Sustainable Cities and Society最新文献_第2页

Machine learning and causal attribution of urban heat in the Phoenix metropolitan 凤凰城都市热的机器学习和因果归因

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

Sustainable Cities and Society

Pub Date : 2026-01-10 DOI: 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.

热环境恶化是城市发展中最严峻的挑战之一，它导致空气质量、环境健康和生态系统服务的退化。虽然已有许多将城市热归因于各种环境因素的研究，但这些因素可解释的潜在因果关系在很大程度上仍未得到充分探讨。在这项研究中，我们将城市热量（由地表温度LST测量）的机器学习（ML）归因到两大类贡献者，即(a)当地景观特征（地表反照率、植被覆盖、建筑密度和人为活动的测量）和(b)气象条件（降水、湿度、风、压力、太阳辐射和土壤湿度），以亚利桑那州凤凰城为试验平台。此外，我们利用收敛交叉映射（CCM）量化了这些环境因子与地表温度之间的潜在因果关系。太阳辐射和植被覆盖度（NDVI）是城市热环境的两个最重要的决定因素，无论在统计上还是因果关系上都是如此。我们还确定了含水量变量（降水、湿度和土壤湿度）的影响，这些变量未被ML归因捕获，但具有显著的因果关系。这些发现有助于加深我们对调节城市热量及其与其他环境因素复杂相互作用的潜在机制的理解，从而为可持续城市规划实践提供信息。

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引用次数: 0

Connection of cold-wet islands to mitigate urban thermal risk 连接冷湿岛屿，降低城市热风险

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

Sustainable Cities and Society

Pub Date : 2026-01-10 DOI: 10.1016/j.scs.2026.107143

Aokang Xu , Jing Shi , Haoyuan Feng , Xiangyun Meng

Under the synergistic effects of global climate change and rapid urbanization, the urban heat island effect is intensifying, posing significant challenges to sustainable urban development and public health and safety. Research indicates that the cooling impact of isolated cooling units on the urban thermal environment is limited, whereas networked cooling systems offer superior cooling benefits by improving landscape connectivity and functional integration. This study focuses on the main urban area of Wuhan, integrating morphological spatial pattern analysis, landscape connectivity assessment, and circuit theory to develop a cold-wet network (CWN). Subsequently, CWN communities were delineated based on connectivity, and the network’s robustness was evaluated under multiple attack strategies. Key conclusions include: The concept of the wet island complements the cold island to form the cold-wet island (CWI), providing a more comprehensive definition of cooling zones; Co-optimizing the structural components of CWN (i.e., CWIs and cold-wet corridors) significantly enhances network efficiency; Differentiated management of CWN communities, through localized optimization and cross-community coordination, strengthens overall network resilience; An actual attack strategy based on “shape-scale” metrics offers a quantitative basis for prioritizing CWI protection; Adding CWIs improves CWN robustness under actual attack scenarios, suggesting that increasing cooling units is a viable strategy for mitigating urban heat. This study aims to alleviate heat stress in the main urban area of Wuhan while providing a theoretical framework for climate-adaptive urban planning and sustainable development.

在全球气候变化和快速城市化的协同作用下，城市热岛效应不断加剧，对城市可持续发展和公共健康安全构成重大挑战。研究表明，孤立冷却装置对城市热环境的冷却影响有限，而网络化冷却系统通过改善景观连通性和功能整合提供了优越的冷却效益。本研究以武汉市主城区为研究对象，结合形态空间格局分析、景观连通性评价和线路理论，构建了武汉市主城区冷湿网络。随后，基于连通性划分了CWN社区，并在多种攻击策略下评估了网络的鲁棒性。主要结论包括：湿岛的概念是对冷岛的补充，形成了冷湿岛（CWI），提供了更全面的冷区定义；协同优化CWN的结构组件（即cws和冷湿走廊）可显著提高网络效率；CWN社区差异化管理，通过局部优化和跨社区协调，增强网络整体弹性；基于“形状尺度”指标的实际攻击策略为CWI保护的优先级提供了定量基础；添加cwi可以提高CWN在实际攻击场景下的鲁棒性，这表明增加冷却单元是缓解城市热量的可行策略。本研究旨在缓解武汉市主城区的热应激，同时为气候适应性城市规划和可持续发展提供理论框架。

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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 : 2026-01-10 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

Reconstruction of Pedestrian-Level pollutant distributions in urban street canyon using physics-informed neural network 基于物理信息神经网络的城市街道峡谷行人级污染物分布重建

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

Sustainable Cities and Society

Pub Date : 2026-01-09 DOI: 10.1016/j.scs.2026.107141

Liang Ma , Tengfei An , Runhan Zhao , Wenli Liu

Rapid urban expansion, coupled with an increasing number of motor vehicles, has rendered street canyons critical zones for PM_2.5 accumulation. However, the limited density of on-site monitoring and the considerable computational cost of conventional computational fluid dynamics simulations impede accurate and real-time assessment of pollution exposure at the pedestrian level. This study proposes a framework termed PINN-RAS, which integrates a physics-informed neural network (PINN) with a residual-softmax adaptive sampling (RAS) strategy. This framework enables the reconstruction of fine-scale pollution distributions based on sparse observational data. First, the incorporation of physical constraints enables the PINN model to maintain predictive robustness as the proportion of training data decreases progressively from 80% to 5%. It captures dominant vortices and pollutant gradients within the canyon, with overall R² values consistently exceeding 0.8. Second, under simulated measurement noise, the model demonstrates strong local error convergence. Prediction errors remain confined to narrow boundary regions, achieving an R² of 0.81, which outperforms baseline models including convolutional neural and long short-term memory networks. Third, the RAS strategy dynamically allocates sampling points in regions with high residuals and sharp concentration gradients, thereby enhancing coverage of critical zones. The reconstruction model achieves an R² of 0.85, while the mean squared error and mean absolute error are reduced to 0.11 and 0.16, respectively, thereby enabling finer-scale reconstruction using an equivalent data proportion. This method provides a cost-effective and efficient solution for real-time, high-resolution air quality assessment and facilitates precise health risk assessment and control at the pedestrian level.

快速的城市扩张，加上机动车数量的增加，使得街道峡谷成为PM2.5聚集的关键区域。然而，有限的现场监测密度和传统计算流体动力学模拟的可观计算成本阻碍了对行人水平污染暴露的准确和实时评估。本研究提出了一个名为PINN-RAS的框架，该框架将物理信息神经网络（PINN）与残数软最大值自适应采样（RAS）策略集成在一起。该框架能够基于稀疏观测数据重建精细尺度的污染分布。首先，当训练数据的比例从80%逐渐下降到5%时，物理约束的加入使PINN模型保持预测稳健性。它捕获了峡谷内的优势涡和污染物梯度，总体R2值始终超过0.8。其次，在模拟测量噪声下，该模型具有较强的局部误差收敛性。预测误差仍然局限于狭窄的边界区域，实现R2为0.81，优于包括卷积神经和长短期记忆网络在内的基线模型。第三，RAS策略在残差高、浓度梯度大的区域动态分配采样点，从而提高关键区域的覆盖率。重建模型的R2为0.85，均方误差和平均绝对误差分别降至0.11和0.16，从而可以使用等效的数据比例进行更精细的重建。该方法为实时、高分辨率的空气质量评估提供了一种经济高效的解决方案，有助于在行人层面进行精确的健康风险评估和控制。

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引用次数: 0

Comprehensive benefits evaluation of the impact of vertical city on solar PV utilization for achieving smart sustainable cities 垂直城市对太阳能光伏利用影响的综合效益评价，实现智慧可持续城市

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

Sustainable Cities and Society

Pub Date : 2026-01-09 DOI: 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.

城市化的快速发展导致城市建筑的垂直扩张，极大地影响了太阳能光伏发电的利用潜力。本研究使用机器学习随机森林模型模拟城市结构的垂直发展，并评估城市三维形态的变化如何影响太阳能光伏利用的综合效益。研究结果表明，当城市平均高度增加12.08%时，光伏发电收益可提高39.91%，发电成本可降低11.1%。进一步分析发现，II类城市地块（中高层高密度）的光伏回报率最高，是III类城市地块（高层低密度）的8.27倍。我们的研究表明，城市三维形态与太阳能光伏利用潜力密切相关。设计合理的城市三维形态，最大限度地利用太阳能资源，对于实现可持续发展目标11 （SDG11）中关于智慧可持续城市的具体目标至关重要。

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引用次数: 0

Breaking the isothermal assumption in CFD air quality modeling: Solar irradiance effects on the wind velocity-concentration relationship 打破CFD空气质量模拟中的等温假设：太阳辐照度对风速-浓度关系的影响

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

Sustainable Cities and Society

Pub Date : 2026-01-09 DOI: 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.

本研究考察了太阳辐照对城市空气质量等温计算流体动力学（CFD）模型中常用的风速-浓度关系的影响程度。主要目的是评估这种广泛使用的关系在非等温条件下的有效性，并为太阳诱导的热效应对城市污染物扩散的影响提供新的见解。虽然这种关系可以通过从有限的模拟中推断出长期的浓度估计，从而提供强大的操作优势，但其在非等温条件下的有效性仍未经检验。然而，最近的监管变化和经验证据日益突出了等温假设的局限性，特别是在捕捉受太阳能驱动的热效应影响的短期污染物动态方面。为了解决这一差距，对理想的5 × 5城市建筑阵列中的污染物扩散进行了系统的CFD分析。在固定的太阳位置和热边界条件下，该阵列暴露于不同的入口风速和太阳辐照度水平。结果表明，热诱导流动结构可以显著改变污染物的扩散模式，特别是在低风和高辐照条件下。然而，随着机械力的增加，流场和由此产生的污染物浓度分布趋于收敛，减少了热扰动的影响。对模拟的污染物场与利用等温风速-浓度关系重新计算的污染物场的对比分析表明，这种方法的可靠性取决于热力和机械力之间的平衡。在有利的条件下，例如：例如，风大，太阳辐照度低——利用这种关系仍然是强有力的。相反，在太阳能主导的情况下，它会引入重大误差。

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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 : 2026-01-09 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

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 : 2026-01-09 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

The synergistic evolution model and simulation of the energy-environment-economy (3E) system in the Yellow River Basin 黄河流域能源-环境-经济（3E）系统协同演化模型与模拟

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

Sustainable Cities and Society

Pub Date : 2026-01-09 DOI: 10.1016/j.scs.2026.107140

Danning Zhao, Yan Song, Xinran Yan

The Yellow River Basin (YRB) serves as a crucial energy base and ecological barrier in China, where the nonlinear dynamic interactions and threshold effects within its Energy-Environment-Economy (3E) system remain insufficiently understood. The system's evolution faces practical challenges due to the destabilization of sequential transition coordination mechanisms. This study employs synergetics theory and incorporates the B-L reaction mechanism to establish a Logistic sequential transition coordination model for the 3E system in the YRB. The model analyzes the evolutionary patterns and external effects of four key parameters: high-quality economic development level (q₁), ecological environment quality (q₂), renewable energy consumption (q₃), and non-renewable energy consumption (q₄). Scenario simulations are conducted to examine the coordinated evolution across different types of cities. The results show that the evolution of the 3E system in the YRB is primarily driven by the positive synergistic effects of q₁, q₂, and q₃, while the development of q₄ remains constrained. Notably, cities prioritizing non-renewable energy exhibit lagging economic-environmental coordination due to path dependence. The threshold effect exhibits spatiotemporal heterogeneity. High-threshold cities (such as Dingxi and Pingliang) face risks of systemic instability, though their numbers are gradually decreasing. In contrast, low-threshold cities (like Xi'an and Jinan) demonstrate stable development across all parameters, with non-renewable energy consumption progressively declining, enabling these systems to achieve stable synergistic states through evolution. Compared to upstream and downstream cities, midstream cities experience slower co-evolution processes due to constraints from energy transition and industrial monoculture. Based on this, this study proposes a categorized governance framework, providing theoretical support and policy tools to address the challenges of synergistic development in the 3E system of the YRB and achieve sustainable development.

黄河流域作为中国重要的能源基地和生态屏障，其能量-环境-经济（3E）系统的非线性动态相互作用和阈值效应尚未得到充分认识。由于序贯过渡协调机制的不稳定性，系统演化面临着现实的挑战。本研究运用协同理论，结合B-L反应机制，建立了YRB中3E系统的Logistic序贯过渡协调模型。该模型分析了经济高质量发展水平（q1）、生态环境质量（q2）、可再生能源消费（q3）和不可再生能源消费（q4）四个关键参数的演化模式和外部效应。通过情景模拟，考察了不同类型城市间的协同演化。结果表明，YRB中3E系统的演化主要受q1、q2和q3的正向协同效应驱动，而q4的发展仍然受到制约。值得注意的是，由于路径依赖，优先考虑不可再生能源的城市表现出滞后的经济环境协调。阈值效应表现出时空异质性。高门槛城市（如定西和平凉）面临着系统性不稳定的风险，尽管它们的数量正在逐渐减少。相比之下，低门槛城市（如西安和济南）在所有参数上都表现出稳定的发展，不可再生能源消耗逐渐下降，使这些系统通过进化达到稳定的协同状态。与上下游城市相比，中游城市受能源转型和产业单一文化的制约，协同演化过程较为缓慢。基于此，本研究提出了分类治理框架，为应对长江三角洲3E系统协同发展挑战，实现可持续发展提供理论支持和政策工具。

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引用次数: 0

Variability of cooling distances and intensities of urban green spaces over six hot summer days affects their relevance for UHI mitigation in Berlin, Germany 德国柏林6个炎热夏季城市绿地降温距离和强度的变异性影响了它们与热岛缓解的相关性

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

Sustainable Cities and Society

Pub Date : 2026-01-09 DOI: 10.1016/j.scs.2026.107139

Britta Stumpe, Niklas Stuhrmann, Bernd Marschner

In the context of global urbanization and increasingly frequent heatwaves, urban green spaces (UGS) play a crucial role in mitigating urban heat islands (UHI). However, the variability and reliability of these cooling services is often overlooked. This study addresses the temporal and inter-site variability of cooling effectiveness across different UGS types in Berlin, Germany. We analyzed inner (land surface temperature, LST) and outer (park cooling distance, PCD; park cooling intensity, PCI) cooling parameters for 228 parks, 512 allotments, and 143 cemeteries using six cloud-free Landsat 8 OLI scenes from hot summer days (T_max > 25 °C). While LST showed low variabilities between sites, PCD and PCI fluctuated more, especially in allotments. Only 9–16% of the UGSs exhibited stable cooling distances over time, with parks achieving the largest PCD and cemeteries providing the most intense and reliable cooling.

Allotments were the least effective and most variable. To investigate drivers of temporal PCD variability, we conducted regression analyses using vegetation indices, spatial metrics, and urban structure. However, the models explained little of the variance (R² up to 0.3), indicating complex external influences not fully captured by the available data. To analyze the multifaceted role of UGS in UHI mitigation, we propose the novel concept of “Cooling Relevance” which integrates intensity, spatial extent, reliability, and the number of potential beneficiaries. Parks emerged as the most relevant UGS type under this framework. Our findings highlight the need to incorporate reliability into urban greening strategies to support equitable and climate-resilient urban planning.

在全球城市化和热浪日益频繁的背景下，城市绿地在缓解城市热岛（UHI）方面发挥着至关重要的作用。然而，这些冷却服务的可变性和可靠性往往被忽视。本研究解决了德国柏林不同UGS类型冷却效率的时间和站点间变异性。利用6个无云Landsat 8 OLI场景分析了228个公园、512个地块和143个墓地的内部（地表温度，LST）和外部（公园冷却距离，PCD，公园冷却强度，PCI）冷却参数。虽然LST在不同地点之间的变化不大，但PCD和PCI的波动更大，特别是在分配中。随着时间的推移，只有9-16%的地下水库表现出稳定的冷却距离，公园达到了最大的PCD，而墓地提供了最强烈和可靠的冷却。分配是最不有效和最不稳定的。为了研究PCD时空变化的驱动因素，我们利用植被指数、空间指标和城市结构进行了回归分析。然而，这些模型解释了很少的方差（R²高达0.3），表明复杂的外部影响没有被现有数据完全捕获。为了分析UGS在缓解城市热岛影响方面的多方面作用，我们提出了“降温相关性”的新概念，该概念综合了强度、空间范围、可靠性和潜在受益者的数量。在这一框架下，公园成为最相关的UGS类型。我们的研究结果强调了将可靠性纳入城市绿化战略以支持公平和气候适应型城市规划的必要性。

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引用次数: 0