Projected risk and vulnerability to heat waves for Montreal, Quebec, using Gaussian processes

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-10-28 DOI:10.1016/j.scs.2024.105907

Jean-Nicolas Côté , Elisabeth Levac , Mickaël Germain , Eric Lavigne

{"title":"Projected risk and vulnerability to heat waves for Montreal, Quebec, using Gaussian processes","authors":"Jean-Nicolas Côté , Elisabeth Levac , Mickaël Germain , Eric Lavigne","doi":"10.1016/j.scs.2024.105907","DOIUrl":null,"url":null,"abstract":"<div><div>Urban areas face increasing climate risks and are at the forefront of adaptation challenges. Despite the growing number of cities that are developing adaptation plans, they often fail to implement, monitor, and evaluate them. This article addresses this issue by modelling a comprehensive risk assessment that includes vulnerability using Gaussian processes. Mortality during heat waves for the City of Montreal, Quebec, is used as a case study. The vulnerability model includes sensitivity components (age and socioeconomic variables) and an adaptive capacity component (a suitable level of vegetation to decrease the urban heat island effect). Various aging and climate scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) are used for projections up to year 2100. SHAP values are used to show features contributions to the model. As the climate warms, Montreal will face increasing summer mortality. The city should therefore increase its vegetation cover in vulnerable neighbourhoods. Despite inherent limitations to the complexity of risk modelling, this approach facilitates the implementation of adaptation solutions and their monitoring. Greater effort should be made in the future to improve comprehensive risk modelling and more research is required to validate which framework is best in closing the gap between science and political decisions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105907"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007315","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Urban areas face increasing climate risks and are at the forefront of adaptation challenges. Despite the growing number of cities that are developing adaptation plans, they often fail to implement, monitor, and evaluate them. This article addresses this issue by modelling a comprehensive risk assessment that includes vulnerability using Gaussian processes. Mortality during heat waves for the City of Montreal, Quebec, is used as a case study. The vulnerability model includes sensitivity components (age and socioeconomic variables) and an adaptive capacity component (a suitable level of vegetation to decrease the urban heat island effect). Various aging and climate scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) are used for projections up to year 2100. SHAP values are used to show features contributions to the model. As the climate warms, Montreal will face increasing summer mortality. The city should therefore increase its vegetation cover in vulnerable neighbourhoods. Despite inherent limitations to the complexity of risk modelling, this approach facilitates the implementation of adaptation solutions and their monitoring. Greater effort should be made in the future to improve comprehensive risk modelling and more research is required to validate which framework is best in closing the gap between science and political decisions.

查看原文本刊更多论文

利用高斯过程预测魁北克省蒙特利尔市的热浪风险和脆弱性

城市地区面临着越来越大的气候风险，并处于适应挑战的最前沿。尽管越来越多的城市正在制定适应计划，但它们往往无法实施、监测和评估这些计划。本文针对这一问题，利用高斯过程建立了一个包括脆弱性在内的综合风险评估模型。本文以魁北克省蒙特利尔市热浪期间的死亡率为案例进行研究。脆弱性模型包括敏感性部分（年龄和社会经济变量）和适应能力部分（降低城市热岛效应的适当植被水平）。各种老化和气候情景（SSP1-2.6、SSP2-4.5 和 SSP5-8.5）用于预测 2100 年之前的情况。SHAP 值用于显示对模型的贡献特征。随着气候变暖，蒙特利尔的夏季死亡率将越来越高。因此，该市应增加脆弱街区的植被覆盖率。尽管风险建模的复杂性有其固有的局限性，但这种方法有助于适应解决方案的实施及其监测。今后应加大力度改进综合风险建模，并需要开展更多研究，以验证哪种框架最能缩小科学与政治决策之间的差距。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;