In the coming decades, millions of urban residents will be exposed to increasingly deadly heat extremes. As an adaptive response to rising temperatures, many cities have begun to install vegetated ”green” roofs, which now vary widely in structure and size, and have generally been shown to have cooling capacity. Yet, little research has been done to differentiate which types of green roofs are most effective at reducing urban heat. We present a method to evaluate the cooling effects associated with green infrastructure that draws on publicly available satellite imagery and open-source software for analysis. Based on a quasi-experimental research design that integrates social and physical science approaches, this technique is able to identify the cooling effects of green infrastructure against background warming trends associated with urbanization and climate change. We demonstrate this method at three green roof sites across the City of Chicago, finding that the study sites with larger, intensive green roofs accompanied by diverse plant species have greater cooling benefits than the extensive, monoculture green roof. Our low-cost method can aid policymakers and planners in empirically evaluating the cooling capacity of green roofs in their own communities. • Green infrastructure can be evaluated with low-cost remote sensing imagery. • Quasi-experimental research designs can identify the cooling effects of green roofs. • Not all green roofs can fully counteract localized warming trends.

Kathryn McConnell

Christian V. Braneon

Equisha Glenn

Natasha Stamler

Evan Mallen

Daniel P. Johnson

Raaghav Pandya

Jacob Abramowitz

Gabriel Fernandez

Cynthia Rosenzweig

Sustainable Cities and Society

A quasi-experimental approach for evaluating the heat mitigation effects of green roofs in Chicago, Illinois

Human mobility, as a fundamental requirement of everyday life, has been most directly impacted during the COVID-19 pandemic. Existing studies have revealed its ensuing changes. However, its resilience, which is defined as people's ability to resist such impact and maintain their normal mobility, still remains unclear. Such resilience reveals people's response capabilities to the pandemic and quantifying it can help us better understand the interplay between them. Herein, we introduced an integrated framework to quantify the resilience of human mobility to COVID-19 based on its change process. Taking Beijing as a case study, the resilience of different mobility characteristics among different population groups, and under different waves of COVID-19, were compared. Overall, the mobility range and diversity were found to be less resilient than decisions on whether to move. Females consistently exhibited lower resilience than males; middle-aged people exhibited the lowest resilience under the first wave of COVID-19 while older adult's resilience became the lowest during the COVID-19 rebound. With the refinement of pandemic-control measures, human mobility resilience was enhanced. These findings reveal heterogeneities and variations in people's response capabilities to the pandemic, which can help formulate targeted and flexible policies, and thereby promote sustainable and resilient urban management.

Yaxi Liu

Xi Wang

Ci Song

Jie Chen

Hua Shu

Mingbo Wu

Sihui Guo

Qiang Huang

Tao Pei

Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China

量化人类流动性对COVID-19大流行的韧性：以中国北京为例

Numerous studies on urban heat island (UHI) effects have examined relationships between land use/land cover (LULC) and land surface temperature (LST), while impacts of environmental temperatures on these relationships have received little attention. This paper has investigated how background temperature affects performances of cool/hot sources in either enhancing or mitigating LST in Shenyang, China based on Landsat 8 image. In specific, according to the method of standard classification method with an interval of 1.0 standard deviation, LST was classified into seven temperature levels: highest, higher, high, medium, low, lower and lowest. Afterwards, the seven levels were merged into three temperature zones (TZ), namely cold TZ (CTZ, highest, higher and high), neutral TZ (NTZ, medium) and hot TZ (HTZ, low, lower and lowest), for investigating relationships between LST and LULC (including building (BU), road (RO), bare land (BA), green land (GR), agricultural land (AG) and water bodies (WA)). LST generally follows the order of BU > RO > BA > AR > GR > WA, but temperature differences between cool/hot sources reduce as background temperature increases. LULC-LST relationships vary greatly with background temperature. Overall, BU and RO have significant positive relationships with LST, while negative relationships are found among GR, AR, WA and LST. However, in CTZ, RO is in negative relationships with LST, GR and WA are no longer significant in NTZ, and GR and BA play significant positive roles in aggravating LST in HTZ. Generally, this study can help orientate scientific decisions in urban environmental governance.

Bao-Jie He

Zi-Qi Zhao

Li-Du Shen

Hong-Bo Wang

Li-Guang Li

An approach to examining performances of cool/hot sources in mitigating/enhancing land surface temperature under different temperature backgrounds based on landsat 8 image

基于Landsat 8影像的不同温度背景下冷/热源对地表温度缓解/增强作用的评估方法

Hong Fang

Shanchuan Guo

Chenghan Yang

Bo Yuan

Chunqiang Li

Xiaoquan Pan

Pengfei Tang

Peijun Du

Influence of urban functional zone change on land surface temperature using multi-source geospatial data: A case study in Nanjing City, China

多源地理空间数据支持下城市功能区变化对地表温度的影响：以中国南京市为例

Urban and industrial forms were widely concerned as influential factors of surface urban heat island (SUHI) at the macroscale. However, there is still a lack of systematic understanding of urban and industrial forms’ nonlinear and threshold effects on SUHI. This study aimed to disclose their nonlinear effects on daytime and nighttime SUHI based on the random forest (RF) model, using the case of 162 prefecture-level cities in China. The RF results showed that industrial factors collectively contributed 43.38% to daytime SUHI variance, whereas urban factors contributed 20.44% to nighttime SUHI variance. Industrial diversity and urban contiguity were the most critical non-natural factors influencing daytime and nighttime SUHI, respectively. Most factors positively affected daytime and nighttime SUHI, except for the urban elongation. Urban boundary complexity had a distinct effect on daytime and nighttime SUHI. Urban compactness/contiguity and industrial agglomeration/diversity had nonlinear and threshold effects on SUHI. Based on the findings, we have a further understanding of the effects of urban and industrial forms on SUHI in the effective ranges.

Yujia Ming

Yong Liu

Jianping Gu

Jinzhu Wang

Xue Liu

Nonlinear effects of urban and industrial forms on surface urban heat island: Evidence from 162 Chinese prefecture-level cities

城市与工业形态对地表城市热岛的非线性影响：来自中国162个地级市的证据

Building energy consumption modeling and forecasting is essential to address buildings energy efficiency problems and take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, data-driven techniques rely on data analysis and machine learning to provide flexible methods for building energy prediction. The present paper offers a review of studies developing data-driven models for building scale applications. The prevalent methods are introduced with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment. A special attention is also given to different machine learning approaches. Based on the results of this review, the latest technical improvements and research efforts are synthesized. The key role of occupants’ behavior integration in data-driven modeling is discussed. Limitations and research gaps are highlighted. Future research opportunities are also identified.

Mathieu Bourdeau

Xiao Qiang Zhai

Elyes Nefzaoui

Xiaofeng Guo

Patrice Chatellier

Modeling and forecasting building energy consumption: A review of data-driven techniques

建筑能耗建模与预测：数据驱动技术的综述

As cities worldwide grapple with the escalating challenges of extreme heat events, afforestation can serve as an effective adaptation measure to mitigate heat stress. This study presents a comprehensive nationwide assessment of urban tree-induced cooling effects and provides a more accurate and robust methodology that accounts for the influence of spatial heterogeneity within the complex urban environment. Results reveal that the conventional method for assessing cooling efficiency (TCE) is substantially influenced by urban heterogeneity, in terms of both horizontal and vertical structures. The revised tree cooling efficiency (RTCE), designed to minimize the impacts of other land cover types on surface temperature variations, was proved to be a more accurate estimation. In 362 out of 379 cities across China, TCE tended to underestimate the cooling effects as a result of spatial heterogeneity. The nationwide average disparity between RTCE and TCE was approximately 0.047±0.0145 ℃ per percent (95 % confidence interval). The largest differences were observed among high-rise environments, highlighting the undervalued heat mitigation potential of urban trees in compact and densely populated urban settings. This study emphasizes the considerable potential of urban tree-cooling effects in mitigating heat stress in the face of a warming planet.

Song Leng

Ranhao Sun

Ming Yan

Liding Chen

Prevalent underestimation of tree cooling efficiency attributed to urban intrinsic heterogeneity

城市内在异质性导致树木降温效率普遍低估

Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest. Forecasting enables informed and efficient responses for electricity demand. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. Over 113 different case studies reported across 41 academic papers have been used for the comparison. The timeframe, inputs, outputs, scale, data sample size, error type and value have been taken into account as criteria for the comparison. The review reveals that despite the relative simplicity of all reviewed models, the regression and/or multiple regression are still widely used and efficient for long and very long-term prediction. For short and very short-term prediction, machine-learning algorithms such as artificial neural networks, support vector machines, and time series analysis (including Autoregressive Integrated Moving Average (ARIMA) and the Autoregressive Moving Average (ARMA)) are favoured. The most widely employed independent variables are the building and occupancy characteristics and environmental data, especially in the machine learning models. In many cases, time series analysis and regressions rely on electricity historical data only, without the introduction of exogenous variables. Overall, if the singularity of the different cases made the comparison difficult, some trends are clearly identifiable. Considering the large amount of use cases studied, the meta-analysis of the references led to the identification of best practices within the expert community in relation to forecasting use for electricity consumption and power load prediction. Therefore, from the findings of the meta-analysis, a taxonomy has been defined in order to help researchers make an informed decision and choose the right model for their problem (long or short term, low or high resolution, building to country level).

Corentin Kuster

Yacine Rezgui

Monjur Mourshed

Electrical load forecasting models: A critical systematic review

电力负荷预测模型：一项关键的系统性综述

• Daily ambient temperature was positively associated with sex offense with 8 days lag. • The association was stronger in hot season and cold season compared to moderate season. • The association can be enhanced by higher relative humidity and precipitation. • The association was only significant for sodomy, fondling and rape. • The association was only significant for out-of-residence sex offenses. There is an increasing interest in the link between ambient temperature and sexual crime in the context of climate change. However, existing studies are limited in evaluating the acute effect of temperature and rarely estimate the attributable burden. Here, we show that in seven large US cities, every 5 °C rise in daily mean temperature was associated with a 4.5 % [95 % confidence interval (CI): 2.8–6.3 %] increase in sex offenses in the following 0–8 days. The associations were stronger in hot and cold season compared to moderate season, and could be enhanced by higher relative humidity and precipitation. The associations were only significant for sodomy, fondling and rape, and for sex offenses happened in certain locations (open space, education, street but not residence). We estimated that 2.6 % (95 %CI: 1.7–3.6 %) sex offenses were attributable to temperatures above city-specific median temperatures, corresponding to a mean annual sex offense rate of 2.9/100,000 (95 %CI: 1.9–4.0/100,000). Our findings highlight the potential rising sexual crime along with climate change and provide useful information for targeted preventions.

Rongbin Xu

Xiuqin Xiong

Michael J. Abramson

Shanshan Li

Yuming Guo

Association between ambient temperature and sex offense: A case-crossover study in seven large US cities, 2007–2017

环境温度与性犯罪之间的关联：美国七大城市2007-2017年病例交叉研究

Block thermal environments affect public health and the sustainable development of cities. However, the study of spatial heterogeneity of block morphology in thermal environments needs further research, and exploring a new classification system of block morphology will also be beneficial to urban management. Taking 1426 blocks of Dalian city as an example, this paper proposed a new classification system of block morphology (HDR system) by a two-step cluster. Pearson coefficient, ordinary least squares (OLS) model, and geographically weighted regression (GWR) model were applied to explore the mechanisms of building height (BH), building density (BD), and plot ratio (PR) on land surface temperature (LST) globally (non-classification) and locally (classification system HDR1-5) respectively. The results are as follows: 1) Low-floor buildings dominate Dalian's blocks. 2) Greater intensity of block development is associated with greater LST. 3) BH, BD, and PR show significant spatial heterogeneity on LST. 4) LST is most notably affected by BD. 5) HDR5 is recommended as the preferred block plan to face the worsening of the thermal environment. The results of this study provide direct references and implications for city managers and designers who want to develop practical and sustainable policies and norms for urban renewal and new blocks.

Chanjuan Wang

Zongmao Li

Yuan Su

Qinfeng Zhao

Xiujuan He

Zhirui Wu

Weijun Gao

Ziting Wu

Impact of block morphology on urban thermal environment with the consideration of spatial heterogeneity

考虑空间异质性的街区形态对城市热环境的影响

可持续城市与社会 (SCS) 是一本国际期刊，专注于旨在减少城市的环境和社会影响的基础和应用研究。

Sustainable Cities and Society is an international journal that focuses on fundamental and applied research aimed at reducing the environmental and social impacts of urban areas.

《可持续城市与社会》（Sustainable Cities and Society）是一本国际期刊，专注于基础和应用研究，旨在减少城市对环境和社会的影响。

期刊	Sustainable Cities and Society
ISSN	2210-6707, 2210-6715
h-index	130
Journal Impact	10.67
国际分区	GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY(Q1)
国内分区	工程技术(1区)工程技术结构与建筑技术(1区)工程技术能源与燃料(1区)工程技术绿色可持续发展技术(1区)