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    MIT Maritime Consortium releases “Nuclear Ship Safety Handbook”

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    Commercial shipping accounts for 3 percent of all greenhouse gas emissions globally. As the sector sets climate goals and chases a carbon-free future, nuclear power — long used as a source for military vessels — presents an enticing solution. To date, however, there has been no clear, unified public document available to guide design safety for certain components of civilian nuclear ships. A new “Nuclear Ship Safety Handbook” by the MIT Maritime Consortium aims to change that and set the standard for safe maritime nuclear propulsion.“This handbook is a critical tool in efforts to support the adoption of nuclear in the maritime industry,” explains Themis Sapsis, the William I. Koch Professor of Mechanical Engineering at MIT, director of the MIT Center for Ocean Engineering, and co-director of the MIT Maritime Consortium. “The goal is to provide a strong basis for initial safety on key areas that require nuclear and maritime regulatory research and development in the coming years to prepare for nuclear propulsion in the maritime industry.”Using research data and standards, combined with operational experiences during civilian maritime nuclear operations, the handbook provides unique insights into potential issues and resolutions in the design efficacy of maritime nuclear operations, a topic of growing importance on the national and international stage. “Right now, the nuclear-maritime policies that exist are outdated and often tied only to specific technologies, like pressurized water reactors,” says Jose Izurieta, a graduate student in the Department of Mechanical Engineering (MechE) Naval Construction and Engineering (2N) Program, and one of the handbook authors. “With the recent U.K.-U.S. Technology Prosperity Deal now including civil maritime nuclear applications, I hope the handbook can serve as a foundation for creating a clear, modern regulatory framework for nuclear-powered commercial ships.”The recent memorandum of understanding signed by the U.S. and U.K calls for the exploration of “novel applications of advanced nuclear energy, including civil maritime applications,” and for the parties to play “a leading role informing the establishment of international standards, potential establishment of a maritime shipping corridor between the Participants’ territories, and strengthening energy resilience for the Participants’ defense facilities.”“The U.S.-U.K. nuclear shipping corridor offers a great opportunity to collaborate with legislators on establishing the critical framework that will enable the United States to invest on nuclear-powered merchant vessels — an achievement that will reestablish America in the shipbuilding space,” says Fotini Christia, the Ford International Professor of the Social Sciences, director of the Institute for Data, Systems, and Society (IDSS), director of the MIT Sociotechnical Systems Research Center, and co-director of the MIT Maritime Consortium.“With over 30 nations now building or planning their first reactors, nuclear energy’s global acceptance is unprecedented — and that momentum is key to aligning safety rules across borders for nuclear-powered ships and the respective ports,” says Koroush Shirvan, the Atlantic Richfield Career Development Professor in Energy Studies at MIT and director of the Reactor Technology Course for Utility Executives.The handbook, which is divided into chapters in areas involving the overlapping nuclear and maritime safety design decisions that will be encountered by engineers, is careful to balance technical and practical guidance with policy considerations.Commander Christopher MacLean, MIT associate professor of the practice in mechanical engineering, naval construction, and engineering, says the handbook will significantly benefit the entire maritime community, specifically naval architects and marine engineers, by providing standardized guidelines for design and operation specific to nuclear powered commercial vessels.“This will assist in enhancing safety protocols, improve risk assessments, and ensure consistent compliance with international regulations,” MacLean says. “This will also help foster collaboration amongst engineers and regulators. Overall, this will further strengthen the reliability, sustainability, and public trust in nuclear-powered maritime systems.”Anthony Valiaveedu, the handbook’s lead author, and co-author Nat Edmonds, are both students in the MIT Master’s Program in Technology and Policy (TPP) within the IDSS. The pair are also co-authors of a paper published in Science Policy Review earlier this year that offered structured advice on the development of nuclear regulatory policies.“It is important for safety and technology to go hand-in-hand,” Valiaveedu explains. “What we have done is provide a risk-informed process to begin these discussions for engineers and policymakers.”“Ultimately, I hope this framework can be used to build strong bilateral agreements between nations that will allow nuclear propulsion to thrive,” says fellow co-author Izurieta.Impact on industry“Maritime designers needed a source of information to improve their ability to understand and design the reactor primary components, and development of the ‘Nuclear Ship Safety Handbook’ was a good step to bridge this knowledge gap,” says Christopher J. Wiernicki, American Bureau of Shipping (ABS) chair and CEO. “For this reason, it is an important document for the industry.”The ABS, which is the American classification society for the maritime industry, develops criteria and provides safety certification for all ocean-going vessels. ABS is among the founding members of the MIT Maritime Consortium. Capital Clean Energy Carriers Corp., HD Korea Shipbuilding and Offshore Engineering, and Delos Navigation Ltd. are also consortium founding members. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.“As we consider a net-zero framework for the shipping industry, nuclear propulsion represents a potential solution. Careful investigation remains the priority, with safety and regulatory standards at the forefront,” says Jerry Kalogiratos, CEO of Capital Clean Energy Carriers Corp. “As first movers, we are exploring all options. This handbook lays the technical foundation for the development of nuclear-powered commercial vessels.”Sangmin Park, senior vice president at HD Korea Shipbuilding and Offshore Engineering, says “The ‘Nuclear Ship Safety Handbook’ marks a groundbreaking milestone that bridges shipbuilding excellence and nuclear safety. It drives global collaboration between industry and academia, and paves the way for the safe advancement of the nuclear maritime era.”Maritime at MITMIT has been a leading center of ship research and design for over a century, with work at the Institute today representing significant advancements in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. Maritime Consortium projects, including the handbook, reflect national priorities aimed at revitalizing the U.S. shipbuilding and commercial maritime industries.The MIT Maritime Consortium, which launched in 2024, brings together MIT and maritime industry leaders to explore data-powered strategies to reduce harmful emissions, optimize vessel operations, and support economic priorities.“One of our most important efforts is the development of technologies, policies, and regulations to make nuclear propulsion for commercial ships a reality,” says Sapsis. “Over the last year, we have put together an interdisciplinary team with faculty and students from across the Institute. One of the outcomes of this effort is this very detailed document providing detailed guidance on how such effort should be implemented safely.”Handbook contributors come from multiple disciplines and MIT departments, labs, and research centers, including the Center for Ocean Engineering, IDSS, MechE’s Course 2N Program, the MIT Technology and Policy Program, and the Department of Nuclear Science and Engineering.MIT faculty members and research advisors on the project include Sapsis; Christia; Shirvan; MacLean; Jacopo Buongiorno, the Battelle Energy Alliance Professor in Nuclear Science and Engineering, director, Center for Advanced Nuclear Energy Systems, and director of science and technology for the Nuclear Reactor Laboratory; and Captain Andrew Gillespy, professor of the practice and director of the Naval Construction and Engineering (2N) Program.“Proving the viability of nuclear propulsion for civilian ships will entail getting the technologies, the economics and the regulations right,” says Buongiorno. “This handbook is a meaningful initial contribution to the development of a sound regulatory framework.”“We were lucky to have a team of students and knowledgeable professors from so many fields,” says Edmonds. “Before even beginning the outline of the handbook, we did significant archival and history research to understand the existing regulations and overarching story of nuclear ships. Some of the most relevant documents we found were written before 1975, and many of them were stored in the bellows of the NS Savannah.”The NS Savannah, which was built in the late 1950s as a demonstration project for the potential peacetime uses of nuclear energy, was the first nuclear-powered merchant ship. The Savannah was first launched on July 21, 1959, two years after the first nuclear-powered civilian vessel, the Soviet ice-breaker Lenin, and was retired in 1971.Historical context for this project is important, because the reactor technologies envisioned for maritime propulsion today are quite different from the traditional pressurized water reactors used by the U.S. Navy. These new reactors are being developed not just in the maritime context, but also to power ports and data centers on land; they all use low-enriched uranium and are passively cooled. For the maritime industry, Sapsis says, “the technology is there, it’s safe, and it’s ready.”“The Nuclear Ship Safety Handbook” is publicly available on the MIT Maritime Consortium website and from the MIT Libraries.  More

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      Fighting for the health of the planet with AI

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      For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS), a shared position between the MIT Schwarzman College of Computing and EECS, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS) — was struck by the disparities in how people live.“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says. “From a young age, I knew that I definitely wanted to address that issue.”That motivation was further stoked by a high school biology teacher, who focused his class on climate and sustainability.“We learned that climate change, this huge, important issue, would exacerbate inequity,” Donti says. “That really stuck with me and put a fire in my belly.”So, when Donti enrolled at Harvey Mudd College, she thought she would direct her energy toward the study of chemistry or materials science to create next-generation solar panels.Those plans, however, were jilted. Donti “fell in love” with computer science, and then discovered work by researchers in the United Kingdom who were arguing that artificial intelligence and machine learning would be essential to help integrate renewables into power grids.“It was the first time I’d seen those two interests brought together,” she says. “I got hooked and have been working on that topic ever since.”Pursuing a PhD at Carnegie Mellon University, Donti was able to design her degree to include computer science and public policy. In her research, she explored the need for fundamental algorithms and tools that could manage, at scale, power grids relying heavily on renewables.“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says. “But I wanted to make sure that the way I was doing the work was grounded both in the actual energy systems domain and working with people in that domain” to provide what was actually needed.While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI. Her objective, she says, was to help the community of people involved in climate and sustainability — “be they computer scientists, academics, practitioners, or policymakers” — to come together and access resources, connection, and education “to help them along that journey.”“In the climate space,” she says, “you need experts in particular climate change-related sectors, experts in different technical and social science tool kits, problem owners, affected users, policymakers who know the regulations — all of those — to have on-the-ground scalable impact.”When Donti came to MIT in September 2023, it was not surprising that she was drawn by its initiatives directing the application of computer science toward society’s biggest problems, especially the current threat to the health of the planet.“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says. “Technology is not just one-and-done and monetizable in the context of a year.”Her work uses deep learning models to incorporate the physics and hard constraints of electric power systems that employ renewables for better forecasting, optimization, and control.“Machine learning is already really widely used for things like solar power forecasting, which is a prerequisite to managing and balancing power grids,” she says. “My focus is, how do you improve the algorithms for actually balancing power grids in the face of a range of time-varying renewables?”Among Donti’s breakthroughs is a promising solution for power grid operators to be able to optimize for cost, taking into account the actual physical realities of the grid, rather than relying on approximations. While the solution is not yet deployed, it appears to work 10 times faster, and far more cheaply, than previous technologies, and has attracted the attention of grid operators.Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization. In general, much data related to the systems is private, either because it is proprietary or because of security concerns. Donti and her research group are working to create synthetic data and benchmarks that, Donti says, “can help to expose some of the underlying problems” in making power systems more efficient.“The question is,” Donti says, “can we bring our datasets to a point such that they are just hard enough to drive progress?”For her efforts, Donti has been awarded the U.S. Department of Energy Computational Science Graduate Fellowship and the NSF Graduate Research Fellowship. She was recognized as part of MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50.”Next spring, Donti will co-teach a class called AI for Climate Action with Sara Beery, EECS assistant professor, whose focus is AI for biodiversity and ecosystems, and Abigail Bodner, assistant professor in the departments of EECS and Earth, Atmospheric and Planetary Sciences, whose focus is AI for climate and Earth science.“We’re all super-excited about it,” Donti says.Coming to MIT, Donti says, “I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society.” More

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      New prediction model could improve the reliability of fusion power plants

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      Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and limitless fusion energy.Today, there are a number of experimental tokamaks in operation around the world, with more underway. Most are small-scale research machines built to investigate how the devices can spin up plasma and harness its energy. One of the challenges that tokamaks face is how to safely and reliably turn off a plasma current that is circulating at speeds of up to 100 kilometers per second, at temperatures of over 100 million degrees Celsius.Such “rampdowns” are necessary when a plasma becomes unstable. To prevent the plasma from further disrupting and potentially damaging the device’s interior, operators ramp down the plasma current. But occasionally the rampdown itself can destabilize the plasma. In some machines, rampdowns have caused scrapes and scarring to the tokamak’s interior — minor damage that still requires considerable time and resources to repair.Now, scientists at MIT have developed a method to predict how plasma in a tokamak will behave during a rampdown. The team combined machine-learning tools with a physics-based model of plasma dynamics to simulate a plasma’s behavior and any instabilities that may arise as the plasma is ramped down and turned off. The researchers trained and tested the new model on plasma data from an experimental tokamak in Switzerland. They found the method quickly learned how plasma would evolve as it was tuned down in different ways. What’s more, the method achieved a high level of accuracy using a relatively small amount of data. This training efficiency is promising, given that each experimental run of a tokamak is expensive and quality data is limited as a result.The new model, which the team highlights this week in an open-access Nature Communications paper, could improve the safety and reliability of future fusion power plants.“For fusion to be a useful energy source it’s going to have to be reliable,” says lead author Allen Wang, a graduate student in aeronautics and astronautics and a member of the Disruption Group at MIT’s Plasma Science and Fusion Center (PSFC). “To be reliable, we need to get good at managing our plasmas.”The study’s MIT co-authors include PSFC Principal Research Scientist and Disruptions Group leader Cristina Rea, and members of the Laboratory for Information and Decision Systems (LIDS) Oswin So, Charles Dawson, and Professor Chuchu Fan, along with Mark (Dan) Boyer of Commonwealth Fusion Systems and collaborators from the Swiss Plasma Center in Switzerland.“A delicate balance”Tokamaks are experimental fusion devices that were first built in the Soviet Union in the 1950s. The device gets its name from a Russian acronym that translates to a “toroidal chamber with magnetic coils.” Just as its name describes, a tokamak is toroidal, or donut-shaped, and uses powerful magnets to contain and spin up a gas to temperatures and energies high enough that atoms in the resulting plasma can fuse and release energy.Today, tokamak experiments are relatively low-energy in scale, with few approaching the size and output needed to generate safe, reliable, usable energy. Disruptions in experimental, low-energy tokamaks are generally not an issue. But as fusion machines scale up to grid-scale dimensions, controlling much higher-energy plasmas at all phases will be paramount to maintaining a machine’s safe and efficient operation.“Uncontrolled plasma terminations, even during rampdown, can generate intense heat fluxes damaging the internal walls,” Wang notes. “Quite often, especially with the high-performance plasmas, rampdowns actually can push the plasma closer to some instability limits. So, it’s a delicate balance. And there’s a lot of focus now on how to manage instabilities so that we can routinely and reliably take these plasmas and safely power them down. And there are relatively few studies done on how to do that well.”Bringing down the pulseWang and his colleagues developed a model to predict how a plasma will behave during tokamak rampdown. While they could have simply applied machine-learning tools such as a neural network to learn signs of instabilities in plasma data, “you would need an ungodly amount of data” for such tools to discern the very subtle and ephemeral changes in extremely high-temperature, high-energy plasmas, Wang says.Instead, the researchers paired a neural network with an existing model that simulates plasma dynamics according to the fundamental rules of physics. With this combination of machine learning and a physics-based plasma simulation, the team found that only a couple hundred pulses at low performance, and a small handful of pulses at high performance, were sufficient to train and validate the new model.The data they used for the new study came from the TCV, the Swiss “variable configuration tokamak” operated by the Swiss Plasma Center at EPFL (the Swiss Federal Institute of Technology Lausanne). The TCV is a small experimental fusion experimental device that is used for research purposes, often as test bed for next-generation device solutions. Wang used the data from several hundred TCV plasma pulses that included properties of the plasma such as its temperature and energies during each pulse’s ramp-up, run, and ramp-down. He trained the new model on this data, then tested it and found it was able to accurately predict the plasma’s evolution given the initial conditions of a particular tokamak run.The researchers also developed an algorithm to translate the model’s predictions into practical “trajectories,” or plasma-managing instructions that a tokamak controller can automatically carry out to for instance adjust the magnets or temperature maintain the plasma’s stability. They implemented the algorithm on several TCV runs and found that it produced trajectories that safely ramped down a plasma pulse, in some cases faster and without disruptions compared to runs without the new method.“At some point the plasma will always go away, but we call it a disruption when the plasma goes away at high energy. Here, we ramped the energy down to nothing,” Wang notes. “We did it a number of times. And we did things much better across the board. So, we had statistical confidence that we made things better.”The work was supported in part by Commonwealth Fusion Systems (CFS), an MIT spinout that intends to build the world’s first compact, grid-scale fusion power plant. The company is developing a demo tokamak, SPARC, designed to produce net-energy plasma, meaning that it should generate more energy than it takes to heat up the plasma. Wang and his colleagues are working with CFS on ways that the new prediction model and tools like it can better predict plasma behavior and prevent costly disruptions to enable safe and reliable fusion power.“We’re trying to tackle the science questions to make fusion routinely useful,” Wang says. “What we’ve done here is the start of what is still a long journey. But I think we’ve made some nice progress.”Additional support for the research came from the framework of the EUROfusion Consortium, via the Euratom Research and Training Program and funded by the Swiss State Secretariat for Education, Research, and Innovation. More

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      Responding to the climate impact of generative AI

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      In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.The energy demands of generative AI are expected to continue increasing dramatically over the next decade.For instance, an April 2025 report from the International Energy Agency predicts that the global electricity demand from data centers, which house the computing infrastructure to train and deploy AI models, will more than double by 2030, to around 945 terawatt-hours. While not all operations performed in a data center are AI-related, this total amount is slightly more than the energy consumption of Japan.Moreover, an August 2025 analysis from Goldman Sachs Research forecasts that about 60 percent of the increasing electricity demands from data centers will be met by burning fossil fuels, increasing global carbon emissions by about 220 million tons. In comparison, driving a gas-powered car for 5,000 miles produces about 1 ton of carbon dioxide.These statistics are staggering, but at the same time, scientists and engineers at MIT and around the world are studying innovations and interventions to mitigate AI’s ballooning carbon footprint, from boosting the efficiency of algorithms to rethinking the design of data centers.Considering carbon emissionsTalk of reducing generative AI’s carbon footprint is typically centered on “operational carbon” — the emissions used by the powerful processors, known as GPUs, inside a data center. It often ignores “embodied carbon,” which are emissions created by building the data center in the first place, says Vijay Gadepally, senior scientist at MIT Lincoln Laboratory, who leads research projects in the Lincoln Laboratory Supercomputing Center.Constructing and retrofitting a data center, built from tons of steel and concrete and filled with air conditioning units, computing hardware, and miles of cable, consumes a huge amount of carbon. In fact, the environmental impact of building data centers is one reason companies like Meta and Google are exploring more sustainable building materials. (Cost is another factor.)Plus, data centers are enormous buildings — the world’s largest, the China Telecomm-Inner Mongolia Information Park, engulfs roughly 10 million square feet — with about 10 to 50 times the energy density of a normal office building, Gadepally adds. “The operational side is only part of the story. Some things we are working on to reduce operational emissions may lend themselves to reducing embodied carbon, too, but we need to do more on that front in the future,” he says.Reducing operational carbon emissionsWhen it comes to reducing operational carbon emissions of AI data centers, there are many parallels with home energy-saving measures. For one, we can simply turn down the lights.“Even if you have the worst lightbulbs in your house from an efficiency standpoint, turning them off or dimming them will always use less energy than leaving them running at full blast,” Gadepally says.In the same fashion, research from the Supercomputing Center has shown that “turning down” the GPUs in a data center so they consume about three-tenths the energy has minimal impacts on the performance of AI models, while also making the hardware easier to cool.Another strategy is to use less energy-intensive computing hardware.Demanding generative AI workloads, such as training new reasoning models like GPT-5, usually need many GPUs working simultaneously. The Goldman Sachs analysis estimates that a state-of-the-art system could soon have as many as 576 connected GPUs operating at once.But engineers can sometimes achieve similar results by reducing the precision of computing hardware, perhaps by switching to less powerful processors that have been tuned to handle a specific AI workload.There are also measures that boost the efficiency of training power-hungry deep-learning models before they are deployed.Gadepally’s group found that about half the electricity used for training an AI model is spent to get the last 2 or 3 percentage points in accuracy. Stopping the training process early can save a lot of that energy.“There might be cases where 70 percent accuracy is good enough for one particular application, like a recommender system for e-commerce,” he says.Researchers can also take advantage of efficiency-boosting measures.For instance, a postdoc in the Supercomputing Center realized the group might run a thousand simulations during the training process to pick the two or three best AI models for their project.By building a tool that allowed them to avoid about 80 percent of those wasted computing cycles, they dramatically reduced the energy demands of training with no reduction in model accuracy, Gadepally says.Leveraging efficiency improvementsConstant innovation in computing hardware, such as denser arrays of transistors on semiconductor chips, is still enabling dramatic improvements in the energy efficiency of AI models.Even though energy efficiency improvements have been slowing for most chips since about 2005, the amount of computation that GPUs can do per joule of energy has been improving by 50 to 60 percent each year, says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory and a principal investigator at MIT’s Initiative on the Digital Economy.“The still-ongoing ‘Moore’s Law’ trend of getting more and more transistors on chip still matters for a lot of these AI systems, since running operations in parallel is still very valuable for improving efficiency,” says Thomspon.Even more significant, his group’s research indicates that efficiency gains from new model architectures that can solve complex problems faster, consuming less energy to achieve the same or better results, is doubling every eight or nine months.Thompson coined the term “negaflop” to describe this effect. The same way a “negawatt” represents electricity saved due to energy-saving measures, a “negaflop” is a computing operation that doesn’t need to be performed due to algorithmic improvements.These could be things like “pruning” away unnecessary components of a neural network or employing compression techniques that enable users to do more with less computation.“If you need to use a really powerful model today to complete your task, in just a few years, you might be able to use a significantly smaller model to do the same thing, which would carry much less environmental burden. Making these models more efficient is the single-most important thing you can do to reduce the environmental costs of AI,” Thompson says.Maximizing energy savingsWhile reducing the overall energy use of AI algorithms and computing hardware will cut greenhouse gas emissions, not all energy is the same, Gadepally adds.“The amount of carbon emissions in 1 kilowatt hour varies quite significantly, even just during the day, as well as over the month and year,” he says.Engineers can take advantage of these variations by leveraging the flexibility of AI workloads and data center operations to maximize emissions reductions. For instance, some generative AI workloads don’t need to be performed in their entirety at the same time.Splitting computing operations so some are performed later, when more of the electricity fed into the grid is from renewable sources like solar and wind, can go a long way toward reducing a data center’s carbon footprint, says Deepjyoti Deka, a research scientist in the MIT Energy Initiative.Deka and his team are also studying “smarter” data centers where the AI workloads of multiple companies using the same computing equipment are flexibly adjusted to improve energy efficiency.“By looking at the system as a whole, our hope is to minimize energy use as well as dependence on fossil fuels, while still maintaining reliability standards for AI companies and users,” Deka says.He and others at MITEI are building a flexibility model of a data center that considers the differing energy demands of training a deep-learning model versus deploying that model. Their hope is to uncover the best strategies for scheduling and streamlining computing operations to improve energy efficiency.The researchers are also exploring the use of long-duration energy storage units at data centers, which store excess energy for times when it is needed.With these systems in place, a data center could use stored energy that was generated by renewable sources during a high-demand period, or avoid the use of diesel backup generators if there are fluctuations in the grid.“Long-duration energy storage could be a game-changer here because we can design operations that really change the emission mix of the system to rely more on renewable energy,” Deka says.In addition, researchers at MIT and Princeton University are developing a software tool for investment planning in the power sector, called GenX, which could be used to help companies determine the ideal place to locate a data center to minimize environmental impacts and costs.Location can have a big impact on reducing a data center’s carbon footprint. For instance, Meta operates a data center in Lulea, a city on the coast of northern Sweden where cooler temperatures reduce the amount of electricity needed to cool computing hardware.Thinking farther outside the box (way farther), some governments are even exploring the construction of data centers on the moon where they could potentially be operated with nearly all renewable energy.AI-based solutionsCurrently, the expansion of renewable energy generation here on Earth isn’t keeping pace with the rapid growth of AI, which is one major roadblock to reducing its carbon footprint, says Jennifer Turliuk MBA ’25, a short-term lecturer, former Sloan Fellow, and former practice leader of climate and energy AI at the Martin Trust Center for MIT Entrepreneurship.The local, state, and federal review processes required for a new renewable energy projects can take years.Researchers at MIT and elsewhere are exploring the use of AI to speed up the process of connecting new renewable energy systems to the power grid.For instance, a generative AI model could streamline interconnection studies that determine how a new project will impact the power grid, a step that often takes years to complete.And when it comes to accelerating the development and implementation of clean energy technologies, AI could play a major role.“Machine learning is great for tackling complex situations, and the electrical grid is said to be one of the largest and most complex machines in the world,” Turliuk adds.For instance, AI could help optimize the prediction of solar and wind energy generation or identify ideal locations for new facilities.It could also be used to perform predictive maintenance and fault detection for solar panels or other green energy infrastructure, or to monitor the capacity of transmission wires to maximize efficiency.By helping researchers gather and analyze huge amounts of data, AI could also inform targeted policy interventions aimed at getting the biggest “bang for the buck” from areas such as renewable energy, Turliuk says.To help policymakers, scientists, and enterprises consider the multifaceted costs and benefits of AI systems, she and her collaborators developed the Net Climate Impact Score.The score is a framework that can be used to help determine the net climate impact of AI projects, considering emissions and other environmental costs along with potential environmental benefits in the future.At the end of the day, the most effective solutions will likely result from collaborations among companies, regulators, and researchers, with academia leading the way, Turliuk adds.“Every day counts. We are on a path where the effects of climate change won’t be fully known until it is too late to do anything about it. This is a once-in-a-lifetime opportunity to innovate and make AI systems less carbon-intense,” she says. More

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      MIT engineers develop a magnetic transistor for more energy-efficient electronics

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      Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.Overcoming the limitsIn an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.Leveraging magnetismThis lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities. More

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      in Energy

      New method could monitor corrosion and cracking in a nuclear reactor

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      MIT researchers have developed a technique that enables real-time, 3D monitoring of corrosion, cracking, and other material failure processes inside a nuclear reactor environment.This could allow engineers and scientists to design safer nuclear reactors that also deliver higher performance for applications like electricity generation and naval vessel propulsion.During their experiments, the researchers utilized extremely powerful X-rays to mimic the behavior of neutrons interacting with a material inside a nuclear reactor.They found that adding a buffer layer of silicon dioxide between the material and its substrate, and keeping the material under the X-ray beam for a longer period of time, improves the stability of the sample. This allows for real-time monitoring of material failure processes.By reconstructing 3D image data on the structure of a material as it fails, researchers could design more resilient materials that can better withstand the stress caused by irradiation inside a nuclear reactor.“If we can improve materials for a nuclear reactor, it means we can extend the life of that reactor. It also means the materials will take longer to fail, so we can get more use out of a nuclear reactor than we do now. The technique we’ve demonstrated here allows to push the boundary in understanding how materials fail in real-time,” says Ericmoore Jossou, who has shared appointments in the Department of Nuclear Science and Engineering (NSE), where he is the John Clark Hardwick Professor, and the Department of Electrical Engineering and Computer Science (EECS), and the MIT Schwarzman College of Computing.Jossou, senior author of a study on this technique, is joined on the paper by lead author David Simonne, an NSE postdoc; Riley Hultquist, a graduate student in NSE; Jiangtao Zhao, of the European Synchrotron; and Andrea Resta, of Synchrotron SOLEIL. The research was published Tuesday by the journal Scripta Materiala.“Only with this technique can we measure strain with a nanoscale resolution during corrosion processes. Our goal is to bring such novel ideas to the nuclear science community while using synchrotrons both as an X-ray probe and radiation source,” adds Simonne.Real-time imagingStudying real-time failure of materials used in advanced nuclear reactors has long been a goal of Jossou’s research group.Usually, researchers can only learn about such material failures after the fact, by removing the material from its environment and imaging it with a high-resolution instrument.“We are interested in watching the process as it happens. If we can do that, we can follow the material from beginning to end and see when and how it fails. That helps us understand a material much better,” he says.They simulate the process by firing an extremely focused X-ray beam at a sample to mimic the environment inside a nuclear reactor. The researchers must use a special type of high-intensity X-ray, which is only found in a handful of experimental facilities worldwide.For these experiments they studied nickel, a material incorporated into alloys that are commonly used in advanced nuclear reactors. But before they could start the X-ray equipment, they had to prepare a sample.To do this, the researchers used a process called solid state dewetting, which involves putting a thin film of the material onto a substrate and heating it to an extremely high temperature in a furnace until it transforms into single crystals.“We thought making the samples was going to be a walk in the park, but it wasn’t,” Jossou says.As the nickel heated up, it interacted with the silicon substrate and formed a new chemical compound, essentially derailing the entire experiment. After much trial-and-error, the researchers found that adding a thin layer of silicon dioxide between the nickel and substrate prevented this reaction.But when crystals formed on top of the buffer layer, they were highly strained. This means the individual atoms had moved slightly to new positions, causing distortions in the crystal structure.Phase retrieval algorithms can typically recover the 3D size and shape of a crystal in real-time, but if there is too much strain in the material, the algorithms will fail.However, the team was surprised to find that keeping the X-ray beam trained on the sample for a longer period of time caused the strain to slowly relax, due to the silicon buffer layer. After a few extra minutes of X-rays, the sample was stable enough that they could utilize phase retrieval algorithms to accurately recover the 3D shape and size of the crystal.“No one had been able to do that before. Now that we can make this crystal, we can image electrochemical processes like corrosion in real time, watching the crystal fail in 3D under conditions that are very similar to inside a nuclear reactor. This has far-reaching impacts,” he says.They experimented with a different substrate, such as niobium doped strontium titanate, and found that only a silicon dioxide buffered silicon wafer created this unique effect.An unexpected resultAs they fine-tuned the experiment, the researchers discovered something else.They could also use the X-ray beam to precisely control the amount of strain in the material, which could have implications for the development of microelectronics.In the microelectronics community, engineers often introduce strain to deform a material’s crystal structure in a way that boosts its electrical or optical properties.“With our technique, engineers can use X-rays to tune the strain in microelectronics while they are manufacturing them. While this was not our goal with these experiments, it is like getting two results for the price of one,” he adds.In the future, the researchers want to apply this technique to more complex materials like steel and other metal alloys used in nuclear reactors and aerospace applications. They also want to see how changing the thickness of the silicon dioxide buffer layer impacts their ability to control the strain in a crystal sample.“This discovery is significant for two reasons. First, it provides fundamental insight into how nanoscale materials respond to radiation — a question of growing importance for energy technologies, microelectronics, and quantum materials. Second, it highlights the critical role of the substrate in strain relaxation, showing that the supporting surface can determine whether particles retain or release strain when exposed to focused X-ray beams,” says Edwin Fohtung, an associate professor at the Rensselaer Polytechnic Institute, who was not involved with this work.This work was funded, in part, by the MIT Faculty Startup Fund and the U.S. Department of Energy. The sample preparation was carried out, in part, at the MIT.nano facilities. More

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      in Environment

      Simpler models can outperform deep learning at climate prediction

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      Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.Their analysis also reveals that a benchmarking technique commonly used to evaluate machine-learning techniques for climate predictions can be distorted by natural variations in the data, like fluctuations in weather patterns. This could lead someone to believe a deep-learning model makes more accurate predictions when that is not the case.The researchers developed a more robust way of evaluating these techniques, which shows that, while simple models are more accurate when estimating regional surface temperatures, deep-learning approaches can be the best choice for estimating local rainfall.They used these results to enhance a simulation tool known as a climate emulator, which can rapidly simulate the effect of human activities onto a future climate.The researchers see their work as a “cautionary tale” about the risk of deploying large AI models for climate science. While deep-learning models have shown incredible success in domains such as natural language, climate science contains a proven set of physical laws and approximations, and the challenge becomes how to incorporate those into AI models.“We are trying to develop models that are going to be useful and relevant for the kinds of things that decision-makers need going forward when making climate policy choices. While it might be attractive to use the latest, big-picture machine-learning model on a climate problem, what this study shows is that stepping back and really thinking about the problem fundamentals is important and useful,” says study senior author Noelle Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and the Department of Earth, Atmospheric and Planetary Sciences (EAPS).Selin’s co-authors are lead author Björn Lütjens, a former EAPS postdoc who is now a research scientist at IBM Research; senior author Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in EAPS and co-director of the Lorenz Center; and Duncan Watson-Parris, assistant professor at the University of California at San Diego. Selin and Ferrari are also co-principal investigators of the Bringing Computation to the Climate Challenge project, out of which this research emerged. The paper appears today in the Journal of Advances in Modeling Earth Systems.Comparing emulatorsBecause the Earth’s climate is so complex, running a state-of-the-art climate model to predict how pollution levels will impact environmental factors like temperature can take weeks on the world’s most powerful supercomputers.Scientists often create climate emulators, simpler approximations of a state-of-the art climate model, which are faster and more accessible. A policymaker could use a climate emulator to see how alternative assumptions on greenhouse gas emissions would affect future temperatures, helping them develop regulations.But an emulator isn’t very useful if it makes inaccurate predictions about the local impacts of climate change. While deep learning has become increasingly popular for emulation, few studies have explored whether these models perform better than tried-and-true approaches.The MIT researchers performed such a study. They compared a traditional technique called linear pattern scaling (LPS) with a deep-learning model using a common benchmark dataset for evaluating climate emulators.Their results showed that LPS outperformed deep-learning models on predicting nearly all parameters they tested, including temperature and precipitation.“Large AI methods are very appealing to scientists, but they rarely solve a completely new problem, so implementing an existing solution first is necessary to find out whether the complex machine-learning approach actually improves upon it,” says Lütjens.Some initial results seemed to fly in the face of the researchers’ domain knowledge. The powerful deep-learning model should have been more accurate when making predictions about precipitation, since those data don’t follow a linear pattern.They found that the high amount of natural variability in climate model runs can cause the deep learning model to perform poorly on unpredictable long-term oscillations, like El Niño/La Niña. This skews the benchmarking scores in favor of LPS, which averages out those oscillations.Constructing a new evaluationFrom there, the researchers constructed a new evaluation with more data that address natural climate variability. With this new evaluation, the deep-learning model performed slightly better than LPS for local precipitation, but LPS was still more accurate for temperature predictions.“It is important to use the modeling tool that is right for the problem, but in order to do that you also have to set up the problem the right way in the first place,” Selin says.Based on these results, the researchers incorporated LPS into a climate emulation platform to predict local temperature changes in different emission scenarios.“We are not advocating that LPS should always be the goal. It still has limitations. For instance, LPS doesn’t predict variability or extreme weather events,” Ferrari adds.Rather, they hope their results emphasize the need to develop better benchmarking techniques, which could provide a fuller picture of which climate emulation technique is best suited for a particular situation.“With an improved climate emulation benchmark, we could use more complex machine-learning methods to explore problems that are currently very hard to address, like the impacts of aerosols or estimations of extreme precipitation,” Lütjens says.Ultimately, more accurate benchmarking techniques will help ensure policymakers are making decisions based on the best available information.The researchers hope others build on their analysis, perhaps by studying additional improvements to climate emulation methods and benchmarks. Such research could explore impact-oriented metrics like drought indicators and wildfire risks, or new variables like regional wind speeds.This research is funded, in part, by Schmidt Sciences, LLC, and is part of the MIT Climate Grand Challenges team for “Bringing Computation to the Climate Challenge.” More

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      in Energy

      Surprisingly diverse innovations led to dramatically cheaper solar panels

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      The cost of solar panels has dropped by more than 99 percent since the 1970s, enabling widespread adoption of photovoltaic systems that convert sunlight into electricity.A new MIT study drills down on specific innovations that enabled such dramatic cost reductions, revealing that technical advances across a web of diverse research efforts and industries played a pivotal role.The findings could help renewable energy companies make more effective R&D investment decisions and aid policymakers in identifying areas to prioritize to spur growth in manufacturing and deployment.The researchers’ modeling approach shows that key innovations often originated outside the solar sector, including advances in semiconductor fabrication, metallurgy, glass manufacturing, oil and gas drilling, construction processes, and even legal domains.“Our results show just how intricate the process of cost improvement is, and how much scientific and engineering advances, often at a very basic level, are at the heart of these cost reductions. A lot of knowledge was drawn from different domains and industries, and this network of knowledge is what makes these technologies improve,” says study senior author Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society.Trancik is joined on the paper by co-lead authors Goksin Kavlak, a former IDSS graduate student and postdoc who is now a senior energy associate at the Brattle Group; Magdalena Klemun, a former IDSS graduate student and postdoc who is now an assistant professor at Johns Hopkins University; former MIT postdoc Ajinkya Kamat; as well as Brittany Smith and Robert Margolis of the National Renewable Energy Laboratory. The research appears today in PLOS ONE.Identifying innovationsThis work builds on mathematical models that the researchers previously developed that tease out the effects of engineering technologies on the cost of photovoltaic (PV) modules and systems.In this study, the researchers aimed to dig even deeper into the scientific advances that drove those cost declines.They combined their quantitative cost model with a detailed, qualitative analysis of innovations that affected the costs of PV system materials, manufacturing steps, and deployment processes.“Our quantitative cost model guided the qualitative analysis, allowing us to look closely at innovations in areas that are hard to measure due to a lack of quantitative data,” Kavlak says.Building on earlier work identifying key cost drivers — such as the number of solar cells per module, wiring efficiency, and silicon wafer area — the researchers conducted a structured scan of the literature for innovations likely to affect these drivers. Next, they grouped these innovations to identify patterns, revealing clusters that reduced costs by improving materials or prefabricating components to streamline manufacturing and installation. Finally, the team tracked industry origins and timing for each innovation, and consulted domain experts to zero in on the most significant innovations.All told, they identified 81 unique innovations that affected PV system costs since 1970, from improvements in antireflective coated glass to the implementation of fully online permitting interfaces.“With innovations, you can always go to a deeper level, down to things like raw materials processing techniques, so it was challenging to know when to stop. Having that quantitative model to ground our qualitative analysis really helped,” Trancik says.They chose to separate PV module costs from so-called balance-of-system (BOS) costs, which cover things like mounting systems, inverters, and wiring.PV modules, which are wired together to form solar panels, are mass-produced and can be exported, while many BOS components are designed, built, and sold at the local level.“By examining innovations both at the BOS level and within the modules, we identify the different types of innovations that have emerged in these two parts of PV technology,” Kavlak says.BOS costs depend more on soft technologies, nonphysical elements such as permitting procedures, which have contributed significantly less to PV’s past cost improvement compared to hardware innovations.“Often, it comes down to delays. Time is money, and if you have delays on construction sites and unpredictable processes, that affects these balance-of-system costs,” Trancik says.Innovations such as automated permitting software, which flags code-compliant systems for fast-track approval, show promise. Though not yet quantified in this study, the team’s framework could support future analysis of their economic impact and similar innovations that streamline deployment processes.Interconnected industriesThe researchers found that innovations from the semiconductor, electronics, metallurgy, and petroleum industries played a major role in reducing both PV and BOS costs, but BOS costs were also impacted by innovations in software engineering and electric utilities.Noninnovation factors, like efficiency gains from bulk purchasing and the accumulation of knowledge in the solar power industry, also reduced some cost variables.In addition, while most PV panel innovations originated in research organizations or industry, many BOS innovations were developed by city governments, U.S. states, or professional associations.“I knew there was a lot going on with this technology, but the diversity of all these fields and how closely linked they are, and the fact that we can clearly see that network through this analysis, was interesting,” Trancik says.“PV was very well-positioned to absorb innovations from other industries — thanks to the right timing, physical compatibility, and supportive policies to adapt innovations for PV applications,” Klemun adds.The analysis also reveals the role greater computing power could play in reducing BOS costs through advances like automated engineering review systems and remote site assessment software.“In terms of knowledge spillovers, what we’ve seen so far in PV may really just be the beginning,” Klemun says, pointing to the expanding role of robotics and AI-driven digital tools in driving future cost reductions and quality improvements.In addition to their qualitative analysis, the researchers demonstrated how this methodology could be used to estimate the quantitative impact of a particular innovation if one has the numerical data to plug into the cost equation.For instance, using information about material prices and manufacturing procedures, they estimate that wire sawing, a technique which was introduced in the 1980s, led to an overall PV system cost decrease of $5 per watt by reducing silicon losses and increasing throughput during fabrication.“Through this retrospective analysis, you learn something valuable for future strategy because you can see what worked and what didn’t work, and the models can also be applied prospectively. It is also useful to know what adjacent sectors may help support improvement in a particular technology,” Trancik says.Moving forward, the researchers plan to apply this methodology to a wide range of technologies, including other renewable energy systems. They also want to further study soft technology to identify innovations or processes that could accelerate cost reductions.“Although the process of technological innovation may seem like a black box, we’ve shown that you can study it just like any other phenomena,” Trancik says.This research is funded, in part, by the U.S. Department of Energy Solar Energies Technology Office. More

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      in Environment

      Eco-driving measures could significantly reduce vehicle emissions

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      Any motorist who has ever waited through multiple cycles for a traffic light to turn green knows how annoying signalized intersections can be. But sitting at intersections isn’t just a drag on drivers’ patience — unproductive vehicle idling could contribute as much as 15 percent of the carbon dioxide emissions from U.S. land transportation.A large-scale modeling study led by MIT researchers reveals that eco-driving measures, which can involve dynamically adjusting vehicle speeds to reduce stopping and excessive acceleration, could significantly reduce those CO2 emissions.Using a powerful artificial intelligence method called deep reinforcement learning, the researchers conducted an in-depth impact assessment of the factors affecting vehicle emissions in three major U.S. cities.Their analysis indicates that fully adopting eco-driving measures could cut annual city-wide intersection carbon emissions by 11 to 22 percent, without slowing traffic throughput or affecting vehicle and traffic safety.Even if only 10 percent of vehicles on the road employ eco-driving, it would result in 25 to 50 percent of the total reduction in CO2 emissions, the researchers found.In addition, dynamically optimizing speed limits at about 20 percent of intersections provides 70 percent of the total emission benefits. This indicates that eco-driving measures could be implemented gradually while still having measurable, positive impacts on mitigating climate change and improving public health.

      An animated GIF compares what 20% eco-driving adoption looks like to 100% eco-driving adoption.Image: Courtesy of the researchers

      “Vehicle-based control strategies like eco-driving can move the needle on climate change reduction. We’ve shown here that modern machine-learning tools, like deep reinforcement learning, can accelerate the kinds of analysis that support sociotechnical decision making. This is just the tip of the iceberg,” says senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of the Laboratory for Information and Decision Systems (LIDS).She is joined on the paper by lead author Vindula Jayawardana, an MIT graduate student; as well as MIT graduate students Ao Qu, Cameron Hickert, and Edgar Sanchez; MIT undergraduate Catherine Tang; Baptiste Freydt, a graduate student at ETH Zurich; and Mark Taylor and Blaine Leonard of the Utah Department of Transportation. The research appears in Transportation Research Part C: Emerging Technologies.A multi-part modeling studyTraffic control measures typically call to mind fixed infrastructure, like stop signs and traffic signals. But as vehicles become more technologically advanced, it presents an opportunity for eco-driving, which is a catch-all term for vehicle-based traffic control measures like the use of dynamic speeds to reduce energy consumption.In the near term, eco-driving could involve speed guidance in the form of vehicle dashboards or smartphone apps. In the longer term, eco-driving could involve intelligent speed commands that directly control the acceleration of semi-autonomous and fully autonomous vehicles through vehicle-to-infrastructure communication systems.“Most prior work has focused on how to implement eco-driving. We shifted the frame to consider the question of should we implement eco-driving. If we were to deploy this technology at scale, would it make a difference?” Wu says.To answer that question, the researchers embarked on a multifaceted modeling study that would take the better part of four years to complete.They began by identifying 33 factors that influence vehicle emissions, including temperature, road grade, intersection topology, age of the vehicle, traffic demand, vehicle types, driver behavior, traffic signal timing, road geometry, etc.“One of the biggest challenges was making sure we were diligent and didn’t leave out any major factors,” Wu says.Then they used data from OpenStreetMap, U.S. geological surveys, and other sources to create digital replicas of more than 6,000 signalized intersections in three cities — Atlanta, San Francisco, and Los Angeles — and simulated more than a million traffic scenarios.The researchers used deep reinforcement learning to optimize each scenario for eco-driving to achieve the maximum emissions benefits.Reinforcement learning optimizes the vehicles’ driving behavior through trial-and-error interactions with a high-fidelity traffic simulator, rewarding vehicle behaviors that are more energy-efficient while penalizing those that are not.The researchers cast the problem as a decentralized cooperative multi-agent control problem, where the vehicles cooperate to achieve overall energy efficiency, even among non-participating vehicles, and they act in a decentralized manner, avoiding the need for costly communication between vehicles.However, training vehicle behaviors that generalize across diverse intersection traffic scenarios was a major challenge. The researchers observed that some scenarios are more similar to one another than others, such as scenarios with the same number of lanes or the same number of traffic signal phases.As such, the researchers trained separate reinforcement learning models for different clusters of traffic scenarios, yielding better emission benefits overall.But even with the help of AI, analyzing citywide traffic at the network level would be so computationally intensive it could take another decade to unravel, Wu says.Instead, they broke the problem down and solved each eco-driving scenario at the individual intersection level.“We carefully constrained the impact of eco-driving control at each intersection on neighboring intersections. In this way, we dramatically simplified the problem, which enabled us to perform this analysis at scale, without introducing unknown network effects,” she says.Significant emissions benefitsWhen they analyzed the results, the researchers found that full adoption of eco-driving could result in intersection emissions reductions of between 11 and 22 percent.These benefits differ depending on the layout of a city’s streets. A denser city like San Francisco has less room to implement eco-driving between intersections, offering a possible explanation for reduced emission savings, while Atlanta could see greater benefits given its higher speed limits.Even if only 10 percent of vehicles employ eco-driving, a city could still realize 25 to 50 percent of the total emissions benefit because of car-following dynamics: Non-eco-driving vehicles would follow controlled eco-driving vehicles as they optimize speed to pass smoothly through intersections, reducing their carbon emissions as well.In some cases, eco-driving could also increase vehicle throughput by minimizing emissions. However, Wu cautions that increasing throughput could result in more drivers taking to the roads, reducing emissions benefits.And while their analysis of widely used safety metrics known as surrogate safety measures, such as time to collision, suggest that eco-driving is as safe as human driving, it could cause unexpected behavior in human drivers. More research is needed to fully understand potential safety impacts, Wu says.Their results also show that eco-driving could provide even greater benefits when combined with alternative transportation decarbonization solutions. For instance, 20 percent eco-driving adoption in San Francisco would cut emission levels by 7 percent, but when combined with the projected adoption of hybrid and electric vehicles, it would cut emissions by 17 percent.“This is a first attempt to systematically quantify network-wide environmental benefits of eco-driving. This is a great research effort that will serve as a key reference for others to build on in the assessment of eco-driving systems,” says Hesham Rakha, the Samuel L. Pritchard Professor of Engineering at Virginia Tech, who was not involved with this research.And while the researchers focus on carbon emissions, the benefits are highly correlated with improvements in fuel consumption, energy use, and air quality.“This is almost a free intervention. We already have smartphones in our cars, and we are rapidly adopting cars with more advanced automation features. For something to scale quickly in practice, it must be relatively simple to implement and shovel-ready. Eco-driving fits that bill,” Wu says.This work is funded, in part, by Amazon and the Utah Department of Transportation. More