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.

Hybridizing existing hydropower plants with energy storage or other generation has the potential to improve energy security in the US and globally. However, permitting delays and direct costs to complete the regulatory process can create barriers. So far in the US, nine hydro-hybrids have received Federal Energy Regulatory Commission (FERC) approval and thirteen total hydro-hybrids have been developed. Analyzing these nine cases along with supplementary data from similar projects shows that regulatory delays have increased over the past five years. While hydro-hybrids have not yet been affected by the worst of the delays, the risk of large delays (>500 days) has increased significantly. The worst delay observed was 762 days, which effectively increased the project cost by up to 28\%. FERC could remove this barrier by reducing regulatory cost and risk with a stronger and broader categorical exclusion under the National Environmental Protection Agency and by narrowly defining FERC’s jurisdiction over hybrid assets. A broader categorical exclusion for typical hybridization projects would reduce repeated work across projects and streamline the part of the process that carries the most timeline risk. Narrowly defining FERC’s jurisdiction over secondary storage or generation assets would increase certainty about the process and streamline many projects. For example, hybridization projects that do not affect hydropower operations and already go through environmental reviews at the state or local level may not need federal regulation to ensure responsible development and operation. This would significantly reduce the cost and risk to licensees without negative environmental or social outcomes.