Energy Catalyst 3103-9952 Caravel Press 10.61552/EC.2025.004 2025.004 Accelerating Whole Life Carbon Assessment in Construction with Artificial Intelligence https://orcid.org/0000-0002-9867-6011 Al-HabaibehAmin amin.al-habaibeh@ntu.ac.uk https://orcid.org/0000-0002-9002-3681 ManuEmmanuel ClementTim https://orcid.org/0000-0003-4534-9196 ShakmakBubaker https://orcid.org/0009-0008-6532-7326 SelvamJanani https://orcid.org/0009-0002-1701-5498 LinTsai-Hsuan Product Innovation Centre, School of Architecture, Design and The Built Environment, Nottingham Trent University, UK Centre for the Built Environment, School of Architecture, Design and The Built Environment, Nottingham Trent University, UK Morgan Sindall Construction, UK 30052025 1 54 67 10 February 2025 21 April 2025 30 May 2025 © 2025 The Author(s). Published by Caravel Press. 2025 This is an open access article under the CC BY 4.0 licence.

The global effort to reduce carbon emission and mitigate the environmental carbon footprint of the construction industry, along with its impact on climate change, has prompted construction organisations to integrate life cycle carbon assessment into their practices. One of the key areas for enhancing sustainability is the immediate evaluation of carbon footprint in the design stage of construction projects. This includes carbon emissions associated with the intrinsic properties of materials, as well as those related to transportation and installation. Additionally, there are carbon emissions linked to the maintenance and operation of the built asset throughout a project life cycle. This paper aims to accelerate whole-life carbon assessment by integrating artificial intelligence with CarboniCa software, an in-house carbon assessment tool utilised by a major UK construction organisation. To speed up the evaluation process, a new development is suggested using AI deep-learning neural networks to learn from experience and to estimate carbon footprint, thus reducing time, energy and cost. By leveraging historical construction project data within the CarboniCa software, the experimental results provided a reasonable estimation (R² = 0.87) of the whole-life carbon for different building types. With the integration of deep learning neural networks, the proposed process is expected to improve efficiency by saving time and resources. It will provide designers with rapid guidance during the early design stage, enabling them to reduce the life-cycle carbon impact more effectively. The paper begins with a literature review on the significance of life cycle carbon assessment in the construction industry, followed by an overview of CarboniCa, a carbon assessment tool. It then explores the integration of artificial intelligence to enhance the software’s ability to rapidly evaluate whole-life carbon, thereby promoting sustainability within the built environment.

 AI Artificial Intelligence Construction Industry Carbon Footprint Sustainable Construction S. Su, Z. Zang, J. Yuan, X. Pan, and M. Shan, “Considering critical building materials for embodied carbon emissions in buildings: A machine learning-based prediction model and tool,” Case Studies in Construction Materials, vol. 20, Jul. 2024, doi: 10.1016/j.cscm.2024.e02887. 10.1016/j.cscm.2024.e02887 X. Zhang, Y. Li, H. Chen, X. Yan, and K. Liu, “Characteristics of embodied carbon emissions for high-rise building construction: A statistical study on 403 residential buildings in China,” Resour Conserv Recycl, vol. 198, Nov. 2023, doi: 10.1016/j.resconrec.2023.107200. 10.1016/j.resconrec.2023.107200 RICS, Whole life carbon assessment for the built environment RICS PROFESSIONAL STANDARD. 2024. [Online]. Available: www.rics.org T. Ibn-Mohammed, R. Greenough, S. Taylor, L. Ozawa-Meida, and A. Acquaye, “Operational vs. embodied emissions in buildings - A review of current trends,” 2013. doi: 10.1016/j.enbuild.2013.07.026. 10.1016/j.enbuild.2013.07.026 M. A. Kamazani and M. K. Dixit, “Multi-objective optimization of embodied and operational energy and carbon emission of a building envelope,” J Clean Prod, vol. 428, Nov. 2023, doi: 10.1016/j.jclepro.2023.139510. 10.1016/j.jclepro.2023.139510 G. Mohebbi, A. Hasan, A. Blay-Armah, A. Bahadori-Jahromi, A. Mylona, and M. Barthorpe, “Comparative analysis of the whole life carbon of three construction methods of a UK-based supermarket,” Building Services Engineering Research and Technology, vol. 44, no. 3, pp. 355– 375, May 2023, doi: 10.1177/01436244231161070. 10.1177/01436244231161070 W. Pan and Y. Teng, “A systematic investigation into the methodological variables of embodied carbon assessment of buildings,” May 01, 2021, Elsevier Ltd. doi: 10.1016/j.rser.2021.110840. 10.1016/j.rser.2021.110840 N. C. Kayaçetin and A. M. Tanyer, “Embodied carbon assessment of residential housing at urban scale,” Renewable and Sustainable Energy Reviews, vol. 117, Jan. 2020, doi: 10.1016/j.rser.2019.109470. 10.1016/j.rser.2019.109470 Y. H. Labaran, V. S. Mathur, and M. M. Farouq, “The carbon footprint of construction industry: A review of direct and indirect emission,” Sep. 01, 2021, Kare Publishing. doi: 10.29187/jscmt.2021.66. 10.29187/jscmt.2021.66 N. F. Arenas and M. Shafique, “Reducing embodied carbon emissions of buildings – a key consideration to meet the net zero target,” Sustainable Futures, vol. 7, Jun. 2024, doi: 10.1016/j.sftr.2024.100166. 10.1016/j.sftr.2024.100166 H. L. Gauch, C. F. Dunant, W. Hawkins, and A. Cabrera Serrenho, “What really matters in multi- storey building design? A simultaneous sensitivity study of embodied carbon, construction cost, and operational energy,” Appl Energy, vol. 333, Mar. 2023, doi: 10.1016/j.apenergy.2022.120585. 10.1016/j.apenergy.2022.120585 A. Akbarnezhad and J. Xiao, “Estimation and minimization of embodied carbon of buildings: A review,” 2017, MDPI AG. doi: 10.3390/buildings7010005. 10.3390/buildings7010005 M. P. Drewniok et al., “Mapping material use and embodied carbon in UK construction,” Resour Conserv Recycl, vol. 197, Oct. 2023, doi: 10.1016/j.resconrec.2023.107056. 10.1016/j.resconrec.2023.107056 H. Gao et al., “A Review of Building Carbon Emission Accounting and Prediction Models,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/buildings13071617. 10.3390/buildings13071617 F. Pomponi and A. Moncaster, “Scrutinising embodied carbon in buildings: The next performance gap made manifest,” Jan. 01, 2018, Elsevier Ltd. doi: 10.1016/j.rser.2017.06.049. 10.1016/j.rser.2017.06.049 X. Zhang, K. Liu, and Z. Zhang, “Life cycle carbon emissions of two residential buildings in China: Comparison and uncertainty analysis of different assessment methods,” J Clean Prod, vol. 266, Sep. 2020, doi: 10.1016/j.jclepro.2020.122037. 10.1016/j.jclepro.2020.122037 S. Gavankar, S. Anderson, and A. A. Keller, “Critical Components of Uncertainty Communication in Life Cycle Assessments of Emerging Technologies: Nanotechnology as a Case Study,” J Ind Ecol, vol. 19, no. 3, pp. 468–479, Jun. 2015, doi: 10.1111/jiec.12183. 10.1111/jiec.12183 Y. Cang, Z. Luo, L. Yang, and B. Han, “A new method for calculating the embodied carbon emissions from buildings in schematic design: Taking ‘building element’ as basic unit,” Build Environ, vol. 185, Nov. 2020, doi: 10.1016/j.buildenv.2020.107306. 10.1016/j.buildenv.2020.107306 Y. Cang, L. Yang, Z. Luo, and N. Zhang, “Prediction of embodied carbon emissions from residential buildings with different structural forms,” Sustain Cities Soc, vol. 54, Mar. 2020, doi: 10.1016/j.scs.2019.101946. 10.1016/j.scs.2019.101946 Y. A. Huang, C. L. Weber, and H. S. Matthews, “Categorization of scope 3 emissions for streamlined enterprise carbon footprinting,” Environ Sci Technol, vol. 43, no. 22, pp. 8509– 8515, Nov. 2009, doi: 10.1021/es901643a. 10.1021/es901643a Y. Chang, Z. Huang, R. J. Ries, and E. Masanet, “The embodied air pollutant emissions and water footprints of buildings in China: A quantification using disaggregated input-output life cycle inventory model,” J Clean Prod, vol. 113, pp. 274– 284, Feb. 2016, doi: 10.1016/j.jclepro.2015.11.014. 10.1016/j.jclepro.2015.11.014 C. K. Chau, T. M. Leung, and W. Y. Ng, “A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings,” 2015, Elsevier Ltd. doi: 10.1016/j.apenergy.2015.01.023. 10.1016/j.apenergy.2015.01.023 H. Islam, M. Jollands, and S. Setunge, “Life cycle assessment and life cycle cost implication of residential buildings - A review,” 2015, Elsevier Ltd. doi: 10.1016/j.rser.2014.10.006. 10.1016/j.rser.2014.10.006 J. Xu, Y. Teng, W. Pan, and Y. Zhang, “BIM- integrated LCA to automate embodied carbon assessment of prefabricated buildings,” J Clean Prod, vol. 374, Nov. 2022, doi: 10.1016/j.jclepro.2022.133894. 10.1016/j.jclepro.2022.133894 M. Lotteau, P. Loubet, and G. Sonnemann, “An analysis to understand how the shape of a concrete residential building influences its embodied energy and embodied carbon,” Energy Build, vol. 154, pp. 1–11, Nov. 2017, doi: 10.1016/j.enbuild.2017.08.048. 10.1016/j.enbuild.2017.08.048 X. Zhang, H. Chen, J. Sun, and X. Zhang, “Predictive models of embodied carbon emissions in building design phases: Machine learning approaches based on residential buildings in China,” Build Environ, vol. 258, Jun. 2024, doi: 10.1016/j.buildenv.2024.111595. 10.1016/j.buildenv.2024.111595 M. Xikai, W. Lixiong, L. Jiwei, Q. Xiaoli, and W. Tongyao, “Comparison of regression models for estimation of carbon emissions during building’s lifecycle using designing factors: a case study of residential buildings in Tianjin, China,” Energy Build, vol. 204, Dec. 2019, doi: 10.1016/j.enbuild.2019.109519. 10.1016/j.enbuild.2019.109519 C. Zhu, Y. Chang, X. Li, and M. Shan, “Factors influencing embodied carbon emissions of China’s building sector: An analysis based on extended STIRPAT modeling,” Energy Build, vol. 255, Jan. 2022, doi: 10.1016/j.enbuild.2021.111607. 10.1016/j.enbuild.2021.111607 M. F. Victoria and S. Perera, “Parametric embodied carbon prediction model for early stage estimating,” Energy Build, vol. 168, pp. 106–119, Jun. 2018, doi: 10.1016/j.enbuild.2018.02.044. 10.1016/j.enbuild.2018.02.044 C. Y. Chen, K. K. Chai, and E. Lau, “AI-Assisted approach for building energy and carbon footprint modeling,” Energy and AI, vol. 5, Sep. 2021, doi: 10.1016/j.egyai.2021.100091. 10.1016/j.egyai.2021.100091 R. O. Yussuf and O. S. Asfour, “Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview,” Feb. 15, 2024, Elsevier Ltd. doi: 10.1016/j.enbuild.2024.113903. 10.1016/j.enbuild.2024.113903 Y. Fang, X. Lu, and H. Li, “A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage,” J Clean Prod, vol. 328, Dec. 2021, doi: 10.1016/j.jclepro.2021.129657. 10.1016/j.jclepro.2021.129657 S. Su et al., “Temporal dynamic assessment of household energy consumption and carbon emissions in China: From the perspective of occupants,” Sustain Prod Consum, vol. 37, pp. 142–155, May 2023, doi: 10.1016/j.spc.2023.02.014. 10.1016/j.spc.2023.02.014 M. Lu, Z. Luo, Y. Cang, N. Zhang, and L. Yang, “Methods for Calculating Building-Embodied Carbon Emissions for the Whole Design Process,” Fundamental Research, Feb. 2024, doi: 10.1016/j.fmre.2022.07.015. 10.1016/j.fmre.2022.07.015 F. Pomponi, M. L. Anguita, M. Lange, B. D’Amico, and E. Hart, “Enhancing the Practicality of Tools to Estimate the Whole Life Embodied Carbon of Building Structures via Machine Learning Models,” Front Built Environ, vol. 7, Oct. 2021, doi: 10.3389/fbuil.2021.745598. 10.3389/fbuil.2021.745598 F. Ascione, N. Bianco, G. M. Mauro, and G. P. Vanoli, “A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin,” Appl Energy, vol. 241, pp. 331–361, May 2019, doi: 10.1016/j.apenergy.2019.03.028. 10.1016/j.apenergy.2019.03.028