Abstract
Organic waste valorization studies are often fragmented, focusing on individual biomass streams or isolated conversion pathways, thereby limiting the comparability and transferability of reuse strategies. To address this gap, this study develops a unified data-driven decision-support framework "Green Tree" for the early-stage selection of sustainable valorization pathways for heterogeneous animal by-products. Twelve matrices derived from fisheries, aquaculture, poultry processing, and insect production systems were characterized and integrated with literature data to construct a compositional dataset for clustering analysis and pathway classification. K-means clustering identified ash, fixed carbon (FC), and nitrogen (N) as the most discriminant variables, leading to the definition of operational thresholds (14 wt% ash, 16 wt% FC, and 10 wt% N) for pathway selection. The resulting Green Tree classified mineral-rich matrices toward mineral recovery and material applications, nitrogen-rich matrices toward biochemical valorization and N-doped carbon production, char-oriented matrices toward biochar and activated carbon generation, and volatile-rich residues toward anaerobic digestion and volatile-oriented thermochemical conversion. External and experimental validation supported the framework predictions. Literature-derived external validation showed good agreement between expected and Green Tree-assigned classifications (90% ). Experimental results further confirmed these trends: chicken feathers showed low pyrolysis conversion (35%-42%), confirming their char-oriented behaviour, whereas fish scales exhibited high mineral content and approximately 71% conversion, supporting their classification as mineral-rich matrices. Anaerobic digestion of fish waste generated biogas containing 47%-63% CH4, confirming the suitability of volatile-rich residues for biochemical conversion. The proposed Green Tree translates compositional data into operational valorization criteria for sustainable waste management and circular bio-refinery strategies.