IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3699-d776222.html
   My bibliography  Save this article

Implementing Artificial Intelligence Techniques to Predict Environmental Impacts: Case of Construction Products

Author

Listed:
  • Anish Koyamparambath

    (Institute of Molecular Sciences, University of Bordeaux, Centre National de la Recherche Scientifique, Bordeaux INP, ISM, UMR 5255, 33400 Talence, France
    WeLOOP, 254 Rue de Bourg, 59130 Lambersart, France)

  • Naeem Adibi

    (WeLOOP, 254 Rue de Bourg, 59130 Lambersart, France)

  • Carolina Szablewski

    (WeLOOP, 254 Rue de Bourg, 59130 Lambersart, France)

  • Sierra A. Adibi

    (William E Boeing Department of Aeronautics and Astronautics, University of Washington, Seattle, WA 98195, USA)

  • Guido Sonnemann

    (Institute of Molecular Sciences, University of Bordeaux, Centre National de la Recherche Scientifique, Bordeaux INP, ISM, UMR 5255, 33400 Talence, France)

Abstract

Nowadays, product designers, manufacturers, and consumers consider the environmental impacts of products, processes, and services in their decision-making process. Life Cycle Assessment (LCA) is a tool that assesses the environmental impacts over a product’s life cycle. Conducting a life cycle assessment (LCA) requires meticulous data sourcing and collection and is often time-consuming for both practitioner and verifier. However, predicting the environmental impacts of products and services can help stakeholders and decision-makers identify the hotspots. Our work proposes using Artificial Intelligence (AI) techniques to predict the environmental performance of a product or service to assist LCA practitioners and verifiers. This approach uses data from environmental product declarations of construction products. The data is processed utilizing natural language processing (NLP) which is then trained to random forest algorithm, an ensemble tree-based machine learning method. Finally, we trained the model with information on the product and their environmental impacts using seven impact category values and verified the results using a testing dataset (20% of EPD data). Our results demonstrate that the model was able to predict the values of impact categories: global warming potential, abiotic depletion potential for fossil resources, acidification potential, and photochemical ozone creation potential with an accuracy (measured using R 2 metrics, a measure to score the correlation of predicted values to real value) of 81%, 77%, 68%, and 70%, respectively. Our method demonstrates the capability to predict environmental performance with a defined variability by learning from the results of the previous LCA studies. The model’s performance also depends on the amount of data available for training. However, this approach does not replace a detailed LCA but is rather a quick prediction and assistance to LCA practitioners and verifiers in realizing an LCA.

Suggested Citation

  • Anish Koyamparambath & Naeem Adibi & Carolina Szablewski & Sierra A. Adibi & Guido Sonnemann, 2022. "Implementing Artificial Intelligence Techniques to Predict Environmental Impacts: Case of Construction Products," Sustainability, MDPI, vol. 14(6), pages 1-12, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3699-:d:776222
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3699/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3699/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anthony Halog & Yosef Manik, 2011. "Advancing Integrated Systems Modelling Framework for Life Cycle Sustainability Assessment," Sustainability, MDPI, vol. 3(2), pages 1-31, February.
    2. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghazi Falah & Michael Hoy & Rakhal Sarker, 2000. "Co-existence in Selected Mixed Arab-Jewish Cities in Israel: By Choice or by Default?," Urban Studies, Urban Studies Journal Limited, vol. 37(4), pages 775-796, April.
    2. Cowell, Frank & Flachaire, Emmanuel & Bandyopadhyay, Sanghamitra, 2009. "Goodness-of-fit: an economic approach," LSE Research Online Documents on Economics 25433, London School of Economics and Political Science, LSE Library.
    3. Li, Jiarong & Li, Xiangdong & Wang, Yong & Tu, Jiyuan, 2020. "A theoretical model of natural circulation flow and heat transfer within horizontal evacuated tube considering the secondary flow," Renewable Energy, Elsevier, vol. 147(P1), pages 630-638.
    4. Paul Ofei-Manu & Satoshi Shimano, 2012. "In Transition towards Sustainability: Bridging the Business and Education Sectors of Regional Centre of Expertise Greater Sendai Using Education for Sustainable Development-Based Social Learning," Sustainability, MDPI, vol. 4(7), pages 1-26, July.
    5. Svjetlana Janković Šoja & Ana Anokić & Dana Bucalo Jelić & Radojka Maletić, 2016. "Ranking EU Countries According to Their Level of Success in Achieving the Objectives of the Sustainable Development Strategy," Sustainability, MDPI, vol. 8(4), pages 1-10, March.
    6. Burkey, Mark L. & Obeng, Kofi, 2005. "Crash Risk Reduction at Signalized Intersections Using Longitudinal Data," MPRA Paper 36281, University Library of Munich, Germany.
    7. Daniel Griffith & David Wong, 2007. "Modeling population density across major US cities: a polycentric spatial regression approach," Journal of Geographical Systems, Springer, vol. 9(1), pages 53-75, April.
    8. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    9. Ming Tang & Huchang Liao & Zhengjun Wan & Enrique Herrera-Viedma & Marc A. Rosen, 2018. "Ten Years of Sustainability (2009 to 2018): A Bibliometric Overview," Sustainability, MDPI, vol. 10(5), pages 1-21, May.
    10. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H., 2011. "Accounting for Heterogeneity in Hedging Behavior: Comparing & Evaluating Grouping Methods," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114787, European Association of Agricultural Economists.
    11. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    12. Sarker, Rakhal & Surry, Yves R., 2003. "The Fast Decay Process In Recreational Demand Activities And The Use Of Alternative Count Data Models," Working Papers 34147, University of Guelph, Department of Food, Agricultural and Resource Economics.
    13. Jiarui Liu & Azusa Oita & Kentaro Hayashi & Kazuyo Matsubae, 2022. "Sustainability of Vertical Farming in Comparison with Conventional Farming: A Case Study in Miyagi Prefecture, Japan, on Nitrogen and Phosphorus Footprint," Sustainability, MDPI, vol. 14(2), pages 1-18, January.
    14. Lionel Janin & Benoît Menoni, 2007. "L e contrôle des concentrations en France : une analyse empirique des avis du Conseil de la concurrence," Economie & Prévision, La Documentation Française, vol. 0(2), pages 93-114.
    15. Hiau LooiKee & Alessandro Nicita & Marcelo Olarreaga, 2009. "Estimating Trade Restrictiveness Indices," Economic Journal, Royal Economic Society, vol. 119(534), pages 172-199, January.
    16. Eleni Iacovidou & Jonathan Busch & John N. Hahladakis & Helen Baxter & Kok Siew Ng & Ben M. J. Herbert, 2017. "A Parameter Selection Framework for Sustainability Assessment," Sustainability, MDPI, vol. 9(9), pages 1-18, August.
    17. Man Yu & Anthony Halog, 2015. "Solar Photovoltaic Development in Australia—A Life Cycle Sustainability Assessment Study," Sustainability, MDPI, vol. 7(2), pages 1-35, January.
    18. Dabao Zhang, 2022. "Coefficients of Determination for Mixed-Effects Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 674-689, December.
    19. Wam, Hilde Karine & Pedersen, Hans Chr. & Hjeljord, Olav, 2012. "Balancing hunting regulations and hunter satisfaction: An integrated biosocioeconomic model to aid in sustainable management," Ecological Economics, Elsevier, vol. 79(C), pages 89-96.
    20. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3699-:d:776222. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.