A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.

Existing biomass resource assessments quantify landscape-scale availability, while conversion-focused technoeconomic models assume idealized feedstock quality, leaving the preprocessing layer connecting the two largely unquantified. A critical gap exists between raw harvested biomass quality and the strict specifications required for thermochemical conversion: how much biomass can bridge that gap, through what steps, and at what delivered cost and carbon intensity. Unlike conventional nth-plant assessments that treat feedstock variability as already solved, this study treats preprocessing constraints as real, first-plant operational conditions that directly determine conversion-ready volumes and their economic and emissions burden. We address this gap for the Western Cascades through a sequential quality-mitigation and supply-curve modeling framework that screens woody biomass against catalytic fast pyrolysis (CFP) feedstock thresholds, moisture, particle size, ash, alkali and alkaline earth metals (AAEM), and carbon concentration, routing off-specification material toward gasification as a complementary pathway. Only 63% of available biomass met CFP specifications, primarily limited by ash content and moisture. Delivered costs averaged $95.76/dry ton (2022$) and carbon intensity averaged 179.26 kg CO2e per dry ton, with drying and acid leaching as dominant cost and emissions drivers. Logging residues were the most cost-effective feedstock; small-diameter trees carried higher unit costs due to preprocessing intensity and limited throughput. The remaining 37%, nearly all qualifying for gasification, could supply approximately 66% of regional hydrogen demand. These results demonstrate that technology-feedstock alignment recovers stranded biomass, constrains delivered cost and carbon intensity, and provides a replicable framework for bridging landscape-scale resource assessments with facility-scale conversion targets.