uniform configurations. Three release scenarios (constant/periodic/decaying) are analyzed, and the proposed optimum configuration significantly enhances the accuracy and stability of estimations on both the location and the strength of the source. Besides, the analysis reveals that accurate source strength estimation requires more sensors than source location estimation.

发生危险气体泄漏事故时，准确的源项估算是及时进行应急响应的必要条件。然而，源的释放速率通常是时变的，使得估计极具挑战性。为了提高时变信号源项估计的性能，本文采用了一种传感器结构优化方法。该方法将基于伴随浓度场梯度的目标函数与遗传算法相结合，识别出最敏感的传感器位置组合。结果表明，与均匀配置相比，该优化配置显著提高了源定位估计的精度。分析了三种释放场景（恒定/周期/衰减），所提出的最优配置显著提高了源位置和强度估计的准确性和稳定性。此外，分析表明，准确的源强度估计比源位置估计需要更多的传感器。

引用次数: 0

Urban resilience through nature-based solutions in an era of environmental extremes: A global systematic analysis 极端环境时代基于自然的城市弹性解决方案：全球系统分析

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-06 DOI: 10.1016/j.scs.2026.107192

Abdul Ghafoor Nizamani , Dung Thi Vu , Rafeeque Ahmed Nizamani , Geir Torgersen

Urban areas face escalating climate extremes that disproportionately affect regions with systemic inequities. Traditional infrastructure is inadequate. Nature-based solutions (NbS) offer transformative pathways but risk maladaptive outcomes without justice-centred frameworks. This systematic review synthesises 144 recent studies on urban NbS, examining governance architectures and distributive justice using PRISMA guidelines. The analysis reveals three persistent imbalances: (1) Geographic bias: 69% of research concentrates in Global North contexts, overlooking high-risk Global South regions. (2) Governance: Polycentric models prove unstable; technocratic approaches entrench inequities; and grassroots initiatives face marginalisation. (3) Equity: 93% of studies fail to disaggregate outcomes, masking disproportionate benefits to affluent groups. Digital tools risk algorithmic exclusion without community data sovereignty. To realise NbS as climate justice instruments, three shifts are required: institutionalising mandatory equity metrics, reallocating funding to high-vulnerability regions, and democratising digital transitions through community data sovereignty. Ultimately, Governance reform, not technical innovation, proves paramount for equitable resilience.

城市地区面临着不断升级的极端气候，这对存在系统性不平等的地区造成了不成比例的影响。传统的基础设施不足。基于自然的解决方案提供了变革的途径，但如果没有以正义为中心的框架，就有可能产生适应不良的结果。本系统综述综合了144项关于城市国家统计局的最新研究，使用PRISMA指南检查治理架构和分配正义。分析揭示了三个持续存在的失衡：(1)地域偏见：69%的研究集中在全球北方背景下，忽视了高风险的全球南方地区。(2)治理：多中心模型被证明是不稳定的；技术官僚的做法加剧了不平等；草根倡议面临边缘化。(3)公平：93%的研究未能分解结果，掩盖了富裕群体的不成比例的利益。如果没有社区数据主权，数字工具可能会面临算法排斥的风险。要实现国家统计局作为气候正义工具的作用，需要做出三个转变：将强制性公平指标制度化，将资金重新分配给高脆弱性地区，以及通过社区数据主权实现数字化转型的民主化。最终，治理改革，而不是技术创新，被证明对公平弹性至关重要。

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引用次数: 0

Typological identification of surface rainwater utilization under a supply–Demand synergy framework: A case study of Tianjin, China 供需协同框架下地表雨水利用的类型识别——以天津市为例

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-05 DOI: 10.1016/j.scs.2026.107212

Yingzhi Lu, Yixuan Wang, Saiyi Chen, Tian Chen

Global climate change has intensified water security threats in rapidly urbanizing areas, highlighting the need to incorporate rainwater utilization (RU) strategies into urban development. Although conventional rainwater harvesting (RWH) has been widely validated in both urban and agricultural areas, the macro-scale relationship between surface characteristics and RU across areas with diverse land-use types remains insufficiently explored. Previous studies often adopt a one-sided perspective, focusing solely on either supply potential or demand regulation, and thus fail to capture the integrated potential of RU. To address this gap, this study introduces the concept of Rainwater Utilization Service (RUS) to classify surface-based RU types and establishes a coordinated assessment framework that integrates supply and demand. On the supply side, factors such as hydrological processes, spatial planning, and policy are considered to assess RUS potential. On the demand side, the Soil Conservation Service-Curve Number (SCS-CN) method and MIKE System Hydrological European (MIKE SHE) model are employed to simulate surface runoff generation and convergence characteristics, respectively. Applying this framework to Tianjin, the study identifies eight RUS types, each with corresponding intervention priorities: 61.3% of the total area is deemed suitable for the implementation of RWH systems, while 38.7% is more appropriate for natural infiltration strategies. Furthermore, the study reveals that areas with high supply potential are primarily driven by hydrological foundations, followed by policy influences. In contrast, demand hotspots are concentrated in coastal zones, along river networks, and in areas with high surface moisture, with runoff convergence showing a zonal pattern of “high–low–moderate” intensity. As rainfall intensity increases to extreme levels, a threshold is reached beyond which natural and artificial surfaces exhibit similar responses. RUS, as developed in this study, provides a universal paradigm of supply–demand matching, which enables cities facing challenges such as flooding or water scarcity to adapt the framework to their local contexts.

全球气候变化加剧了快速城市化地区的水安全威胁，凸显了将雨水利用（RU）战略纳入城市发展的必要性。尽管传统的雨水收集（RWH）在城市和农业地区都得到了广泛的验证，但在不同土地利用类型的地区，地表特征与雨水收集之间的宏观尺度关系仍未得到充分的探索。以往的研究往往采取片面的视角，只关注供给潜力或需求调节，因此未能捕捉到RU的综合潜力。为了解决这一差距，本研究引入了雨水利用服务（RUS）的概念，对基于地表的雨水利用服务类型进行分类，并建立了一个整合供需的协调评估框架。在供应方面，考虑水文过程、空间规划和政策等因素来评估RUS潜力。在需求侧，分别采用土壤保持服务曲线数（SCS-CN）方法和MIKE System水文欧洲（MIKE SHE）模型模拟地表径流生成和收敛特征。将该框架应用于天津，研究确定了8种RUS类型，每种类型都有相应的干预优先级：61.3%的总面积被认为适合实施RWH系统，而38.7%的面积更适合实施自然渗透策略。此外，研究表明，高供应潜力地区主要受水文基础驱动，其次是政策影响。需求热点集中在沿海地区、河网沿线和地表湿度高的地区，径流辐合呈现“高-低-中”强度的地带性格局。当降雨强度增加到极端水平时，就会达到一个阈值，超过这个阈值，自然表面和人工表面就会表现出类似的反应。本研究开发的RUS提供了供需匹配的通用范例，使面临洪水或水资源短缺等挑战的城市能够根据当地情况调整框架。

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引用次数: 0

A global insight into integration of metro and electric vehicle charging stations 地铁与电动汽车充电站一体化的全球视角

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-06 DOI: 10.1016/j.scs.2026.107217

Dingkang Teng , Justin Hayse Chiwing G. Tang , Ruichen Ma , Xiong Yang , Chengxiang Zhuge

This study investigates the potential of park-charge-ride (PCR) worldwide in the era of transportation electrification, covering 153 urban study units across the United States, Europe and China. This research uses a set of spatial analysis methods to profile the proximity, density, and clustering patterns of electric vehicle charging stations (EVCS) and parking lots relative to metro stations. The results reveal several substantial regional disparities. The United States demonstrates strong yet uneven PCR implementation with remarkable EVCS aggregation; Europe reaches balanced improvement of PCR services; and China, though possessing the greater metro infrastructure, demonstrates limited EVCS integration metro stations nearby. Intriguingly, the study found no significant policy-driven prioritization of PCR deployment by several types of metro stations (i.e., normal, interchange, terminal). These findings stress the necessity for spatial planning to optimize the coverage of PCR, foster multimodal transport efficiency, and advance sustainable mobility objectives worldwide.

本研究以美国、欧洲和中国的153个城市为研究对象，探讨了在交通电气化时代，全球范围内停车收费换乘（PCR）的潜力。本研究采用一套空间分析方法，分析了电动汽车充电站和停车场相对于地铁站的接近度、密度和集群模式。研究结果揭示了几个重大的地区差异。美国表现出强大但不均匀的PCR实施，EVCS聚集显著；欧洲实现了对聚合酶链反应服务的平衡改善；而中国虽然拥有更大的地铁基础设施，但附近的EVCS集成地铁站有限。有趣的是，该研究发现，几种类型的地铁站（即普通、换乘、终点站）没有明显的政策驱动的PCR部署优先级。这些发现强调了空间规划的必要性，以优化聚合链的覆盖范围，促进多式联运效率，并在全球范围内推进可持续交通目标。

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引用次数: 0

Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona 公民科学与机器学习相结合，量化了亚利桑那州马里科帕县绿蓝基础设施的冷却潜力

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

Sustainable Cities and Society

Pub Date : 2026-03-15 Epub Date: 2026-02-05 DOI: 10.1016/j.scs.2026.107211

Alamin Molla , Katia Lamer , David J. Sailor

This study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R²: 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.

本研究考察了亚利桑那州凤凰城多布森牧场城市社区的绿蓝基础设施（GBI）的时空制冷性能。我们利用公民科学近地表（2米）空气温度（Tair）测量来训练一个高度精确的Tair预测LightGBM机器学习模型（R2: 0.986, MAE: 0.251°C, RMSE: 0.585°C）。2024年6月16日，公园区域白天和夜间均表现出约1°C的降温效应（相对于附近平均值）。附近人工湖白天降温效果较强，降温幅度为2.4°C，夜间升温幅度为0.3°C。00:00时，园区下风50 m位置比园区温度高0.3℃，上风50 m位置比园区温度高0.8℃。在11:00时，我们观察到相对于公园相同的50 m距离下，下风区温度降低0.8°C，上风区温度升高0.6°C。2024年6月17日19:00，在相同的50 m下风和50 m上风位置分别观测到1°C的降温和升温效应。我们的数据驱动分析强调了汽车穿越测量的潜在局限性，表明未能考虑穿越期间的时间变化可能导致夜间对Tair的高估和白天的低估。我们的分析还显示，Landsat衍生的地表温度（LST）与当地Landsat立交桥（~ 11.00）时模式预测的Tair之间只有弱相关（系数：0.48）。这凸显了仅依靠地表温度进行人类热暴露分析的潜在错误，特别是在异质建筑环境中。

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引用次数: 0