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    How artificial intelligence can help achieve a clean energy future

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    There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.Controlling real-time operationsCustomers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”Using AI to help plan investments in infrastructure for the futureGrid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.Harnessing AI to discover and exploit advanced materials needed for the energy transition“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” MITEI’s contributionsAt MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.” More

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      MIT Energy Initiative conference spotlights research priorities amidst a changing energy landscape

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      “We’re here to talk about really substantive changes, and we want you to be a participant in that,” said Desirée Plata, the School of Engineering Distinguished Professor of Climate and Energy in MIT’s Department of Civil and Environmental Engineering, at Energizing@MIT: the MIT Energy Initiative’s (MITEI) Annual Research Conference that was held on Sept. 9-10.Plata’s words resonated with the 150-plus participants from academia, industry, and government meeting in Cambridge for the conference, whose theme was “tackling emerging energy challenges.” Meeting such challenges and ultimately altering the trajectory of global climate outcomes requires partnerships, speakers agreed.“We have to be humble and open,” said Giacomo Silvestri, chair of Eniverse Ventures at Eni, in a shared keynote address. “We cannot develop innovation just focusing on ourselves and our competencies … so we need to partner with startups, venture funds, universities like MIT and other public and private institutions.” Added his Eni colleague, Annalisa Muccioli, head of research and technology, “The energy transition is a race we can win only by combining mature solutions ready to deploy, together with emerging technologies that still require acceleration and risk management.”Research targetsIn a conference that showcased a suite of research priorities MITEI has identified as central to ensuring a low-carbon energy future, participants shared both promising discoveries and strategies for advancing proven technologies in the face of shifting political winds and policy uncertainties.One panel focused on grid resiliency — a topic that has moved from the periphery to the center of energy discourse as climate-driven disruptions, cyber threats, and the integration of renewables challenge legacy systems. A dramatic case in point: the April 2025 outage in Spain and Portugal that left millions without power for eight to 15 hours. “I want to emphasize that this failure was about more than the power system,” said MITEI research scientist Pablo Duenas-Martinez. While he pinpointed technical problems with reactive power and voltage control behind the system collapse, Duenas-Martinez also called out a lack of transmission capacity with Central Europe and out-of-date operating procedures, and recommended better preparation and communication among transmission systems and utility operators.“You can’t plan for every single eventuality, which means we need to broaden the portfolio of extreme events we prepare for,” noted Jennifer Pearce, vice president at energy company Avangrid. “We are making the system smarter, stronger, and more resilient to better protect from a wide range of threats such as storms, flooding, and extreme heat events.” Pearce noted that Avangrid’s commitment to deliver safe, reliable power to its customers necessitates “meticulous emergency planning procedures.”The resiliency of the electric grid under greatly increased demand is an important motivation behind MITEI’s September 2025 launch of the Data Center Power Forum, which was also announced during the annual research conference. The forum will include research projects, webinars, and other content focused on energy supply and storage, grid design and management, infrastructure, and public and economic policy related to data centers. The forum’s members include MITEI companies that also participate in MIT’s Center for Environmental and Energy Policy Research (CEEPR).Storage and transportation: Staggering challengesMeeting climate goals to decarbonize the world by 2050 requires building around 300 terawatt-hours of storage, according to Asegun Henry, a professor in the MIT Department of Mechanical Engineering. “It’s an unbelievably enormous problem people have to wrap their minds around,” he said. Henry has been developing a high-temperature thermal energy storage system he has nicknamed “sun in a box.” His system uses liquid metal and graphite to hold electricity as heat and then convert it back to electricity, enabling storage anywhere from five to 500 hours.“At the end of the day, storage provides a service, and the type of technology that you need is a function of the service that you value the most,” said Nestor Sepulveda, commercial lead for advanced energy investments and partnerships at Google. “I don’t think there is one winner-takes-all type of market here.”Another panel explored sustainable fuels that could help decarbonize hard-to-electrify sectors like aviation, shipping, and long-haul trucking. Randall Field, MITEI’s director of research, noted that sustainably produced drop-in fuels — fuels that are largely compatible with existing engines — “could eliminate potentially trillions of dollars of cost for fleet replacement and for infrastructure build-out, while also helping us to accelerate the rate of decarbonization of the transportation sectors.”Erik G. Birkerts is the chief growth officer of LanzaJet, which produces a drop-in, high-energy-density aviation fuel derived from agricultural residue and other waste carbon sources. “The key to driving broad sustainable aviation fuel adoption is solving both the supply-side challenge through more production and the demand-side hurdle by reducing costs,” he said.“We think a good policy framework [for sustainable fuels] would be something that is technology-neutral, does not exclude any pathways to produce, is based on life cycle accounting practices, and on market mechanisms,” said Veronica L. Robertson, energy products technology portfolio manager at ExxonMobil.MITEI plans a major expansion of its research on sustainable fuels, announcing a two-year study, “The future of fuels: Pathways to sustainable transportation,” starting in early 2026. According to Field, the study will analyze and assess biofuels and e-fuels.Solutions from labs big and smallGlobal energy leaders offered glimpses of their research projects. A panel on carbon capture in power generation featured three takes on the topic: Devin Shaw, commercial director of decarbonization technologies at Shell, described post-combustion carbon capture in power plants using steam for heat recovery; Jan Marsh, a global program lead at Siemens Energy, discussed deploying novel materials to capture carbon dioxide directly from the air; and Jeffrey Goldmeer, senior director of technology strategy at GE Vernova, explained integrating carbon capture into gas-powered turbine systems.During a panel on vehicle electrification, Brian Storey, vice president of energy and materials at the Toyota Research Institute, provided an overview of Toyota’s portfolio of projects for decarbonization, including solid-state batteries, flexible manufacturing lines, and grid-forming inverters to support EV charging infrastructure.A session on MITEI seed fund projects revealed promising early-stage research inside MIT’s own labs. A new process for decarbonizing the production of ethylene was presented by Yogesh Surendranath, Donner Professor of Science in the MIT Department of Chemistry. Materials Science and Engineering assistant professor Aristide Gumyusenge also discussed the development of polymers essential for a new kind of sodium-ion battery.Shepherding bold, new technologies like these from academic labs into the real world cannot succeed without ample support and deft management. A panel on paths to commercialization featured the work of Iwnetim Abate, Chipman Career Development Professor and assistant professor in the MIT Department of Materials Science and Engineering, who has spun out a company, Addis Energy, based on a novel geothermal process for harvesting clean hydrogen and ammonia from subsurface, iron-rich rocks. Among his funders: ARPA-E and MIT’s own The Engine Ventures.The panel also highlighted the MIT Proto Ventures Program, an initiative to seize early-stage MIT ideas and unleash them as world-changing startups. “A mere 4.2 percent of all the patents that are actually prosecuted in the world are ever commercialized, which seems like a shocking number,” said Andrew Inglis, an entrepreneur working with Proto Ventures to translate geothermal discoveries into businesses. “Can’t we do this better? Let’s do this better!”Geopolitical hazardsThroughout the conference, participants often voiced concern about the impacts of competition between the United States and China. Kelly Sims Gallagher, dean of the Fletcher School at Tufts University and an expert on China’s energy landscape, delivered the sobering news in her keynote address: “U.S. competitiveness in low-carbon technologies has eroded in nearly every category,” she said. “The Chinese are winning the clean tech race.”China enjoys a 51 percent share in global wind turbine manufacture and 75 percent in solar modules. It also controls low-carbon supply chains that much of the world depends on. “China is getting so dominant that nobody can carve out a comparative advantage in anything,” said Gallagher. “China is just so big, and the scale is so huge that the Chinese can truly conquer markets and make it very hard for potential competitors to find a way in.”And for the United States, the problem is “the seesaw of energy policy,” she says. “It’s incredibly difficult for the private sector to plan and to operate, given the lack of predictability and policy here.”Nevertheless, Gallagher believes the United States still has a chance of at least regaining competitiveness, by setting up a stable, bipartisan energy policy, rebuilding domestic manufacturing and supply chains; providing consistent fiscal incentives; attracting and retaining global talent; and fostering international collaboration.The conference shone a light on one such collaboration: a China-U.S. joint venture to manufacture lithium iron phosphate batteries for commercial vehicles in the United States. The venture brings together Eve Energy, a Chinese battery technology and manufacturing company; Daimler, a global commercial vehicle manufacturer; PACCAR Inc., a U.S.-based truck manufacturer; and Accelera, the zero-emissions business of Cummins Inc. “Manufacturing batteries in the U.S. makes the supply chain more robust and reduces geopolitical risks,” said Mike Gerty, of PACCAR.While she acknowledged the obstacles confronting her colleagues in the room, Plata nevertheless concluded her remarks as a panel moderator with some optimism: “I hope you all leave this conference and look back on it in the future, saying I was in the room when they actually solved some of the challenges standing between now and the future that we all wish to manifest.” More

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      Concrete “battery” developed at MIT now packs 10 times the power

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      Concrete already builds our world, and now it’s one step closer to powering it, too. Made by combining cement, water, ultra-fine carbon black (with nanoscale particles), and electrolytes, electron-conducting carbon concrete (ec3, pronounced “e-c-cubed”) creates a conductive “nanonetwork” inside concrete that could enable everyday structures like walls, sidewalks, and bridges to store and release electrical energy. In other words, the concrete around us could one day double as giant “batteries.”As MIT researchers report in a new PNAS paper, optimized electrolytes and manufacturing processes have increased the energy storage capacity of the latest ec3 supercapacitors by an order of magnitude. In 2023, storing enough energy to meet the daily needs of the average home would have required about 45 cubic meters of ec3, roughly the amount of concrete used in a typical basement. Now, with the improved electrolyte, that same task can be achieved with about 5 cubic meters, the volume of a typical basement wall.“A key to the sustainability of concrete is the development of ‘multifunctional concrete,’ which integrates functionalities like this energy storage, self-healing, and carbon sequestration. Concrete is already the world’s most-used construction material, so why not take advantage of that scale to create other benefits?” asks Admir Masic, lead author of the new study, MIT Electron-Conducting Carbon-Cement-Based Materials Hub (EC³ Hub) co-director, and associate professor of civil and environmental engineering (CEE) at MIT.The improved energy density was made possible by a deeper understanding of how the nanocarbon black network inside ec3 functions and interacts with electrolytes. Using focused ion beams for the sequential removal of thin layers of the ec3 material, followed by high-resolution imaging of each slice with a scanning electron microscope (a technique called FIB-SEM tomography), the team across the EC³ Hub and MIT Concrete Sustainability Hub was able to reconstruct the conductive nanonetwork at the highest resolution yet. This approach allowed the team to discover that the network is essentially a fractal-like “web” that surrounds ec3 pores, which is what allows the electrolyte to infiltrate and for current to flow through the system. “Understanding how these materials ‘assemble’ themselves at the nanoscale is key to achieving these new functionalities,” adds Masic.Equipped with their new understanding of the nanonetwork, the team experimented with different electrolytes and their concentrations to see how they impacted energy storage density. As Damian Stefaniuk, first author and EC³ Hub research scientist, highlights, “we found that there is a wide range of electrolytes that could be viable candidates for ec3. This even includes seawater, which could make this a good material for use in coastal and marine applications, perhaps as support structures for offshore wind farms.”At the same time, the team streamlined the way they added electrolytes to the mix. Rather than curing ec3 electrodes and then soaking them in electrolyte, they added the electrolyte directly into the mixing water. Since electrolyte penetration was no longer a limitation, the team could cast thicker electrodes that stored more energy.The team achieved the greatest performance when they switched to organic electrolytes, especially those that combined quaternary ammonium salts — found in everyday products like disinfectants — with acetonitrile, a clear, conductive liquid often used in industry. A cubic meter of this version of ec3 — about the size of a refrigerator — can store over 2 kilowatt-hours of energy. That’s about enough to power an actual refrigerator for a day.While batteries maintain a higher energy density, ec3 can in principle be incorporated directly into a wide range of architectural elements — from slabs and walls to domes and vaults — and last as long as the structure itself.“The Ancient Romans made great advances in concrete construction. Massive structures like the Pantheon stand to this day without reinforcement. If we keep up their spirit of combining material science with architectural vision, we could be at the brink of a new architectural revolution with multifunctional concretes like ec3,” proposes Masic.Taking inspiration from Roman architecture, the team built a miniature ec3 arch to show how structural form and energy storage can work together. Operating at 9 volts, the arch supported its own weight and additional load while powering an LED light.However, something unique happened when the load on the arch increased: the light flickered. This is likely due to the way stress impacts electrical contacts or the distribution of charges. “There may be a kind of self-monitoring capacity here. If we think of an ec3 arch at architectural scale, its output may fluctuate when it’s impacted by a stressor like high winds. We may be able to use this as a signal of when and to what extent a structure is stressed, or monitor its overall health in real time,” envisions Masic.The latest developments in ec³ technology bring it a step closer to real-world scalability. It’s already been used to heat sidewalk slabs in Sapporo, Japan, due to its thermally conductive properties, representing a potential alternative to salting. “With these higher energy densities and demonstrated value across a broader application space, we now have a powerful and flexible tool that can help us address a wide range of persistent energy challenges,” explains Stefaniuk. “One of our biggest motivations was to help enable the renewable energy transition. Solar power, for example, has come a long way in terms of efficiency. However, it can only generate power when there’s enough sunlight. So, the question becomes: How do you meet your energy needs at night, or on cloudy days?”Franz-Josef Ulm, EC³ Hub co-director and CEE professor, continues the thread: “The answer is that you need a way to store and release energy. This has usually meant a battery, which often relies on scarce or harmful materials. We believe that ec3 is a viable substitute, letting our buildings and infrastructure meet our energy storage needs.” The team is working toward applications like parking spaces and roads that could charge electric vehicles, as well as homes that can operate fully off the grid.“What excites us most is that we’ve taken a material as ancient as concrete and shown that it can do something entirely new,” says James Weaver, a co-author on the paper who is an associate professor of design technology and materials science and engineering at Cornell University, as well as a former EC³ Hub researcher. “By combining modern nanoscience with an ancient building block of civilization, we’re opening a door to infrastructure that doesn’t just support our lives, it powers them.” 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-Africa launches new collaboration with Angola

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      The MIT Center for International Studies announced the launch of a new pilot initiative with Angola, to be implemented through its MIT-Africa Program.The new initiative marks a significant collaboration between MIT-Africa, Sonangol (Angola’s national energy company), and the Instituto Superior Politécnico de Tecnologias e Ciências (ISPTEC). The collaboration was formalized at a signing ceremony on MIT’s campus in June with key stakeholders from all three institutions present, including Diamantino Pedro Azevedo, the Angolan minister of mineral resources, petroleum, and gas, and Sonangol CEO Gaspar Martins.“This partnership marks a pivotal step in the Angolan government’s commitment to leveraging knowledge as the cornerstone of the country’s economic transformation,” says Azevedo. “By connecting the oil and gas sector with science, innovation, and world-class training, we are equipping future generations to lead Angola into a more technological, sustainable, and globally competitive era.”The sentiment is shared by the MIT-Africa Program leaders. “This initiative reflects MIT’s deep commitment to fostering meaningful, long-term relationships across the African continent,” says Mai Hassan, faculty director of the MIT-Africa Program. “It supports our mission of advancing knowledge and educating students in ways that are globally informed, and it provides a platform for mutual learning. By working with Angolan partners, we gain new perspectives and opportunities for innovation that benefit both MIT and our collaborators.”In addition to its new collaboration with MIT-Africa, Sonangol has joined MIT’s Industrial Liaison Program (ILP), breaking new ground as its first corporate member based in sub-Saharan Africa. ILP enables companies worldwide to harness MIT resources to address current challenges and to anticipate future needs. As an ILP member, Sonangol seeks to facilitate collaboration in key sectors such as natural resources and mining, energy, construction, and infrastructure.The MIT-Africa Program manages a portfolio of research, teaching, and learning initiatives that emphasize two-way value — offering impactful experiences to MIT students and faculty while collaborating closely with institutions and communities across Africa. The new Angola collaboration is aligned with this ethos, and will launch with two core activities during the upcoming academic year:Global Classroom: An MIT course on geo-spatial technologies for environmental monitoring, taught by an MIT faculty member, will be brought directly to the ISPTEC campus, offering Angolan students and MIT participants a collaborative, in-country learning experience.Global Teaching Labs: MIT students will travel to ISPTEC to teach science, technology, engineering, arts, and mathematics subjects on renewable energy technologies, engaging Angolan students through hands-on instruction.“This is not a traditional development project,” says Ari Jacobovits, managing director of MIT-Africa. “This is about building genuine partnerships rooted in academic rigor, innovation, and shared curiosity. The collaboration has been designed from the ground up with our partners at ISPTEC and Sonangol. We’re coming in with a readiness to learn as much as we teach.”The pilot marks an important first step in establishing a long-term collaboration with Angola. By investing in collaborative education and innovation, the new initiative aims to spark novel approaches to global challenges and strengthen academic institutions on both sides.These agreements with MIT-Africa and ILP “not only enhance our innovation and technological capabilities, but also create opportunities for sustainable development and operational excellence,” says Gaspar. “They advance our mission to be a leading force in the African energy sector.”“The vision behind this initiative is bold,” says Hassan. “It’s about co-creating knowledge and building capacity that lasts.” More

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      in Environment

      AI stirs up the recipe for concrete in MIT study

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      For weeks, the whiteboard in the lab was crowded with scribbles, diagrams, and chemical formulas. A research team across the Olivetti Group and the MIT Concrete Sustainability Hub (CSHub) was working intensely on a key problem: How can we reduce the amount of cement in concrete to save on costs and emissions? The question was certainly not new; materials like fly ash, a byproduct of coal production, and slag, a byproduct of steelmaking, have long been used to replace some of the cement in concrete mixes. However, the demand for these products is outpacing supply as industry looks to reduce its climate impacts by expanding their use, making the search for alternatives urgent. The challenge that the team discovered wasn’t a lack of candidates; the problem was that there were too many to sort through.On May 17, the team, led by postdoc Soroush Mahjoubi, published an open-access paper in Nature’s Communications Materials outlining their solution. “We realized that AI was the key to moving forward,” notes Mahjoubi. “There is so much data out there on potential materials — hundreds of thousands of pages of scientific literature. Sorting through them would have taken many lifetimes of work, by which time more materials would have been discovered!”With large language models, like the chatbots many of us use daily, the team built a machine-learning framework that evaluates and sorts candidate materials based on their physical and chemical properties. “First, there is hydraulic reactivity. The reason that concrete is strong is that cement — the ‘glue’ that holds it together — hardens when exposed to water. So, if we replace this glue, we need to make sure the substitute reacts similarly,” explains Mahjoubi. “Second, there is pozzolanicity. This is when a material reacts with calcium hydroxide, a byproduct created when cement meets water, to make the concrete harder and stronger over time.  We need to balance the hydraulic and pozzolanic materials in the mix so the concrete performs at its best.”Analyzing scientific literature and over 1 million rock samples, the team used the framework to sort candidate materials into 19 types, ranging from biomass to mining byproducts to demolished construction materials. Mahjoubi and his team found that suitable materials were available globally — and, more impressively, many could be incorporated into concrete mixes just by grinding them. This means it’s possible to extract emissions and cost savings without much additional processing. “Some of the most interesting materials that could replace a portion of cement are ceramics,” notes Mahjoubi. “Old tiles, bricks, pottery — all these materials may have high reactivity. That’s something we’ve observed in ancient Roman concrete, where ceramics were added to help waterproof structures. I’ve had many interesting conversations on this with Professor Admir Masic, who leads a lot of the ancient concrete studies here at MIT.”The potential of everyday materials like ceramics and industrial materials like mine tailings is an example of how materials like concrete can help enable a circular economy. By identifying and repurposing materials that would otherwise end up in landfills, researchers and industry can help to give these materials a second life as part of our buildings and infrastructure.Looking ahead, the research team is planning to upgrade the framework to be capable of assessing even more materials, while experimentally validating some of the best candidates. “AI tools have gotten this research far in a short time, and we are excited to see how the latest developments in large language models enable the next steps,” says Professor Elsa Olivetti, senior author on the work and member of the MIT Department of Materials Science and Engineering. She serves as an MIT Climate Project mission director, a CSHub principal investigator, and the leader of the Olivetti Group.“Concrete is the backbone of the built environment,” says Randolph Kirchain, co-author and CSHub director. “By applying data science and AI tools to material design, we hope to support industry efforts to build more sustainably, without compromising on strength, safety, or durability.In addition to Mahjoubi, Olivetti, and Kirchain, co-authors on the work include MIT postdoc Vineeth Venugopal, Ipek Bensu Manav SM ’21, PhD ’24; and CSHub Deputy Director Hessam AzariJafari. More

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      Workshop explores new advanced materials for a growing world

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      It is clear that humankind needs increasingly more resources, from computing power to steel and concrete, to meet the growing demands associated with data centers, infrastructure, and other mainstays of society. New, cost-effective approaches for producing the advanced materials key to that growth were the focus of a two-day workshop at MIT on March 11 and 12.A theme throughout the event was the importance of collaboration between and within universities and industries. The goal is to “develop concepts that everybody can use together, instead of everybody doing something different and then trying to sort it out later at great cost,” said Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering at MIT.The workshop was produced by MIT’s Materials Research Laboratory (MRL), which has an industry collegium, and MIT’s Industrial Liaison Program. The program included an address by Javier Sanfelix, lead of the Advanced Materials Team for the European Union. Sanfelix gave an overview of the EU’s strategy to developing advanced materials, which he said are “key enablers of the green and digital transition for European industry.”That strategy has already led to several initiatives. These include a material commons, or shared digital infrastructure for the design and development of advanced materials, and an advanced materials academy for educating new innovators and designers. Sanfelix also described an Advanced Materials Act for 2026 that aims to put in place a legislative framework that supports the entire innovation cycle.Sanfelix was visiting MIT to learn more about how the Institute is approaching the future of advanced materials. “We see MIT as a leader worldwide in technology, especially on materials, and there is a lot to learn about [your] industry collaborations and technology transfer with industry,” he said.Innovations in steel and concreteThe workshop began with talks about innovations involving two of the most common human-made materials in the world: steel and cement. We’ll need more of both but must reckon with the huge amounts of energy required to produce them and their impact on the environment due to greenhouse-gas emissions during that production.One way to address our need for more steel is to reuse what we have, said C. Cem Tasan, the POSCO Associate Professor of Metallurgy in the Department of Materials Science and Engineering (DMSE) and director of the Materials Research Laboratory.But most of the existing approaches to recycling scrap steel involve melting the metal. “And whenever you are dealing with molten metal, everything goes up, from energy use to carbon-dioxide emissions. Life is more difficult,” Tasan said.The question he and his team asked is whether they could reuse scrap steel without melting it. Could they consolidate solid scraps, then roll them together using existing equipment to create new sheet metal? From the materials-science perspective, Tasan said, that shouldn’t work, for several reasons.But it does. “We’ve demonstrated the potential in two papers and two patent applications already,” he said. Tasan noted that the approach focuses on high-quality manufacturing scrap. “This is not junkyard scrap,” he said.Tasan went on to explain how and why the new process works from a materials-science perspective, then gave examples of how the recycled steel could be used. “My favorite example is the stainless-steel countertops in restaurants. Do you really need the mechanical performance of stainless steel there?” You could use the recycled steel instead.Hessam Azarijafari addressed another common, indispensable material: concrete. This year marks the 16th anniversary of the MIT Concrete Sustainability Hub (CSHub), which began when a set of industry leaders and politicians reached out to MIT to learn more about the benefits and environmental impacts of concrete.The hub’s work now centers around three main themes: working toward a carbon-neutral concrete industry; the development of a sustainable infrastructure, with a focus on pavement; and how to make our cities more resilient to natural hazards through investment in stronger, cooler construction.Azarijafari, the deputy director of the CSHub, went on to give several examples of research results that have come out of the CSHub. These include many models to identify different pathways to decarbonize the cement and concrete sector. Other work involves pavements, which the general public thinks of as inert, Azarijafari said. “But we have [created] a state-of-the-art model that can assess interactions between pavement and vehicles.” It turns out that pavement surface characteristics and structural performance “can influence excess fuel consumption by inducing an additional rolling resistance.”Azarijafari emphasized  the importance of working closely with policymakers and industry. That engagement is key “to sharing the lessons that we have learned so far.”Toward a resource-efficient microchip industryConsider the following: In 2020 the number of cell phones, GPS units, and other devices connected to the “cloud,” or large data centers, exceeded 50 billion. And data-center traffic in turn is scaling by 1,000 times every 10 years.But all of that computation takes energy. And “all of it has to happen at a constant cost of energy, because the gross domestic product isn’t changing at that rate,” said Kimerling. The solution is to either produce much more energy, or make information technology much more energy-efficient. Several speakers at the workshop focused on the materials and components behind the latter.Key to everything they discussed: adding photonics, or using light to carry information, to the well-established electronics behind today’s microchips. “The bottom line is that integrating photonics with electronics in the same package is the transistor for the 21st century. If we can’t figure out how to do that, then we’re not going to be able to scale forward,” said Kimerling, who is director of the MIT Microphotonics Center.MIT has long been a leader in the integration of photonics with electronics. For example, Kimerling described the Integrated Photonics System Roadmap – International (IPSR-I), a global network of more than 400 industrial and R&D partners working together to define and create photonic integrated circuit technology. IPSR-I is led by the MIT Microphotonics Center and PhotonDelta. Kimerling began the organization in 1997.Last year IPSR-I released its latest roadmap for photonics-electronics integration, “which  outlines a clear way forward and specifies an innovative learning curve for scaling performance and applications for the next 15 years,” Kimerling said.Another major MIT program focused on the future of the microchip industry is FUTUR-IC, a new global alliance for sustainable microchip manufacturing. Begun last year, FUTUR-IC is funded by the National Science Foundation.“Our goal is to build a resource-efficient microchip industry value chain,” said Anuradha Murthy Agarwal, a principal research scientist at the MRL and leader of FUTUR-IC. That includes all of the elements that go into manufacturing future microchips, including workforce education and techniques to mitigate potential environmental effects.FUTUR-IC is also focused on electronic-photonic integration. “My mantra is to use electronics for computation, [and] shift to photonics for communication to bring this energy crisis in control,” Agarwal said.But integrating electronic chips with photonic chips is not easy. To that end, Agarwal described some of the challenges involved. For example, currently it is difficult to connect the optical fibers carrying communications to a microchip. That’s because the alignment between the two must be almost perfect or the light will disperse. And the dimensions involved are minuscule. An optical fiber has a diameter of only millionths of a meter. As a result, today each connection must be actively tested with a laser to ensure that the light will come through.That said, Agarwal went on to describe a new coupler between the fiber and chip that could solve the problem and allow robots to passively assemble the chips (no laser needed). The work, which was conducted by researchers including MIT graduate student Drew Wenninger, Agarwal, and Kimerling, has been patented, and is reported in two papers. A second recent breakthrough in this area involving a printed micro-reflector was described by Juejun “JJ” Hu, John F. Elliott Professor of Materials Science and Engineering.FUTUR-IC is also leading educational efforts for training a future workforce, as well as techniques for detecting — and potentially destroying — the perfluroalkyls (PFAS, or “forever chemicals”) released during microchip manufacturing. FUTUR-IC educational efforts, including virtual reality and game-based learning, were described by Sajan Saini, education director for FUTUR-IC. PFAS detection and remediation were discussed by Aristide Gumyusenge, an assistant professor in DMSE, and Jesus Castro Esteban, a postdoc in the Department of Chemistry.Other presenters at the workshop included Antoine Allanore, the Heather N. Lechtman Professor of Materials Science and Engineering; Katrin Daehn, a postdoc in the Allanore lab; Xuanhe Zhao, the Uncas (1923) and Helen Whitaker Professor in the Department of Mechanical Engineering; Richard Otte, CEO of Promex; and Carl Thompson, the Stavros V. Salapatas Professor in Materials Science and Engineering. More

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      Taking the “training wheels” off clean energy

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      Renewable power sources have seen unprecedented levels of investment in recent years. But with political uncertainty clouding the future of subsidies for green energy, these technologies must begin to compete with fossil fuels on equal footing, said participants at the 2025 MIT Energy Conference.“What these technologies need less is training wheels, and more of a level playing field,” said Brian Deese, an MIT Institute Innovation Fellow, during a conference-opening keynote panel.The theme of the two-day conference, which is organized each year by MIT students, was “Breakthrough to deployment: Driving climate innovation to market.” Speakers largely expressed optimism about advancements in green technology, balanced by occasional notes of alarm about a rapidly changing regulatory and political environment.Deese defined what he called “the good, the bad, and the ugly” of the current energy landscape. The good: Clean energy investment in the United States hit an all-time high of $272 billion in 2024. The bad: Announcements of future investments have tailed off. And the ugly: Macro conditions are making it more difficult for utilities and private enterprise to build out the clean energy infrastructure needed to meet growing energy demands.“We need to build massive amounts of energy capacity in the United States,” Deese said. “And the three things that are the most allergic to building are high uncertainty, high interest rates, and high tariff rates. So that’s kind of ugly. But the question … is how, and in what ways, that underlying commercial momentum can drive through this period of uncertainty.”A shifting clean energy landscapeDuring a panel on artificial intelligence and growth in electricity demand, speakers said that the technology may serve as a catalyst for green energy breakthroughs, in addition to putting strain on existing infrastructure. “Google is committed to building digital infrastructure responsibly, and part of that means catalyzing the development of clean energy infrastructure that is not only meeting the AI need, but also benefiting the grid as a whole,” said Lucia Tian, head of clean energy and decarbonization technologies at Google.Across the two days, speakers emphasized that the cost-per-unit and scalability of clean energy technologies will ultimately determine their fate. But they also acknowledged the impact of public policy, as well as the need for government investment to tackle large-scale issues like grid modernization.Vanessa Chan, a former U.S. Department of Energy (DoE) official and current vice dean of innovation and entrepreneurship at the University of Pennsylvania School of Engineering and Applied Sciences, warned of the “knock-on” effects of the move to slash National Institutes of Health (NIH) funding for indirect research costs, for example. “In reality, what you’re doing is undercutting every single academic institution that does research across the nation,” she said.During a panel titled “No clean energy transition without transmission,” Maria Robinson, former director of the DoE’s Grid Deployment Office, said that ratepayers alone will likely not be able to fund the grid upgrades needed to meet growing power demand. “The amount of investment we’re going to need over the next couple of years is going to be significant,” she said. “That’s where the federal government is going to have to play a role.”David Cohen-Tanugi, a clean energy venture builder at MIT, noted that extreme weather events have changed the climate change conversation in recent years. “There was a narrative 10 years ago that said … if we start talking about resilience and adaptation to climate change, we’re kind of throwing in the towel or giving up,” he said. “I’ve noticed a very big shift in the investor narrative, the startup narrative, and more generally, the public consciousness. There’s a realization that the effects of climate change are already upon us.”“Everything on the table”The conference featured panels and keynote addresses on a range of emerging clean energy technologies, including hydrogen power, geothermal energy, and nuclear fusion, as well as a session on carbon capture.Alex Creely, a chief engineer at Commonwealth Fusion Systems, explained that fusion (the combining of small atoms into larger atoms, which is the same process that fuels stars) is safer and potentially more economical than traditional nuclear power. Fusion facilities, he said, can be powered down instantaneously, and companies like his are developing new, less-expensive magnet technology to contain the extreme heat produced by fusion reactors.By the early 2030s, Creely said, his company hopes to be operating 400-megawatt power plants that use only 50 kilograms of fuel per year. “If you can get fusion working, it turns energy into a manufacturing product, not a natural resource,” he said.Quinn Woodard Jr., senior director of power generation and surface facilities at geothermal energy supplier Fervo Energy, said his company is making the geothermal energy more economical through standardization, innovation, and economies of scale. Traditionally, he said, drilling is the largest cost in producing geothermal power. Fervo has “completely flipped the cost structure” with advances in drilling, Woodard said, and now the company is focused on bringing down its power plant costs.“We have to continuously be focused on cost, and achieving that is paramount for the success of the geothermal industry,” he said.One common theme across the conference: a number of approaches are making rapid advancements, but experts aren’t sure when — or, in some cases, if — each specific technology will reach a tipping point where it is capable of transforming energy markets.“I don’t want to get caught in a place where we often descend in this climate solution situation, where it’s either-or,” said Peter Ellis, global director of nature climate solutions at The Nature Conservancy. “We’re talking about the greatest challenge civilization has ever faced. We need everything on the table.”The road aheadSeveral speakers stressed the need for academia, industry, and government to collaborate in pursuit of climate and energy goals. Amy Luers, senior global director of sustainability for Microsoft, compared the challenge to the Apollo spaceflight program, and she said that academic institutions need to focus more on how to scale and spur investments in green energy.“The challenge is that academic institutions are not currently set up to be able to learn the how, in driving both bottom-up and top-down shifts over time,” Luers said. “If the world is going to succeed in our road to net zero, the mindset of academia needs to shift. And fortunately, it’s starting to.”During a panel called “From lab to grid: Scaling first-of-a-kind energy technologies,” Hannan Happi, CEO of renewable energy company Exowatt, stressed that electricity is ultimately a commodity. “Electrons are all the same,” he said. “The only thing [customers] care about with regards to electrons is that they are available when they need them, and that they’re very cheap.”Melissa Zhang, principal at Azimuth Capital Management, noted that energy infrastructure development cycles typically take at least five to 10 years — longer than a U.S. political cycle. However, she warned that green energy technologies are unlikely to receive significant support at the federal level in the near future. “If you’re in something that’s a little too dependent on subsidies … there is reason to be concerned over this administration,” she said.World Energy CEO Gene Gebolys, the moderator of the lab-to-grid panel, listed off a number of companies founded at MIT. “They all have one thing in common,” he said. “They all went from somebody’s idea, to a lab, to proof-of-concept, to scale. It’s not like any of this stuff ever ends. It’s an ongoing process.” More

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      Reducing carbon emissions from residential heating: A pathway forward

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      In the race to reduce climate-warming carbon emissions, the buildings sector is falling behind. While carbon dioxide (CO2) emissions in the U.S. electric power sector dropped by 34 percent between 2005 and 2021, emissions in the building sector declined by only 18 percent in that same time period. Moreover, in extremely cold locations, burning natural gas to heat houses can make up a substantial share of the emissions portfolio. Therefore, steps to electrify buildings in general, and residential heating in particular, are essential for decarbonizing the U.S. energy system.But that change will increase demand for electricity and decrease demand for natural gas. What will be the net impact of those two changes on carbon emissions and on the cost of decarbonizing? And how will the electric power and natural gas sectors handle the new challenges involved in their long-term planning for future operations and infrastructure investments?A new study by MIT researchers with support from the MIT Energy Initiative (MITEI) Future Energy Systems Center unravels the impacts of various levels of electrification of residential space heating on the joint power and natural gas systems. A specially devised modeling framework enabled them to estimate not only the added costs and emissions for the power sector to meet the new demand, but also any changes in costs and emissions that result for the natural gas sector.The analyses brought some surprising outcomes. For example, they show that — under certain conditions — switching 80 percent of homes to heating by electricity could cut carbon emissions and at the same time significantly reduce costs over the combined natural gas and electric power sectors relative to the case in which there is only modest switching. That outcome depends on two changes: Consumers must install high-efficiency heat pumps plus take steps to prevent heat losses from their homes, and planners in the power and the natural gas sectors must work together as they make long-term infrastructure and operations decisions. Based on their findings, the researchers stress the need for strong state, regional, and national policies that encourage and support the steps that homeowners and industry planners can take to help decarbonize today’s building sector.A two-part modeling approachTo analyze the impacts of electrification of residential heating on costs and emissions in the combined power and gas sectors, a team of MIT experts in building technology, power systems modeling, optimization techniques, and more developed a two-part modeling framework. Team members included Rahman Khorramfar, a senior postdoc in MITEI and the Laboratory for Information and Decision Systems (LIDS); Morgan Santoni-Colvin SM ’23, a former MITEI graduate research assistant, now an associate at Energy and Environmental Economics, Inc.; Saurabh Amin, a professor in the Department of Civil and Environmental Engineering and principal investigator in LIDS; Audun Botterud, a principal research scientist in LIDS; Leslie Norford, a professor in the Department of Architecture; and Dharik Mallapragada, a former MITEI principal research scientist, now an assistant professor at New York University, who led the project. They describe their new methods and findings in a paper published in the journal Cell Reports Sustainability on Feb. 6.The first model in the framework quantifies how various levels of electrification will change end-use demand for electricity and for natural gas, and the impacts of possible energy-saving measures that homeowners can take to help. “To perform that analysis, we built a ‘bottom-up’ model — meaning that it looks at electricity and gas consumption of individual buildings and then aggregates their consumption to get an overall demand for power and for gas,” explains Khorramfar. By assuming a wide range of building “archetypes” — that is, groupings of buildings with similar physical characteristics and properties — coupled with trends in population growth, the team could explore how demand for electricity and for natural gas would change under each of five assumed electrification pathways: “business as usual” with modest electrification, medium electrification (about 60 percent of homes are electrified), high electrification (about 80 percent of homes make the change), and medium and high electrification with “envelope improvements,” such as sealing up heat leaks and adding insulation.The second part of the framework consists of a model that takes the demand results from the first model as inputs and “co-optimizes” the overall electricity and natural gas system to minimize annual investment and operating costs while adhering to any constraints, such as limits on emissions or on resource availability. The modeling framework thus enables the researchers to explore the impact of each electrification pathway on the infrastructure and operating costs of the two interacting sectors.The New England case study: A challenge for electrificationAs a case study, the researchers chose New England, a region where the weather is sometimes extremely cold and where burning natural gas to heat houses contributes significantly to overall emissions. “Critics will say that electrification is never going to happen [in New England]. It’s just too expensive,” comments Santoni-Colvin. But he notes that most studies focus on the electricity sector in isolation. The new framework considers the joint operation of the two sectors and then quantifies their respective costs and emissions. “We know that electrification will require large investments in the electricity infrastructure,” says Santoni-Colvin. “But what hasn’t been well quantified in the literature is the savings that we generate on the natural gas side by doing that — so, the system-level savings.”Using their framework, the MIT team performed model runs aimed at an 80 percent reduction in building-sector emissions relative to 1990 levels — a target consistent with regional policy goals for 2050. The researchers defined parameters including details about building archetypes, the regional electric power system, existing and potential renewable generating systems, battery storage, availability of natural gas, and other key factors describing New England.They then performed analyses assuming various scenarios with different mixes of home improvements. While most studies assume typical weather, they instead developed 20 projections of annual weather data based on historical weather patterns and adjusted for the effects of climate change through 2050. They then analyzed their five levels of electrification.Relative to business-as-usual projections, results from the framework showed that high electrification of residential heating could more than double the demand for electricity during peak periods and increase overall electricity demand by close to 60 percent. Assuming that building-envelope improvements are deployed in parallel with electrification reduces the magnitude and weather sensitivity of peak loads and creates overall efficiency gains that reduce the combined demand for electricity plus natural gas for home heating by up to 30 percent relative to the present day. Notably, a combination of high electrification and envelope improvements resulted in the lowest average cost for the overall electric power-natural gas system in 2050.Lessons learnedReplacing existing natural gas-burning furnaces and boilers with heat pumps reduces overall energy consumption. Santoni-Colvin calls it “something of an intuitive result” that could be expected because heat pumps are “just that much more efficient than old, fossil fuel-burning systems. But even so, we were surprised by the gains.”Other unexpected results include the importance of homeowners making more traditional energy efficiency improvements, such as adding insulation and sealing air leaks — steps supported by recent rebate policies. Those changes are critical to reducing costs that would otherwise be incurred for upgrading the electricity grid to accommodate the increased demand. “You can’t just go wild dropping heat pumps into everybody’s houses if you’re not also considering other ways to reduce peak loads. So it really requires an ‘all of the above’ approach to get to the most cost-effective outcome,” says Santoni-Colvin.Testing a range of weather outcomes also provided important insights. Demand for heating fuel is very weather-dependent, yet most studies are based on a limited set of weather data — often a “typical year.” The researchers found that electrification can lead to extended peak electric load events that can last for a few days during cold winters. Accordingly, the researchers conclude that there will be a continuing need for a “firm, dispatchable” source of electricity; that is, a power-generating system that can be relied on to produce power any time it’s needed — unlike solar and wind systems. As examples, they modeled some possible technologies, including power plants fired by a low-carbon fuel or by natural gas equipped with carbon capture equipment. But they point out that there’s no way of knowing what types of firm generators will be available in 2050. It could be a system that’s not yet mature, or perhaps doesn’t even exist today.In presenting their findings, the researchers note several caveats. For one thing, their analyses don’t include the estimated cost to homeowners of installing heat pumps. While that cost is widely discussed and debated, that issue is outside the scope of their current project.In addition, the study doesn’t specify what happens to existing natural gas pipelines. “Some homes are going to electrify and get off the gas system and not have to pay for it, leaving other homes with increasing rates because the gas system cost now has to be divided among fewer customers,” says Khorramfar. “That will inevitably raise equity questions that need to be addressed by policymakers.”Finally, the researchers note that policies are needed to drive residential electrification. Current financial support for installation of heat pumps and steps to make homes more thermally efficient are a good start. But such incentives must be coupled with a new approach to planning energy infrastructure investments. Traditionally, electric power planning and natural gas planning are performed separately. However, to decarbonize residential heating, the two sectors should coordinate when planning future operations and infrastructure needs. Results from the MIT analysis indicate that such cooperation could significantly reduce both emissions and costs for residential heating — a change that would yield a much-needed step toward decarbonizing the buildings sector as a whole. More