Packaging industry must harness AI to meet circular economy goals

The study highlights AI’s ability to process large, fragmented datasets, track packaging flows across systems, and update assessments in near real time. With predictive analytics and machine learning, businesses can shift from retrospective compliance to proactive design and operational strategies that reduce waste and emissions.

A comprehensive review sheds light on the powerful intersection of artificial intelligence, life cycle sustainability assessment, and circular economy strategies in packaging. The study stresses that packaging, a major driver of global waste, is now at the center of a critical transformation where digital technologies and sustainability frameworks converge.

The paper, titled “Life Cycle-Based Sustainability Assessment and Circularity Mapping for Packaging Materials: Integrating Artificial Intelligence” and published in the Journal of Artificial Intelligence by Tech Science Press. It synthesizes findings from 85 peer-reviewed studies and 15 industry case examples, making it one of the first sector-focused reviews to unify ISO-aligned life cycle methods, circularity mapping, and AI solutions for packaging.

Why is packaging sustainability demanding new approaches?

Packaging accounts for a significant portion of municipal solid waste worldwide, with plastics posing particular challenges in recycling and recovery. Traditional assessments like life cycle analysis (LCA) have guided improvements, but static reporting and limited data integration have left gaps in real-time decision-making.

The authors argue that life cycle sustainability assessment (LCSA), aligned with ISO 14040, provides a more comprehensive framework by combining environmental, economic, and social dimensions. Yet even with LCSA, the dynamic nature of supply chains, consumer behavior, and material innovation requires more adaptive tools.

This is where artificial intelligence enters the picture. The study highlights AI’s ability to process large, fragmented datasets, track packaging flows across systems, and update assessments in near real time. With predictive analytics and machine learning, businesses can shift from retrospective compliance to proactive design and operational strategies that reduce waste and emissions.

How is artificial intelligence being applied across the packaging life cycle?

The review maps AI applications across different stages of the packaging value chain, with end-of-life management emerging as the most mature area. Vision-based AI sorting systems are achieving high accuracy in distinguishing plastics and other materials in recycling facilities. These systems not only increase recovery rates but also lower contamination, which has long undermined recycling efficiency.

Besides waste sorting, AI is increasingly used in operations and design. Predictive analytics help companies forecast material demand, optimize inventory, and reduce overproduction. Machine learning models are being applied to simulate different packaging configurations, enabling lighter, modular, and more recyclable designs.

Lifecycle optimization is another area where AI is proving valuable. By integrating live data into LCA models, businesses can test multiple scenarios for energy use, emissions, and circularity outcomes. This dynamic approach provides clearer insights into trade-offs between material choice, transportation, and recovery systems.

However, the review points out that only a small fraction of studies fully integrate all three strands - LCSA, circular economy mapping, and AI. Most current deployments are siloed, meaning the transformative potential of combining them is still largely untapped.

What policy and practice shifts are needed to accelerate progress?

The authors outline a series of recommendations for policymakers, industry leaders, and researchers. At the regulatory level, they call for mandatory LCSA reporting for packaging materials, aligned with ISO standards. Such requirements, supported by digital platforms and benchmarking tools, would raise accountability and comparability across industries.

Economic incentives are also deemed essential. Extended producer responsibility (EPR) schemes should be restructured to reward circular designs and high recyclability formats while penalizing difficult-to-recycle packaging. Public procurement can further reinforce these signals by prioritizing sustainable packaging options in large-scale purchasing.

From a practice perspective, the review identifies capacity building as a critical barrier. Packaging engineers and designers need AI literacy to effectively interpret outputs from predictive models and digital twins. The authors recommend cross-sector knowledge hubs to bridge gaps between material science, computer science, and design teams. Consumer awareness is also highlighted as a necessary driver, since proper disposal and reuse behavior remain weak links in many circular systems.

Finally, the review calls for equity and inclusivity. AI systems must be trained on diverse datasets to ensure their outputs are valid across regions and socio-economic contexts. Without such attention, advances risk widening gaps between well-resourced and underserved communities in waste management and recycling.

What future directions and research gaps remain?

While the integration of AI into packaging sustainability is accelerating, several challenges remain. The review stresses the need for scalable AI models that can adapt to different materials, recycling plants, and geographic contexts. Edge AI and digital twin technologies are identified as promising next steps to deliver real-time monitoring and decision support.

Material innovation is another frontier. Biodegradable and bio-based alternatives such as mushroom-based packaging show promise, but more rigorous evidence is needed on their barrier properties, cost competitiveness, and end-of-life impacts. AI could help evaluate these emerging materials within LCSA frameworks.

The review also calls for expanding LCSA practice to include social metrics and the systemic impacts of digital technologies. For example, the socio-economic consequences of introducing AI-based sorting systems, such as job displacement or new skill requirements, must be considered alongside environmental gains.

Governance and ethics emerge as equally important. Transparency, bias, and data rights are flagged as unresolved risks in applying AI to sustainability. Independent auditing and regulatory oversight will be required to ensure trust in AI-driven packaging systems. Finally, the authors emphasize the need for human-AI collaboration. Tools and dashboards must be designed so that non-specialists, including designers and sustainability leads, can easily incorporate AI insights into day-to-day decisions.