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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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DOE OSTI2026

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↗
DOE OSTI2020

China’s Booming Plug-in Electric Vehicle Market—How Will It Continue?

The plug-in electric vehicle (PEV) is recognized as a pivotal component in transportation energy security and technology innovation in China. With government incentives and substantial capital investments, the Chinese PEV market has become one of the largest in the world. However, is this market boom ephemeral or will it be continuing? This chapter provides a general review of the Chinese PEV market mainly from the consumer per-spective, by summarizing the characteristics of the vehicle policies and consumer concerns in the PEV market in recent years. Moreover, it intro-duces detailed examples on how the policies, technologies, and infrastruc-ture shape the market. In addition to employing large amounts of reliable data from the China Automotive Technology and Research Center for the review, the authors also adopted a quantitative analysis tool—the New Energy and Oil Consumption Credits model—to project the potential in-dustrial impacts of the government policies in multiple scenarios. Based on the findings, it seems that the prosperity of the PEV market, incentivized by the government, will continue in the short run. The direct subsidies to PEV from the government will gradually phase out, and the supply-side push policies such as the dual-credit policy and other charging infrastruc-ture incentives will work as the invisible hand to force the automakers to invest more in the PEV segment. However, in the long run, innovation in PEV technology and business models, together with consumer acceptance will be the major factors that can sustain further market growth.

Ou, Shawn↗
DOE OSTI2022

Aggregation in bottom-up vulnerability assessments and equity implications: The case of Jordanian households’ water supply

Bottom-up methods for water resources modeling rely on acceptability thresholds to find, through a response surface, which deeply uncertain futures lead to system failure. They commonly treat water users as aggregate actors, which may preclude analysis of the equity impacts of interventions. This paper explores how aggregation choices for large groups of water users lead to different policy recommendations in response surface assessments. Herein, two aggregation methods with varying parameters are considered: percentile satisfaction targets and generalized mean. A 2-dimensional stress-test assessment across groundwater availability and population is applied to household water supply in Jordan. The study compares six different policies covering supply enhancement and rebalancing, using a country-wide multi-agent model that characterizes households across socioeconomic strata. For different aggregation levels, policies are ordered by their associated robustness index. Results show that aggregation choices may modify response surfaces as much as policy changes and strongly determine policy preference. Modifying allocation rules can substantially reduce the disparity in household vulnerability. Preferences defined by aggregation intervals provide a finer understanding of trade-offs among water users and may improve deliberation over equity under deep uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Win-win transportation strategies for India: Linking air pollution and climate mitigation

Here, this article analyzes road transport in India to explore linkages between air pollution and climate change policies in the transportation sector. Five teams modeled five policy scenarios – fuel efficiency, electrification, alternative fuels, modal shifts, and moderation in transport demand – to explore which policy brings the largest synergetic effects in reducing carbon dioxide (CO 2 ) and particulate matter (PM 2.5 ) emissions. The teams also modeled the comprehensive scenario which included policy measures from individual scenarios. The paper concludes that all of the measures provide strong co-benefits in reducing air pollutant and CO 2 emissions. The modeling results show that increased energy efficiency of passenger and freight vehicles has the largest protentional for reducing both CO 2 and PM 2.5 emissions. It is possible to reach even a larger reduction of air pollutants and CO 2 emissions by combining several policy measures in the comprehensive scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A reinforcement learning approach to long-horizon operations, health, and maintenance supervisory control of advanced energy systems

In this work, we develop a Reinforcement Learning (RL) approach to the supervisory control problem for advanced energy systems, such as novel nuclear reactors and other demand-driven, mission-critical, and component-health-sensitive energy plants. The inclusive problem landscape considered captures the stochastic confluence of plant performance, component health evolution, power demand from the grid, diverse maintenance actions, and operator-defined goals and constraints, all considered over meaningfully long-enough reasoning horizons. Key aspects of the proposed approach are a receding horizon control-inspired technique dictating time- or event-triggered supervisory policy (re-)constructions, as well as additional capability-enabling contributions such as timescale compression, to handle long reasoning horizons and uncertainty in parts of the problem, and practical yet demonstrably-effective handling of hybrid action spaces with continuous and discrete decision variables. The resulting algorithm consists of a simulation-based RL agent constructing stochastic supervisory control policies over nontrivial action spaces and for long horizons, applying the learned policy to the system for a much shorter interval, and perpetually repeating, to construct the next long-horizon policy. That next policy will only be applied, again, for a short interval, yet originally far-in-time events move progressively closer, their associated uncertainty decreases, and new events and aspects enter the reasoning horizon. The proposed methodology bridges fundamental receding horizon concepts with the unequivocally stronger and more scalable reasoning of contemporary RL. Numerical examples using Soft Actor–Critic Deep RL illustrate the operation and efficacy of the proposed technique for a power plant tasked with health-aware load following missions in a dynamic electricity market landscape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bundling measures for food systems transformation: a global, multimodel assessment

Background Current food systems leave one in ten individuals at risk of hunger while driving unsustainable environmental impacts. Inaction risks further exacerbating negative impacts on both human and planetary health. These challenges emerge from complex system interactions, requiring approaches that engage with this complexity and consider how transformation measures interact across food systems. We aimed to quantify the magnitude and uncertainty of the impacts of key food systems transformation measures both individually and in a bundle using an ensemble of global economic models. Methods In this global multimodel assessment, we applied an ensemble of ten state-of-the-art global economic models to evaluate the potential of four key measures in transforming food systems: increasing agricultural productivity, halving food loss and waste, shifting towards healthier diets, and economy-wide climate mitigation policies aligned with limiting warming to 1·5°C. The scenarios used a middle-of-the-road shared socioeconomic pathway for population and gross domestic product growth, climate impact data from Jägermeyr and colleagues, Thornton and colleagues, and Nelson and colleagues, and dietary targets based on the EAT–Lancet healthy reference diet, with model simulations conducted from 2020 to 2050. We then assessed the effect of these measures in isolation and in combination in a bundled scenario. To further understand the interactions between these measures, we conducted a decomposition analysis that distinguishes between the individual effects of a measure (effect when implemented alone), total effects (its contribution within the bundle), and interaction effects (the difference between total and individual effects). This approach aimed to show complementarities and trade-offs that emerge when multiple measures are implemented simultaneously. Findings Our analysis showed that individual measures in isolation are insufficient to achieve high-level environmental objectives and might generate unintended consequences. In contrast, bundling measures produces co-benefits: avoiding 50% of projected agricultural greenhouse gas emissions by 2050 and almost 20% of anticipated land conversion, while moderating food price increases associated with ambitious climate change mitigation policies. Our decomposition analysis further shows that measures can have varying effects across different dimensions. Although dietary shifts and climate mitigation policies are the largest drivers of environmental benefits (each contributing to a median decline of >10 percentage points in non-CO 2 emissions and 5 percentage points in agricultural land use globally), productivity improvements and reducing food loss and waste play essential roles in moderating price increases (each contributing to a median decline of >5 percentage points in average prices). Interpretation This study highlights the importance of implementing coordinated approaches to food system transformation and climate change mitigation rather than relying on isolated interventions. Comprehensive transformation requires understanding how supply-side and demand-side changes can interact with climate mitigation policies, enabling policy makers to design intervention packages that maximise benefits while minimising trade-offs across environmental, economic, and social dimensions.

Sundiang, Marina [Cornell Univ., Ithaca, NY (Unite↗
DOE OSTI2021

Comparison of Current and Future PM2.5 Air Quality in China Under CMIP6 and DPEC Emission Scenarios

The latest scenarios for the sixth Coupled Model Intercomparison Project (CMIP6) provide opportunities to explore future climate and air pollution mitigation pathways with socioeconomic features. However, to what extent inadequate consideration of region-specific policies in the CMIP6 scenarios would affect regional air quality projections is still not clear. Here, we simulated China’s future PM2.5 concentrations and compositions with local policy-based scenarios and the original CMIP6 datasets, for the first time estimating future air quality differences induced by the lack of local pollution control policies in the CMIP6 scenarios. We found that the CMIP6-driven projections overestimate major chemical aerosols by 10% – 70% in 2015 and fail to capture the rapid PM2.5 concentration decline during 2015–2019. Near-term differences further lead to 43% – 49% (5 – 9 µg/m3) PM2.5 overpredictions in 2050 in CMIP6 scenarios compared with policy-based mitigation scenarios. Our study demonstrates the indispensable influences of local policies on regional analyses and suggests that the research community should incorporate more region-specific information in future scenario designs.

Cheng, Jing↗
DOE OSTI2021

The surprisingly inexpensive cost of state-driven emission control strategies

Traditionally, analysis of the costs of cutting greenhouse gas emissions has assumed that governments would implement idealized, optimal policies such as uniform economy-wide carbon taxes. Yet actual policies in the real world, especially in large federal governments, are often highly heterogeneous and vary in political support and administrative capabilities within a country. While the benefits of heterogeneous action have been discussed widely for experimentation and leadership, little is known about its costs. Focusing on the United States, we represent plausible variation (by more than a factor of 3) in the stringency of state-led climate policy in a process-based integrated assessment model (GCAM-USA). For a wide array of national decarbonization targets, we find that the nationwide cost from heterogeneous subnational policies is only one-tenth higher than nationally uniform policies. Such results hinge on two critical technologies (advanced biofuels and electricity) for which inter-state trade ameliorates the economic efficiencies that might arise with heterogeneous action.

Peng, Wei↗
DOE OSTI2024

High-ambition climate action in all sectors can achieve a 65% greenhouse gas emissions reduction in the United States by 2035

Under the next cycle of target setting under the Paris Agreement, countries will be updating and submitting new nationally determined contributions (NDCs) over the coming year. To this end, there is a growing need for the United States to assess potential pathways toward a new, maximally ambitious 2035 NDC. In this study, we use an integrated assessment model with state-level detail to model existing policies from both federal and non-federal actors, including the Inflation Reduction Act, Bipartisan Infrastructure Law, and key state policies, across all sectors and gases. Additionally, we develop a high-ambition scenario, which includes new and enhanced policies from these actors. We find that existing policies can reduce net greenhouse gas (GHG) emissions by 44% (with a range of 37% to 52%) by 2035, relative to 2005 levels. The high-ambition scenario can deliver net GHG reductions up to 65% (with a range of 59% to 71%) by 2035 under accelerated implementation of federal regulations and investments, as well as state policies such as renewable portfolio standards, EV sales targets, and zero-emission appliance standards. This level of reductions would provide a basis for continued progress toward the country’s 2050 net-zero emissions goal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Plan evaluation for heat resilience: complementary methods to comprehensively assess heat planning in Tempe and Tucson, Arizona

Abstract Escalating impacts from climate change and urban heat are increasing the urgency for communities to equitably plan for heat resilience. Cities in the desert Southwest are among the hottest and fastest warming in the U.S., placing them on the front lines of heat planning. Urban heat resilience requires an integrated planning approach that coordinates strategies across the network of plans that shape the built environment and risk patterns. To date, few studies have assessed cities’ progress on heat planning. This research is the first to combine two emerging plan evaluation approaches to examine how networks of plans shape urban heat resilience through case studies of Tempe and Tucson, Arizona. The first methodology, Plan Quality Evaluation for Heat Resilience, adapts existing plan quality assessment approaches to heat. We assess whether plans meet 56 criteria across seven principles of high-quality planning and the types of heat strategies included in the plans. The second methodology, the Plan Integration for Resilience Scorecard™ (PIRS™) for Heat, focuses on plan policies that could influence urban heat hazards. We categorize policies by policy tool and heat mitigation strategy and score them based on their heat impact. Scored policies are then mapped to evaluate their spatial distribution and the net effect of the plan network. The resulting PIRS™ for Heat scorecard is compared with heat vulnerability indicators to assess policy alignment with risks. We find that both cities are proactively planning for heat resilience using similar plan and strategy types, however, there are clear and consistent opportunities for improvement. Combining these complementary plan evaluation methods provides a more comprehensive understanding of how plans address heat and a generalizable approach that communities everywhere could use to identify opportunities for improved heat resilience planning.

Environmental Sciences & Ecology↗
DOE OSTI2020

Deep Reinforcement Learning-Based Edge Caching in Wireless Networks

With the purpose to offload data traffic in wireless networks, content caching techniques have recently been studied intensively. Using these techniques and caching a portion of the popular files at the local content servers, the users can be served with less delay. Most of the content replacement policies are based on the content popularity, that depends on the users' preferences. In practice, such information varies over time. Therefore, an approach to determine the file popularity patterns must be incorporated into caching policies. In this context, we study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. For centralized edge caching, we aim at maximizing the cache hit rate. In decentralized edge caching, we consider both the cache hit rate and transmission delay as performance metrics. These proposed frameworks are assumed to neither have any prior information on the file popularities nor know the potential variations in such information. Via simulation results, the superiority of the proposed frameworks is verified by comparing them with other policies, including least frequently used (LFU), least recently used (LRU), and first-in-first-out (FIFO) policies.

42 ENGINEERING↗
DOE OSTI2020

The Study of Environmental Arctic Change (SEARCH) Arctic Futures 2050 Conference. Final Report

The Arctic Futures 2050 Conference was designed as a novel convening of Arctic scientists, Indigenous people, and decision makers, including policy makers at all levels of government. The conference was held 4–6 September 2019 in Washington, D.C. and gathered nearly 400 participants to explore six questions: What do we know, and what do we need to know about the Arctic—and why does it matter? What challenges confront policy makers in the rapidly changing Arctic? What basic research is needed to inform responses to Arctic change? What applied research is needed to inform responses to change? What tools can facilitate informing policy making with science and Indigenous knowledge? What opportunities exist for partnerships between policy makers and scientists? A conference report (see: https://www.searcharcticscience.org/arctic-2050/conference-2019/products) detailed all conference discussions. The main conference conclusion was that holistic understanding and useful adaptation to rapid Arctic change requires bringing together scientists, Indigenous Knowledge holders, and policy makers in all phases of defining the problems, conducting research, and sharing knowledge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling Photovoltaics Innovation and Deployment Dynamics

PV’s historically rapid cost reduction is exceptional among technologies. Further cost reductions could play a major role in increasing deployment in the future. To enable such cost reductions, new modeling frameworks are needed to understand the determinants of innovation in PV. In this project, we study the mechanisms driving PV module and system cost reductions, delving deeply into the specific technological innovations that have occurred in the past and the policies that encouraged them, and also opportunities for future cost reduction and widespread deployment. The project contributes new fundamental insight on the determinants of technological innovation by developing novel methods and insights that are generalizable and can therefore be applied to other technologies. The results will allow policy makers, engineers, and other stakeholders to better prioritize their efforts and investments in the future. The project is organized around four journal articles as described below. The first article [1] identifies ‘low-level’ (e.g. conversion efficiency improvement) and ‘high- level’ (e.g. R&D efforts) mechanisms of cost reduction in PV systems (Tasks 1-4). This work builds on a previous DOE grant, where we developed a framework for technological innovation leading to PV module cost reduction [2]. We advance a method to disentangle the contributions of physical (‘hardware’) and non-physical (‘soft technology’) changes. Our results uncover reasons behind the relatively slow evolution of soft technology and can inform new innovation approaches to these technologies. The second article [3] identifies specific engineering or institutional innovations that enabled the low-level mechanisms of cost reduction in PV module and balance-of-systems (BOS) (Tasks 5, 9, 10). We identify 85 innovations and connect them to the cost variables they affected. By developing an innovations typology, this study shows the differences between the types of innovations affecting PV modules and BOS components. Finally, by analyzing the industry origins of innovations, this study also finds that PV was well-positioned within an ecosystem of continuously advancing technologies. The third article [4] studies prospective cost reduction opportunities (Task 10). We explore how design approaches that emphasize standardization and automation, such as plug-and-play PV systems, can create cost reduction opportunities by reducing interactions and speeding up activities with high process costs. We show that this can lead to cost reduction in cost components with the most untapped opportunity for improvement such as installation labor, overhead, electrical BOS, and customer acquisition. The fourth article [5] analyzes how various policies supporting PV deployment and R&D contributed to PV’s cost improvement by enabling high-level mechanisms, specific innovations, and ultimately low-level mechanisms of cost reduction (Tasks 8, 13, 14). We investigate examples from different countries and connect these policies to quantifiable cost change mechanisms. Our study sheds light on the roles that different nations played over time, through a diverse set of policy approaches.

14 SOLAR ENERGY↗
DOE OSTI2021

Accessing protected data by a high-performance computing cluster

A data protection system is provided that allows applications to access protected data in a way that restricts applications from outputting to unauthorized targets any unprotected data derived from the protected data and that ensures that the applications do not have access to a key that allows access to the unprotected data. The data protection system provides a policy server that may execute on a service node of a high performance computing system and a data encryption process that may execute on each compute node that is allocated to an application or batch job. The policy server maintains policies of entities specifying access control for protected data. The data encryption process generates a secure execution environment for an application process and interfaces with the policy server to retrieve keys for decrypting protected data in accordance with a policy, and it decrypts and provides the decrypted data to the application process.

Barnes, Peter↗
DOE OSTI2023

Building Environmental Justice and Equity into the Development of Critical Mineral Industries

The global transition toward low-carbon energy not only means an increase in demand for clean electricity and renewable resources, but also an increase in demand for the critical minerals (CMs) and rare earth elements (REEs) that these technologies rely upon. Indeed, low-carbon energy technologies, such as those used for wind turbines and electric vehicle motors, require significantly more lithium, nickel, cobalt, manganese, graphite, and other CMs than fossil-based energy technologies. The growing demand for new forms of low- carbon energy will necessitate a proportional scale-up of CM and REE extraction and processing. Presently, the majority of extraction and processing activities for CMs and REEs are concentrated in very few countries, primarily in China and the Global South. CM and REE supply chain activities conducted in or controlled by these countries are commonly associated with widespread and well- documented human rights abuses and environmental degradation. As a result of concerns related to supply chain security, worker welfare, and the overall need for additional sources of CMs and REEs, governments worldwide have begun to explore policy pathways for the development of new supply chains. The development of new supply chains for CMs and REEs re s a multifold opportunity to accelerate the global deployment of low-carbon energy fleets, reduce global carbon emissions, build political resilience—and also to enhance policy objectives of environmental justice and equity in the clean energy transition. Fulfilling these policy objectives requires energy producers to navigate a maze of global supply chain policies designed to shape and accelerate the growth of new markets. In the United States, for instance, President Biden recently implemented fiscal and trade policies that incentivize both domestic and global reliance on U.S.-produced CMs and REEs, leveraging key relationships in Asia and Europe to ensure the accelerated buildout of the U.S. supply chain. The Biden Administration’s framework also emphasizes that new supply chains for CMs, REEs, and other low-carbon energy pathways must generate benefits for marginalized and disadvantaged communities, build energy equity, and contribute to the Biden Administration’s vision of environmental justice. present CM and REE supply chains include several stages, including mining, processing, transport, utilization, and disposal, each of which involves different environmental justice considerations and potential injustices. This study explores how technical innovation, paired with responsible community engagement and empowerment, can help inform the development of energy equity and environmental justice at all stages of new supply chains. The study highlights these opportunities on a broad scale. We also lend a specific focus to research at the University of Wyoming School of Energy Resources that aims to guide the development of CM and REE industries in communities with high economic dependence on coal and other fossil fuel industries, identifying pathways to grow a U.S. domestic supply chain by producing CMs from coal, coal by-products, and coal waste streams. These production pathways represent a potential new supply for CMs across the U.S. and elsewhere, thereby enhancing national security and accelerating the widespread deployment of low-carbon energy technologies, while also generating alternative applications for remaining coal reserves and aiding in a just transition for rural energy-producing communities.

Gerace, Selena↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2022

Achieving an 80% carbon-free electricity system in China by 2035

Dramatic reductions in solar, wind, and battery storage costs create new opportunities to reduce emissions and costs in China’s electricity sector, beyond current policy goals. This study examines the cost, reliability, emissions, public health, and employment implications of increasing the share of non-fossil fuel (“carbon free”) electricity generation in China to 80% by 2035. The analysis uses state-of-the-art modeling with high resolution load, wind, and solar inputs. The study finds that achieving an 80% carbon free electricity system in China by 2035 could reduce wholesale electricity costs, relative to a current policy baseline, while maintaining high levels of reliability, reducing deaths from air pollution, and increasing employment. In our 80% scenario, wind and solar generation capacity reach 3 TW and battery storage capacity reaches 0.4 TW by 2035, implying a rapid scale up in these resources that will require changes in policy targets, markets and regulation, and land use policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Enabling Floating Solar Photovoltaic (FPV) Deployment: Review of Barriers to FPV Deployment in Southeast Asia

The purpose of this report is to improve understanding of the policy landscape for FPV systems and identify potential pathways to create an enabling policy and regulatory environment FPV systems in Southeast Asia. In this literature review, we discuss the case for FPVs in Southeast Asia by presenting the energy context and state of the grid in select Southeast Asia countries and conduct a literature review of existing and potential non-technical barriers to FPV deployment, concluding with potential policy pathways to address some of the barriers identified. Given the lack of extensive literature on the policy and regulatory environment for FPVs and the similarity with other RE technologies, this review adapts lessons from other RE technologies to the FPV context.

14 SOLAR ENERGY↗