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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Single electron in a storage ring: a probe into the fundamental properties of synchrotron radiation and a powerful diagnostic tool

An experimental study of undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. Photons were detected with a single-photon avalanche diode (SPAD) at an average rate of 20 kHz, corresponding to one detection every few hundred revolutions. Events were continuously recorded by a picosecond event timer for as long as 5 minutes at a time. The collected data were used to directly test the predictions of classical Poisson photostatistics. The study was motivated in part by a previous observation of sub-Poissonian statistics in a similar experiment with synchrotron radiation. In addition, we show that the time series of recorded events can be used to study the synchrotron motion of a single electron, yielding dynamical parameters such as the phase jitter of the radiofrequency cavity and the dependence of the synchrotron period on amplitude. Research plans for further experiments are also discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Kepler-90: Giant Transit-timing Variations Reveal a Super-puff

Exoplanet transit-timing variations (TTVs) caused by gravitational forces between planets can be used to determine planetary masses and orbital parameters. Most of the observed TTVs are small and sinusoidal in time, leading to degeneracies between the masses and orbital parameters. Here we report a TTV analysis of Kepler-90g and Kepler-90h, which exhibit large TTVs up to 25 hr. With optimization, we find a unique solution that allows us to constrain all of the orbital parameters. The best-fit masses for Kepler-90g and 90h are 15.0{sub −0.8}{sup +0.9} M {sub ⊕} (Earth mass) and 203{sub −5}{sup +5}M{sub ⊕}, respectively, with Kepler-90g having an unusually low apparent density of 0.15 ± 0.05 g cm{sup −3}. The uniqueness of orbital parameter solution enables a long-term dynamical integration, which reveals that although their periods are close to 2:3 orbital resonance, they are not locked in resonance, and the configuration is stable over billions of years. The dynamical history of the system suggests that planet interactions are able to raise the eccentricities and break the resonant lock after the initial formation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Numerical fluid dynamics for FRG flow equations: Zero-dimensional QFTs as numerical test cases. II. Entropy production and irreversibility of RG flows

Herein we demonstrate that the reformulation of renormalization group (RG) flow equations as nonlinear heat equations has severe implications on the understanding of RG flows in general. We demonstrate by explicitly constructing an entropy function for a zero-dimensional Z 2 -symmetric model that the dissipative character of generic nonlinear diffusion equations is also hard-coded in the functional RG equation. This renders RG flows manifestly irreversible, revealing the semigroup property of RG transformations on the level of the flow equation itself. Additionally, we argue that the dissipative character of RG flows, its irreversibility and the entropy production during the RG flow may be linked to the existence of a so-called C– / A-function. In total, this introduces an asymmetry in the so-called RG time—in complete analogy to the thermodynamic arrow of time—and allows for an interpretation of infrared actions as equilibrium solutions of dissipative RG flows equations. The impossibility of resolving microphysics from macrophysics is evident in this framework. Furthermore, we directly link the irreversibility and the entropy production in RG flows to an explicit numerical entropy production, which is manifest in diffusive and non-linear partial differential equations (PDEs) and a standard mathematical tool for the analysis of PDEs. Using exactly solvable zero-dimensional Z 2 -symmetric models, we explicitly compute the (numerical) entropy production related to the total variation nonincreasing property of the PDE during RG flows toward the infrared limit. Finally, we discuss generalizations of our findings and relations to the C– / A-theorem as well as how our work may help to construct truncations of RG flow equations in the future, including numerically stable schemes for solving the corresponding PDEs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Ultraviolet and X-ray properties of Coma’s ultra-diffuse galaxies

ABSTRACT Many ultra-diffuse galaxies (UDGs) have been discovered in the Coma cluster, and there is evidence that some, notably Dragonfly 44, have Milky Way-like dynamical masses despite dwarf-like stellar masses. We used X-ray, ultraviolet (UV), and optical data to investigate the star formation and nuclear activity in the Coma UDGs, and we obtained deep UV and X-ray data (Swift and XMM–Newton) for Dragonfly 44 to search for low-level star formation, hot circumgalactic gas, and the integrated emission from X-ray binaries. Among the Coma UDGs, we find UV luminosities consistent with quiescence but NUV − r colours indicating star formation in the past Gyr. This indicates that the UDGs were recently quenched. The r-band luminosity declines with projected distance from the Coma core. The Dragonfly 44 UV luminosity is also consistent with quiescence, with SFR$\lt 6\times 10^{-4} \, \mathrm{M}_{\odot }$ yr−1, and no X-rays are detected down to a sensitivity of 1038 erg s−1. This rules out a hot corona with a $M \gt 10^8 \, \mathrm{M}_{\odot }$ within the virial radius, which would normally be expected for a dynamically massive galaxy. The absence of bright, low-mass X-ray binaries is consistent with the expectation from the Galaxy total stellar mass, but it is unlikely if most low-mass X-ray binaries form in globular clusters, as Dragonfly 44 has a very large population. Based on the UV and X-ray analysis, the Coma UDGs are consistent with quenched dwarf galaxies, although we cannot rule out a dynamically massive population.

Lee, Chris H.↗
DOE OSTI2023

Combining Agent Based Modeling and System Dynamics to Investigate the Circularity of Plastics

The United States currently produces about 1 million metric ton of ocean plastic pollution annually. One proposed solution to combat plastic waste is a circular economy (CE), which aims to transition from today's take-make-waste linear pattern of production and consumption to a system where the value of resources is maximized over time. Two key methods in industrial ecology are useful in assessing the viability of CE: (1) System Dynamics (SD) and (2) Agent Based Modeling (ABM). In prior work, the plastic life cycle was modeled with SD and ABM. The two models calculate recycling rates and costs in different ways, making it difficult to pinpoint necessary next steps. We integrate the ABM and SD models - linking the emergent patterns from micro-level human decisions to system level processes - which allows a more comprehensive understanding of feedbacks, costs, and environmental impacts. The integrated model is more accurate, and can be used to visualize recycling rates and human health and environmental impacts over time. The difference between the integrated and original SD model prompts a Sobol sensitivity analysis, which is used to understand which behavioral factors most affect plastic recycling patterns. We find that the habitual component is typically the most influential in promoting positive recycling behavior. Additionally, we utilize the combined model to understand and visualize how various behavioral intervention scenarios, like improved access to recycling programs and cart tagging, influence recycling patterns; these results can guide future policy-making.

agent-based modeling↗
DOE OSTI2021

Data-Driven Learning of Nonautonomous Systems

In this work, we present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model

Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge--Kutta framework. The inexpensive surrogate model is integrated with a small time step to guide the solution trajectory, and the full model is treated with a large time step to occasionally correct for the surrogate model error and ensure convergence. Here, we provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.

Surrogate models↗
DOE OSTI2024

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Quantum computing Floquet energy spectra

Quantum systems can be dynamically controlled using time-periodic external fields, leading to the concept of Floquet engineering, with promising technological applications. Computing Floquet energy spectra is harder than only computing ground state properties or single time-dependent trajectories, and scales exponentially with the Hilbert space dimension. Especially for strongly correlated systems in the low frequency limit, classical approaches based on truncation break down. Here, we present two quantum algorithms to determine effective Floquet modes and energy spectra. We combine the defining properties of Floquet modes in time and frequency domains with the expressiveness of parametrized quantum circuits to overcome the limitations of classical approaches. We benchmark our algorithms and provide an analysis of the key properties relevant for near-term quantum hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DaYu: Optimizing Distributed Scientific Workflows by Decoding Dataflow Semantics and Dynamics

The combination of ever-growing scientific datasets and distributed workflow complexity creates I/O performance bottlenecks due to data volume, velocity, and variety. Although the increasing use of descriptive data formats (e.g., HDF5, netCDF) helps organize these datasets, it also creates obscure bottlenecks due to the need to translate high level operations into file addresses and then into low-level I/O operations. To address this challenge, we introduce DaYu, a method and toolset for analyzing (a) semantic relationships between logical datasets and file addresses, (b) how dataset operations translate into I/O, and (c) the combination across entire workflows. DaYu's analysis and visualization enables identification of critical bottlenecks and reasoning about remediation. We describe our methodology and propose optimization guidelines. Evaluation on scientific workflows demonstrates up to 3.7x performance improvements in I/O time for obscure bottlenecks. The time and storage overhead for DaYu's time-ordered data is typically under 0.2% of runtime and 0.25% of data volume, respectively.

Tang, Meng↗
DOE OSTI2022

Numerical analysis of dynamic load following response in a natural circulation molten salt power reactor system

The Molten Salt Reactor (MSR) concept is a rapidly evolving Generation IV design that has recently attracted favorable attention due to the potential for reducing waste generation, realizing passive safety features, and seizing on the opportunity for cost effective economics. An investigation into the power transient behavior of an autonomous load-following, closed-loop, natural circulation MSR system is important to quantifying operational and safety performance under dynamic conditions. This paper presents the results of a STAR-CCM+ and a comparatively simple asymmetric, one-dimensional, numerical model to solve the compound dynamic MSR power behavior subject to flow and temperature reactivity feedback only. Results show that reactor power is affected by fuel salt flow velocity (global) and temperatures (local bulk volume averages) in a coupled, time-delayed manner that results in a unique compound dynamic, closed-loop power feedback mechanism. The 1-D simulation approach opens the possibility of performing inexpensive computations to evaluate time-dependent reactor performance relative to thermo-physical fuel salt limitations. Results also potentially motivate a convincing conclusion that natural circulation MSRs provide a leap in safety and reliability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Unusual layer-by-layer growth of epitaxial oxide islands during Cu oxidation

Elucidating metal oxide growth mechanisms is essential for precisely designing and fabricating nanostructured oxides with broad applications in energy and electronics. However, current epitaxial oxide growth methods are based on macroscopic empirical knowledge, lacking fundamental guidance at the nanoscale. Using correlated in situ environmental transmission electron microscopy, statistically-validated quantitative analysis, and density functional theory calculations, we show epitaxial Cu 2 O nano-island growth on Cu is layer-by-layer along Cu 2 O(110) planes, regardless of substrate orientation, contradicting classical models that predict multi-layer growth parallel to substrate surfaces. Growth kinetics show cubic relationships with time, indicating individual oxide monolayers follow Frank-van der Merwe growth whereas oxide islands follow Stranski-Krastanov growth. Cu sources for island growth transition from step edges to bulk substrates during oxidation, contrasting with classical corrosion theories which assume subsurface sources predominate. Our results resolve alternative epitaxial island growth mechanisms, improving the understanding of oxidation dynamics critical for advanced manufacturing at the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗
DOE OSTI2020

Molecular Transducers of Physical Activity Consortium (MoTrPAC): Mapping the Dynamic Responses to Exercise

Exercise provides a robust physiological stimulus that evokes cross-talk between a wide range of tissues via hormonal, cellular and molecular signals that when repeated regularly over time (i.e., training) improves physiological capacity, benefits numerous organ systems and decreases the risk for premature mortality. In spite of its fundamental importance, a gap remains in identifying the detailed molecular signals and responses to exercise and its benefits for health and disease prevention. To further knowledge in this emerging area, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to generate a molecular map of the effects of acute and chronic exercise. MoTrPAC investigators are conducting preclinical and clinical studies to better understand the systemic effects of exercise across multiple tissues. The preclinical studies were performed on more than 700 rats with the analysis of twenty or more organs per rat. The clinical study involves a large cohort of ~2600 individuals with a range of ages and fitness levels (untrained, trained, highly trained) who will be evaluated physiologically and by molecular probing of blood, muscle and adipose tissues before and after acute and chronic endurance or resistance exercise. Multi-omic analyses (genomics, epigenomics, transcriptomics, proteomics and metabolomics/lipidomics) will be performed followed by state-of-the-art bioinformatics to create a molecular map of exercise. This mapping project will provide a public database that is expected to enhance our understanding of the health benefits of physical activity in many organ systems and potentially provide insight into mitigating diseases through activity.

Sanford, James A.↗
DOE OSTI2024

Phonon dynamics in the site-disordered Kitaev spin liquid

The Kitaev honeycomb model provides a paradigmatic example of an exactly solvable quantum spin liquid (QSL), where spins fractionalize into itinerant Majorana fermions coupled to a static background of ℤ 2 gauge fluxes. This model has attracted significant interest due to its potential experimental realization in spin-orbit Mott insulators like 𝛼−RuCl 3 . Among various experimental techniques, ultrasound measurements of sound attenuation have emerged as a promising approach to detect spin fractionalization in these materials. However, deviations from the ideal Kitaev model, often due to disorder, introduce localized modes that dominate the low-energy physics. To investigate these effects, we calculate the sound attenuation coefficient in the site-disordered Kitaev honeycomb model under an applied magnetic field that breaks time-reversal symmetry. Our analysis reveals that quasilocalized modes from quasivacancies impact the phonon self-energy, yet the sound attenuation coefficient maintains its sixfold symmetry and linear temperature dependence at low temperatures, even with disorder. This robustness underscores sound attenuation's reliability as a probe for spin fractionalization. As a result, we show that this linear behavior persists under an external field and in the random-flux sector, highlighting the persistent influence of fractionalized excitations, despite disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Single-Molecule Tracking Measurements Reveal the Detailed Mechanisms of Molecular Diffusion in Solvent Mixtures under Nanoconfinement

Understanding mass transport mechanisms in nanopores is important for developing advanced materials for chemical separations, chemical sensing, and energy storage. This paper reports a novel imaging platform that is employed for the first time to investigate the detailed diffusion dynamics of single rhodamine B (RhB) dye molecules confined within solution-filled cylindrical anodic aluminum oxide (AAO) nanopores. The imaging platform relies on illumination of horizontally-oriented AAO nanopores in a highly inclined and laminated optical (HILO) light sheet microscopy geometry. The method was used to investigate the translational and orientational dynamics of single rhodamine B (RhB) molecules within horizontally-oriented 5- and 10-nm diameter AAO nanopores filled with water–ethanol mixtures. The established platform enabled the observation of one-dimensional motion along the pore axis involving occasional short- or long-term immobilization at the single-molecule level. Analysis of cumulative squared-displacement distributions revealed fast (5 – 30 µm²/s), intermediate (1 – 5 µm²/s), and slow (< 1 µm²/s) diffusion components. From the effects of mixture composition and pore size on the contributions of these three components, we inferred that the fast, intermediate, and slow components could be assigned to desorption-mediated hopping, crawling, and wiggling motions, respectively. The platform based on the horizontally-oriented AAO nanopores also permitted single-molecule emission polarization measurements that revealed the negligible steric confinement of individual diffusing RhB molecules within the AAO nanopores. The imaging platform based on AAO membranes and HILO microscopy provided a unique means to investigate how solvation-mediated surface interactions and nanoconfinement govern molecular transport in nanoporous environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Modeling the Economic and Environmental Impacts of Land Scarcity Under Deep Uncertainty

Abstract Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi‐faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi‐metric and multi‐sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross‐sector linkages, and acknowledge the important uncertainties confronting the human‐Earth system when making any analysis of land scarcity impacts.

54 ENVIRONMENTAL SCIENCES↗