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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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      Confronting the AI/energy conundrum

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      The explosive growth of AI-powered computing centers is creating an unprecedented surge in electricity demand that threatens to overwhelm power grids and derail climate goals. At the same time, artificial intelligence technologies could revolutionize energy systems, accelerating the transition to clean power.“We’re at a cusp of potentially gigantic change throughout the economy,” said William H. Green, director of the MIT Energy Initiative (MITEI) and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, at MITEI’s Spring Symposium, “AI and energy: Peril and promise,” held on May 13. The event brought together experts from industry, academia, and government to explore solutions to what Green described as both “local problems with electric supply and meeting our clean energy targets” while seeking to “reap the benefits of AI without some of the harms.” The challenge of data center energy demand and potential benefits of AI to the energy transition is a research priority for MITEI.AI’s startling energy demandsFrom the start, the symposium highlighted sobering statistics about AI’s appetite for electricity. After decades of flat electricity demand in the United States, computing centers now consume approximately 4 percent of the nation’s electricity. Although there is great uncertainty, some projections suggest this demand could rise to 12-15 percent by 2030, largely driven by artificial intelligence applications.Vijay Gadepally, senior scientist at MIT’s Lincoln Laboratory, emphasized the scale of AI’s consumption. “The power required for sustaining some of these large models is doubling almost every three months,” he noted. “A single ChatGPT conversation uses as much electricity as charging your phone, and generating an image consumes about a bottle of water for cooling.”Facilities requiring 50 to 100 megawatts of power are emerging rapidly across the United States and globally, driven both by casual and institutional research needs relying on large language programs such as ChatGPT and Gemini. Gadepally cited congressional testimony by Sam Altman, CEO of OpenAI, highlighting how fundamental this relationship has become: “The cost of intelligence, the cost of AI, will converge to the cost of energy.”“The energy demands of AI are a significant challenge, but we also have an opportunity to harness these vast computational capabilities to contribute to climate change solutions,” said Evelyn Wang, MIT vice president for energy and climate and the former director at the Advanced Research Projects Agency-Energy (ARPA-E) at the U.S. Department of Energy.Wang also noted that innovations developed for AI and data centers — such as efficiency, cooling technologies, and clean-power solutions — could have broad applications beyond computing facilities themselves.Strategies for clean energy solutionsThe symposium explored multiple pathways to address the AI-energy challenge. Some panelists presented models suggesting that while artificial intelligence may increase emissions in the short term, its optimization capabilities could enable substantial emissions reductions after 2030 through more efficient power systems and accelerated clean technology development.Research shows regional variations in the cost of powering computing centers with clean electricity, according to Emre Gençer, co-founder and CEO of Sesame Sustainability and former MITEI principal research scientist. Gençer’s analysis revealed that the central United States offers considerably lower costs due to complementary solar and wind resources. However, achieving zero-emission power would require massive battery deployments — five to 10 times more than moderate carbon scenarios — driving costs two to three times higher.“If we want to do zero emissions with reliable power, we need technologies other than renewables and batteries, which will be too expensive,” Gençer said. He pointed to “long-duration storage technologies, small modular reactors, geothermal, or hybrid approaches” as necessary complements.Because of data center energy demand, there is renewed interest in nuclear power, noted Kathryn Biegel, manager of R&D and corporate strategy at Constellation Energy, adding that her company is restarting the reactor at the former Three Mile Island site, now called the “Crane Clean Energy Center,” to meet this demand. “The data center space has become a major, major priority for Constellation,” she said, emphasizing how their needs for both reliability and carbon-free electricity are reshaping the power industry.Can AI accelerate the energy transition?Artificial intelligence could dramatically improve power systems, according to Priya Donti, assistant professor and the Silverman Family Career Development Professor in MIT’s Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision Systems. She showcased how AI can accelerate power grid optimization by embedding physics-based constraints into neural networks, potentially solving complex power flow problems at “10 times, or even greater, speed compared to your traditional models.”AI is already reducing carbon emissions, according to examples shared by Antonia Gawel, global director of sustainability and partnerships at Google. Google Maps’ fuel-efficient routing feature has “helped to prevent more than 2.9 million metric tons of GHG [greenhouse gas] emissions reductions since launch, which is the equivalent of taking 650,000 fuel-based cars off the road for a year,” she said. Another Google research project uses artificial intelligence to help pilots avoid creating contrails, which represent about 1 percent of global warming impact.AI’s potential to speed materials discovery for power applications was highlighted by Rafael Gómez-Bombarelli, the Paul M. Cook Career Development Associate Professor in the MIT Department of Materials Science and Engineering. “AI-supervised models can be trained to go from structure to property,” he noted, enabling the development of materials crucial for both computing and efficiency.Securing growth with sustainabilityThroughout the symposium, participants grappled with balancing rapid AI deployment against environmental impacts. While AI training receives most attention, Dustin Demetriou, senior technical staff member in sustainability and data center innovation at IBM, quoted a World Economic Forum article that suggested that “80 percent of the environmental footprint is estimated to be due to inferencing.” Demetriou emphasized the need for efficiency across all artificial intelligence applications.Jevons’ paradox, where “efficiency gains tend to increase overall resource consumption rather than decrease it” is another factor to consider, cautioned Emma Strubell, the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Strubell advocated for viewing computing center electricity as a limited resource requiring thoughtful allocation across different applications.Several presenters discussed novel approaches for integrating renewable sources with existing grid infrastructure, including potential hybrid solutions that combine clean installations with existing natural gas plants that have valuable grid connections already in place. These approaches could provide substantial clean capacity across the United States at reasonable costs while minimizing reliability impacts.Navigating the AI-energy paradoxThe symposium highlighted MIT’s central role in developing solutions to the AI-electricity challenge.Green spoke of a new MITEI program on computing centers, power, and computation that will operate alongside the comprehensive spread of MIT Climate Project research. “We’re going to try to tackle a very complicated problem all the way from the power sources through the actual algorithms that deliver value to the customers — in a way that’s going to be acceptable to all the stakeholders and really meet all the needs,” Green said.Participants in the symposium were polled about priorities for MIT’s research by Randall Field, MITEI director of research. The real-time results ranked “data center and grid integration issues” as the top priority, followed by “AI for accelerated discovery of advanced materials for energy.”In addition, attendees revealed that most view AI’s potential regarding power as a “promise,” rather than a “peril,” although a considerable portion remain uncertain about the ultimate impact. When asked about priorities in power supply for computing facilities, half of the respondents selected carbon intensity as their top concern, with reliability and cost following. More

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      Decarbonizing steel is as tough as steel

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      The long-term aspirational goal of the Paris Agreement on climate change is to cap global warming at 1.5 degrees Celsius above preindustrial levels, and thereby reduce the frequency and severity of floods, droughts, wildfires, and other extreme weather events. Achieving that goal will require a massive reduction in global carbon dioxide (CO2) emissions across all economic sectors. A major roadblock, however, could be the industrial sector, which accounts for roughly 25 percent of global energy- and process-related CO2 emissions — particularly within the iron and steel sector, industry’s largest emitter of CO2.Iron and steel production now relies heavily on fossil fuels (coal or natural gas) for heat, converting iron ore to iron, and making steel strong. Steelmaking could be decarbonized by a combination of several methods, including carbon capture technology, the use of low- or zero-carbon fuels, and increased use of recycled steel. Now a new study in the Journal of Cleaner Production systematically explores the viability of different iron-and-steel decarbonization strategies.Today’s strategy menu includes improving energy efficiency, switching fuels and technologies, using more scrap steel, and reducing demand. Using the MIT Economic Projection and Policy Analysis model, a multi-sector, multi-region model of the world economy, researchers at MIT, the University of Illinois at Urbana-Champaign, and ExxonMobil Technology and Engineering Co. evaluate the decarbonization potential of replacing coal-based production processes with electric arc furnaces (EAF), along with either scrap steel or “direct reduced iron” (DRI), which is fueled by natural gas with carbon capture and storage (NG CCS DRI-EAF) or by hydrogen (H2 DRI-EAF).Under a global climate mitigation scenario aligned with the 1.5 C climate goal, these advanced steelmaking technologies could result in deep decarbonization of the iron and steel sector by 2050, as long as technology costs are low enough to enable large-scale deployment. Higher costs would favor the replacement of coal with electricity and natural gas, greater use of scrap steel, and reduced demand, resulting in a more-than-50-percent reduction in emissions relative to current levels. Lower technology costs would enable massive deployment of NG CCS DRI-EAF or H2 DRI-EAF, reducing emissions by up to 75 percent.Even without adoption of these advanced technologies, the iron-and-steel sector could significantly reduce its CO2 emissions intensity (how much CO2 is released per unit of production) with existing steelmaking technologies, primarily by replacing coal with gas and electricity (especially if it is generated by renewable energy sources), using more scrap steel, and implementing energy efficiency measures.“The iron and steel industry needs to combine several strategies to substantially reduce its emissions by mid-century, including an increase in recycling, but investing in cost reductions in hydrogen pathways and carbon capture and sequestration will enable even deeper emissions mitigation in the sector,” says study supervising author Sergey Paltsev, deputy director of the MIT Center for Sustainability Science and Strategy (MIT CS3) and a senior research scientist at the MIT Energy Initiative (MITEI).This study was supported by MIT CS3 and ExxonMobil through its membership in MITEI. More

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      Recovering from the past and transitioning to a better energy future

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      As the frequency and severity of extreme weather events grow, it may become increasingly necessary to employ a bolder approach to climate change, warned Emily A. Carter, the Gerhard R. Andlinger Professor in Energy and the Environment at Princeton University. Carter made her case for why the energy transition is no longer enough in the face of climate change while speaking at the MIT Energy Initiative (MITEI) Presents: Advancing the Energy Transition seminar on the MIT campus.“If all we do is take care of what we did in the past — but we don’t change what we do in the future — then we’re still going to be left with very serious problems,” she said. Our approach to climate change mitigation must comprise transformation, intervention, and adaption strategies, said Carter. Transitioning to a decarbonized electricity system is one piece of the puzzle. Growing amounts of solar and wind energy — along with nuclear, hydropower, and geothermal — are slowly transforming the energy electricity landscape, but Carter noted that there are new technologies farther down the pipeline.  “Advanced geothermal may come on in the next couple of decades. Fusion will only really start to play a role later in the century, but could provide firm electricity such that we can start to decommission nuclear,” said Carter, who is also a senior strategic advisor and associate laboratory director at the Department of Energy’s Princeton Plasma Physics Laboratory. Taking this a step further, Carter outlined how this carbon-free electricity should then be used to electrify everything we can. She highlighted the industrial sector as a critical area for transformation: “The energy transition is about transitioning off of fossil fuels. If you look at the manufacturing industries, they are driven by fossil fuels right now. They are driven by fossil fuel-driven thermal processes.” Carter noted that thermal energy is much less efficient than electricity and highlighted electricity-driven strategies that could replace heat in manufacturing, such as electrolysis, plasmas, light-emitting diodes (LEDs) for photocatalysis, and joule heating. The transportation sector is also a key area for electrification, Carter said. While electric vehicles have become increasingly common in recent years, heavy-duty transportation is not as easily electrified. The solution? “Carbon-neutral fuels for heavy-duty aviation and shipping,” she said, emphasizing that these fuels will need to become part of the circular economy. “We know that when we burn those fuels, they’re going to produce CO2 [carbon dioxide] again. They need to come from a source of CO2 that is not fossil-based.” The next step is intervention in the form of carbon dioxide removal, which then necessitates methods of storage and utilization, according to Carter. “There’s a lot of talk about building large numbers of pipelines to capture the CO2 — from fossil fuel-driven power plants, cement plants, steel plants, all sorts of industrial places that emit CO2 — and then piping it and storing it in underground aquifers,” she explained. Offshore pipelines are much more expensive than those on land, but can mitigate public concerns over their safety. Europe is exclusively focusing their efforts offshore for this very reason, and the same could be true for the United States, Carter said.  Once carbon dioxide is captured, commercial utilization may provide economic leverage to accelerate sequestration, even if only a few gigatons are used per year, Carter noted. Through mineralization, CO2 can be converted into carbonates, which could be used in building materials such as concrete and road-paving materials.  There is another form of intervention that Carter currently views as a last resort: solar geoengineering, sometimes known as solar radiation management or SRM. In 1991, Mount Pinatubo in the Philippines erupted and released sulfur dioxide into the stratosphere, which caused a temporary cooling of the Earth by approximately 0.5 degree Celsius for over a year. SRM seeks to recreate that cooling effect by injecting particles into the atmosphere that reflect sunlight. According to Carter, there are three main strategies: stratospheric aerosol injection, cirrus cloud thinning (thinning clouds to let more infrared radiation emitted by the earth escape to space), and marine cloud brightening (brightening clouds with sea salt so they reflect more light).  “My view is, I hope we don’t ever have to do it, but I sure think we should understand what would happen in case somebody else just decides to do it. It’s a global security issue,” said Carter. “In principle, it’s not so difficult technologically, so we’d like to really understand and to be able to predict what would happen if that happened.” With any technology, stakeholder and community engagement is essential for deployment, Carter said. She emphasized the importance of both respectfully listening to concerns and thoroughly addressing them, stating, “Hopefully, there’s enough information given to assuage their fears. We have to gain the trust of people before any deployment can be considered.” A crucial component of this trust starts with the responsibility of the scientific community to be transparent and critique each other’s work, Carter said. “Skepticism is good. You should have to prove your proof of principle.” MITEI Presents: Advancing the Energy Transition is an MIT Energy Initiative speaker series highlighting energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. The series will continue in fall 2025. For more information on this and additional events, visit the MITEI website. More

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      Study helps pinpoint areas where microplastics will accumulate

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      The accumulation of microplastics in the environment, and within our bodies, is an increasingly worrisome issue. But predicting where these ubiquitous particles will accumulate, and therefore where remediation efforts should be focused, has been difficult because of the many factors that contribute to their dispersal and deposition.New research from MIT shows that one key factor in determining where microparticles are likely to build up has to do with the presence of biofilms. These thin, sticky biopolymer layers are shed by microorganisms and can accumulate on surfaces, including along sandy riverbeds or seashores. The study found that, all other conditions being equal, microparticles are less likely to accumulate in sediment infused with biofilms, because if they land there, they are more likely to be resuspended by flowing water and carried away.The open-access findings appear in the journal Geophysical Research Letters, in a paper by MIT postdoc Hyoungchul Park and professor of civil and environmental engineering Heidi Nepf. “Microplastics are definitely in the news a lot,” Nepf says, “and we don’t fully understand where the hotspots of accumulation are likely to be. This work gives a little bit of guidance” on some of the factors that can cause these particles, and small particles in general, to accumulate in certain locations.Most experiments looking at the ways microparticles are transported and deposited have been conducted over bare sand, Park says. “But in nature, there are a lot of microorganisms, such as bacteria, fungi, and algae, and when they adhere to the stream bed they generate some sticky things.” These substances are known as extracellular polymeric substances, or EPS, and they “can significantly affect the channel bed characteristics,” he says. The new research focused on determining exactly how these substances affected the transport of microparticles, including microplastics.The research involved a flow tank with a bottom lined with fine sand, and sometimes with vertical plastic tubes simulating the presence of mangrove roots. In some experiments the bed consisted of pure sand, and in others the sand was mixed with a biological material to simulate the natural biofilms found in many riverbed and seashore environments.Water mixed with tiny plastic particles was pumped through the tank for three hours, and then the bed surface was photographed under ultraviolet light that caused the plastic particles to fluoresce, allowing a quantitative measurement of their concentration.The results revealed two different phenomena that affected how much of the plastic accumulated on the different surfaces. Immediately around the rods that stood in for above-ground roots, turbulence prevented particle deposition. In addition, as the amount of simulated biofilms in the sediment bed increased, the accumulation of particles also decreased.Nepf and Park concluded that the biofilms filled up the spaces between the sand grains, leaving less room for the microparticles to fit in. The particles were more exposed because they penetrated less deeply in between the sand grains, and as a result they were much more easily resuspended and carried away by the flowing water.“These biological films fill the pore spaces between the sediment grains,” Park explains, “and that makes the deposited particles — the particles that land on the bed — more exposed to the forces generated by the flow, which makes it easier for them to be resuspended. What we found was that in a channel with the same flow conditions and the same vegetation and the same sand bed, if one is without EPS and one is with EPS, then the one without EPS has a much higher deposition rate than the one with EPS.”Nepf adds: “The biofilm is blocking the plastics from accumulating in the bed because they can’t go deep into the bed. They just stay right on the surface, and then they get picked up and moved elsewhere. So, if I spilled a large amount of microplastic in two rivers, and one had a sandy or gravel bottom, and one was muddier with more biofilm, I would expect more of the microplastics to be retained in the sandy or gravelly river.”All of this is complicated by other factors, such as the turbulence of the water or the roughness of the bottom surface, she says. But it provides a “nice lens” to provide some suggestions for people who are trying to study the impacts of microplastics in the field. “They’re trying to determine what kinds of habitats these plastics are in, and this gives a framework for how you might categorize those habitats,” she says. “It gives guidance to where you should go to find more plastics versus less.”As an example, Park suggests, in mangrove ecosystems, microplastics may preferentially accumulate in the outer edges, which tend to be sandy, while the interior zones have sediment with more biofilm. Thus, this work suggests “the sandy outer regions may be potential hotspots for microplastic accumulation,” he says, and can make this a priority zone for monitoring and protection.“This is a highly relevant finding,” says Isabella Schalko, a research scientist at ETH Zurich, who was not associated with this research. “It suggests that restoration measures such as re-vegetation or promoting biofilm growth could help mitigate microplastic accumulation in aquatic systems. It highlights the powerful role of biological and physical features in shaping particle transport processes.”The work was supported by Shell International Exploration and Production through the MIT Energy Initiative. More

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      A new approach could fractionate crude oil using much less energy

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      Separating crude oil into products such as gasoline, diesel, and heating oil is an energy-intensive process that accounts for about 6 percent of the world’s CO2 emissions. Most of that energy goes into the heat needed to separate the components by their boiling point.In an advance that could dramatically reduce the amount of energy needed for crude oil fractionation, MIT engineers have developed a membrane that filters the components of crude oil by their molecular size.“This is a whole new way of envisioning a separation process. Instead of boiling mixtures to purify them, why not separate components based on shape and size? The key innovation is that the filters we developed can separate very small molecules at an atomistic length scale,” says Zachary P. Smith, an associate professor of chemical engineering at MIT and the senior author of the new study.The new filtration membrane can efficiently separate heavy and light components from oil, and it is resistant to the swelling that tends to occur with other types of oil separation membranes. The membrane is a thin film that can be manufactured using a technique that is already widely used in industrial processes, potentially allowing it to be scaled up for widespread use.Taehoon Lee, a former MIT postdoc who is now an assistant professor at Sungkyunkwan University in South Korea, is the lead author of the paper, which appears today in Science.Oil fractionationConventional heat-driven processes for fractionating crude oil make up about 1 percent of global energy use, and it has been estimated that using membranes for crude oil separation could reduce the amount of energy needed by about 90 percent. For this to succeed, a separation membrane needs to allow hydrocarbons to pass through quickly, and to selectively filter compounds of different sizes.Until now, most efforts to develop a filtration membrane for hydrocarbons have focused on polymers of intrinsic microporosity (PIMs), including one known as PIM-1. Although this porous material allows the fast transport of hydrocarbons, it tends to excessively absorb some of the organic compounds as they pass through the membrane, leading the film to swell, which impairs its size-sieving ability.To come up with a better alternative, the MIT team decided to try modifying polymers that are used for reverse osmosis water desalination. Since their adoption in the 1970s, reverse osmosis membranes have reduced the energy consumption of desalination by about 90 percent — a remarkable industrial success story.The most commonly used membrane for water desalination is a polyamide that is manufactured using a method known as interfacial polymerization. During this process, a thin polymer film forms at the interface between water and an organic solvent such as hexane. Water and hexane do not normally mix, but at the interface between them, a small amount of the compounds dissolved in them can react with each other.In this case, a hydrophilic monomer called MPD, which is dissolved in water, reacts with a hydrophobic monomer called TMC, which is dissolved in hexane. The two monomers are joined together by a connection known as an amide bond, forming a polyamide thin film (named MPD-TMC) at the water-hexane interface.While highly effective for water desalination, MPD-TMC doesn’t have the right pore sizes and swelling resistance that would allow it to separate hydrocarbons.To adapt the material to separate the hydrocarbons found in crude oil, the researchers first modified the film by changing the bond that connects the monomers from an amide bond to an imine bond. This bond is more rigid and hydrophobic, which allows hydrocarbons to quickly move through the membrane without causing noticeable swelling of the film compared to the polyamide counterpart.“The polyimine material has porosity that forms at the interface, and because of the cross-linking chemistry that we have added in, you now have something that doesn’t swell,” Smith says. “You make it in the oil phase, react it at the water interface, and with the crosslinks, it’s now immobilized. And so those pores, even when they’re exposed to hydrocarbons, no longer swell like other materials.”The researchers also introduced a monomer called triptycene. This shape-persistent, molecularly selective molecule further helps the resultant polyimines to form pores that are the right size for hydrocarbons to fit through.This approach represents “an important step toward reducing industrial energy consumption,” says Andrew Livingston, a professor of chemical engineering at Queen Mary University of London, who was not involved in the study.“This work takes the workhorse technology of the membrane desalination industry, interfacial polymerization, and creates a new way to apply it to organic systems such as hydrocarbon feedstocks, which currently consume large chunks of global energy,” Livingston says. “The imaginative approach using an interfacial catalyst coupled to hydrophobic monomers leads to membranes with high permeance and excellent selectivity, and the work shows how these can be used in relevant separations.”Efficient separationWhen the researchers used the new membrane to filter a mixture of toluene and triisopropylbenzene (TIPB) as a benchmark for evaluating separation performance, it was able to achieve a concentration of toluene 20 times greater than its concentration in the original mixture. They also tested the membrane with an industrially relevant mixture consisting of naphtha, kerosene, and diesel, and found that it could efficiently separate the heavier and lighter compounds by their molecular size.If adapted for industrial use, a series of these filters could be used to generate a higher concentration of the desired products at each step, the researchers say.“You can imagine that with a membrane like this, you could have an initial stage that replaces a crude oil fractionation column. You could partition heavy and light molecules and then you could use different membranes in a cascade to purify complex mixtures to isolate the chemicals that you need,” Smith says.Interfacial polymerization is already widely used to create membranes for water desalination, and the researchers believe it should be possible to adapt those processes to mass produce the films they designed in this study.“The main advantage of interfacial polymerization is it’s already a well-established method to prepare membranes for water purification, so you can imagine just adopting these chemistries into existing scale of manufacturing lines,” Lee says.The research was funded, in part, by ExxonMobil through the MIT Energy Initiative.  More

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      How to solve a bottleneck for CO2 capture and conversion

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      Removing carbon dioxide from the atmosphere efficiently is often seen as a crucial need for combatting climate change, but systems for removing carbon dioxide suffer from a tradeoff. Chemical compounds that efficiently remove CO₂ from the air do not easily release it once captured, and compounds that release CO₂ efficiently are not very efficient at capturing it. Optimizing one part of the cycle tends to make the other part worse.Now, using nanoscale filtering membranes, researchers at MIT have added a simple intermediate step that facilitates both parts of the cycle. The new approach could improve the efficiency of electrochemical carbon dioxide capture and release by six times and cut costs by at least 20 percent, they say.The new findings are reported today in the journal ACS Energy Letters, in a paper by MIT doctoral students Simon Rufer, Tal Joseph, and Zara Aamer, and professor of mechanical engineering Kripa Varanasi.“We need to think about scale from the get-go when it comes to carbon capture, as making a meaningful impact requires processing gigatons of CO₂,” says Varanasi. “Having this mindset helps us pinpoint critical bottlenecks and design innovative solutions with real potential for impact. That’s the driving force behind our work.”Many carbon-capture systems work using chemicals called hydroxides, which readily combine with carbon dioxide to form carbonate. That carbonate is fed into an electrochemical cell, where the carbonate reacts with an acid to form water and release carbon dioxide. The process can take ordinary air with only about 400 parts per million of carbon dioxide and generate a stream of 100 percent pure carbon dioxide, which can then be used to make fuels or other products.Both the capture and release steps operate in the same water-based solution, but the first step needs a solution with a high concentration of hydroxide ions, and the second step needs one high in carbonate ions. “You can see how these two steps are at odds,” says Varanasi. “These two systems are circulating the same sorbent back and forth. They’re operating on the exact same liquid. But because they need two different types of liquids to operate optimally, it’s impossible to operate both systems at their most efficient points.”The team’s solution was to decouple the two parts of the system and introduce a third part in between. Essentially, after the hydroxide in the first step has been mostly chemically converted to carbonate, special nanofiltration membranes then separate ions in the solution based on their charge. Carbonate ions have a charge of 2, while hydroxide ions have a charge of 1. “The nanofiltration is able to separate these two pretty well,” Rufer says.Once separated, the hydroxide ions are fed back to the absorption side of the system, while the carbonates are sent ahead to the electrochemical release stage. That way, both ends of the system can operate at their more efficient ranges. Varanasi explains that in the electrochemical release step, protons are being added to the carbonate to cause the conversion to carbon dioxide and water, but if hydroxide ions are also present, the protons will react with those ions instead, producing just water.“If you don’t separate these hydroxides and carbonates,” Rufer says, “the way the system fails is you’ll add protons to hydroxide instead of carbonate, and so you’ll just be making water rather than extracting carbon dioxide. That’s where the efficiency is lost. Using nanofiltration to prevent this was something that we aren’t aware of anyone proposing before.”Testing showed that the nanofiltration could separate the carbonate from the hydroxide solution with about 95 percent efficiency, validating the concept under realistic conditions, Rufer says. The next step was to assess how much of an effect this would have on the overall efficiency and economics of the process. They created a techno-economic model, incorporating electrochemical efficiency, voltage, absorption rate, capital costs, nanofiltration efficiency, and other factors.The analysis showed that present systems cost at least $600 per ton of carbon dioxide captured, while with the nanofiltration component added, that drops to about $450 a ton. What’s more, the new system is much more stable, continuing to operate at high efficiency even under variations in the ion concentrations in the solution. “In the old system without nanofiltration, you’re sort of operating on a knife’s edge,” Rufer says; if the concentration varies even slightly in one direction or the other, efficiency drops off drastically. “But with our nanofiltration system, it kind of acts as a buffer where it becomes a lot more forgiving. You have a much broader operational regime, and you can achieve significantly lower costs.”He adds that this approach could apply not only to the direct air capture systems they studied specifically, but also to point-source systems — which are attached directly to the emissions sources such as power plant emissions — or to the next stage of the process, converting captured carbon dioxide into useful products such as fuel or chemical feedstocks.  Those conversion processes, he says, “are also bottlenecked in this carbonate and hydroxide tradeoff.”In addition, this technology could lead to safer alternative chemistries for carbon capture, Varanasi says. “A lot of these absorbents can at times be toxic, or damaging to the environment. By using a system like ours, you can improve the reaction rate, so you can choose chemistries that might not have the best absorption rate initially but can be improved to enable safety.”Varanasi adds that “the really nice thing about this is we’ve been able to do this with what’s commercially available,” and with a system that can easily be retrofitted to existing carbon-capture installations. If the costs can be further brought down to about $200 a ton, it could be viable for widespread adoption. With ongoing work, he says, “we’re confident that we’ll have something that can become economically viable” and that will ultimately produce valuable, saleable products.Rufer notes that even today, “people are buying carbon credits at a cost of over $500 per ton. So, at this cost we’re projecting, it is already commercially viable in that there are some buyers who are willing to pay that price.” But by bringing the price down further, that should increase the number of buyers who would consider buying the credit, he says. “It’s just a question of how widespread we can make it.” Recognizing this growing market demand, Varanasi says, “Our goal is to provide industry scalable, cost-effective, and reliable technologies and systems that enable them to directly meet their decarbonization targets.”The research was supported by Shell International Exploration and Production Inc. through the MIT Energy Initiative, and the U.S. National Science Foundation, and made use of the facilities at MIT.nano. More

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      How can India decarbonize its coal-dependent electric power system?

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      As the world struggles to reduce climate-warming carbon emissions, India has pledged to do its part, and its success is critical: In 2023, India was the third-largest carbon emitter worldwide. The Indian government has committed to having net-zero carbon emissions by 2070.To fulfill that promise, India will need to decarbonize its electric power system, and that will be a challenge: Fully 60 percent of India’s electricity comes from coal-burning power plants that are extremely inefficient. To make matters worse, the demand for electricity in India is projected to more than double in the coming decade due to population growth and increased use of air conditioning, electric cars, and so on.Despite having set an ambitious target, the Indian government has not proposed a plan for getting there. Indeed, as in other countries, in India the government continues to permit new coal-fired power plants to be built, and aging plants to be renovated and their retirement postponed.To help India define an effective — and realistic — plan for decarbonizing its power system, key questions must be addressed. For example, India is already rapidly developing carbon-free solar and wind power generators. What opportunities remain for further deployment of renewable generation? Are there ways to retrofit or repurpose India’s existing coal plants that can substantially and affordably reduce their greenhouse gas emissions? And do the responses to those questions differ by region?With funding from IHI Corp. through the MIT Energy Initiative (MITEI), Yifu Ding, a postdoc at MITEI, and her colleagues set out to answer those questions by first using machine learning to determine the efficiency of each of India’s current 806 coal plants, and then investigating the impacts that different decarbonization approaches would have on the mix of power plants and the price of electricity in 2035 under increasingly stringent caps on emissions.First step: Develop the needed datasetAn important challenge in developing a decarbonization plan for India has been the lack of a complete dataset describing the current power plants in India. While other studies have generated plans, they haven’t taken into account the wide variation in the coal-fired power plants in different regions of the country. “So, we first needed to create a dataset covering and characterizing all of the operating coal plants in India. Such a dataset was not available in the existing literature,” says Ding.Making a cost-effective plan for expanding the capacity of a power system requires knowing the efficiencies of all the power plants operating in the system. For this study, the researchers used as their metric the “station heat rate,” a standard measurement of the overall fuel efficiency of a given power plant. The station heat rate of each plant is needed in order to calculate the fuel consumption and power output of that plant as plans for capacity expansion are being developed.Some of the Indian coal plants’ efficiencies were recorded before 2022, so Ding and her team used machine-learning models to predict the efficiencies of all the Indian coal plants operating now. In 2024, they created and posted online the first comprehensive, open-sourced dataset for all 806 power plants in 30 regions of India. The work won the 2024 MIT Open Data Prize. This dataset includes each plant’s power capacity, efficiency, age, load factor (a measure indicating how much of the time it operates), water stress, and more.In addition, they categorized each plant according to its boiler design. A “supercritical” plant operates at a relatively high temperature and pressure, which makes it thermodynamically efficient, so it produces a lot of electricity for each unit of heat in the fuel. A “subcritical” plant runs at a lower temperature and pressure, so it’s less thermodynamically efficient. Most of the Indian coal plants are still subcritical plants running at low efficiency.Next step: Investigate decarbonization optionsEquipped with their detailed dataset covering all the coal power plants in India, the researchers were ready to investigate options for responding to tightening limits on carbon emissions. For that analysis, they turned to GenX, a modeling platform that was developed at MITEI to help guide decision-makers as they make investments and other plans for the future of their power systems.Ding built a GenX model based on India’s power system in 2020, including details about each power plant and transmission network across 30 regions of the country. She also entered the coal price, potential resources for wind and solar power installations, and other attributes of each region. Based on the parameters given, the GenX model would calculate the lowest-cost combination of equipment and operating conditions that can fulfill a defined future level of demand while also meeting specified policy constraints, including limits on carbon emissions. The model and all data sources were also released as open-source tools for all viewers to use.Ding and her colleagues — Dharik Mallapragada, a former principal research scientist at MITEI who is now an assistant professor of chemical and biomolecular energy at NYU Tandon School of Engineering and a MITEI visiting scientist; and Robert J. Stoner, the founding director of the MIT Tata Center for Technology and Design and former deputy director of MITEI for science and technology — then used the model to explore options for meeting demands in 2035 under progressively tighter carbon emissions caps, taking into account region-to-region variations in the efficiencies of the coal plants, the price of coal, and other factors. They describe their methods and their findings in a paper published in the journal Energy for Sustainable Development.In separate runs, they explored plans involving various combinations of current coal plants, possible new renewable plants, and more, to see their outcome in 2035. Specifically, they assumed the following four “grid-evolution scenarios:”Baseline: The baseline scenario assumes limited onshore wind and solar photovoltaics development and excludes retrofitting options, representing a business-as-usual pathway.High renewable capacity: This scenario calls for the development of onshore wind and solar power without any supply chain constraints.Biomass co-firing: This scenario assumes the baseline limits on renewables, but here all coal plants — both subcritical and supercritical — can be retrofitted for “co-firing” with biomass, an approach in which clean-burning biomass replaces some of the coal fuel. Certain coal power plants in India already co-fire coal and biomass, so the technology is known.Carbon capture and sequestration plus biomass co-firing: This scenario is based on the same assumptions as the biomass co-firing scenario with one addition: All of the high-efficiency supercritical plants are also retrofitted for carbon capture and sequestration (CCS), a technology that captures and removes carbon from a power plant’s exhaust stream and prepares it for permanent disposal. Thus far, CCS has not been used in India. This study specifies that 90 percent of all carbon in the power plant exhaust is captured.Ding and her team investigated power system planning under each of those grid-evolution scenarios and four assumptions about carbon caps: no cap, which is the current situation; 1,000 million tons (Mt) of carbon dioxide (CO2) emissions, which reflects India’s announced targets for 2035; and two more-ambitious targets, namely 800 Mt and 500 Mt. For context, CO2 emissions from India’s power sector totaled about 1,100 Mt in 2021. (Note that transmission network expansion is allowed in all scenarios.)Key findingsAssuming the adoption of carbon caps under the four scenarios generated a vast array of detailed numerical results. But taken together, the results show interesting trends in the cost-optimal mix of generating capacity and the cost of electricity under the different scenarios.Even without any limits on carbon emissions, most new capacity additions will be wind and solar generators — the lowest-cost option for expanding India’s electricity-generation capacity. Indeed, this is observed to be the case now in India. However, the increasing demand for electricity will still require some new coal plants to be built. Model results show a 10 to 20 percent increase in coal plant capacity by 2035 relative to 2020.Under the baseline scenario, renewables are expanded up to the maximum allowed under the assumptions, implying that more deployment would be economical. More coal capacity is built, and as the cap on emissions tightens, there is also investment in natural gas power plants, as well as batteries to help compensate for the now-large amount of intermittent solar and wind generation. When a 500 Mt cap on carbon is imposed, the cost of electricity generation is twice as high as it was with no cap.The high renewable capacity scenario reduces the development of new coal capacity and produces the lowest electricity cost of the four scenarios. Under the most stringent cap — 500 Mt — onshore wind farms play an important role in bringing the cost down. “Otherwise, it’ll be very expensive to reach such stringent carbon constraints,” notes Ding. “Certain coal plants that remain run only a few hours per year, so are inefficient as well as financially unviable. But they still need to be there to support wind and solar.” She explains that other backup sources of electricity, such as batteries, are even more costly. The biomass co-firing scenario assumes the same capacity limit on renewables as in the baseline scenario, and the results are much the same, in part because the biomass replaces such a low fraction — just 20 percent — of the coal in the fuel feedstock. “This scenario would be most similar to the current situation in India,” says Ding. “It won’t bring down the cost of electricity, so we’re basically saying that adding this technology doesn’t contribute effectively to decarbonization.”But CCS plus biomass co-firing is a different story. It also assumes the limits on renewables development, yet it is the second-best option in terms of reducing costs. Under the 500 Mt cap on CO2 emissions, retrofitting for both CCS and biomass co-firing produces a 22 percent reduction in the cost of electricity compared to the baseline scenario. In addition, as the carbon cap tightens, this option reduces the extent of deployment of natural gas plants and significantly improves overall coal plant utilization. That increased utilization “means that coal plants have switched from just meeting the peak demand to supplying part of the baseline load, which will lower the cost of coal generation,” explains Ding.Some concernsWhile those trends are enlightening, the analyses also uncovered some concerns for India to consider, in particular, with the two approaches that yielded the lowest electricity costs.The high renewables scenario is, Ding notes, “very ideal.” It assumes that there will be little limiting the development of wind and solar capacity, so there won’t be any issues with supply chains, which is unrealistic. More importantly, the analyses showed that implementing the high renewables approach would create uneven investment in renewables across the 30 regions. Resources for onshore and offshore wind farms are mainly concentrated in a few regions in western and southern India. “So all the wind farms would be put in those regions, near where the rich cities are,” says Ding. “The poorer cities on the eastern side, where the coal power plants are, will have little renewable investment.”So the approach that’s best in terms of cost is not best in terms of social welfare, because it tends to benefit the rich regions more than the poor ones. “It’s like [the government will] need to consider the trade-off between energy justice and cost,” says Ding. Enacting state-level renewable generation targets could encourage a more even distribution of renewable capacity installation. Also, as transmission expansion is planned, coordination among power system operators and renewable energy investors in different regions could help in achieving the best outcome.CCS plus biomass co-firing — the second-best option for reducing prices — solves the equity problem posed by high renewables, and it assumes a more realistic level of renewable power adoption. However, CCS hasn’t been used in India, so there is no precedent in terms of costs. The researchers therefore based their cost estimates on the cost of CCS in China and then increased the required investment by 10 percent, the “first-of-a-kind” index developed by the U.S. Energy Information Administration. Based on those costs and other assumptions, the researchers conclude that coal plants with CCS could come into use by 2035 when the carbon cap for power generation is less than 1,000 Mt.But will CCS actually be implemented in India? While there’s been discussion about using CCS in heavy industry, the Indian government has not announced any plans for implementing the technology in coal-fired power plants. Indeed, India is currently “very conservative about CCS,” says Ding. “Some researchers say CCS won’t happen because it’s so expensive, and as long as there’s no direct use for the captured carbon, the only thing you can do is put it in the ground.” She adds, “It’s really controversial to talk about whether CCS will be implemented in India in the next 10 years.”Ding and her colleagues hope that other researchers and policymakers — especially those working in developing countries — may benefit from gaining access to their datasets and learning about their methods. Based on their findings for India, she stresses the importance of understanding the detailed geographical situation in a country in order to design plans and policies that are both realistic and equitable. More