Oral Presentation World Sustainable Built Environment Conference 2026

A Model for Delivering Resilience and Adaptive Capacity in the Built Environment.  (131343)

Mark Mulville 1
  1. TU Dublin, Dublin, IRELAND, Ireland

The buildings we design, develop and renovate today must have resilience and be capable of adapting to the predicted impacts of climate change. Without such resilience and adaptative capacity buildings risk becoming prematurely obsolete, presenting both investment and environmental risks. This research presents a model for climate change adaptation planning in the built environment.  

Utilising an analysis of the predominant typologies within the built stock in Ireland, the research determines key climate related risks. These key risk factors are considered, for the first time, using standarised climate projects specific to Ireland (from the Irish Meteorological Servies, TRANSLATE project). The stock analysis identifies ‘typical’ configurations against the most numerous and high-risk typologies. Each configuration is then evaluated against key risks drawn from the literature. In turn the model is developed to provide a process to support the delivery of resilience and adaptive capacity through design and planning, operation and management within the stock. Proposed changes to the regulatory framework are suggested to support the implementation of the key measures to ensure long-term building performance can be supported. 

The research identified that dwellings, commercial office space and public buildings are the most prevalent typologies in Ireland. While their configuration varies significantly, key risks for each typology include overheating, flood, rainwater penetration, materials degradation and wind damage. The model presents a process for managing the risk presented to ensure long-term building performance. 

The findings of the research have implications for the built environment regulatory framework and highlight areas where reform is required. While focused on Ireland, the methods and model are generlisable to a wide range of locations. Where implemented the proposed model has the potential to reduce the risk of premature obsolescence.