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Python-Based AI-Assisted Modeling and Computation of Life Cycle Assessment of European Polymeric Waste: Application in Manufacturing and Recycling Industries Regarding Sustainability
Institute of Sustainable Building Materials and Engineering System, Riga Technical University, Paula Valdenaiela 1, LV-1007 Riga, Latvia.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.ORCID iD: 0000-0001-5084-6336
Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Ehitajate Tee 5, 19086 Tallinn, Estonia.
CiTin—Centro de Interface Tecnológico Industrial, 4970-786 Arcos de Valdevez, Portugal; ProMetheus, Instituto Politécnico de Viana do Castelo (IPVC), 4900-347 Viana do Castelo, Portugal.
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2026 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 18, no 11, article id 5445Article in journal (Refereed) Published
Abstract [en]

Development of sustainability systems for assessment of environmental impacts remains a paramount challenge for green and circular manufacturing of polymers. In this study, a comprehensive life cycle assessment (LCA) framework is developed for European polymeric waste by integrating OpenLCA, Ecoinvent v3.11, and Python-based machine learning (ML) algorithms. Cradle-to-gate, service-life, and cradle-to-grave assessments are performed for representative thermoplastic composite systems, including PP–PET–cotton, HDPE–glass fiber, and PEEK–carbon fiber composites, covering domestic, engineering, and high-performance polymer categories. The results demonstrate that raw material extraction and manufacturing stages dominate environmental impacts, contributing the highest shares to climate change, ecotoxicity, and non-renewable energy consumption. PP-based composite systems exhibit the lowest overall environmental burdens due to lower processing energy and simpler molecular structures, while HDPE-based systems show moderate impacts. PEEK-based composites present the highest impacts per unit mass, driven by energy-intensive synthesis and high processing temperature. Environmental impacts are evaluated using EF v3.1 and ReCiPe methodologies, supported by Monte Carlo simulations and ML-assisted uncertainty quantification. Monte Carlo simulations and ML-assisted LCA provide probabilistic ranges, uncertainty quantification, and predictive insights into impact indicators, enabling the development of a quantitative sustainability system based on probability–impact relationships. A Europe-wide assessment of 57 Mt of polymeric waste highlights that environmental burdens are concentrated in countries with high polymer production and consumption, emphasizing the importance of energy mix, recycling efficiency, and waste management strategies. Overall, this work demonstrates that digitalized LCA coupled with ML offers a powerful decision-support framework for sustainable polymer design, recycling optimization, and circular economy policy development, supporting the transition toward low-carbon and resource-efficient polymer systems in Europe.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI) , 2026. Vol. 18, no 11, article id 5445
Keywords [en]
life cycle assessment, polymeric waste, sustainability, environmental impacts computation, LCA artificial intelligence, LCA machine learning studies
National Category
Environmental Management Other Environmental Engineering
Research subject
Engineering Materials
Identifiers
URN: urn:nbn:se:ltu:diva-118701DOI: 10.3390/su18115445ISI: 001790310100001Scopus ID: 2-s2.0-105041422973OAI: oai:DiVA.org:ltu-118701DiVA, id: diva2:2079386
Note

For funding, see link: https://www.mdpi.com/2071-1050/18/11/5445;

Fulltext license: CC BY;

Part of Special Issue: Circular Economy and Competitiveness in the Era of Global Sustainability Transitions

Available from: 2026-06-25 Created: 2026-06-25 Last updated: 2026-06-25Bibliographically approved

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Maurya, Himanshu Singh

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