A Decarbonization Framework for Existing Building Portfolios

现有建筑组合的脱碳框架

• 批准号: 577383-2022
• 负责人: Lee, BrunoB
• 金额: $ 14.82万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746359
• 关键词: Decarbonization; Framework; Existing; Building; Portfolios

Canada's pathway to a net-zero carbon target by 2050 must include revamping Canada's building stock. Our study aims to achieve the followings:- First, we will extend the existing knowledge base that typically focuses on retrofitting individual buildings (a case-specific and time-consuming process) and apply it to large portfolios of buildings. This requires research that goes beyond building science and construction; this project will incorporate data-driven analysis and machine-learning techniques over large portfolios of buildings in Canada.- Buildings that perform well now will operate at sub-optimal conditions in the future under climate change. This project will consider different climate change scenarios to optimize retrofitting measures to yield robust performance over time.- Volatility in the carbon market tends to discourage the willingness of building owners to invest in retrofitting measures, as payback period can be long and full of uncertainty. This project will apply stochastic risk analyses to devise policy recommendations that will continue incentivizing building owners to engage in retrofitting measures while making long-term financing affordable and sustainable.This project will develop a multidisciplinary decarbonization framework, by engaging experts from both engineering and finance backgrounds, for large portfolios of buildings. The project outcome supports decision-making and prioritizes retrofitting measures on a significant portion of building stock in Canada.

加拿大到2050年实现净零碳目标的途径必须包括改造加拿大的建筑存量。我们的研究旨在实现以下目标：-首先，我们将扩展现有的知识库，这些知识库通常侧重于对个别建筑进行翻新(这是一个针对具体情况和耗时的过程)，并将其应用于大型建筑组合。这需要超越建筑科学和建筑的研究；这个项目将在加拿大的大型建筑组合中纳入数据驱动的分析和机器学习技术。-现在表现良好的建筑未来将在气候变化下处于次优条件下运行。该项目将考虑不同的气候变化情景，以优化改造措施，以随着时间的推移产生强劲的业绩。-碳市场的波动往往会抑制建筑物所有者投资于改造措施的意愿，因为回收期可能很长，充满不确定性。该项目将应用随机风险分析来制定政策建议，以继续激励建筑物业主参与翻新措施，同时使长期融资变得负担得起和可持续。该项目将通过聘请来自工程和金融背景的专家，为大型建筑物投资组合开发一个多学科的脱碳框架。该项目成果支持决策，并确定了加拿大相当大一部分建筑存量的翻新措施的优先次序。