Author(s)

Alexander Dupont^*, Claire Marchand

Affiliation(s)

The Bordeaux Institute of Technology, Bordeaux, France.

Corresponding Author

Alexander Dupont

ABSTRACT

As global concerns about plastic pollution and carbon emissions intensify, the food packaging industry is under increasing pressure to transition toward sustainable materials. However, assessing the environmental impact of new packaging alternatives remains a complex, time-consuming task that traditionally relies on manual life cycle assessment (LCA). This paper introduces an artificial intelligence (AI)-enabled framework designed to streamline environmental impact assessments of bio-based and biodegradable food packaging options. By integrating machine learning algorithms with LCA datasets, the proposed system rapidly evaluates packaging materials across multiple environmental indicators, including global warming potential (GWP), water usage, eutrophication, and end-of-life scenarios. The study applies supervised learning models trained on historical LCA data to predict the environmental performance of novel packaging materials, enabling faster material selection and iterative eco-design. Results show that AI models can accurately classify high-impact contributors and provide actionable insights for material optimization. This approach not only accelerates sustainability evaluations but also supports more transparent, data-driven decision-making processes in the packaging development lifecycle. The paper concludes with a discussion of challenges and future research directions, including the incorporation of real-time environmental data and model explainability.

KEYWORDS

Artificial intelligence; Environmental impact assessment; Life cycle analysis; Sustainable packaging; Machine learning; Food packaging; Bio-based materials; Eco-design; GWP prediction; Circular economy

CITE THIS PAPER

Alexander Dupont, Claire Marchand. AI-enabled environmental impact assessment for sustainable food packaging alternatives. Chemical Innovation & Technology. 2025, 2(1): 32-37. DOI: https://doi.org/10.61784/cit3006.

REFERENCES

[1] Barrowclough D, Birkbeck C D. Transforming the global plastics economy: the role of economic policies in the global governance of plastic pollution. Social Sciences, 2022, 11(1): 26.

[2] Wang J, Tan Y, Jiang B, et al. Dynamic Marketing Uplift Modeling: A Symmetry-Preserving Framework Integrating Causal Forests with Deep Reinforcement Learning for Personalized Intervention Strategies. Symmetry, 2025, 17(4): 610.

[3] Binhazzaa Z. Plastics Are Paving the Way for a Greener Future and Accelerating Decarbonization. 2024.

[4] Tan Y, Wu B, Cao J, et al. LLaMA-UTP: Knowledge-Guided Expert Mixture for Analyzing Uncertain Tax Positions. IEEE Access, 2025.

[5] Reichert C L, Bugnicourt E, Coltelli M B, et al. Bio-based packaging: Materials, modifications, industrial applications and sustainability. Polymers, 2020, 12(7): 1558.

[6] Daramola O M, Apeh C E, Basiru J O, et al. Sustainable packaging operations: Balancing cost, functionality, and environmental concerns. 2025.

[7] Barbhuiya S, Das B B. Life Cycle Assessment of construction materials: Methodologies, applications and future directions for sustainable decision-making. Case Studies in Construction Materials, 2023, 19: e02326.

[8] Yang Y, Wang M, Wang J, et al. Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains. Sensors (Basel, Switzerland), 2025, 25(8): 2428.

[9] Versino F, Ortega F, Monroy Y, et al. Sustainable and bio-based food packaging: A review on past and current design innovations. Foods, 2023, 12(5): 1057.

[10] Wang J, Zhang H, Wu B, et al. Symmetry-Guided Electric Vehicles Energy Consumption Optimization Based on Driver Behavior and Environmental Factors: A Reinforcement Learning Approach. Symmetry, 2025.

[11] Koyamparambath A, Adibi N, Szablewski C, et al. Implementing artificial intelligence techniques to predict environmental impacts: case of construction products. Sustainability, 2020, 14(6): 3699.

[12] Xing S, Wang Y, Liu W. Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing. Sensors, 2025.

[13] McCarthy A, Holland C, Shapira P. The development and testing of an early, rapid sustainability assessment tool for responsible innovation in engineering biology. SocArXiv, 2024.

[14] Dey A, Dhumal C V, Sengupta P, et al. Challenges and possible solutions to mitigate the problems of single-use plastics used for packaging food items: A review. Journal of Food Science and Technology, 2021, 58(9): 3251-3269.

[15] Rahim A A, Musa S N, Ramesh S, et al. A systematic review on material selection methods. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2020, 234(7): 1032-1059.

[16] Panyaram S, Hullurappa M. Data-Driven Approaches to Equitable Green Innovation Bridging Sustainability and Inclusivity. In Advancing Social Equity Through Accessible Green Innovation. IGI Global Scientific Publishing, 2025: 139-152.

[17] Dey A, Dhumal C V, Sengupta P, et al. Challenges and possible solutions to mitigate the problems of single-use plastics used for packaging food items: A review. Journal of Food Science and Technology, 2021, 58(9): 3251-3269.

[18] Qi R. DecisionFlow for SMEs: A Lightweight Visual Framework for Multi-Task Joint Prediction and Anomaly Detection. 2025.

[19] Acharjee S A, Gogoi B, Bharali P, et al. Recent trends in the development of Polyhydroxyalkanoates (PHAs) based biocomposites by blending with different bio-based polymers. Journal of Polymer Research, 2024, 31(4): 98.

[20] Rossi F, Zuffi C, Parisi M L, et al. Comparative scenario-based LCA of renewable energy technologies focused on the end-of-life evaluation. Journal of Cleaner Production, 2023, 405: 136931.

[21] Pauer E, Wohner B, Heinrich V, et al. Assessing the environmental sustainability of food packaging: An extended life cycle assessment including packaging-related food losses and waste and circularity assessment. Sustainability, 2019, 11(3): 925.

[22] Barbhuiya S, Das B B. Life Cycle Assessment of construction materials: Methodologies, applications and future directions for sustainable decision-making. Case Studies in Construction Materials, 2023, 19: e02326.

[23] Johnson A L. Overcoming Barriers to R&D Investment: A Case Study on US Small and Medium-Sized Enterprises (Doctoral dissertation, University of Arizona Global Campus), 2024.

[24] Ghai S, Thériault R, Forscher P, et al. A manifesto for a globally diverse, equitable, and inclusive open science. Communications Psychology, 2025, 3(1): 16.

[25] Liu Y, Guo L, Hu X, Zhou M. Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks. Sensors, 2025, 25(11): 3320.

[26] Masih A. Machine learning algorithms in air quality modeling. Global Journal of Environmental Science & Management (GJESM), 2019, 5(4).

[27] Qi R. Interpretable Slow-Moving Inventory Forecasting: A Hybrid Neural Network Approach with Interactive Visualization. 2025.

[28] Ibn-Mohammed T, Mustapha K B, Abdulkareem M, Fet al. Toward artificial intelligence and machine learning-enabled frameworks for improved predictions of lifecycle environmental impacts of functional materials and devices. MRS Communications, 2023, 13(5): 795-811.

[29] Ghoroghi A, Rezgui Y, Petri I, et al. Advances in application of machine learning to life cycle assessment: a literature review. The International Journal of Life Cycle Assessment, 2022, 27(3): 433-456.

[30] Bidyalakshmi T, Jyoti B, Mansuri S M, et al. Application of Artificial Intelligence in Food Processing: Current Status and Future Prospects. Food Engineering Reviews, 2024: 1-28.

[31] Liu Y, Guo L, Hu X, Zhou M. A symmetry-based hybrid model of computational fluid dynamics and machine learning for cold storage temperature management. Symmetry, 2025, 17(4): 539.