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AI-driven Risk–Benefit Analysis of Plastic Packaging: Balancing Economic Upside Risks and Environmental Downside Risks
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This systematic review found that AI and machine learning tools can optimize plastic packaging production, improve recycling rates, and reduce environmental pollution through smarter material selection and predictive waste analytics. Reducing plastic waste at the source through AI-enabled circular economy strategies is a critical upstream intervention for limiting the generation of environmental microplastics.
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.
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Integrating Artificial Intelligence into Circular Strategies for Plastic Recycling and Upcycling
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This review shows how artificial intelligence — from ML-powered sorting to AI-assisted catalyst design and digital twins — is transforming plastic recycling and upcycling by improving material identification, process efficiency, and lifecycle transparency. Accelerating the circular economy for plastics is one of the most direct strategies for reducing the volume of plastic waste that ultimately fragments into microplastics in the environment.
Integrating Artificial Intelligence into Circular Strategies for Plastic Recycling and Upcycling
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This review shows that AI-integrated sensing (FTIR, Raman, hyperspectral imaging) and machine-learning tools are transforming plastic recycling by improving material sorting, kinetic modeling, enzyme design for depolymerization, and life cycle assessment — moving AI from a supporting tool to a structural enabler of circular plastic systems. These advances directly support reducing the plastic waste flows that generate microplastic pollution.
The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
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This paper examines how artificial intelligence and machine learning can help transition the plastics industry toward a circular economy. AI tools can optimize recycling processes, predict material degradation, and identify opportunities to reduce plastic waste before it enters the environment.
AI-Integrated Chemistry Solutions for Sustainable Plastic Waste Management
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This paper reviews how artificial intelligence is being applied to improve plastic waste recycling efficiency, expand recycling technologies, and support development of biodegradable plastic alternatives. Real-world case studies illustrate AI's potential to transform plastic waste management and advance sustainable solutions.
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
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This study analyzed global plastic waste management practices and used artificial intelligence models to predict future waste trends. The researchers found that current waste management systems are struggling to keep up with rising plastic production, posing threats to ecosystems, human health, and the economy. The AI models help forecast where waste generation is headed, which could inform better policy decisions.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.