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    Simpler models can outperform deep learning at climate prediction

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

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      Eco-driving measures could significantly reduce vehicle emissions

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

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

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

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      in Environment

      School of Architecture and Planning welcomes new faculty for 2025

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      Four new faculty members join the School of Architecture and Planning (SA+P) this fall, offering the MIT community creativity, knowledge, and scholarship in multidisciplinary roles.“These individuals add considerable strength and depth to our faculty,” says Hashim Sarkis, dean of the School of Architecture and Planning. “We are excited for the academic vigor they bring to research and teaching.”Karrie G. Karahalios ’94, MEng ’95, SM ’97, PhD ’04 joins the MIT Media Lab as a full professor of media arts and sciences. Karahalios is a pioneer in the exploration of social media and of how people communicate in environments that are increasingly mediated by algorithms that, as she has written, “shape the world around us.” Her work combines computing, systems, artificial intelligence, anthropology, sociology, psychology, game theory, design, and infrastructure studies. Karahalios’ work has received numerous honors including the National Science Foundation CAREER Award, Alfred P. Sloan Research Fellowship, SIGMOD Best Paper Award, and recognition as an ACM Distinguished Member.Pat Pataranutaporn SM ’18, PhD ’20 joins the MIT Media Lab as an assistant professor of media arts and sciences. A visionary technologist, scientist, and designer, Pataranutaporn explores the frontier of human-AI interaction, inventing and investigating AI systems that support human thriving. His research focuses on how personalized AI systems can amplify human cognition, from learning and decision-making to self-development, reflection, and well-being. Pataranutaporn will co-direct the Advancing Humans with AI Program.Mariana Popescu joins the Department of Architecture as an assistant professor. Popescu is a computational architect and structural designer with a strong interest and experience in innovative ways of approaching the fabrication process and use of materials in construction. Her area of expertise is computational and parametric design, with a focus on digital fabrication and sustainable design. Her extensive involvement in projects related to promoting sustainability has led to a multilateral development of skills, which combine the fields of architecture, engineering, computational design, and digital fabrication. Popescu earned her doctorate at ETH Zurich. She was named a “Pioneer” on the MIT Technology Review global list of “35 innovators under 35” in 2019.Holly Samuelson joins the Department of Architecture as an associate professor in the Building Technology Program at MIT, teaching architectural technology courses. Her teaching and research focus on issues of building design that impact human and environmental health. Her current projects harness advanced building simulation to investigate issues of greenhouse gas emissions, heat vulnerability, and indoor environmental quality while considering the future of buildings in a changing electricity grid. Samuelson has co-authored over 40 peer-reviewed papers, winning a best paper award from the journal Energy and Building. As a recognized expert in architectural technology, she has been featured in news outlets including The Washington Post, The Boston Globe, the BBC, and The Wall Street Journal. Samuelson earned her doctor of design from Harvard University Graduate School of Design. More

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      in Energy

      Model predicts long-term effects of nuclear waste on underground disposal systems

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      As countries across the world experience a resurgence in nuclear energy projects, the questions of where and how to dispose of nuclear waste remain as politically fraught as ever. The United States, for instance, has indefinitely stalled its only long-term underground nuclear waste repository. Scientists are using both modeling and experimental methods to study the effects of underground nuclear waste disposal and ultimately, they hope, build public trust in the decision-making process.New research from scientists at MIT, Lawrence Berkeley National Lab, and the University of Orléans makes progress in that direction. The study shows that simulations of underground nuclear waste interactions, generated by new, high-performance-computing software, aligned well with experimental results from a research facility in Switzerland.The study, which was co-authored by MIT PhD student Dauren Sarsenbayev and Assistant Professor Haruko Wainwright, along with Christophe Tournassat and Carl Steefel, appears in the journal PNAS.“These powerful new computational tools, coupled with real-world experiments like those at the Mont Terri research site in Switzerland, help us understand how radionuclides will migrate in coupled underground systems,” says Sarsenbayev, who is first author of the new study.The authors hope the research will improve confidence among policymakers and the public in the long-term safety of underground nuclear waste disposal.“This research — coupling both computation and experiments — is important to improve our confidence in waste disposal safety assessments,” says Wainwright. “With nuclear energy re-emerging as a key source for tackling climate change and ensuring energy security, it is critical to validate disposal pathways.”Comparing simulations with experimentsDisposing of nuclear waste in deep underground geological formations is currently considered the safest long-term solution for managing high-level radioactive waste. As such, much effort has been put into studying the migration behaviors of radionuclides from nuclear waste within various natural and engineered geological materials.Since its founding in 1996, the Mont Terri research site in northern Switzerland has served as an important test bed for an international consortium of researchers interested in studying materials like Opalinus clay — a thick, water-tight claystone abundant in the tunneled areas of the mountain.“It is widely regarded as one of the most valuable real-world experiment sites because it provides us with decades of datasets around the interactions of cement and clay, and those are the key materials proposed to be used by countries across the world for engineered barrier systems and geological repositories for nuclear waste,” explains Sarsenbayev.For their study, Sarsenbayev and Wainwright collaborated with co-authors Tournassat and Steefel, who have developed high-performance computing software to improve modeling of interactions between the nuclear waste and both engineered and natural materials.To date, several challenges have limited scientists’ understanding of how nuclear waste reacts with cement-clay barriers. For one thing, the barriers are made up of irregularly mixed materials deep underground. Additionally, the existing class of models commonly used to simulate radionuclide interactions with cement-clay do not take into account electrostatic effects associated with the negatively charged clay minerals in the barriers.Tournassat and Steefel’s new software accounts for electrostatic effects, making it the only one that can simulate those interactions in three-dimensional space. The software, called CrunchODiTi, was developed from established software known as CrunchFlow and was most recently updated this year. It is designed to be run on many high-performance computers at once in parallel.For the study, the researchers looked at a 13-year-old experiment, with an initial focus on cement-clay rock interactions. Within the last several years, a mix of both negatively and positively charged ions were added to the borehole located near the center of the cement emplaced in the formation. The researchers focused on a 1-centimeter-thick zone between the radionuclides and cement-clay referred to as the “skin.” They compared their experimental results to the software simulation, finding the two datasets aligned.“The results are quite significant because previously, these models wouldn’t fit field data very well,” Sarsenbayev says. “It’s interesting how fine-scale phenomena at the ‘skin’ between cement and clay, the physical and chemical properties of which changes over time, could be used to reconcile the experimental and simulation data.” The experimental results showed the model successfully accounted for electrostatic effects associated with the clay-rich formation and the interaction between materials in Mont Terri over time.“This is all driven by decades of work to understand what happens at these interfaces,” Sarsenbayev says. “It’s been hypothesized that there is mineral precipitation and porosity clogging at this interface, and our results strongly suggest that.”“This application requires millions of degrees of freedom because these multibarrier systems require high resolution and a lot of computational power,” Sarsenbayev says. “This software is really ideal for the Mont Terri experiment.”Assessing waste disposal plansThe new model could now replace older models that have been used to conduct safety and performance assessments of underground geological repositories.“If the U.S. eventually decides to dispose nuclear waste in a geological repository, then these models could dictate the most appropriate materials to use,” Sarsenbayev says. “For instance, right now clay is considered an appropriate storage material, but salt formations are another potential medium that could be used. These models allow us to see the fate of radionuclides over millennia. We can use them to understand interactions at timespans that vary from months to years to many millions of years.”Sarsenbayev says the model is reasonably accessible to other researchers and that future efforts may focus on the use of machine learning to develop less computationally expensive surrogate models.Further data from the experiment will be available later this month. The team plans to compare those data to additional simulations.“Our collaborators will basically get this block of cement and clay, and they’ll be able to run experiments to determine the exact thickness of the skin along with all of the minerals and processes present at this interface,” Sarsenbayev says. “It’s a huge project and it takes time, but we wanted to share initial data and this software as soon as we could.”For now, the researchers hope their study leads to a long-term solution for storing nuclear waste that policymakers and the public can support.“This is an interdisciplinary study that includes real world experiments showing we’re able to predict radionuclides’ fate in the subsurface,” Sarsenbayev says. “The motto of MIT’s Department of Nuclear Science and Engineering is ‘Science. Systems. Society.’ I think this merges all three domains.” More

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      in Energy

      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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      in Environment

      Study: Climate change may make it harder to reduce smog in some regions

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      Global warming will likely hinder our future ability to control ground-level ozone, a harmful air pollutant that is a primary component of smog, according to a new MIT study.The results could help scientists and policymakers develop more effective strategies for improving both air quality and human health. Ground-level ozone causes a host of detrimental health impacts, from asthma to heart disease, and contributes to thousands of premature deaths each year.The researchers’ modeling approach reveals that, as the Earth warms due to climate change, ground-level ozone will become less sensitive to reductions in nitrogen oxide emissions in eastern North America and Western Europe. In other words, it will take greater nitrogen oxide emission reductions to get the same air quality benefits.However, the study also shows that the opposite would be true in northeast Asia, where cutting emissions would have a greater impact on reducing ground-level ozone in the future. The researchers combined a climate model that simulates meteorological factors, such as temperature and wind speeds, with a chemical transport model that estimates the movement and composition of chemicals in the atmosphere.By generating a range of possible future outcomes, the researchers’ ensemble approach better captures inherent climate variability, allowing them to paint a fuller picture than many previous studies.“Future air quality planning should consider how climate change affects the chemistry of air pollution. We may need steeper cuts in nitrogen oxide emissions to achieve the same air quality goals,” says Emmie Le Roy, a graduate student in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and lead author of a paper on this study.Her co-authors include Anthony Y.H. Wong, a postdoc in the MIT Center for Sustainability Science and Strategy; Sebastian D. Eastham, principal research scientist in the MIT Center for Sustainability Science and Strategy; Arlene Fiore, the Peter H. Stone and Paola Malanotte Stone Professor of EAPS; and senior author Noelle Selin, a professor in the Institute for Data, Systems, and Society (IDSS) and EAPS. The research appears today in Environmental Science and Technology.Controlling ozoneGround-level ozone differs from the stratospheric ozone layer that protects the Earth from harmful UV radiation. It is a respiratory irritant that is harmful to the health of humans, animals, and plants.Controlling ground-level ozone is particularly challenging because it is a secondary pollutant, formed in the atmosphere by complex reactions involving nitrogen oxides and volatile organic compounds in the presence of sunlight.“That is why you tend to have higher ozone days when it is warm and sunny,” Le Roy explains.Regulators typically try to reduce ground-level ozone by cutting nitrogen oxide emissions from industrial processes. But it is difficult to predict the effects of those policies because ground-level ozone interacts with nitrogen oxide and volatile organic compounds in nonlinear ways.Depending on the chemical environment, reducing nitrogen oxide emissions could cause ground-level ozone to increase instead.“Past research has focused on the role of emissions in forming ozone, but the influence of meteorology is a really important part of Emmie’s work,” Selin says.To conduct their study, the researchers combined a global atmospheric chemistry model with a climate model that simulate future meteorology.They used the climate model to generate meteorological inputs for each future year in their study, simulating factors such as likely temperature and wind speeds, in a way that captures the inherent variability of a region’s climate.Then they fed those inputs to the atmospheric chemistry model, which calculates how the chemical composition of the atmosphere would change because of meteorology and emissions.The researchers focused on Eastern North America, Western Europe, and Northeast China, since those regions have historically high levels of the precursor chemicals that form ozone and well-established monitoring networks to provide data.They chose to model two future scenarios, one with high warming and one with low warming, over a 16-year period between 2080 and 2095. They compared them to a historical scenario capturing 2000 to 2015 to see the effects of a 10 percent reduction in nitrogen oxide emissions.Capturing climate variability“The biggest challenge is that the climate naturally varies from year to year. So, if you want to isolate the effects of climate change, you need to simulate enough years to see past that natural variability,” Le Roy says.They could overcome that challenge due to recent advances in atmospheric chemistry modeling and by taking advantage of parallel computing to simulate multiple years at the same time. They simulated five 16-year realizations, resulting in 80 model years for each scenario.The researchers found that eastern North America and Western Europe are especially sensitive to increases in nitrogen oxide emissions from the soil, which are natural emissions driven by increases in temperature.Due to that sensitivity, as the Earth warms and more nitrogen oxide from soil enters the atmosphere, reducing nitrogen oxide emissions from human activities will have less of an impact on ground-level ozone.“This shows how important it is to improve our representation of the biosphere in these models to better understand how climate change may impact air quality,” Le Roy says.On the other hand, since industrial processes in northeast Asia cause more ozone per unit of nitrogen oxide emitted, cutting emissions there would cause greater reductions in ground-level ozone in future warming scenarios.“But I wouldn’t say that is a good thing because it means that, overall, there are higher levels of ozone,” Le Roy adds.Running detailed meteorology simulations, rather than relying on annual average weather data, gave the researchers a more complete picture of the potential effects on human health.“Average climate isn’t the only thing that matters. One high ozone day, which might be a statistical anomaly, could mean we don’t meet our air quality target and have negative human health impacts that we should care about,” Le Roy says.In the future, the researchers want to continue exploring the intersection of meteorology and air quality. They also want to expand their modeling approach to consider other climate change factors with high variability, like wildfires or biomass burning.“We’ve shown that it is important for air quality scientists to consider the full range of climate variability, even if it is hard to do in your models, because it really does affect the answer that you get,” says Selin.This work is funded, in part, by the MIT Praecis Presidential Fellowship, the J.H. and E.V. Wade Fellowship, and the MIT Martin Family Society of Fellows for Sustainability. More

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      in Energy

      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

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      in Environment

      Hundred-year storm tides will occur every few decades in Bangladesh, scientists report

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      Tropical cyclones are hurricanes that brew over the tropical ocean and can travel over land, inundating coastal regions. The most extreme cyclones can generate devastating storm tides — seawater that is heightened by the tides and swells onto land, causing catastrophic flood events in coastal regions. A new study by MIT scientists finds that, as the planet warms, the recurrence of destructive storm tides will increase tenfold for one of the hardest-hit regions of the world.In a study appearing today in One Earth, the scientists report that, for the highly populated coastal country of Bangladesh, what was once a 100-year event could now strike every 10 years — or more often — by the end of the century. In a future where fossil fuels continue to burn as they do today, what was once considered a catastrophic, once-in-a-century storm tide will hit Bangladesh, on average, once per decade. And the kind of storm tides that have occurred every decade or so will likely batter the country’s coast more frequently, every few years.Bangladesh is one of the most densely populated countries in the world, with more than 171 million people living in a region roughly the size of New York state. The country has been historically vulnerable to tropical cyclones, as it is a low-lying delta that is easily flooded by storms and experiences a seasonal monsoon. Some of the most destructive floods in the world have occurred in Bangladesh, where it’s been increasingly difficult for agricultural economies to recover.The study also finds that Bangladesh will likely experience tropical cyclones that overlap with the months-long monsoon season. Until now, cyclones and the monsoon have occurred at separate times during the year. But as the planet warms, the scientists’ modeling shows that cyclones will push into the monsoon season, causing back-to-back flooding events across the country.“Bangladesh is very active in preparing for climate hazards and risks, but the problem is, everything they’re doing is more or less based on what they’re seeing in the present climate,” says study co-author Sai Ravela, principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “We are now seeing an almost tenfold rise in the recurrence of destructive storm tides almost anywhere you look in Bangladesh. This cannot be ignored. So, we think this is timely, to say they have to pause and revisit how they protect against these storms.”Ravela’s co-authors are Jiangchao Qiu, a postdoc in EAPS, and Kerry Emanuel, professor emeritus of atmospheric science at MIT.Height of tidesIn recent years, Bangladesh has invested significantly in storm preparedness, for instance in improving its early-warning system, fortifying village embankments, and increasing access to community shelters. But such preparations have generally been based on the current frequency of storms.In this new study, the MIT team aimed to provide detailed projections of extreme storm tide hazards, which are flooding events where tidal effects amplify cyclone-induced storm surge, in Bangladesh under various climate-warming scenarios and sea-level rise projections.“A lot of these events happen at night, so tides play a really strong role in how much additional water you might get, depending on what the tide is,” Ravela explains.To evaluate the risk of storm tide, the team first applied a method of physics-based downscaling, which Emanuel’s group first developed over 20 years ago and has been using since to study hurricane activity in different parts of the world. The technique involves a low-resolution model of the global ocean and atmosphere that is embedded with a finer-resolution model that simulates weather patterns as detailed as a single hurricane. The researchers then scatter hurricane “seeds” in a region of interest and run the model forward to observe which seeds grow and make landfall over time.To the downscaled model, the researchers incorporated a hydrodynamical model, which simulates the height of a storm surge, given the pattern and strength of winds at the time of a given storm. For any given simulated storm, the team also tracked the tides, as well as effects of sea level rise, and incorporated this information into a numerical model that calculated the storm tide, or the height of the water, with tidal effects as a storm makes landfall.Extreme overlapWith this framework, the scientists simulated tens of thousands of potential tropical cyclones near Bangladesh, under several future climate scenarios, ranging from one that resembles the current day to one in which the world experiences further warming as a result of continued fossil fuel burning. For each simulation, they recorded the maximum storm tides along the coast of Bangladesh and noted the frequency of storm tides of various heights in a given climate scenario.“We can look at the entire bucket of simulations and see, for this storm tide of say, 3 meters, we saw this many storms, and from that you can figure out the relative frequency of that kind of storm,” Qiu says. “You can then invert that number to a return period.”A return period is the time it takes for a storm of a particular type to make landfall again. A storm that is considered a “100-year event” is typically more powerful and destructive, and in this case, creates more extreme storm tides, and therefore more catastrophic flooding, compared to a 10-year event.From their modeling, Ravela and his colleagues found that under a scenario of increased global warming, the storms that previously were considered 100-year events, producing the highest storm tide values, can recur every decade or less by late-century. They also observed that, toward the end of this century, tropical cyclones in Bangladesh will occur across a broader seasonal window, potentially overlapping in certain years with the seasonal monsoon season.“If the monsoon rain has come in and saturated the soil, a cyclone then comes in and it makes the problem much worse,” Ravela says. “People won’t have any reprieve between the extreme storm and the monsoon. There are so many compound and cascading effects between the two. And this only emerges because warming happens.”Ravela and his colleagues are using their modeling to help experts in Bangladesh better evaluate and prepare for a future of increasing storm risk. And he says that the climate future for Bangladesh is in some ways not unique to this part of the world.“This climate change story that is playing out in Bangladesh in a certain way will be playing out in a different way elsewhere,” Ravela notes. “Maybe where you are, the story is about heat stress, or amplifying droughts, or wildfires. The peril is different. But the underlying catastrophe story is not that different.”This research is supported in part by the MIT Climate Resilience Early Warning Systems Climate Grand Challenges project, the Jameel Observatory JO-CREWSNet project; MIT Weather and Climate Extremes Climate Grand Challenges project; and Schmidt Sciences, LLC.  More

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      in Energy

      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