Circular bioeconomy in carbon footprint components of nonthermal processing technologies towards sustainable food system: A review

Background: The environmental impact in terms of the number of greenhouse gases released due to human activities is measured by the method known as carbon footprint. This method is used to assess and quantify the contribution of individual organizations, products, or processes to the global climate. The environmental impact of processing throughout its life cycle is evaluated by a comprehensive method known as life cycle assessment. Sustainability criteria assessment and utilization of sustainability indicators are important to conduct circularity in research. Effective food supply chain management plays an essential role in achieving sustainability goals and increasing food safety. Scope and approach: The present review highlights the evaluation of carbon footprint by methods such as nonthermal techniques, artificial intelligence, and machine learning. Artificial intelligence and machine learning include the use of electronic sensors, digital twin technology, and the current version system to evaluate the quality and organoleptic conditions. These methods result in optimizing energy and resource consumption and promote sustainability. Key findings and conclusion: This review emphasizes that nonthermal processing technologies and artificial intelligence demonstrate significant potential in reducing energy use, water consumption, and greenhouse gas emissions, thereby contributing to the sustainability goals of the circular bioeconomy. Furthermore, AI-driven technologies offer promising solutions for monitoring agricultural outputs, optimizing supply chains, and reducing waste. Therefore, adopting these technologies within the framework of the circular bioeconomy not only offers a viable pathway toward a more sustainable food system but also aligns with global sustainability objectives by promoting resource efficiency and reducing waste.