AI-driven Risk–Benefit Analysis of Plastic Packaging: Balancing Economic Upside Risks and Environmental Downside Risks

Abstrak

Plastic packaging remains a critical yet controversial component of modern supply chains, offering economic efficiencies while posing significant environmental challenges. This study conducts a systematic literature review to explore how artificial intelligence (AI), automation, and machine learning (ML) can enhance plastic production processes, minimize waste, and promote sustainability. Key findings indicate that AI enables smarter material selection, design optimization, and energy-efficient manufacturing, addressing economic upside risks such as cost reduction and operational efficiency. Concurrently, AI mitigates environmental downside risks by improving recycling rates, reducing CO₂ emissions, and minimizing plastic pollution through advanced sorting and predictive analytics. Despite these benefits, challenges remain, including substantial implementation expenses, limited adoption among small and medium enterprises (SMEs), and insufficient data availability. The research emphasizes the necessity for balanced regulatory frameworks and technological investments to align economic objectives with ecological preservation. By consolidating current knowledge, this paper offers actionable insights for stakeholders navigating the complexities of plastic packaging within a circular economy framework, positioning AI as a pivotal tool in achieving sustainable equilibrium.