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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 “Right now, the nuclear-maritime policies that exist are outdated and often tied only to specific technologies, like pressurized water reactors,” says Jose Izurieta, a graduate student in the Department of Mechanical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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      Book reviews technologies aiming to remove carbon from the atmosphere

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      Two leading experts in the field of carbon capture and sequestration (CCS) — Howard J. Herzog, a senior research engineer in the MIT Energy Initiative, and Niall Mac Dowell, a professor in energy systems engineering at Imperial College London — explore methods for removing carbon dioxide already in the atmosphere in their new book, “Carbon Removal.” Published in October, the book is part of the Essential Knowledge series from the MIT Press, which consists of volumes “synthesizing specialized subject matter for nonspecialists” and includes Herzog’s 2018 book, “Carbon Capture.”Burning fossil fuels, as well as other human activities, cause the release of carbon dioxide (CO2) into the atmosphere, where it acts like a blanket that warms the Earth, resulting in climate change. Much attention has focused on mitigation technologies that reduce emissions, but in their book, Herzog and Mac Dowell have turned their attention to “carbon dioxide removal” (CDR), an approach that removes carbon already present in the atmosphere.In this new volume, the authors explain how CO2 naturally moves into and out of the atmosphere and present a brief history of carbon removal as a concept for dealing with climate change. They also describe the full range of “pathways” that have been proposed for removing CO2 from the atmosphere. Those pathways include engineered systems designed for “direct air capture” (DAC), as well as various “nature-based” approaches that call for planting trees or taking steps to enhance removal by biomass or the oceans. The book offers easily accessible explanations of the fundamental science and engineering behind each approach.The authors compare the “quality” of the different pathways based on the following metrics:Accounting. For public acceptance of any carbon-removal strategy, the authors note, the developers need to get the accounting right — and that’s not always easy. “If you’re going to spend money to get CO2 out of the atmosphere, you want to get paid for doing it,” notes Herzog. It can be tricky to measure how much you have removed, because there’s a lot of CO2 going in and out of the atmosphere all the time. Also, if your approach involves, say, burning fossil fuels, you must subtract the amount of CO2 that’s emitted from the total amount you claim to have removed. Then there’s the timing of the removal. With a DAC device, the removal happens right now, and the removed CO2 can be measured. “But if I plant a tree, it’s going to remove CO2 for decades. Is that equivalent to removing it right now?” Herzog queries. How to take that factor into account hasn’t yet been resolved.Permanence. Different approaches keep the CO2 out of the atmosphere for different durations of time. How long is long enough? As the authors explain, this is one of the biggest issues, especially with nature-based solutions, where events such as wildfires or pestilence or land-use changes can release the stored CO2 back into the atmosphere. How do we deal with that?Cost. Cost is another key factor. Using a DAC device to remove CO2 costs far more than planting trees, but it yields immediate removal of a measurable amount of CO2 that can then be locked away forever. How does one monetize that trade-off?Additionality. “You’re doing this project, but would what you’re doing have been done anyway?” asks Herzog. “Is your effort additional to business as usual?” This question comes into play with many of the nature-based approaches involving trees, soils, and so on.Permitting and governance. These issues are especially important — and complicated — with approaches that involve doing things in the ocean. In addition, Herzog points out that some CCS projects could also achieve carbon removal, but they would have a hard time getting permits to build the pipelines and other needed infrastructure.The authors conclude that none of the CDR strategies now being proposed is a clear winner on all the metrics. However, they stress that carbon removal has the potential to play an important role in meeting our climate change goals — not by replacing our emissions-reduction efforts, but rather by supplementing them. However, as Herzog and Mac Dowell make clear in their book, many challenges must be addressed to move CDR from today’s speculation to deployment at scale, and the book supports the wider discussion about how to move forward. Indeed, the authors have fulfilled their stated goal: “to provide an objective analysis of the opportunities and challenges for CDR and to separate myth from reality.” More

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      in Environment

      How to reduce greenhouse gas emissions from ammonia production

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      Ammonia is one of the most widely produced chemicals in the world, used mostly as fertilizer, but also for the production of some plastics, textiles, and other applications. Its production, through processes that require high heat and pressure, accounts for up to 20 percent of all the greenhouse gases from the entire chemical industry, so efforts have been underway worldwide to find ways to reduce those emissions.Now, researchers at MIT have come up with a clever way of combining two different methods of producing the compound that minimizes waste products, that, when combined with some other simple upgrades, could reduce the greenhouse emissions from production by as much as 63 percent, compared to the leading “low-emissions” approach being used today.The new approach is described in the journal Energy & Fuels, in a paper by MIT Energy Initiative (MITEI) Director William H. Green, graduate student Sayandeep Biswas, MITEI Director of Research Randall Field, and two others.“Ammonia has the most carbon dioxide emissions of any kind of chemical,” says Green, who is the Hoyt C. Hottel Professor in Chemical Engineering. “It’s a very important chemical,” he says, because its use as a fertilizer is crucial to being able to feed the world’s population.Until late in the 19th century, the most widely used source of nitrogen fertilizer was mined deposits of bat or bird guano, mostly from Chile, but that source was beginning to run out, and there were predictions that the world would soon be running short of food to sustain the population. But then a new chemical process, called the Haber-Bosch process after its inventors, made it possible to make ammonia out of nitrogen from the air and hydrogen, which was mostly derived from methane. But both the burning of fossil fuels to provide the needed heat and the use of methane to make the hydrogen led to massive climate-warming emissions from the process.To address this, two newer variations of ammonia production have been developed: so-called “blue ammonia,” where the greenhouse gases are captured right at the factory and then sequestered deep underground, and “green ammonia,” produced by a different chemical pathway, using electricity instead of fossil fuels to hydrolyze water to make hydrogen.Blue ammonia is already beginning to be used, with a few plants operating now in Louisiana, Green says, and the ammonia mostly being shipped to Japan, “so that’s already kind of commercial.” Other parts of the world are starting to use green ammonia, especially in places that have lots of hydropower, solar, or wind to provide inexpensive electricity, including a giant plant now under construction in Saudi Arabia.But in most places, both blue and green ammonia are still more expensive than the traditional fossil-fuel-based version, so many teams around the world have been working on ways to cut these costs as much as possible so that the difference is small enough to be made up through tax subsidies or other incentives.The problem is growing, because as the population grows, and as wealth increases, there will be ever-increasing demands for nitrogen fertilizer. At the same time, ammonia is a promising substitute fuel to power hard-to-decarbonize transportation such as cargo ships and heavy trucks, which could lead to even greater needs for the chemical.“It definitely works” as a transportation fuel, by powering fuel cells that have been demonstrated for use by everything from drones to barges and tugboats and trucks, Green says. “People think that the most likely market of that type would be for shipping,” he says, “because the downside of ammonia is it’s toxic and it’s smelly, and that makes it slightly dangerous to handle and to ship around.” So its best uses may be where it’s used in high volume and in relatively remote locations, like the high seas. In fact, the International Maritime Organization will soon be voting on new rules that might give a strong boost to the ammonia alternative for shipping.The key to the new proposed system is to combine the two existing approaches in one facility, with a blue ammonia factory next to a green ammonia factory. The process of generating hydrogen for the green ammonia plant leaves a lot of leftover oxygen that just gets vented to the air. Blue ammonia, on the other hand, uses a process called autothermal reforming that requires a source of pure oxygen, so if there’s a green ammonia plant next door, it can use that excess oxygen.“Putting them next to each other turns out to have significant economic value,” Green says. This synergy could help hybrid “blue-green ammonia” facilities serve as an important bridge toward a future where eventually green ammonia, the cleanest version, could finally dominate. But that future is likely decades away, Green says, so having the combined plants could be an important step along the way.“It might be a really long time before [green ammonia] is actually attractive” economically, he says. “Right now, it’s nowhere close, except in very special situations.” But the combined plants “could be a really appealing concept, and maybe a good way to start the industry,” because so far only small, standalone demonstration plants of the green process are being built.“If green or blue ammonia is going to become the new way of making ammonia, you need to find ways to make it relatively affordable in a lot of countries, with whatever resources they’ve got,” he says. This new proposed combination, he says, “looks like a really good idea that can help push things along. Ultimately, there’s got to be a lot of green ammonia plants in a lot of places,” and starting out with the combined plants, which could be more affordable now, could help to make that happen. The team has filed for a patent on the process.Although the team did a detailed study of both the technology and the economics that show the system has great promise, Green points out that “no one has ever built one. We did the analysis, it looks good, but surely when people build the first one, they’ll find funny little things that need some attention,” such as details of how to start up or shut down the process. “I would say there’s plenty of additional work to do to make it a real industry.” But the results of this study, which shows the costs to be much more affordable than existing blue or green plants in isolation, “definitely encourages the possibility of people making the big investments that would be needed to really make this industry feasible.”This proposed integration of the two methods “improves efficiency, reduces greenhouse gas emissions, and lowers overall cost,” says Kevin van Geem, a professor in the Center for Sustainable Chemistry at Ghent University, who was not associated with this research. “The analysis is rigorous, with validated process models, transparent assumptions, and comparisons to literature benchmarks. By combining techno-economic analysis with emissions accounting, the work provides a credible and balanced view of the trade-offs.”He adds that, “given the scale of global ammonia production, such a reduction could have a highly impactful effect on decarbonizing one of the most emissions-intensive chemical industries.”The research team also included MIT postdoc Angiras Menon and MITEI research lead Guiyan Zang. The work was supported by IHI Japan through the MIT Energy Initiative and the Martin Family Society of Fellows for Sustainability.  More

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      in Environment

      Report: Sustainability in supply chains is still a firm-level priority

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      Corporations are actively seeking sustainability advances in their supply chains — but many need to improve the business metrics they use in this area to realize more progress, according to a new report by MIT researchers.   During a time of shifting policies globally and continued economic uncertainty, the survey-based report finds 85 percent of companies say they are continuing supply chain sustainability practices at the same level as in recent years, or are increasing those efforts.“What we found is strong evidence that sustainability still matters,” says Josué Velázquez Martínez, a research scientist and director of the MIT Sustainable Supply Chain Lab, which helped produce the report. “There are many things that remain to be done to accomplish those goals, but there’s a strong willingness from companies in all parts of the world to do something about sustainability.”The new analysis, titled “Sustainability Still Matters,” was released today. It is the sixth annual report on the subject prepared by the MIT Sustainable Supply Chain Lab, which is part of MIT’s Center for Transportation and Logistics. The Council of Supply Chain Management Professionals collaborated on the project as well.The report is based on a global survey, with responses from 1,203 professionals in 97 countries. This year, the report analyzes three issues in depth, including regulations and the role they play in corporate approaches to supply chain management. A second core topic is management and mitigation of what industry professionals call “Scope 3” emissions, which are those not from a firm itself, but from a firm’s supply chain. And a third issue of focus is the future of freight transportation, which by itself accounts for a substantial portion of supply chain emissions.Broadly, the survey finds that for European-based firms, the principal driver of action in this area remains government mandates, such as the Corporate Sustainability Reporting Directive, which requires companies to publish regular reports on their environmental impact and the risks to society involved. In North America, firm leadership and investor priorities are more likely to be decisive factors in shaping a company’s efforts.“In Europe the pressure primarily comes more from regulation, but in the U.S. it comes more from investors, or from competitors,” Velázquez Martínez says.The survey responses on Scope 3 emissions reveal a number of opportunities for improvement. In business and sustainability terms, Scope 1 greenhouse gas emissions are those a firm produces directly. Scope 2 emissions are the energy it has purchased. And Scope 3 emissions are those produced across a firm’s value chain, including the supply chain activities involved in producing, transporting, using, and disposing of its products.The report reveals that about 40 percent of firms keep close track of Scope 1 and 2 emissions, but far fewer tabulate Scope 3 on equivalent terms. And yet Scope 3 may account for roughly 75 percent of total firm emissions, on aggregate. About 70 percent of firms in the survey say they do not have enough data from suppliers to accurately tabulate the total greenhouse gas and climate impact of their supply chains.Certainly it can be hard to calculate the total emissions when a supply chain has many layers, including smaller suppliers lacking data capacity. But firms can upgrade their analytics in this area, too. For instance, 50 percent of North American firms are still using spreadsheets to tabulate emissions data, often making rough estimates that correlate emissions to simple economic activity. An alternative is life cycle assessment software that provides more sophisticated estimates of a product’s emissions, from the extraction of its materials to its post-use disposal. By contrast, only 32 percent of European firms are still using spreadsheets rather than life cycle assessment tools.“You get what you measure,” Velázquez Martínez says. “If you measure poorly, you’re going to get poor decisions that most likely won’t drive the reductions you’re expecting. So we pay a lot of attention to that particular issue, which is decisive to defining an action plan. Firms pay a lot of attention to metrics in their financials, but in sustainability they’re often using simplistic measurements.”When it comes to transportation, meanwhile, the report shows that firms are still grappling with the best ways to reduce emissions. Some see biofuels as the best short-term alternative to fossil fuels; others are investing in electric vehicles; some are waiting for hydrogen-powered vehicles to gain traction. Supply chains, after all, frequently involve long-haul trips. For firms, as for individual consumers, electric vehicles are more practical with a larger infrastructure of charging stations. There are advances on that front but more work to do as well.That said, “Transportation has made a lot of progress in general,” Velázquez Martínez says, noting the increased acceptance of new modes of vehicle power in general.Even as new technologies loom on the horizon, though, supply chain sustainability is not wholly depend on their introduction. One factor continuing to propel sustainability in supply chains is the incentives companies have to lower costs. In a competitive business environment, spending less on fossil fuels usually means savings. And firms can often find ways to alter their logistics to consume and spend less.“Along with new technologies, there is another side of supply chain sustainability that is related to better use of the current infrastructure,” Velázquez Martínez observes. “There is always a need to revise traditional ways of operating to find opportunities for more efficiency.”  More

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      in Energy

      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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      in Environment

      A beacon of light

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      Placing a lit candle in a window to welcome friends and strangers is an old Irish tradition that took on greater significance when Mary Robinson was elected president of Ireland in 1990. At the time, Robinson placed a lamp in Áras an Uachtaráin — the official residence of Ireland’s presidents — noting that the Irish diaspora and all others are always welcome in Ireland. Decades later, a lit lamp remains in a window in Áras an Uachtaráin.The symbolism of Robinson’s lamp was shared by Hashim Sarkis, dean of the MIT School of Architecture and Planning (SA+P), at the school’s graduation ceremony in May, where Robinson addressed the class of 2025. To replicate the generous intentions of Robinson’s lamp and commemorate her visit to MIT, Sarkis commissioned a unique lantern as a gift for Robinson. He commissioned an identical one for his office, which is in the front portico of MIT at 77 Massachusetts Ave.“The lamp will welcome all citizens of the world to MIT,” says Sarkis.

      Geolectric: Sustainable, Low-Carbon Ceramics for Embedded Electronics and Interaction DesignVideo: MIT Design Intelligence Lab

      No ordinary lanternThe bespoke lantern was created by Marcelo Coelho SM ’08, PhD ’12, director of the Design Intelligence Lab and associate professor of the practice in the Department of Architecture.One of several projects in the Geoletric research at the Design Intelligence Lab, the lantern showcases the use of geopolymers as a sustainable material alternative for embedded computers and consumer electronics.“The materials that we use to make computers have a negative impact on climate, so we’re rethinking how we make products with embedded electronics — such as a lamp or lantern — from a climate perspective,” says Coelho.Consumer electronics rely on materials that are high in carbon emissions and difficult to recycle. As the demand for embedded computing increases, so too does the need for alternative materials that have a reduced environmental impact while supporting electronic functionality.The Geolectric lantern advances the formulation and application of geopolymers — a class of inorganic materials that form covalently bonded, non-crystalline networks. Unlike traditional ceramics, geopolymers do not require high-temperature firing, allowing electronic components to be embedded seamlessly during production.Geopolymers are similar to ceramics, but have a lower carbon footprint and present a sustainable alternative for consumer electronics, product design, and architecture. The minerals Coelho uses to make the geopolymers — aluminum silicate and sodium silicate — are those regularly used to make ceramics.“Geopolymers aren’t particularly new, but are becoming more popular,” says Coelho. “They have high strength in both tension and compression, superior durability, fire resistance, and thermal insulation. Compared to concrete, geopolymers don’t release carbon dioxide. Compared to ceramics, you don’t have to worry about firing them. What’s even more interesting is that they can be made from industrial byproducts and waste materials, contributing to a circular economy and reducing waste.”The lantern is embedded with custom electronics that serve as a proximity and touch sensor. When a hand is placed over the top, light shines down the glass tubes.The timeless design of the Geoelectric lantern — minimalist, composed of natural materials — belies its future-forward function. Coelho’s academic background is in fine arts and computer science. Much of his work, he says, “bridges these two worlds.”Working at the Design Intelligence Lab with Coelho on the lanterns are Jacob Payne, a graduate architecture student, and Jean-Baptiste Labrune, a research affiliate.A light for MITA few weeks before commencement, Sarkis saw the Geoelectric lantern in Palazzo Diedo Berggruen Arts and Culture in Venice, Italy. The exhibition, a collateral event of the Venice Biennale’s 19th International Architecture Exhibition, featured the work of 40 MIT architecture faculty.The sustainability feature of Geolectric is the key reason Sarkis regarded the lantern as the perfect gift for Robinson. After her career in politics, Robinson founded the Mary Robinson Foundation — Climate Justice, an international center addressing the impacts of climate change on marginalized communities.The third iteration of Geolectric for Sarkis’ office is currently underway. While the lantern was a technical prototype and an opportunity to showcase his lab’s research, Coelho — an immigrant from Brazil — was profoundly touched by how Sarkis created the perfect symbolism to both embody the welcoming spirit of the school and honor President Robinson.“When the world feels most fragile, we need to urgently find sustainable and resilient solutions for our built environment. It’s in the darkest times when we need light the most,” says Coelho.  More

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      in Energy

      Climate Action Learning Lab helps state and local leaders identify and implement effective climate mitigation strategies

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      This spring, J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — launched its first ever Learning Lab, centered on climate action. The Learning Lab convened a cohort of government leaders who are enacting a broad range of policies and programs to support the transition to a low-carbon economy. Through the Learning Lab, participants explored how to embed randomized evaluation into promising solutions to determine how to maximize changes in behavior — a strategy that can help advance decarbonization in the most cost-effective ways to benefit all communities. The inaugural cohort included more than 25 participants from state agencies and cities, including the Massachusetts Clean Energy Center, the Minnesota Housing Finance Agency, and the cities of Lincoln, Nebraska; Newport News, Virginia; Orlando, Florida; and Philadelphia.“State and local governments have demonstrated tremendous leadership in designing and implementing decarbonization policies and climate action plans over the past few years,” said Peter Christensen, scientific advisor of the J-PAL North America Environment, Energy, and Climate Change Sector. “And while these are informed by scientific projections on which programs and technologies may effectively and equitably reduce emissions, the projection methods involve a lot of assumptions. It can be challenging for governments to determine whether their programs are actually achieving the expected level of emissions reductions that we desperately need. The Climate Action Learning Lab was designed to support state and local governments in addressing this need — helping them to rigorously evaluate their programs to detect their true impact.”From May to July, the Learning Lab offered a suite of resources for participants to leverage rigorous evaluation to identify effective and equitable climate mitigation solutions. Offerings included training lectures, one-on-one strategy sessions, peer learning engagements, and researcher collaboration. State and local leaders built skills and knowledge in evidence generation and use, reviewed and applied research insights to their own programmatic areas, and identified priority research questions to guide evidence-building and decision-making practices. Programs prioritized for evaluation covered topics such as compliance with building energy benchmarking policies, take-up rates of energy-efficient home improvement programs such as heat pumps and Solar for All, and scoring criteria for affordable housing development programs.“We appreciated the chance to learn about randomized evaluation methodology, and how this impact assessment tool could be utilized in our ongoing climate action planning. With so many potential initiatives to pursue, this approach will help us prioritize our time and resources on the most effective solutions,” said Anna Shugoll, program manager at the City of Philadelphia’s Office of Sustainability.This phase of the Learning Lab was possible thanks to grant funding from J-PAL North America’s longtime supporter and collaborator Arnold Ventures. The work culminated in an in-person summit in Cambridge, Massachusetts, on July 23, where Learning Lab participants delivered a presentation on their jurisdiction’s priority research questions and strategic evaluation plans. They also connected with researchers in the J-PAL network to further explore impact evaluation opportunities for promising decarbonization programs.“The Climate Action Learning Lab has helped us identify research questions for some of the City of Orlando’s deep decarbonization goals. J-PAL staff, along with researchers in the J-PAL network, worked hard to bridge the gap between behavior change theory and the applied, tangible benefits that we achieve through rigorous evaluation of our programs,” said Brittany Sellers, assistant director for sustainability, resilience and future-ready for Orlando. “Whether we’re discussing an energy-efficiency policy for some of the biggest buildings in the City of Orlando or expanding [electric vehicle] adoption across the city, it’s been very easy to communicate some of these high-level research concepts and what they can help us do to actually pursue our decarbonization goals.”The next phase of the Climate Action Learning Lab will center on building partnerships between jurisdictions and researchers in the J-PAL network to explore the launch of randomized evaluations, deepening the community of practice among current cohort members, and cultivating a broad culture of evidence building and use in the climate space. “The Climate Action Learning Lab provided a critical space for our city to collaborate with other cities and states seeking to implement similar decarbonization programs, as well as with researchers in the J-PAL network to help rigorously evaluate these programs,” said Daniel Collins, innovation team director at the City of Newport News. “We look forward to further collaboration and opportunities to learn from evaluations of our mitigation efforts so we, as a city, can better allocate resources to the most effective solutions.”The Climate Action Learning Lab is one of several offerings under the J-PAL North America Evidence for Climate Action Project. The project’s goal is to convene an influential network of researchers, policymakers, and practitioners to generate rigorous evidence to identify and advance equitable, high-impact policy solutions to climate change in the United States. In addition to the Learning Lab, J-PAL North America will launch a climate special topic request for proposals this fall to fund research on climate mitigation and adaptation initiatives. J-PAL will welcome applications from both research partnerships formed through the Learning Lab as well as other eligible applicants.Local government leaders, researchers, potential partners, or funders committed to advancing climate solutions that work, and who want to learn more about the Evidence for Climate Action Project, may email na_eecc@povertyactionlab.org or subscribe to the J-PAL North America Climate Action newsletter. More

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      in Energy

      New self-assembling material could be the key to recyclable EV batteries

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      Today’s electric vehicle boom is tomorrow’s mountain of electronic waste. And while myriad efforts are underway to improve battery recycling, many EV batteries still end up in landfills.A research team from MIT wants to help change that with a new kind of self-assembling battery material that quickly breaks apart when submerged in a simple organic liquid. In a new paper published in Nature Chemistry, the researchers showed the material can work as the electrolyte in a functioning, solid-state battery cell and then revert back to its original molecular components in minutes.The approach offers an alternative to shredding the battery into a mixed, hard-to-recycle mass. Instead, because the electrolyte serves as the battery’s connecting layer, when the new material returns to its original molecular form, the entire battery disassembles to accelerate the recycling process.“So far in the battery industry, we’ve focused on high-performing materials and designs, and only later tried to figure out how to recycle batteries made with complex structures and hard-to-recycle materials,” says the paper’s first author Yukio Cho PhD ’23. “Our approach is to start with easily recyclable materials and figure out how to make them battery-compatible. Designing batteries for recyclability from the beginning is a new approach.”Joining Cho on the paper are PhD candidate Cole Fincher, Ty Christoff-Tempesta PhD ’22, Kyocera Professor of Ceramics Yet-Ming Chiang, Visiting Associate Professor Julia Ortony, Xiaobing Zuo, and Guillaume Lamour.Better batteriesThere’s a scene in one of the “Harry Potter” films where Professor Dumbledore cleans a dilapidated home with the flick of the wrist and a spell. Cho says that image stuck with him as a kid. (What better way to clean your room?) When he saw a talk by Ortony on engineering molecules so that they could assemble into complex structures and then revert back to their original form, he wondered if it could be used to make battery recycling work like magic.That would be a paradigm shift for the battery industry. Today, batteries require harsh chemicals, high heat, and complex processing to recycle. There are three main parts of a battery: the positively charged cathode, the negatively charged electrode, and the electrolyte that shuttles lithium ions between them. The electrolytes in most lithium-ion batteries are highly flammable and degrade over time into toxic byproducts that require specialized handling.To simplify the recycling process, the researchers decided to make a more sustainable electrolyte. For that, they turned to a class of molecules that self-assemble in water, named aramid amphiphiles (AAs), whose chemical structures and stability mimic that of Kevlar. The researchers further designed the AAs to contain polyethylene glycol (PEG), which can conduct lithium ions, on one end of each molecule. When the molecules are exposed to water, they spontaneously form nanoribbons with ion-conducting PEG surfaces and bases that imitate the robustness of Kevlar through tight hydrogen bonding. The result is a mechanically stable nanoribbon structure that conducts ions across its surface.“The material is composed of two parts,” Cho explains. “The first part is this flexible chain that gives us a nest, or host, for lithium ions to jump around. The second part is this strong organic material component that is used in the Kevlar, which is a bulletproof material. Those make the whole structure stable.”When added to water, the nanoribbons self-assemble to form millions of nanoribbons that can be hot-pressed into a solid-state material.“Within five minutes of being added to water, the solution becomes gel-like, indicating there are so many nanofibers formed in the liquid that they start to entangle each other,” Cho says. “What’s exciting is we can make this material at scale because of the self-assembly behavior.”The team tested the material’s strength and toughness, finding it could endure the stresses associated with making and running the battery. They also constructed a solid-state battery cell that used lithium iron phosphate for the cathode and lithium titanium oxide as the anode, both common materials in today’s batteries. The nanoribbons moved lithium ions successfully between the electrodes, but a side-effect known as polarization limited the movement of lithium ions into the battery’s electrodes during fast bouts of charging and discharging, hampering its performance compared to today’s gold-standard commercial batteries.“The lithium ions moved along the nanofiber all right, but getting the lithium ion from the nanofibers to the metal oxide seems to be the most sluggish point of the process,” Cho says.When they immersed the battery cell into organic solvents, the material immediately dissolved, with each part of the battery falling away for easier recycling. Cho compared the materials’ reaction to cotton candy being submerged in water.“The electrolyte holds the two battery electrodes together and provides the lithium-ion pathways,” Cho says. “So, when you want to recycle the battery, the entire electrolyte layer can fall off naturally and you can recycle the electrodes separately.”Validating a new approachCho says the material is a proof of concept that demonstrates the recycle-first approach.“We don’t want to say we solved all the problems with this material,” Cho says. “Our battery performance was not fantastic because we used only this material as the entire electrolyte for the paper, but what we’re picturing is using this material as one layer in the battery electrolyte. It doesn’t have to be the entire electrolyte to kick off the recycling process.”Cho also sees a lot of room for optimizing the material’s performance with further experiments.Now, the researchers are exploring ways to integrate these kinds of materials into existing battery designs as well as implementing the ideas into new battery chemistries.“It’s very challenging to convince existing vendors to do something very differently,” Cho says. “But with new battery materials that may come out in five or 10 years, it could be easier to integrate this into new designs in the beginning.”Cho also believes the approach could help reshore lithium supplies by reusing materials from batteries that are already in the U.S.“People are starting to realize how important this is,” Cho says. “If we can start to recycle lithium-ion batteries from battery waste at scale, it’ll have the same effect as opening lithium mines in the U.S. Also, each battery requires a certain amount of lithium, so extrapolating out the growth of electric vehicles, we need to reuse this material to avoid massive lithium price spikes.”The work was supported, in part, by the National Science Foundation and the U.S. Department of Energy. 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