Sustainability assessment of hydrothermal carbonization of food/agro-industrial waste: integrating life cycle, techno-economic, and machine learning perspectives

Food and agro-industrial waste, exceeding 1.3 billion tons annually, represents both a significant environmental burden and an untapped resource. Hydrothermal carbonization (HTC) has emerged as a promising thermochemical pathway for converting wet biomass into hydrochar and nutrient-rich process water, offering reduced greenhouse gas emissions compared to conventional disposal methods. However, the sustainability of HTC remains insufficiently characterized across environmental, economic, and digital dimensions. This study presents a comprehensive framework that integrates life cycle assessment (LCA), techno-economic analysis (TEA), and machine learning (ML) for HTC systems. Published LCA studies are synthesized to highlight greenhouse gas mitigation potentials and environmental hotspots, while TEA investigations are examined to identify cost drivers, investment risks, and market barriers. The emerging role of ML is critically analyzed, with emphasis on its ability to predict HTC performance, mitigate data scarcity, and support real-time process optimization. An integrative framework is proposed to bridge LCA, TEA, and ML, enabling multi-objective decision-making and policy-relevant evaluation. By aligning HTC deployment with circular economy principles and the United Nations Sustainable Development Goals, this framework advances the methodological foundation of HTC sustainability assessment. Finally, key research gaps are identified, providing a decision-grade basis for the sustainable scale-up of HTC systems.