Machine learning-assisted life cycle assessment for sustainable and environmentally responsible manufacturing
Vandana GUPTA, P. L. PARMAR, Krunal P. MODI, Mukta SHARMA, Syed Hassan Imam GARDEZI, Pramod KUMAR
Abstract. Life Cycle Assessment (LCA) represents the de facto standard of measuring the environmental footprint of manufactured products throughout their full value chain, but its usability in industrial decision-making is hampered by its prohibitive computational demands, the large amount of inventory data required, and its lack of real-time process-level capabilities. This paper proposes a comprehensive Machine Learning-Assisted LCA (ML-LCA) framework that addresses these barriers through five tightly integrated components: (1) an eXtreme Gradient Boosting (XGBoost) regressor for rapid Global Warming Potential (GWP) prediction achieving R² = 0.974 and RMSE = 3.8 kg CO₂-eq per functional unit; (2) a Long Short-Term Memory (LSTM) network for dynamic Life Cycle Inventory (LCI) modeling that captures temporal manufacturing variability; (3) SHapley Additive exPlanations (SHAP) for interpretable identification of environmental hotspots; (4) a Non-dominated Sorting Genetic Algorithm III (NSGA-III) multi-objective eco-design optimizer; and (5) an ISO 14040/44-compliant uncertainty quantification module achieving 94% confidence interval coverage. When applied to the case study of aluminum alloy precision machining as a representative gate-to-gate case study, the proposed framework is able to cut down GWP by 27.6, acidification potential by 31.9 and eutrophication potential by 24.7 compared to conventional process baselines and compresses LCA computation time down to less than 0.02 seconds per functional unit. The findings make ML-LCA a technically sound, industrially viable roadmap to a manufacturing process that is environmentally responsible.
Keywords
Machine Learning, Life Cycle Assessment, Environmental Impact, Sustainable Manufacturing, XGBoost, SHAP Interpretability, Eco-Design Optimization, NSGA-III
Published online 8/15/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Vandana GUPTA, P. L. PARMAR, Krunal P. MODI, Mukta SHARMA, Syed Hassan Imam GARDEZI, Pramod KUMAR, Machine learning-assisted life cycle assessment for sustainable and environmentally responsible manufacturing, Materials Research Proceedings, Vol. 71, pp 191-196, 2026
DOI: https://doi.org/10.21741/97816449042312-26
The article was published as article 26 of the book AI-Driven Innovations in Mechanical, Manufacturing, and Energy Engineering
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] G. Finnveden et al., “Recent developments in life cycle assessment,” Journal of Environmental Management, vol. 91, no. 1, pp. 1-21, Oct. 2009. https://doi.org/10.1016/j.jenvman.2009.06.018
[2] ISO 14040:2006, Environmental Management – Life Cycle Assessment – Principles and Framework. Geneva: International Organization for Standardization, 2006.
[3] ISO 14044:2006, Environmental Management – Life Cycle Assessment – Requirements and Guidelines. Geneva: International Organization for Standardization, 2006.
[4] M. Rebitzer et al., “Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications,” Environment International, vol. 30, no. 5, pp. 701-720, Jul. 2004.
[5] J. B. Guinée et al., “Life cycle assessment: Past, present, and future,” Environmental Science & Technology, vol. 45, no. 1, pp. 90-96, Jan. 2011. https://doi.org/10.1021/es101316v
[6] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. ACM KDD, San Francisco, CA, USA, 2016, pp. 785-794. https://doi.org/10.1145/2939672.2939785
[7] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NeurIPS, Long Beach, CA, USA, 2017, pp. 4765-4774.
[8] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001. https://doi.org/10.1023/A:1010933404324
[9] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, Oct. 2001. https://doi.org/10.1214/aos/1013203451
[10] K. Deb and H. Jain, “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, Aug. 2014. https://doi.org/10.1109/TEVC.2013.2281535
[11] W. Haas, F. Krausmann, D. Wiedenhofer, and M. Heinz, “How circular is the global economy?: An assessment of material flows, waste production, and recycling in the European Union and the world in 2005,” Journal of Industrial Ecology, vol. 19, no. 5, pp. 765-777, Oct. 2015. https://doi.org/10.1111/jiec.12244
[12] P. Frees and H. Wettstein, “Prediction of life cycle assessment results using artificial neural networks,” International Journal of Life Cycle Assessment, vol. 26, no. 4, pp. 777-789, Apr. 2021.
[13] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, Nov. 1997. https://doi.org/10.1162/neco.1997.9.8.1735
[14] Rout, P., Jha, A. K., Gupta, P., Singh, B. and Choudhury, S., “Failure analysis of composite plate under ballistic impact,” Mater. Today Proc., vol. 74, pp. 1008-1011, 2023. https://doi.org/10.1016/j.matpr.2022.11.357
[15] Ruhela, H. S., Bhardwaj, S., Agrawal, T. and Gupta, P., “Explicit dynamics analysis of shin pads using finite element analysis,” in Int. Conf. Industrial Problems on Machines and Mechanism, Springer, Singapore, 2022, pp. 683-690. https://doi.org/10.1007/978-981-99-4270-1_67
[16] Kumar, M., Rajiyan, J. and Gupta, P., “A computational approach for solving elasto-statics problems,” Mater. Today Proc., vol. 46, pp. 6876-6879, 2021. https://doi.org/10.1016/j.matpr.2021.04.462
