Biobased materials, derived from renewable sources, offer a promising alternative to conventional/synthetic materials by reducing reliance on fossil fuels and lowering embodied energy and carbon footprints. However, despite the potential of the biobased industry to advance global sustainability goals, biobased materials face challenges in sustainability assessment (SA). These include variability in production systems, inconsistent environmental impact results, and a lack of standardized evaluation methodologies. Additionally, data scarcity, limited stakeholder collaboration, and inadequate SA tools further complicate the situation. Industry 4.0 technologies, such as cloud computing and artificial intelligence (AI), present a transformative opportunity to address these challenges. By enabling real-time data collection, enhancing traceability, and improving stakeholder engagement across the value chain, these technologies can reshape the SA of conventional and biobased products. My doctoral research is motivated by the need for a holistic, transparent, and dynamic SA framework that enables accurate evaluation of the biobased materials, processes, and products. Thus, to contribute to the actualization of a more sustainable biobased industry, the overarching goal of my research is to develop a cloud-enabled SA framework that integrates cloud computing technologies for real-time, data-driven sustainability performance evaluations. This framework will foster stakeholder collaboration and holistic engineering solutions, ensuring accurate, scalable, and actionable SA across the biobased product life cycle. To accomplish this objective, I am undertaking three interrelated tasks. The first task in my dissertation research was to define the requirements for a cloud-enabled SA framework to support the comprehensive SA of biobased products across the entire life cycle. The SA requirements for each stage, from crop cultivation to product end-of-life, were examined, with particular emphasis on the manufacturing phase. Industrial hemp, recognized as a biobased crop that addresses many of the United Nations’ Sustainable Development Goals, was chosen as a representative case for this research. The key requirements include addressing data scarcity, enabling a holistic evaluation of the biobased value chain, and integrating emerging technologies to enhance stakeholder engagement, ensuring a more transparent and comprehensive SA. Based on the requirements defined in the first research task, the second task in my dissertation research was to develop a conceptual framework for the SA of biobased products, integrating cloud-enabled technologies with SA methodologies. The model promotes stakeholder collaboration at each stage and aggregates contributions across multiple stages to provide a holistic, dynamic SA. This framework enables real-time monitoring, trend analysis, and strategic decision-making to enhance sustainability efforts. Time dependencies are facilitated by cloud computing technologies, which allow for the collection, processing, and visualization of data in real time. Characterization factors are used to quantify impacts across sustainability performance categories, including climate change, ozone depletion, acidification, human health, labor costs, and employment. The third research task of my dissertation research involves the integration and validation of the developed conceptual framework within a cloud-enabled environment, using the industrial hemp decortication process as a case study. A cloud-based life cycle assessment (LCA) tool is being developed using Python to incorporate the proposed framework to assess environmental impacts. This tool will retrieve data from an AWS-hosted cloud database, conduct life cycle inventory (LCI) calculations, and apply life cycle impact assessment (LCIA) methods. Envisioned future enhancements of the cloud-based framework will integrate AI models to further improve SA by identifying patterns and providing deeper insights into biobased products. To assist non-experts, AI-driven impact assessments will leverage generative AI and large language models (LLMs) to evaluate both existing and hypothetical biobased products, ensuring transparency and objectivity. The broader impact of my doctoral research extends beyond the hemp industry; the cloud-based approach has the potential to influence other biobased industries, aiding the global transition toward a more circular and resilient bioeconomy.