Economic and sustainability prospects for wet waste valorization: The case for sustainable aviation fuel from arrested anaerobic digestion
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Wiatrowski, Matthew R. (ORCID:0000000206587178)↗
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Utility Finder (U-Finder) Tool
U-Finder allows users to search for and identify local utility partners and electric vehicle charger incentives by state or ZIP code. U-Finder pulls from the Homeland Infrastructure Foundation-Level Database of Electric Retail Service Territories to identify utility service territories. Utility incentive listings are provided by utility associations, and state government incentive listings are pulled from the Alternative Fuels Data Center Laws and Incentives website. 
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
The Realistic Potential of Soil Carbon Sequestration in U.S. Croplands for Climate Mitigation
Existing estimates of the climate mitigation potential from cropland carbon sequestration (C-sequestration) are limited because they tend to assume constant rates of soil organic carbon change over all available cropland area, use relatively coarse land delineations, and often fail to adequately consider the agronomic and socioeconomic dimensions of agricultural land use. This results in an inflated estimate of the C-sequestration potential. We address this gap by defining a more appropriate land base for cover cropping in the United States for C-sequestration purposes: stable croplands in annual production systems that can integrate cover cropping without irrigation. Our baseline estimate of this suitable stable cropland area is 32% of current U.S. cropland extent. Even an alternative, less restrictive definition of stability results in a large reduction in area (44% of current U.S. croplands). Focusing cover crop implementation to this constrained land base would increase durability of associated C-sequestration and limit soil carbon loss from land conversion to qualify for carbon-specific incentives. Applying spatially-variable C-sequestration rates from the literature to our baseline area yields a technical potential of 19.4 Tg CO 2 e yr –1 annually, about one-fifth of previous estimates. We also find the cost of realizing about half (10 Tg CO 2 e yr –1 ) of this potential could exceed 100 USD Mg CO 2 e –1 , an order of magnitude higher than previously thought. While our economic analyses suggest that financial incentives are necessary for large-scale adoption of cover cropping in the U.S., they also imply any C-sequestration realized under such incentives is likely to be additional.
54 ENVIRONMENTAL SCIENCES↗
Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity
Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).
14 SOLAR ENERGY↗
Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions
Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.
Biomass Co-firing↗
Herbicide‐resistant weed management with robots: A weed ecological–economic model
The heavy reliance on herbicides for weed control has led to an increase in resistant weeds in the United States. Robotic weed control is emerging as an alternative technology for removing weeds mechanically using artificial intelligence. We develop an integrated weed ecological and economic dynamic (I‐WEED) model to examine the biophysical and economic drivers of adopting robotic weed management and simulate the optimal timing and intensity of robotic adoption within and across growing seasons. We specify a cohort‐based weed growth model that relates yield damages to effective weed density and treats the susceptibility of weeds to herbicides as a renewable resource that can be regenerated by using mechanical weeding robots, due to a fitness cost that makes resistant weeds less prolific. Compared to myopic weed management which ignores resistance development, forward‐looking management leads to earlier adoption of robots and treating robots as complements instead of substitutes to herbicides. This weed management results in adopting fewer robots, deploying robots on a smaller portion of the land, higher profitability, and lower yield loss in the long run, relative to myopic management. Counterintuitively, myopic management leads to a lower resistance level through its higher robot adoption intensity. We also find that a lower level of initial weed seed resistance and/or a higher fitness cost result in a higher level of resistance because they create incentives for farmers to delay the adoption of robotic weed control. Our analysis shows the importance of jointly considering the interactions between weed ecology and economics in analyzing the incentives and effects of robotic weed management on weed resistance.
agricultural robotics↗
Designing Cost‐Effective Carbon Payments to Induce Cellulosic Feedstock Production for Sustainable Aviation Fuel
Perennial bioenergy crops, such as miscanthus and switchgrass, and crop residues have the potential to scale up sustainable aviation fuel (SAF) production and mitigate carbon emissions. However, high establishment costs, delayed returns, and risk–return profiles that diverge from those of conventional crops can hinder incentives to adopt bioenergy crops. We develop an economic model that incorporates spatially varying joint yield and price distributions for the multiple crop choices a farmer faces and apply it to examine the incentives for risk-averse, present-biased, and credit-constrained farmers to produce cellulosic feedstocks under various biomass prices. We link this model to a biogeochemical model to quantify the spatially varying carbon mitigation benefits from these feedstocks in the rainfed region of the United States. We also analyze the cost-effectiveness of two carbon payment policies: annual and upfront. We find that risk-averse, present-biased, or credit-constrained farmers prefer to grow the lower-yielding but less risky switchgrass and harvest corn stover instead of the lower carbon, higher-yielding but riskier feedstock miscanthus, resulting in lower SAF production. Upfront carbon payments incentivize higher quantities of less carbon-intensive SAF production by risk-averse, credit-constrained, and present-biased farmers because they offset a part of the establishment costs of miscanthus. We also find that when farmers are credit-constrained, upfront payments are more cost-effective in terms of carbon mitigation per dollar spent. In contrast, annual payments are more cost-effective when farmers can access credit.
09 BIOMASS FUELS↗
Economics of land‐based carbon mitigation
Agricultural land holds tremendous potential to contribute to net zero greenhouse gas emission goals by providing low carbon renewable energy to displace fossil fuels and by serving as a sink for sequestering carbon in the soil with climate‐smart practices. This potential is, however, far from being realized. This paper examines the economic incentives and barriers to implementing land‐based carbon mitigation strategies and discusses the specific features of land‐based carbon mitigation practices on carbon emissions that need to be considered in designing policy incentives to induce adoption. Although a carbon price‐based policy is socially efficient, the more commonly observed policies to promote land‐based carbon mitigation include practice‐based conservation programs, technology mandates, and sector‐specific standards. The paper discusses the rationale for these alternative policy approaches and concludes with a discussion of emerging opportunities for designing policy and market‐based approaches for promoting land‐based carbon‐mitigation and future directions for economics research.
additionality↗
Simulating competition in the US bioeconomy to produce hard‐to‐electrify transportation fuels using limited biomass resources
This study presents a novel bioeconomy optimization framework, BiOpt, designed to address critical questions regarding the strategic use of limited US biomass resources for biofuel production. By integrating detailed techno-economic analyses, life cycle assessments, and resource assessment data, BiOpt optimizes resource distributions across competing technologies to maximize economic performance and/or minimize greenhouse gas emissions. Using feedstock scenarios from the 2023 Billion Ton Study, the analysis explores optimal biomass allocations across sustainable aviation fuel, diesel, and marine biofuel conversion pathways given varying production targets and policy incentives. Results demonstrate distinct feedstock preferences and pathway utilizations when prioritizing economic returns vs. emissions reductions. For instance, fats, oils, and greases were highly favored in cost-optimized scenarios, while low-carbon feedstocks such as wet waste dominated greenhouse gas-minimized strategies. The findings underscore the pivotal role of policy incentives and technological advances in shaping biofuel supply chains and provide actionable insights for scaling sustainable biofuel production to decarbonize hard-to-electrify sectors. This framework offers a robust tool for policymakers and stakeholders to evaluate biofuel strategies that balance energy output, economic viability, and environmental impact.
09 BIOMASS FUELS↗
Soft costs and EVSE – Knowledge gaps as a barrier to successful projects
There has been a recent push to increase access to electric vehicle (EV) charging infrastructure. The National Electric Vehicle Infrastructure (NEVI) program, part of the Bipartisan Infrastructure Law (BIL) has made significant funding available for major charging infrastructure projects along state thruways, and many state and local incentives exist for EV owners to install chargers in their homes. However, deployment of these chargers has not kept up with demand, primarily due to issues in project planning, permitting processes, and unforeseen delays. This paper serves as a review of the current understanding of these and other non-hardware costs in EV charging infrastructure projects (collectively known as “soft costs”). We found that soft costs in EV charging infrastructure projects are not well understood. Specifically, there is little agreement on how soft costs should be categorized and tracked, and less agreement still on best practices for controlling these costs and lowering barriers to infrastructure deployment. A broader review of EV charging infrastructure cost analyses shows that these costs can have significant impacts on project outcomes. EV charging infrastructure projects may be able to examine the success of the solar industry in lowering soft costs, and a similar effort may lower project costs significantly. Further work on standardizing and collecting data on EV charging infrastructure costs is required to begin addressing and controlling these costs.
32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization
The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.
42 ENGINEERING↗
Solar cities: A case study analysis of city-level enablers of expanded solar energy access
Rooftop solar photovoltaic (PV) adoption can benefit households by reducing electricity bills and enhancing energy resiliency. Low and moderate-income (LMI) households have been less likely to adopt PV and experience these benefits in the United States than higher-income households. Adopter income trends are often explored through quantitative analysis with limited explanatory power. Our quantitative analysis only explains around one-third of city-level variation in LMI adoption trends through socioeconomic factors such as median home values and income inequality and PV market factors such as cumulative adoption and incentives. We implement semi-structured interviews in three case studies of cities with relatively high rates of LMI PV adoption to better understand the factors that explain PV adopter income trends. The case studies partly reiterate findings from quantitative analysis, such as the role of PV incentives. The case studies reveal a broader set of LMI adoption drivers that are missed in quantitative analyses. The case studies show how city contexts can affect LMI adoption, such as the role of supportive city governments. The case studies also reveal the importance of partnerships, such as partnerships between city governments and state LMI PV program implementers. Finally, interviewees emphasized the importance of building trust among prospective LMI PV adopters. Interviewees suggested that partnerships, outreach, and consumer protection measures were crucial to building trust in PV installers among LMI households.
Adoption↗
Modeling distributed energy resource aggregations in security constrained unit commitment and economic dispatch
The Federal Energy Regulatory Commission (FERC) recently issued Order 2222, which requires all wholesale electricity markets in the US to allow distributed energy resources (DERs) to participate in the market as aggregated resources. These DER aggregations may be composed of many individual resources that are offered and dispatched by the market as a single entity. We present here a model of a distributed energy resource aggregator (DERA) that is scheduled by a market operator’s security constrained unit commitment (SCUC) and security constrained economic dispatch (SCED). The DERA model includes constraints for battery energy storage systems (BESSs), demand response resources (DRRs), and a simple distributed energy resource (DER). This paper describes a model for each resource type and presents two methods for the DERA to generate market offer curves: a profit-maximizing optimization to compute cost curves and a direct cost algorithm to determine dispatch costs for each resource and combine into cost curves. Once all participating DERAs are scheduled in SCUC/SCED, the model is then modified to dispatch individual DERs to maximize profit or minimize schedule deviation of the DERAs. A simulation of a representative day illustrates the DERA offers, the scheduled generation, and the DERA dispatch. Findings show the potential for unavoidable schedule deviations due to internal DER constraints and due to economic incentives to deviate from the SCUC/SCED schedules. This highlights the importance of DERA offer construction on market efficiency and system reliability. Novel aspects of our approach include: (1) We consider the asymmetry of price incentives impacting DERAs from the wholesale market compared to those impacting consumers from the retail market, as imposed by current regulations and laws. (2) We model aggregate consumer response through statistically parameterizable utility functions rather than a potentially impractical approach of modeling each individual consumer. (3) We show how to use the DERA operational dispatch model to create offers into the wholesale electricity market. (4) We show how DERAs may fail to meet their scheduled dispatch because the market offer format may not permit them to fully express their operational features such as intertemporal costs and constraints to the market.
aggregations↗
Large-scale spatially explicit analysis of carbon capture at cellulosic biorefineries
The large-scale production of cellulosic biofuels would involve spatially distributed systems including biomass fields, logistics networks and biorefineries. Better understanding of the interactions between landscape-related decisions and the design of biorefineries with carbon capture and storage (CCS) in a supply chain context is needed to enable efficient systems. Here we analyse the cost and greenhouse gas mitigation potential for cellulosic biofuel supply chains in the US Midwest using realistic spatially explicit land availability and crop productivity data and consider fuel conversion technologies with detailed CCS design for their associated CO 2 streams. Optimization methods identify trade-offs and design strategies leading to systems with attractive environmental and economic performance. Strategic and operational decisions depend on underlying spatial features and are sensitive to biofuel demand and CCS incentives. US CCS incentives neglect to motivate greenhouse gas mitigation from all supply chain emission sources, which leverage spatial interactions between CCS, electricity prices and the biomass landscape.
09 BIOMASS FUELS↗
Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains
The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Land conversion to energy crops for sustainable aviation fuel production reduces greenhouse gas emissions in the United States
Energy crops will be critical for scaling up production of Sustainable Aviation Fuel in the United States and reducing greenhouse gas emissions. Here we examine the economic incentives for the extent and type of land conversion needed to scale up fuel production from a mix of cellulosic feedstocks and quantify its greenhouse gas intensity. We show that even with the availability of marginal non-cropland, there will be incentives for converting cropland to produce energy crops as the price of sustainable aviation fuel increases. But contrary to expectations, we find that scaling up fuel production by converting more cropland and more non-cropland from existing uses to energy crops lowers its net greenhouse gas intensity, due to high soil carbon sequestration rate of energy crops, even after considering land use change emissions. The potential savings in emissions are larger than the foregone soil carbon accumulation benefits from keeping that land in current uses.
54 ENVIRONMENTAL SCIENCES↗
Potential for large-scale deployment of offshore wind-to-hydrogen systems in the United States
This study explores the role of producing low-carbon hydrogen using water electrolysis powered by offshore wind in facilitating the United States’ transition to a net-zero emissions economy by 2050. This research introduces an open-source scenario analysis tool for offshore wind-to-hydrogen systems, aiming to assess the impact of technology, regional considerations, and policy incentives on the cost of producing low-carbon hydrogen through offshore wind. Conducting a regional techno-economic analysis at four U.S. coastal sites, the study evaluates two energy transmission configurations and examines associated costs for the years 2025, 2030, and 2035. The results highlight that locations using fixed-bottom technology may achieve cost-competitive water electrolysis hydrogen production by 2030 through leveraging geologic hydrogen storage and federal policy incentives. Furthermore, floating technology locations are expected to see an average 38% reduction in the levelized cost of hydrogen from 2025 to 2035.
17 WIND ENERGY↗
Decision Support System
Forest-based value chains involve decisions that begin at the landscape level and extend through processing, product manufacturing, and end-use markets. However, these decisions are often made independently across sectors, with limited visibility into how upstream resource conditions, incentives, and land management choices influence downstream production systems. In forested regions of the United States, wildfire risk, fragmented ownership, and uncertain markets for low-value residues complicate efforts to align extraction, processing, and utilization decisions. Without tools that link these stages, stakeholders may overlook opportunities to improve resource utilization or inadvertently shift impacts elsewhere in the value chain. This repository introduces a decision support system (DSS) that applies a system-impact-analysis approach to forest biomass residues and co-products. The framework integrates forest inventory data, geospatial resource assessments, and economic modeling to evaluate how biomass extraction decisions influence downstream product pathways. By linking regional feedstock avail- ability with market incentives and processing options—such as fuels, wood products, or soil amendments like biochar—the tool allows decision-makers to compare value chain outcomes across multiple utilization strategies.
Davis, Maggie [Oak Ridge National Laboratory (ORNL↗