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At least 325 records · Page 18

Evaluating the potential of plastic waste upcycling using thermochemical technologies: A case study in Spain

A plastic waste upcycling value chain model has been applied to assess the potential of processing packaging waste in Spain using thermo-chemical technologies to produce low-density polyethylene (LDPE) and polypropylene (PP), which are highly valuable materials. The model projects an annual profit of 120.6 M$\$$/yr, with a capital investment of 789.3 M$\$$, generating 3285 jobs and contributing 65.5 M$\$$/yr to Spain’s economy. The achieved circularity rate of the waste processing infrastructure exceeds 40 %, incorporating recycled HDPE and PET. Despite these advantages, regulatory gaps and market hesitancy toward recycled materials due to quality concerns hinder adoption. Additionally, economies of scale remain underutilized in Spain due to lower plastic waste collection levels compared to countries such as the United States. This network, while less profitable, is environmentally superior, yielding upcycled products with a Global Warming Potential 20–35 % lower than their virgin, fossil-fuel counterparts, confirming this as a viable and sustainable alternative.

Chemical upcycling↗

express: Extensible, high-level workflows for swifter ab initio materials modeling

In this work, we introduce an open-source Julia project, express, an extensible, lightweight, high-throughput, high-level workflow framework that aims to automate ab initio calculations for the materials science community. express is shipped with well-tested workflow templates, including structure optimization, equation of state (EOS) fitting, phonon spectrum (lattice dynamics) calculation, and thermodynamic property calculation in the framework of the quasi-harmonic approximation (QHA). It is designed to be highly modularized so that its components can be reused across various occasions, and customized workflows can be built on top of that. Users can also track the status of workflows in real-time, and rerun failed jobs thanks to the data lineage feature express provides. Finally, two working examples, i.e., all workflows applied to lime and akimotoite, are also presented in the code and this paper.

36 MATERIALS SCIENCE↗

Communication breakdown: Energy efficiency recommendations to address the disconnect between building operators and occupants

As technology advances, progressive building performance goals are met with ease and occupant behavior plays an increasingly significant role in preventing building operators from achieving those goals. However, removing occupants from participating in building control and operation is counterintuitive to the purpose of building operation. Occupant-centric control (OCC) has been suggested as a means of incorporating the occupant while simultaneously reducing the negative impact their behavior can have on building performance. As a result, operators are now tasked with incorporating OCC into their daily operation. The relationship between building occupants and operators is a critical component to OCC, and balancing occupant comfort and building performance. In this paper, we present findings from an international qualitative study of building operators with a focus on the operator and occupant relationship. This paper identifies the role operators play in OCC, how these relationships develop, and how these relationships impact building operation through two key research questions: What factors influence the quality of relationships between occupants and operators? How can the relationships between operators and occupants be improved? Subsequently, these questions revealed this relationship becomes strained when building performance is prioritized over comfort, occupants are ignored or uneducated, and feedback is negative or sparse. In short: there is a disconnection between operators and occupants. Here we propose several solutions including modifying job requirements to prioritize occupant comfort, educating occupants to make autonomous decisions that are not detrimental to operation, and creating effective communication channels for instances where operator intervention is required.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Benchmarking GEANT4 and PHITS for 14.8-MeV neutron transport in polyethylene and graphite materials

The feasibility of GEANT4 and PHITS with ENDF/B-VIII.0, JEFF-3.3 and JENDL-4.0 evaluated nuclear data libraries in 14.8-MeV neutron transport simulation are verified by comparing with the MCNP calculations and the experimental data. Here the measured data are from neutron integral experiments including graphite and polyethylene samples. For 6 cm-thick polyethylene and 20 cm-thick graphite samples, the results from GEANT4 and PHITS with three evaluated nuclear data libraries agree well with the MCNP calculations. For a 2 cm-thick graphite sample, the simulated results from GEANT4 and PHITS agree with the MCNP calculations, and they all underestimate the experiment data in below 3 MeV and overestimate them in the energy range of 5–7 MeV. The difference between the simulation results and the experimental data depends on the evaluated nuclear data libraries. In overall, GEANT4 and PHITS are performing reasonable jobs for 14.8-MeV neutron transport simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

Oak Ridge Computing Academy: An HPC cluster deployment and management pilot

The High Performance Computing Technologies (HPCT) course is a hands-on High Performance Computing (HPC) cluster deployment and management training program offered as part of the International School for Advanced Studies (SISSA) and the International Center for Theoretical Physics (ICTP) Master in High Performance Computing (MHPC) specialization. Here, this training program introduces students to key concepts in cluster configuration. which include networking, software stack provisioning, job scheduling, and monitoring. The publicly available course materials feature several examples and underlying methods that are broadly applicable to cluster deployment and management. This paper discusses the design of a new workforce development program at the Oak Ridge National Laboratory that is based on HPCT, the Oak Ridge Computing Academy (ORCA). The ORCA pilot program was hosted by the Oak Ridge Leadership Computing Facility (OLCF) in Summer 2025. As a part of this discussion, HPCT and ORCA course contents and infrastructure are outlined, ORCA participant experiences are detailed, and potential opportunities for improvement are discussed.

Education↗

Scalable Generation of High-fidelity Synthetic Population Ensembles

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the U.S. via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. The study involves two scenarios: creating ensembles for (1) 17 U.S. metropolitan areas in 2019 and (2) full U.S. Census Divisions in 2023, with each scenario consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system within a research cloud, comprised of virtual containerizations, GPU-enhanced functionality, and orchestrated deployments of UrbanPop’s maturing Likeness Python ecosystem. Results demonstrate we maintained high-fidelity approximations of residential totals by areas of interest and the demographic characteristics of neighborhoods while reducing manual workflow burdens. Finally, we discuss plans to fine-tune and further develop our automated workflows for truly distributed job orchestration to increase computational efficiency, as well as provide an outlook for broadening applications of the ensembles.

Cluster computing↗

The Holman Research Pathway in Radiation Oncology: 2010 to 2019

There has not been an assessment of the Holman Research Pathway (HRP) in radiation oncology (RO) in nearly 10 years. In this study, we sought to review the demographic characteristics, research productivity during and after residency, job placements, and National Institutes of Health (NIH) grant funding of RO residents who completed the HRP in the modern era.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

The role of corporate investment in start-ups for climate-tech innovation

Enabling and accelerating the full potential of energy innovation is a critical component of the global policy response to climate change. Incentivizing start-ups advancing climate-tech (i.e., products and services related to clean energy and climate change), has become central to innovation policy because start-ups are nimble (compared to incumbents), can quickly focus on bringing new technologies to market, and simultaneously create new jobs and catalyze local industries. Public policies to support climate-tech start-ups and efforts such as Mission Innovation tend to focus on increasing government spending (e.g., grants for research and development) or on creating demand pull for new technologies (e.g., feed-in-tariffs). But to go from research and development to widespread adoption of new products and services, start-ups need to grow and scale, and this requires support from private investors. Yet, research on how different investors can shape the direction of climate innovation—and how public policy can incentivize private investment in climate-tech start-ups to support the public good—is surprisingly sparse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

How fast can one resize a distributed file system?

Efficient resource utilization becomes a major concern as large-scale distributed computing infrastructures keep growing in size. Malleability, the possibility for resource managers to dynamically increase or decrease the amount of resources allocated to a job, is a promising way to save energy and costs. However, state-of-the-art parallel and distributed storage systems have not been designed with malleability in mind. The reason is mainly the supposedly high cost of data transfers required by resizing operations. Nevertheless, as network and storage technologies evolve, old assumptions about potential bottlenecks can be revisited. In this study, we evaluate the viability of malleability as a design principle for a distributed storage system. We specifically model the minimal duration of the commission and decommission operations. To show how our models can be used in practice, we evaluate the performance of these operations in HDFS, a relevant state-of-the-art distributed file system. We show that the existing decommission mechanism of HDFS is good when the network is the bottleneck, but can be accelerated by up to a factor 3 when storage is the limiting factor. We also show that the commission in HDFS can be substantially accelerated. With the highlights provided by our model, we suggest improvements to speed both operations in HDFS. We discuss how the proposed models can be generalized for distributed file systems with different assumptions and what perspectives are open for the design of efficient malleable distributed file systems.

97 MATHEMATICS AND COMPUTING↗

Enriching OpenStreetMap network data for transportation applications: Insights into the impact of urban congestion on accessibility

OpenStreetMap (OSM) data is a valuable open-source resource for various transportation, traffic, and planning applications. However, OSM network data lack operating traffic speed information, which is critical for transport planning and operations. Addressing this shortcoming, this study leverages commercial vendor data (to serve as ground truth) with exogenous, open-source variables characterizing local transport infrastructure, land use, and demographic information to predict average congested traffic speeds on OSM networks. Three machine-learning models were tested and estimated for OSM links with and without speed limit information in the Denver metropolitan region. Among these, XGBoost performed best, with mean absolute errors of 3.27 and 3.62 mph for links with and without speed limits, respectively. The developed models accurately predicted traffic speeds for different hours and days of the week compared to ground truth data. Using these predicted speeds, drive accessibility scores were computed for the Denver region for different time periods using the Mobility Energy Productivity (MEP) metric to understand the impact of congestion on energy-efficient accessibility. Results show that congestion-adjusted drive accessibility can be significantly lower compared to accessibility calculated using free flow speeds. Specifically, weekday evening hours saw a 42 % drop in accessibility due to reduced speeds, particularly around downtown Denver. Across the Denver metro region, approximately half as many opportunities and jobs are accessible in under 20 min by car during the evening peak period relative to free flow conditions. These findings underscore the importance of using congestion-adjusted operating speeds rather than speed limits in accessibility calculations, as reliance on speed limits can substantially overestimate energy-efficient drive accessibility in large, car-centric cities susceptible to significant congestion. In conclusion, the methodology presented here could further enrich OSM network data, making them useful for an even broader range of transportation applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Key insights from US Department of Energy Better Plants workforce development bootcamps (2022–2025)

This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.

Energy efficiency↗

Techno-economic-environmental impacts of industrial energy assessment: Sustainable industrial motor systems of small and medium-sized enterprises

Energy assessments can provide an effective way of identifying and implementing energy efficiency measures to save energy costs and avoid emissions throughout manufacturing facilities. Consequently, it generates significant economic and environmental benefits to localities, states, and the nation. Quantifying these benefits requires a systematic techno-economic-environmental framework for capturing the interactions. This article employs methodologies to improve the energy efficiency of small and medium-size industries through their sustainable industrial motor systems. Motor systems offer large opportunities to enhance energy efficiency through adopting advanced technologies and better-informed operations. Case studies presented illuminate the potential savings and impacts from implementing sustainable motor systems and the importance of energy assessments. Here, the integrated macro-economic analysis quantifies the regional sustainability impacts of implementing the industrial energy efficiency offered by an energy auditing program in Ohio over a ten-year period. Results show that implementing all the center’s motor recommendations have directly saved $\$702$ M in energy costs, avoided 2.7 million metric tons of carbon dioxide emissions, and created 3,445 jobs, resulting in a total annual economic impact of $\$788$ M stemming from direct, indirect, and induced regional economic impacts. It provides policy implications for encouraging sustainable industrial motor systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Funding a Just Transition Away from Coal in the U.S. Considering Avoided Damage from Air Pollution

Abstract Coal is declining in the U.S. as part of the clean energy transition, resulting in remarkable air pollution benefits for the American public and significant costs for the industry. Using the AP3 integrated assessment model, we estimate that fewer emissions of sulfur dioxide, nitrogen oxides, and primary fine particulate matter driven by coal’s decline led to $300 billion in benefits from 2014 to 2019. Conversely, we find that job losses driven by less coal plant and mining activity resulted in $7.84 billion in foregone wages over the same timeframe. While the benefits were greatly distributed (mostly throughout the East), costs were highly concentrated in coal communities. Transferring a small fraction of the benefits to workers could cover these costs while maintaining societal net benefits. Forecasting coal fleet damages from 2020 to 2035, we find that buying out or replacing these plants would result in $589 billion in air quality benefits, which considerably outweigh the costs. The return on investment increases when policy targets the most damaging capacity, and net benefits are maximized when removing just facilities where marginal benefits exceed marginal costs. Evaluating competitive reverse auction policy designs akin to Germany’s Coal Exit Act, we find that adjusting bids based on monetary damages rather than based only on carbon dioxide emissions – the German design – provides a welfare advantage. Our benefit–cost analyses clearly support policies that drive a swift and just transition away from coal, thereby clearing the air while supporting communities needing assistance.

Dennin, Luke R. (ORCID:0000000205405520)↗

Coil optimization for quasi-helically symmetric stellarator configurations

Filament-based coil optimizations are performed for several quasi-helical stellarator configurations, beginning with the one from Landreman & Paul ( Phys. Rev. Lett. , vol. 128, 2022, 035001), demonstrating that precise quasi-helical symmetry can be achieved with realistic coils. Several constraints are placed on the shape and spacing of the coils, such as low curvature and sufficient plasma–coil distance for neutron shielding. The coils resulting from this optimization have a maximum curvature 0.8 times that of the coils of the Helically Symmetric eXperiment (HSX) and a mean squared curvature 0.4 times that of the HSX coils when scaled to the same plasma minor radius. When scaled up to reactor size and magnetic field strength, no fast particle losses were found in the free-boundary configuration when simulating 5000 alpha particles launched at $3.5\,\mathrm {MeV}$ on the flux surface with a normalized toroidal flux of $s=0.5$ . An analysis of the tolerance of the coils to manufacturing errors is performed using a Gaussian process model, and the coils are found to maintain low particle losses for smooth, large-scale errors up to amplitudes of approximately $0.15\,\mathrm {m}$ . Another coil optimization is performed for the Landreman–Paul configuration with the additional constraint that the coils are purely planar. Visual inspection of the Poincaré plot of the resulting magnetic field-lines reveal that the planar modular coils alone do a poor job of reproducing the target equilibrium. Additional non-planar coil optimizations are performed for the quasi-helical configuration with $5\,\%$ volume-averaged plasma beta from Landreman et al. ( Phys. Plasma , vol. 29, issue 8, 2022, 082501), and a similar configuration also optimized to satisfy the Mercier criterion. The finite beta configurations had larger fast-particle losses, with the free-boundary Mercier-optimized configuration performing the worst, losing approximately $5.5\,\%$ of alpha particles launched at $s=0.5$ .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The Story of 5d Metallocorroles: From Metal–Ligand Misfits to New Building Blocks for Cancer Phototherapeutics

Porphyrin chemistry is Shakespearean: over a century of study has not withered the field’s apparently infinite variety. Heme proteins continually astonish us with novel molecular mechanisms, while new porphyrin analogues bowl us over with unprecedented optical, electronic, and metal-binding properties. Within the latter domain, corroles occupy a special place, exhibiting a unique and rich coordination chemistry. The 5d metallocorroles are arguably the icing on that cake. New Zealand chemist Penny Brothers has used the word “misfit” to describe the interactions of boron, a small atom with a predilection for tetrahedral coordination, and porphyrins, classic square-planar ligands. Steve Jobs lionized misfits as those who see things differently and push humanity forward. Both perspectives have inspired us. The 5d metallocorroles are misfits in that they encapsulate a large 5d transition metal ion within the tight cavity of a contracted porphyrin ligand. Given the steric mismatch inherent in their structures, the syntheses of some 5d metallocorroles are understandably capricious, proceeding under highly specific conditions and affording poor yields. Three broad approaches may be distinguished. (a) In the metal–alkyl approach, a free-base corrole is exposed to an alkyllithium and the resulting lithio-corrole is treated with an early transition metal chloride; a variant of the method eschews alkyllithium and deploys a transition metal–alkyl instead, resulting in elimination of the alkyl group as an alkane and insertion of the metal into the corrole. This approach is useful for inserting transition metals from groups 4, 5, and, to some extent, 6, as well as lanthanides and actinides. (b) In our laboratory, we have often deployed a low-valent organometallic approach for the middle transition elements (groups 6, 7, 8, and 9). The reagents are low-valent metal–carbonyl or −olefin complexes, which lose one or more carbon ligands at high temperature, affording coordinatively unsaturated, sticky metal fragments that are trapped by the corrole nitrogens. (c) Finally, a metal acetate approach provides the method of choice for gold and platinum insertion (groups 10 and 11). This Account provides a first-hand perspective of the three approaches, focusing on the last two, which were largely developed in our laboratory. In general, the products were characterized with X-ray crystallography, electrochemistry, and a variety of spectroscopic methods. The physicochemical data, supplemented by relativistic DFT calculations, have provided fascinating insights into periodic trends and relativistic effects. An unexpected feature of many 5d metallocorroles, given their misfit character, is their remarkable stability under thermal, chemical, and photochemical stimulation. Many of them also exhibit long triplet lifetimes on the order of 100 μs and effectively sensitize singlet oxygen formation. Many exhibit phosphorescence in the near-infrared under ambient conditions. Furthermore, water-soluble ReO and Au corroles exhibit impressive photocytotoxicity against multiple cancer cell lines, promising potential applications as cancer phototherapeutics. We thus envision a bright future for the compounds as rugged building blocks for new generations of therapeutic and diagnostic (theranostic) agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluating How Climate Adaptation Measures Affect the Interconnected Water‐Energy Resource Systems of the Western United States

Abstract The Western US faces increasing water stress from the impacts of climate change, making it difficult to meet water demands for the region's cities, agriculture, and hydropower generators. Existing literature suggests that climate adaptation measures such as water conservation, cropland retirement, wastewater recycling, and managed aquifer recharge can alleviate some of these challenges. Few analyses, however, compare the relative efficacy and system‐wide effects of these adaptations under different climate projections across the entire Western United States. Here we use a Western US‐wide water systems model to evaluate, by sector and sub‐region, how the widespread implementation of these adaptive measures impacts water demands, water deliveries, and electricity use related to the water system for three different climate projections. We find that wastewater recycling has greater potential to lower unmet indoor water demands than urban indoor water conservation measures. However, when implemented at scale, indoor water conservation reduces electricity use by an average of 683 Terawatt hours while wastewater recycling increases energy use by an average of 721 Terawatt hours, cumulatively from 2020 to 2070. Cropland retirement and aquifer recharge adaptations increase the ability to meet agricultural water demand, increase groundwater storage, and reduce summertime electricity use. While most of these findings are consistent across different climate projections, the benefits of aquifer recharge are sensitive to spatial variation of precipitation. Given the limitations and tradeoffs of each individually, the results suggest that a portfolio of adaptation measures will be needed for a climate‐resilient water and energy future in the Western US. Plain Language Summary The Western US faces increased water stress from the impacts of climate change, making it difficult to meet demands for cities, agriculture, and hydropower facilities. Adaptation measures like water conservation, retiring agricultural lands, recycling wastewater, and storing water underground can address these challenges. However, there is little modeling to understand the impact of these adaptations if they were implemented across the entire Western US while also considering climate change. We use a Western US‐wide water systems model to evaluate how implementing adaptation measures impacts groundwater levels, the ability to meet water demands, and electricity use related to water in three different possible climate futures. We find that recycling wastewater to drinking water standards does a better job of meeting urban water demands than water conservation measures. However, indoor water conservation reduces energy use for water while recycling wastewater increases energy use for water. Retiring agricultural lands and storing water underground both increase the ability to meet agricultural water demands, increase underground water storage, and reduce summertime energy use. No adaptation measures provides benefits across every metric we track. Therefore, multiple adaptation measures will likely be needed to achieve a climate‐resilient future for energy and water in the Western US. Key Points Water‐related climate change adaptations have different benefits and tradeoffs for interconnected water‐energy systems in the Western U.S. Wastewater recycling most increases urban water coverage, but has a significant tradeoff in the form of greater electricity use Cropland retirement and aquifer recharge benefit water and electricity systems most in the summer when those systems are most stressed

Singhal, A↗