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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tightest Mixed-Integer Programming Formulations for Quadratic SCUC Optimization

In this project, we developed new, tighter Mixed-Integer Programming (MIP) formulations for the combined Alternating Current (AC) Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). The work addresses a critical challenge in power system operations: efficiently determining which generation units to commit and how to optimally dispatch them while maintaining network reliability constraints for both normal and contingency scenarios. Our efforts: 1. Advance the Understanding of SCUC/SCOPF Modeling: By introducing tighter MIP formulations and leveraging cutting-edge optimization tools (Julia/JuMP, PowerModels.jl), this project has pushed forward the state of the art in efficient power systems scheduling. 2. Enhance Technical and Economic Feasibility: The methods developed provide more accurate and potentially faster solutions to large-scale, realistic scheduling and dispatch problems in electric power systems, which can translate into improved reliability and potentially lower costs for grid operations. 3. Benefit to the Public: Greater efficiency in power system operations leads to cost savings for utilities and end-users. Improved reliability and integration of advanced modeling approaches can facilitate the adoption of clean energy resources and better accommodate uncertainties in renewable generation. Because this technology could impact bulk power markets and reliability, these innovations have far-reaching public benefits in terms of cost savings, reliability, and sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Phenotypically anchored transcriptomics across diverse agrichemicals reveals conserved pathways and unique gene expression signatures in zebrafish

Agrichemicals such as herbicides, fungicides, insecticides, and biocides are widely used in agriculture, yet some are associated with adverse effects in humans and the environment. While many of these chemicals have been extensively studied in vitro and are included in the EPA’s ToxCast program, comprehensive in vivo comparisons using RNA sequencing across structurally diverse agrichemicals, in a single screening platform, are lacking. In this study, we examined structurally diverse agrichemicals found in the U.S. Environmental Protection Agency’s (EPA) Toxcast Phase I and II library by statically exposing early life stage zebrafish at 6 h post fertilization (hpf) until 120 hpf at concentrations ranging from 0.25 to 100 µM. Morphological outcomes were assessed at 120 hpf across 10 endpoints, including yolk sac edema, craniofacial malformations, and axis abnormalities. Chemicals that produced robust concentration-response relationships were selected for transcriptomic profiling. For transcriptomic analysis, zebrafish were statically exposed to each chemical and sampled at 48 hpf, prior to the onset of morphological effects observed at 120 hpf. Differential expression analysis identified between 0 and 4,538 differentially expressed genes (DEGs) per chemical, with no clear correlation to morphological severity. Both DEG and co-expression network analyses revealed chemical-specific expression patterns that converged on shared biological pathways, including neurodevelopment and cytoskeletal organization. Key regulatory genes such as mylpfa and krt4 were identified within co-expression modules, suggesting their potential role in conserved toxicity mechanisms. Semantic similarity analysis of enriched gene ontology (GO) terms, when compared to existing datasets, highlighted gaps in the annotation of neurodevelopmental processes, indicating that some in vivo effects may not be fully captured by current curated resources. The results provide new insights into the modes of action of diverse agrichemicals and establish a framework for understanding how agrichemical structure relates to biological function in a vertebrate model.

agrichemical↗
DOE OSTI2026

Innovations Driven by Advanced Characterization to Strategize Critical Mineral Production and Beneficial Reuse from Fossil Energy Waste

Critical minerals (CM), such as rare earth elements (REE), cobalt, nickel, and lithium, have important uses in modern electronics and advanced manufacturing, yet are vulnerable to potential supply chain disruptions. Relatively abundant and readily available fossil energy (FE) wastes, such as coal combustion ash, acid mine drainage (AMD) and treatment solids (AMD solids), and Oil and Gas (O&G) drilling wastes (drill cuttings and produced waters) are under consideration as CM feedstocks. The National Energy Technology Laboratory (NETL) has studied CM resources for various FE wastes as part of the U.S. Department of Energy’s mission of bolstering the domestic CM supply, and makes the data available to the public on EDX at sites such as the NEWTS group. Advanced characterization utilizing synchrotron x-ray techniques coupled with laboratory extractions has been performed to identify CM hosting phases in these FE wastes to inform CM recoverability mechanisms. Novel methods to selectively recover CMs while co-producing other valuable byproducts have been developed. Successful examples discussed here include: (1) The identification of REE/Co/Ni/Sc binding and hosting phases in select FE waste (coal combustion ash and AMD solids), resulting in the development of a patented CM step-extraction process, (2) coupled production of functional sorbents from these extraction wastes and for CM recovery. A pilot-scale testing to evaluate the patent’s technical feasibility for extracting REE from coal ash on a barrel scale has been successfully performed. Additionally, (3) evaluation and measurements of brine geochemistry from U.S. O&G produced waters has informed a high Li recovery potential from Marcellus Shale produced water. NETL researchers have been developing tailored pre-treatment processes, an innovative and highly durable lithium sorbent, and geochemical model guided precipitation to accelerate Li production from the Marcellus Shale produced waters. These innovations driven by characterization are integral for maximizing and advancing the potential for CM recovery while offsetting the cost and environmental footprint for FE waste management.

critical mineral processing↗
DOE OSTI2021

Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum

Abstract Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35–43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

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↗
DOE OSTI2025

Parallelizing autotuning for HPC applications: Unveiling the potential of the speculation strategy in Bayesian optimization

In the exascale computing era, tuning High-Performance Computing (HPC) applications has become a significant computational challenge. Although Bayesian optimization (BO) has emerged as a promising tool for HPC performance tuning, the BO workflow is inherently sequential (i.e., one function evaluation at a time) and cannot leverage the huge amount of parallel resources present in modern supercomputers, resulting in a considerable underutilization of their computational capabilities. This paper explores the trade-off between search quality and parallelism in BO, investigating a diverse set of methods. Building upon both previous approaches from the literature and novel methodologies introduced in this work, our study provides a deep analysis to accelerate BO performance tuning. By examining a set of synthetic functions and practical HPC applications, our exploration analyzes the interaction among various BO methods for parallelization, the quantity of parallel resources, the runtime distribution of target HPC applications, and the costs associated with different search orchestration mechanisms that have been overlooked in previous studies. Compared to sequential BO, our novel methodology achieves comparable quality while demonstrating robust scalability in search time as the amount of parallel resources increases; it also outperforms a state-of-the-art tuner, which supports parallelization, achieving up to 3.67x faster search time. We provide high-value insights for practitioners seeking to leverage the power of parallel computing for efficient HPC application tuning. Additionally, to further assist researchers in accelerating the performance tuning of their HPC applications, we provide an extension of an existing open-source tuning framework that incorporates our methods.

Bayesian optimization↗
DOE OSTI2024

Antigua and Barbuda Distributed Energy Resources Deployment Guidelines

This document is designed to provide comprehensive considerations, best practices and guidance for deployment of Distributed Energy Resources (DERs) in Antigua and Barbuda. The document provides lists of various technology selection guidance including technical specifications, requirements and applicable key standards and code associated with key DERs and balance of plant equipment. The document also presents the key microgrid design considerations to integrate the DERs to achieve the economic, decarbonization and resiliency goals. Taking into account sensitive grid for such islands, for grid readiness and screening the potential DER interconnection, general approach is highlighted along with the potential technical interconnection requirements. Furthermore, this document shows the key stakeholders involved in such DER integration and/or microgrid project and how key responsible project team can be qualified. Finally, this document refers to key information about warranties and consumer rights and maintenance of the systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

SIW21-95: Hybridizing Synchronous Condensers with Grid-Forming Battery Energy Storage Systems

One of the main challenges associated with deployment of high shares of inverter-based resources (IBRs) in power grid is not only reduced system inertia but also degrading system strength that may cause severe stability impacts. A minimum level of system strength is needed for the power system to remain stable under normal conditions and to return to a steady state condition following a system disturbance. Significant system strength reduction is expected in almost all planned areas for solar and wind generation deployment. Synchronous condensers (SC) have been considered as one main technology to address the system strength issues for the areas with high levels of IBRs. SCs can help improving reliability and resiliency of power system but they do not provide the full range of services needed by power systems for reliable and economic integration of high shares of inverter-coupled variable generation such as PV generation. Services related to active power controls cannot be provided by SCs due to lack of prime mover. Even for provision of reactive power SCs have certain constraints based on their thermal and stability limits. NREL has been conducting research on a hybridized concept that combines SCs with grid forming (GFM) battery energy storage systems (BESS). This super flexible AC transmission system (SuperFACTS) that combines these two technologies in a single plant under the same controller offers a unique scalable set of services to the power system at all levels (transmission, sub-transmission, distribution, islands and isolated microgrids). Depending on use cases, SuperFACTS can be controlled to provide fully dispatchable and flexible operation using energy storage component, provide a full range of existing and future ancillary and reliability services to the grid (similar or better than conventional sources), maintain adequate levels of grid strength and inertia, and provide fault current for proper operation of protection systems. Therefore, this economic and easy to commercialize solution has potential for significant impacts on certain segments of global energy sector. All types of gird services (market based, reliability and resiliency) can be provided by SuperFACTS plants. GFM BESS can act as a self-black start source for each SuperFACTS module, which in turn can act a black start resource for co-located PV and wind power plants, segments of transmission and distribution networks, for conventional power plants, etc. The issue of in-rush currents during black start is addressed by overcurrent capability of SCs. In this paper we describe the results of modeling for SuperFACTS concept.

grid forming↗
DOE OSTI2022

Derivation and assessment of regional electricity generation emission factors in the USA

Electricity production is one of the largest sources of environmental emissions - especially greenhouse gases (GHGs) - in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs). To address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs. We explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated. Here this work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers - considering the detailed sources of the information and the breadth of emissions covered by it.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Assessing synergies and trade-offs of diverging Paris-compliant mitigation strategies with long-term SDG objectives

The Sustainable Development Goals (SDGs) and the Paris Agreement are the two transformative agendas, which set the benchmarks for nations to address urgent social, economic and environmental challenges. Aside from setting long-term goals, the pathways followed by nations will involve a series of synergies and trade-offs both between and within these agendas. Since it will not be possible to optimise across the 17 SDGs while simultaneously transitioning to low-carbon societies, it will be necessary to implement policies to address the most critical aspects of the agendas and understand the implications for the other dimensions. Here, we rely on a modelling exercise to analyse the long-term implications of a variety of Paris-compliant mitigation strategies suggested in the recent scientific literature on multiple dimensions of the SDG Agenda. The strategies included rely on technological solutions such as renewable energy deployment or carbon capture and storage, nature-based solutions such as afforestation and behavioural changes in the demand side. Results for a selection of energy-environment SDGs suggest that some mitigation pathways could have negative implications on food and water prices, forest cover and increase pressure on water resources depending on the strategy followed, while renewable energy shares, household energy costs, ambient air pollution and yield impacts could be improved simultaneously while reducing greenhouse gas emissions. Overall, results indicate that promoting changes in the demand side could be beneficial to limit potential trade-offs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Energy-water interdependencies across the three major United States electric grids: A multi-sectoral analysis

As water availability and timing of delivery fluctuates and the US electric grid sees rapid transformation and reconfiguration under decarbonization and resource adequacy strategies, there is a critical need for information and data that supports understanding the water-energy interdependency landscape. The United States currently lacks comprehensive data, informative visualizations, and analysis of energy-water interdependencies at scales necessary to support resource and operational decision-making. This article provides US electricity interconnection-level Sankey diagrams that show the relative reliance of water and energy across various economic sectors. A deeper analysis is additionally provided at the county level to illustrate trends and potential opportunities related to resiliency and efficiency in multi-sectoral water and energy flow distributions and intensities. We find that the electricity interconnections in the US vary dramatically in their water and energy interdependencies across applications and economic sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

High electrification futures: Impacts to the U.S. bulk power system

Electrification of end-use technologies could have important implications for bulk power sector planning, operations, and impacts. By combining previously-defined electrification scenarios with capacity expansion modeling, we evaluate the potential challenges and opportunities associated with electrification. Our scenario analysis reveals that electrification reduces energy consumption and air emissions from across the energy sector, and its impacts on energy system costs are modest due, in part, to the abundance of low-cost resources in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Trade and Climate Mitigation Interactions Create Agro–Economic Opportunities With Social and Environmental Trade–Offs in Latin America and the Caribbean

The Latin America and the Caribbean (LAC) region plays key roles in both meeting global agricultural demands and maintaining carbon sinks due to its abundant land and water resources. In this study we use the Global Change Analysis Model (GCAM) to evaluate the opportunities and challenges posed by two global-scale drivers: agricultural market integration (i.e., reduction of trade barriers) and land-based climate mitigation policy. We evaluate their potential individual and combined impacts on agricultural production and trade revenues across LAC’s economies through mid-century, as well as the resulting impacts on agricultural consumers and integrated land-water-climate systems across the region’s diverse sub-regions. Increased global market integration results in increased agricultural production and trade revenues for many LAC economies driven by their evolving comparative advantages. Climate mitigation measures on CO 2 and non-CO 2 greenhouse gases increase revenues due to increased agricultural prices from land competition and emissions abatement. The combined outcomes from both drivers are complex and sometimes non-linear, highlighting the importance of understanding the interactions between multiple drivers. Our results show that increased agricultural production and trade opportunities pose significant trade-offs that require careful multi-sectoral planning, such as emissions reduction challenges, potential loss of livestock production when pursuing land-based climate mitigation strategies, increased consumer expenditures, and changes in land-use or water withdrawals, resulting in deforestation or water scarcity pressures. There is considerable heterogeneity in economic and environmental outcomes across LAC sub-regions and agricultural commodities, illustrating the value of considering outcomes at finer scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Four-Layer Cyber-Physical Security Model for Electric Machine Drives Considering Control Information Flow

Despite the IEEE Power Electronics Society (PELS) establishing Technical Committee 10 on Design Methodologies with a focus on the cyber-physical security of power electronics systems, a holistic design methodology for addressing security vulnerabilities remains underdeveloped. This gap largely stems from the limited integration of computer science and power/control engineering studies in this interdisciplinary field. Addressing the inadequacy of unilateral cyber or control perspectives, this article presents a novel four-layer cyber-physical security model specifically designed for electric machine drives. Central to this model is the innovative control information flow (CIF) model, residing within the control layer, which serves as a pivotal link between the cyber layer's vulnerable resources and the physical layer's state-space models. By mapping vulnerable resources to control variable space and tracing attack propagation, the CIF model facilitates accurate impact predictions based on tainted control laws. The effectiveness and validity of this proposed model are demonstrated through hardware experiments involving two typical cyber-attack scenarios, underscoring its potential as a comprehensive framework for multidisciplinary security strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗
DOE OSTI2020

Final Reports of the 2020 Los Alamos National Laboratory Computational Physics Student Summer Workshop

For the past ten years, the workshop has been bringing a highly talented and diverse group of student every summer. Students work in teams of two, alongside typically two mentors, on research projects reflecting a broad range of topics within computational physics. In addition, students attend a series of lectures on topics within computational physics, facility tours, and networking events. The program lasts ten weeks, with this year’s workshop running from June 8 to August 14. At the end of the summer, students give a final presentation, along with a written report. Those reports are what make up the remaining sections of this document. Admission to the workshop is by a competitive process, with the mentors forming the selection committee. One of the important accomplishments of the workshop has been to create a student pipeline from diverse schools that sometimes are not normally tapped by LANL recruiting. Many workshop students maintain a continuing relationship with LANL, returning as students interns, post-doctoral researchers, and staff members. Additionally, workshop alumni act as ambassadors for LANL. The result is a wider awareness both of LANL as a potential employer, and of the technical work that happens at LANL. This year, the workshop format was changed in several ways, in order to accommodate the off-site, virtual format. Students worked on LANL virtual desktop systems remotely, also accessing LANL HPC resources. In order to facilitate communication, student were given accounts on both Webex, a video teleconferencing platform, and Mattermost, an online team collaboration and chat platform, similar to Slack. Daily communication between students and mentors was primarily on Mattermost, with Webex conferencing as needed. The lectures were all done on Webex. Given the difficulty of the virtual format, and a concern that students might have video teleconferencing burn-out after an academic semester largely moved to that format, all lectures were optional this year. In spite of this, the attendance was generally high. Lecturers were asked to try to move to a more high-level, ”What is it?,” format. Once again, the students did a tremendous job. Over the course of ten weeks, they did important research across a staggering array of disciplines. The following pages contain the final report for each team’s research efforts. We hope you will find reading them as exciting as it was for us to produce them.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accelerating Informed Local Climate Action by Improving Access to DOE Predictive Modeling and Science-Based Tools

The threat of climate change is becoming increasingly urgent, and local governments across the United States are working to develop and implement climate action plans to reduce greenhouse gas emissions and mitigate the impacts of climate change. At the same time, the U.S. Department of Energy's Office of Science, Earth and Environmental Systems Sciences Division (EESSD) has been developing a range of tools and technologies to help predict the potential multi-faceted impacts from climate change given varying environmental and socioeconomic scenarios. However, local governments have been slow to engage with these scientific assets due to a lack of understanding and awareness. The DOE researchers interviewed highlighted three primary types of resources that could benefit local governments and climate practitioners: climate models, modeled datasets, and scientific journal articles. Although many of these resources are open source and available to the public, there were several challenges identified in translating these resources and their research insights directly to decision-makers.

54 ENVIRONMENTAL SCIENCES↗