Abstract
Mitigating climate change challenges in the built environment through the decarbonisation of energy and construction materials remains a pressing challenge. The circular economy (CE) has been identified as a critical pathway to achieving this objective. CE promotes the efficient use of resources, extending their lifecycle and minimising their environmental impact using a plethora of methods. The link between CE and decarbonisation becomes evident when the intertwined relationship between materials, energy, and the environment is considered. By reducing waste and ensuring the continuous use of materials and energy resources, CE significantly lowers carbon emissions. This approach is inherently aligned with the overarching goals of the decarbonisation agenda. The emergence of digital technologies such as artificial intelligence (AI) has continued to transform how the built environment activities are conducted and improved. However, the utility of AI models in engendering the actualisation of the decarbonisation agenda through improved circular economy performance within the built environment context remains under-researched. This study addresses this knowledge-practice gap, using a scientometric and scoping analysis of relevant peer-reviewed and grey literature. Findings from the scientometric analysis revealed AI has been explored separately in circular economy and decarbonisation. Yet, studies exploring AI in relation to the circularity performance of the built environment for improved decarbonisation remain scant. The narrative review from the scoping analysis further revealed the usefulness of AI in driving optimal decarbonisation and levels through improved circularity performance of materials and energy across various economic sectors, including the built environment for optimal decision making which in turn, encourages responsible producer and consumer behaviour for improved CE performance.