Mora Humberto, Bardhan Ronita
Cambridge Institute for Sustainability Leadership (CISL), University of Cambridge, Cambridge CB2 1TN, UK.
Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK.
R Soc Open Sci. 2025 Jan 15;12(1):241337. doi: 10.1098/rsos.241337. eCollection 2025 Jan.
This study proposes a methodology and a proof of concept to target and prioritize mass retrofitting of residential buildings in the UK using open building datasets that combine fabric energy efficiency and fuel poverty to meet the net-zero targets. The methodological framework uses a series of multi-variate statistical and geospatial methods that consider urban, socio-economic and physical attributes. In addition, thermal imaging is implemented to provide insights at the building scale. We define a hard-to-decarbonize (HtD) metric to enable the clustering of different residential types to establish retrofitting priorities. Using Cambridge, UK, as a case study, five neighbourhoods were identified and characterized to help determine decarbonization intervention priorities. We found that one of five clusters of neighbourhoods is HtD and requires more policy support from government for the implementation of retrofit strategies. The achieved framework has the potential to inform policy and decision making. Of relevance, it is applicable to different urban contexts.
本研究提出了一种方法和概念验证，旨在利用结合了建筑结构能源效率和燃料贫困情况的开放建筑数据集，确定英国住宅建筑大规模改造的目标并对其进行优先排序，以实现净零目标。该方法框架使用了一系列考虑城市、社会经济和物理属性的多变量统计和地理空间方法。此外，还采用了热成像技术，以便在建筑尺度上提供见解。我们定义了一个难以脱碳（HtD）指标，用于对不同住宅类型进行聚类，以确定改造优先级。以英国剑桥为例，识别并描述了五个社区，以帮助确定脱碳干预的优先级。我们发现，五个社区集群中有一个属于难以脱碳类型，需要政府提供更多政策支持以实施改造策略。所达成的框架有可能为政策制定和决策提供参考。重要的是，它适用于不同的城市环境。
