Marine fishery and aquaculture processing generate substantial volumes of biological byproducts—fish skin, bone, scales, and viscera—that remain underutilized as feedstocks for higher-value bio-based materials. The gap between feedstock availability and quality-controlled manufacturing capacity in many coastal economies is not solely scientific; the scientific characterization of marine-derived biomaterials is comparatively more developed than the governance literature addressing their distributed manufacture. It is a governance gap: The absence of a framework capable of transmitting sufficient process knowledge to geographically distributed manufacturing entities while retaining intellectual property integrity, capturing local adaptation learning, and maintaining quality coherence across nodes and biological input sources. Existing organizational models—centralized production and conventional technology licensing—each address part of this challenge but leave important governance problems unresolved. This review examines the governance and technology transfer literature bearing on distributed biomanufacturing, identifies the specific design gap those literatures leave open, and proposes a platform governance model in which intellectual property, research and development authority, feedstock qualification, and quality standards are maintained centrally while geographically defined industrial nodes conduct local sourcing, manufacturing, and regulatory engagement. The central argument is that, for biologically variable feedstocks, governance architecture may function as a technical control mechanism—not merely a commercial arrangement—for reproducibility, quality assurance, and responsible technology transfer. The model is presented as a conceptual framework requiring proof-of-concept validation, regulatory classification, quality system development, and partner governance design before any operational, regulatory, or commercial claims can be made. Governance risks, implementation considerations, and a staged pathway to node deployment are discussed.
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Mohammed Alhalees (2026) studied this question.
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