Recent research relating to energy use and carbon emissions by buildings has started to move from operational energy carbon impacts to the embodied energy/carbon impacts of buildings, and the methods and approaches used in architectural design to reduce embodied carbon have become more prominent. From a practitioner’s perspective, working with an ‘in-house’ Life Cycle Assessment (LCA) tool has become a growing trend for architects, and one perceived way of improving the LCA outcomes of a proposed building is to consider prefabrication of the construction process. Initiatives such as the Low Energy Transformation Initiative (LETI) and government bodies such as Greater London Authorities (GLA) provide guidance on LCA and upfront carbon emission targets for transitioning to net zero by 2050. The aim of this study was to establish (i) the LCA impacts from prefabricated residential buildings against current benchmarks; (ii) boundaries and opportunities in architectural practice in the UK when conducting an LCA; (iii) the effectiveness of an in-house LCA tool. This study shows that, although the life-cycle emissions of this prefabricated building achieved a low band in the LETI labelling system, with 1076 kgCO_2e/m², it still performs better than the business-as-usual model value of 1200 kgCO_2e/m². The results also reveal that the construction industry is not ready to provide realistic data on the prefabrication process to test its advantages compared to conventional constructional methods. However, having an in-house LCA tool provides a faster and more comprehensive LCA due to the commitment to carbon assessment in the office and saves time compared to manual calculations.

Until recently, reducing the energy required to service a building (the operational energy) was the main aim of controlling carbon emissions from the built environment. It is now recognised that the energy required to make a building (the embodied energy) also has a crucial role in creating a net zero carbon future. The methodologies for quantifying embodied carbon are less developed than those for operational carbon, and more research is required to refine the embodied carbon metrics used when a building’s whole-life carbon emissions are calculated in a Life Cycle Assessment (LCA). One such metric is the Environmental Product Declaration (EPD), a document which can be used in different countries to quantify a product's environmental performance. EPDs are crucial data for conducting an LCA study of a building. However, despite recent efforts to standardise them, there are still inconsistencies between EPDs produced by different countries or manufacturers, even for materials with similar thermal and physical properties. This study considered some of the reasons for variations in EPDs for one product type, expanded polystyrene insulation (EPS). Factors such as (i) the LCA databases and software generators used for the EPDs, (ii) material mixes and manufacturing methods, (iii) country energy production mixes, and (iv) transportation distance from material source to the factory were considered in the analysis. As a case study, this paper examined the effects of selecting different EPDs for expanded polystyrene insulation on the final LCA results from the retrofit of a mid-rise residential building in Turkiye. Differences in EPDs demonstrated a fourfold difference between the highest and lowest upfront carbon impact results of building retrofit. This size of discrepancy indicates the need to choose the most appropriate EPD for a building/location when performing an LCA. Practical Applications: Selecting an EPD when conducting an LCA for a new building or retrofit is generally left to the assessor’s judgment and knowledge, which varies greatly depending on the assessor’s background, especially in the construction sector. This study suggests an informed decision-making method over an example of EPS insulation material when the EPD options were none or limited to building locations like Turkiye.

Artificial Intelligence (AI) has rapidly reshaped residential building energy research; however, the field lacks a data‐driven synthesis of its global structure and evolving themes. This study addresses this gap by combining bibliometric analysis with latent Dirichlet allocation (LDA) topic modelling to analyse 1368 articles (2020–2025) from Web of Science Core Collection (WoSCC) and Scopus. Results show a dramatic increase in publications within a global but uneven landscape: China and the United States (US) lead in volume, while Vietnam and Denmark produce the most influential outputs per paper. The United Kingdom (UK) maintains a balanced profile, and several EU member states, including Italy, Spain and Germany, rank among the leading contributors. Collaboration networks are primarily academic, with limited industry involvement. LDA identifies four main themes: energy performance modelling, HVAC optimisation, load forecasting and management and smart‐home energy management. Within these, research centres on operational applications and shows a methodological progression from conventional machine learning (ML) toward hybrid deep learning (DL), including emerging transformer‐based and graph‐based models. The analysis scale is expanding from single‐buildings to district‐level and interconnected systems. The comprehensive review identifies a persistent gap in occupant‐centric modelling and highlights the need for real‐time data integration and improved transparency to deliver practical, human‐responsive, and scalable energy efficiency solutions.