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    Power when the sun doesn’t shine

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    In 2016, at the huge Houston energy conference CERAWeek, MIT materials scientist Yet-Ming Chiang found himself talking to a Tesla executive about a thorny problem: how to store the output of solar panels and wind turbines for long durations.        

    Chiang, the Kyocera Professor of Materials Science and Engineering, and Mateo Jaramillo, a vice president at Tesla, knew that utilities lacked a cost-effective way to store renewable energy to cover peak levels of demand and to bridge the gaps during windless and cloudy days. They also knew that the scarcity of raw materials used in conventional energy storage devices needed to be addressed if renewables were ever going to displace fossil fuels on the grid at scale.

    Energy storage technologies can facilitate access to renewable energy sources, boost the stability and reliability of power grids, and ultimately accelerate grid decarbonization. The global market for these systems — essentially large batteries — is expected to grow tremendously in the coming years. A study by the nonprofit LDES (Long Duration Energy Storage) Council pegs the long-duration energy storage market at between 80 and 140 terawatt-hours by 2040. “That’s a really big number,” Chiang notes. “Every 10 people on the planet will need access to the equivalent of one EV [electric vehicle] battery to support their energy needs.”

    In 2017, one year after they met in Houston, Chiang and Jaramillo joined forces to co-found Form Energy in Somerville, Massachusetts, with MIT graduates Marco Ferrara SM ’06, PhD ’08 and William Woodford PhD ’13, and energy storage veteran Ted Wiley.

    “There is a burgeoning market for electrical energy storage because we want to achieve decarbonization as fast and as cost-effectively as possible,” says Ferrara, Form’s senior vice president in charge of software and analytics.

    Investors agreed. Over the next six years, Form Energy would raise more than $800 million in venture capital.

    Bridging gaps

    The simplest battery consists of an anode, a cathode, and an electrolyte. During discharge, with the help of the electrolyte, electrons flow from the negative anode to the positive cathode. During charge, external voltage reverses the process. The anode becomes the positive terminal, the cathode becomes the negative terminal, and electrons move back to where they started. Materials used for the anode, cathode, and electrolyte determine the battery’s weight, power, and cost “entitlement,” which is the total cost at the component level.

    During the 1980s and 1990s, the use of lithium revolutionized batteries, making them smaller, lighter, and able to hold a charge for longer. The storage devices Form Energy has devised are rechargeable batteries based on iron, which has several advantages over lithium. A big one is cost.

    Chiang once declared to the MIT Club of Northern California, “I love lithium-ion.” Two of the four MIT spinoffs Chiang founded center on innovative lithium-ion batteries. But at hundreds of dollars a kilowatt-hour (kWh) and with a storage capacity typically measured in hours, lithium-ion was ill-suited for the use he now had in mind.

    The approach Chiang envisioned had to be cost-effective enough to boost the attractiveness of renewables. Making solar and wind energy reliable enough for millions of customers meant storing it long enough to fill the gaps created by extreme weather conditions, grid outages, and when there is a lull in the wind or a few days of clouds.

    To be competitive with legacy power plants, Chiang’s method had to come in at around $20 per kilowatt-hour of stored energy — one-tenth the cost of lithium-ion battery storage.

    But how to transition from expensive batteries that store and discharge over a couple of hours to some as-yet-undefined, cheap, longer-duration technology?

    “One big ball of iron”

    That’s where Ferrara comes in. Ferrara has a PhD in nuclear engineering from MIT and a PhD in electrical engineering and computer science from the University of L’Aquila in his native Italy. In 2017, as a research affiliate at the MIT Department of Materials Science and Engineering, he worked with Chiang to model the grid’s need to manage renewables’ intermittency.

    How intermittent depends on where you are. In the United States, for instance, there’s the windy Great Plains; the sun-drenched, relatively low-wind deserts of Arizona, New Mexico, and Nevada; and the often-cloudy Pacific Northwest.

    Ferrara, in collaboration with Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society and her MIT team, modeled four representative locations in the United States and concluded that energy storage with capacity costs below roughly $20/kWh and discharge durations of multiple days would allow a wind-solar mix to provide cost-competitive, firm electricity in resource-abundant locations.

    Now that they had a time frame, they turned their attention to materials. At the price point Form Energy was aiming for, lithium was out of the question. Chiang looked at plentiful and cheap sulfur. But a sulfur, sodium, water, and air battery had technical challenges.

    Thomas Edison once used iron as an electrode, and iron-air batteries were first studied in the 1960s. They were too heavy to make good transportation batteries. But this time, Chiang and team were looking at a battery that sat on the ground, so weight didn’t matter. Their priorities were cost and availability.

    “Iron is produced, mined, and processed on every continent,” Chiang says. “The Earth is one big ball of iron. We wouldn’t ever have to worry about even the most ambitious projections of how much storage that the world might use by mid-century.” If Form ever moves into the residential market, “it’ll be the safest battery you’ve ever parked at your house,” Chiang laughs. “Just iron, air, and water.”

    Scientists call it reversible rusting. While discharging, the battery takes in oxygen and converts iron to rust. Applying an electrical current converts the rusty pellets back to iron, and the battery “breathes out” oxygen as it charges. “In chemical terms, you have iron, and it becomes iron hydroxide,” Chiang says. “That means electrons were extracted. You get those electrons to go through the external circuit, and now you have a battery.”

    Form Energy’s battery modules are approximately the size of a washer-and-dryer unit. They are stacked in 40-foot containers, and several containers are electrically connected with power conversion systems to build storage plants that can cover several acres.

    The right place at the right time

    The modules don’t look or act like anything utilities have contracted for before.

    That’s one of Form’s key challenges. “There is not widespread knowledge of needing these new tools for decarbonized grids,” Ferrara says. “That’s not the way utilities have typically planned. They’re looking at all the tools in the toolkit that exist today, which may not contemplate a multi-day energy storage asset.”

    Form Energy’s customers are largely traditional power companies seeking to expand their portfolios of renewable electricity. Some are in the process of decommissioning coal plants and shifting to renewables.

    Ferrara’s research pinpointing the need for very low-cost multi-day storage provides key data for power suppliers seeking to determine the most cost-effective way to integrate more renewable energy.

    Using the same modeling techniques, Ferrara and team show potential customers how the technology fits in with their existing system, how it competes with other technologies, and how, in some cases, it can operate synergistically with other storage technologies.

    “They may need a portfolio of storage technologies to fully balance renewables on different timescales of intermittency,” he says. But other than the technology developed at Form, “there isn’t much out there, certainly not within the cost entitlement of what we’re bringing to market.”  Thanks to Chiang and Jaramillo’s chance encounter in Houston, Form has a several-year lead on other companies working to address this challenge. 

    In June 2023, Form Energy closed its biggest deal to date for a single project: Georgia Power’s order for a 15-megawatt/1,500-megawatt-hour system. That order brings Form’s total amount of energy storage under contracts with utility customers to 40 megawatts/4 gigawatt-hours. To meet the demand, Form is building a new commercial-scale battery manufacturing facility in West Virginia.

    The fact that Form Energy is creating jobs in an area that lost more than 10,000 steel jobs over the past decade is not lost on Chiang. “And these new jobs are in clean tech. It’s super exciting to me personally to be doing something that benefits communities outside of our traditional technology centers.

    “This is the right time for so many reasons,” Chiang says. He says he and his Form Energy co-founders feel “tremendous urgency to get these batteries out into the world.”

    This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Environment

      MIT researchers remotely map crops, field by field

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      Crop maps help scientists and policymakers track global food supplies and estimate how they might shift with climate change and growing populations. But getting accurate maps of the types of crops that are grown from farm to farm often requires on-the-ground surveys that only a handful of countries have the resources to maintain.

      Now, MIT engineers have developed a method to quickly and accurately label and map crop types without requiring in-person assessments of every single farm. The team’s method uses a combination of Google Street View images, machine learning, and satellite data to automatically determine the crops grown throughout a region, from one fraction of an acre to the next. 

      The researchers used the technique to automatically generate the first nationwide crop map of Thailand — a smallholder country where small, independent farms make up the predominant form of agriculture. The team created a border-to-border map of Thailand’s four major crops — rice, cassava, sugarcane, and maize — and determined which of the four types was grown, at every 10 meters, and without gaps, across the entire country. The resulting map achieved an accuracy of 93 percent, which the researchers say is comparable to on-the-ground mapping efforts in high-income, big-farm countries.

      The team is applying their mapping technique to other countries such as India, where small farms sustain most of the population but the type of crops grown from farm to farm has historically been poorly recorded.

      “It’s a longstanding gap in knowledge about what is grown around the world,” says Sherrie Wang, the d’Arbeloff Career Development Assistant Professor in MIT’s Department of Mechanical Engineering, and the Institute for Data, Systems, and Society (IDSS). “The final goal is to understand agricultural outcomes like yield, and how to farm more sustainably. One of the key preliminary steps is to map what is even being grown — the more granularly you can map, the more questions you can answer.”

      Wang, along with MIT graduate student Jordi Laguarta Soler and Thomas Friedel of the agtech company PEAT GmbH, will present a paper detailing their mapping method later this month at the AAAI Conference on Artificial Intelligence.

      Ground truth

      Smallholder farms are often run by a single family or farmer, who subsist on the crops and livestock that they raise. It’s estimated that smallholder farms support two-thirds of the world’s rural population and produce 80 percent of the world’s food. Keeping tabs on what is grown and where is essential to tracking and forecasting food supplies around the world. But the majority of these small farms are in low to middle-income countries, where few resources are devoted to keeping track of individual farms’ crop types and yields.

      Crop mapping efforts are mainly carried out in high-income regions such as the United States and Europe, where government agricultural agencies oversee crop surveys and send assessors to farms to label crops from field to field. These “ground truth” labels are then fed into machine-learning models that make connections between the ground labels of actual crops and satellite signals of the same fields. They then label and map wider swaths of farmland that assessors don’t cover but that satellites automatically do.

      “What’s lacking in low- and middle-income countries is this ground label that we can associate with satellite signals,” Laguarta Soler says. “Getting these ground truths to train a model in the first place has been limited in most of the world.”

      The team realized that, while many developing countries do not have the resources to maintain crop surveys, they could potentially use another source of ground data: roadside imagery, captured by services such as Google Street View and Mapillary, which send cars throughout a region to take continuous 360-degree images with dashcams and rooftop cameras.

      In recent years, such services have been able to access low- and middle-income countries. While the goal of these services is not specifically to capture images of crops, the MIT team saw that they could search the roadside images to identify crops.

      Cropped image

      In their new study, the researchers worked with Google Street View (GSV) images taken throughout Thailand — a country that the service has recently imaged fairly thoroughly, and which consists predominantly of smallholder farms.

      Starting with over 200,000 GSV images randomly sampled across Thailand, the team filtered out images that depicted buildings, trees, and general vegetation. About 81,000 images were crop-related. They set aside 2,000 of these, which they sent to an agronomist, who determined and labeled each crop type by eye. They then trained a convolutional neural network to automatically generate crop labels for the other 79,000 images, using various training methods, including iNaturalist — a web-based crowdsourced  biodiversity database, and GPT-4V, a “multimodal large language model” that enables a user to input an image and ask the model to identify what the image is depicting. For each of the 81,000 images, the model generated a label of one of four crops that the image was likely depicting — rice, maize, sugarcane, or cassava.

      The researchers then paired each labeled image with the corresponding satellite data taken of the same location throughout a single growing season. These satellite data include measurements across multiple wavelengths, such as a location’s greenness and its reflectivity (which can be a sign of water). 

      “Each type of crop has a certain signature across these different bands, which changes throughout a growing season,” Laguarta Soler notes.

      The team trained a second model to make associations between a location’s satellite data and its corresponding crop label. They then used this model to process satellite data taken of the rest of the country, where crop labels were not generated or available. From the associations that the model learned, it then assigned crop labels across Thailand, generating a country-wide map of crop types, at a resolution of 10 square meters.

      This first-of-its-kind crop map included locations corresponding to the 2,000 GSV images that the researchers originally set aside, that were labeled by arborists. These human-labeled images were used to validate the map’s labels, and when the team looked to see whether the map’s labels matched the expert, “gold standard” labels, it did so 93 percent of the time.

      “In the U.S., we’re also looking at over 90 percent accuracy, whereas with previous work in India, we’ve only seen 75 percent because ground labels are limited,” Wang says. “Now we can create these labels in a cheap and automated way.”

      The researchers are moving to map crops across India, where roadside images via Google Street View and other services have recently become available.

      “There are over 150 million smallholder farmers in India,” Wang says. “India is covered in agriculture, almost wall-to-wall farms, but very small farms, and historically it’s been very difficult to create maps of India because there are very sparse ground labels.”

      The team is working to generate crop maps in India, which could be used to inform policies having to do with assessing and bolstering yields, as global temperatures and populations rise.

      “What would be interesting would be to create these maps over time,” Wang says. “Then you could start to see trends, and we can try to relate those things to anything like changes in climate and policies.” More

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      in Environment

      Study: Global deforestation leads to more mercury pollution

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      About 10 percent of human-made mercury emissions into the atmosphere each year are the result of global deforestation, according to a new MIT study.

      The world’s vegetation, from the Amazon rainforest to the savannahs of sub-Saharan Africa, acts as a sink that removes the toxic pollutant from the air. However, if the current rate of deforestation remains unchanged or accelerates, the researchers estimate that net mercury emissions will keep increasing.

      “We’ve been overlooking a significant source of mercury, especially in tropical regions,” says Ari Feinberg, a former postdoc in the Institute for Data, Systems, and Society (IDSS) and lead author of the study.

      The researchers’ model shows that the Amazon rainforest plays a particularly important role as a mercury sink, contributing about 30 percent of the global land sink. Curbing Amazon deforestation could thus have a substantial impact on reducing mercury pollution.

      The team also estimates that global reforestation efforts could increase annual mercury uptake by about 5 percent. While this is significant, the researchers emphasize that reforestation alone should not be a substitute for worldwide pollution control efforts.

      “Countries have put a lot of effort into reducing mercury emissions, especially northern industrialized countries, and for very good reason. But 10 percent of the global anthropogenic source is substantial, and there is a potential for that to be even greater in the future. [Addressing these deforestation-related emissions] needs to be part of the solution,” says senior author Noelle Selin, a professor in IDSS and MIT’s Department of Earth, Atmospheric and Planetary Sciences.

      Feinberg and Selin are joined on the paper by co-authors Martin Jiskra, a former Swiss National Science Foundation Ambizione Fellow at the University of Basel; Pasquale Borrelli, a professor at Roma Tre University in Italy; and Jagannath Biswakarma, a postdoc at the Swiss Federal Institute of Aquatic Science and Technology. The paper appears today in Environmental Science and Technology.

      Modeling mercury

      Over the past few decades, scientists have generally focused on studying deforestation as a source of global carbon dioxide emissions. Mercury, a trace element, hasn’t received the same attention, partly because the terrestrial biosphere’s role in the global mercury cycle has only recently been better quantified.

      Plant leaves take up mercury from the atmosphere, in a similar way as they take up carbon dioxide. But unlike carbon dioxide, mercury doesn’t play an essential biological function for plants. Mercury largely stays within a leaf until it falls to the forest floor, where the mercury is absorbed by the soil.

      Mercury becomes a serious concern for humans if it ends up in water bodies, where it can become methylated by microorganisms. Methylmercury, a potent neurotoxin, can be taken up by fish and bioaccumulated through the food chain. This can lead to risky levels of methylmercury in the fish humans eat.

      “In soils, mercury is much more tightly bound than it would be if it were deposited in the ocean. The forests are doing a sort of ecosystem service, in that they are sequestering mercury for longer timescales,” says Feinberg, who is now a postdoc in the Blas Cabrera Institute of Physical Chemistry in Spain.

      In this way, forests reduce the amount of toxic methylmercury in oceans.

      Many studies of mercury focus on industrial sources, like burning fossil fuels, small-scale gold mining, and metal smelting. A global treaty, the 2013 Minamata Convention, calls on nations to reduce human-made emissions. However, it doesn’t directly consider impacts of deforestation.

      The researchers launched their study to fill in that missing piece.

      In past work, they had built a model to probe the role vegetation plays in mercury uptake. Using a series of land use change scenarios, they adjusted the model to quantify the role of deforestation.

      Evaluating emissions

      This chemical transport model tracks mercury from its emissions sources to where it is chemically transformed in the atmosphere and then ultimately to where it is deposited, mainly through rainfall or uptake into forest ecosystems.

      They divided the Earth into eight regions and performed simulations to calculate deforestation emissions factors for each, considering elements like type and density of vegetation, mercury content in soils, and historical land use.

      However, good data for some regions were hard to come by.

      They lacked measurements from tropical Africa or Southeast Asia — two areas that experience heavy deforestation. To get around this gap, they used simpler, offline models to simulate hundreds of scenarios, which helped them improve their estimations of potential uncertainties.

      They also developed a new formulation for mercury emissions from soil. This formulation captures the fact that deforestation reduces leaf area, which increases the amount of sunlight that hits the ground and accelerates the outgassing of mercury from soils.

      The model divides the world into grid squares, each of which is a few hundred square kilometers. By changing land surface and vegetation parameters in certain squares to represent deforestation and reforestation scenarios, the researchers can capture impacts on the mercury cycle.

      Overall, they found that about 200 tons of mercury are emitted to the atmosphere as the result of deforestation, or about 10 percent of total human-made emissions. But in tropical and sub-tropical countries, deforestation emissions represent a higher percentage of total emissions. For example, in Brazil deforestation emissions are 40 percent of total human-made emissions.

      In addition, people often light fires to prepare tropical forested areas for agricultural activities, which causes more emissions by releasing mercury stored by vegetation.

      “If deforestation was a country, it would be the second highest emitting country, after China, which emits around 500 tons of mercury a year,” Feinberg adds.

      And since the Minamata Convention is now addressing primary mercury emissions, scientists can expect deforestation to become a larger fraction of human-made emissions in the future.

      “Policies to protect forests or cut them down have unintended effects beyond their target. It is important to consider the fact that these are systems, and they involve human activities, and we need to understand them better in order to actually solve the problems that we know are out there,” Selin says.

      By providing this first estimate, the team hopes to inspire more research in this area.

      In the future, they want to incorporate more dynamic Earth system models into their analysis, which would enable them to interactively track mercury uptake and better model the timescale of vegetation regrowth.

      “This paper represents an important advance in our understanding of global mercury cycling by quantifying a pathway that has long been suggested but not yet quantified. Much of our research to date has focused on primary anthropogenic emissions — those directly resulting from human activity via coal combustion or mercury-gold amalgam burning in artisanal and small-scale gold mining,” says Jackie Gerson, an assistant professor in the Department of Earth and Environmental Sciences at Michigan State University, who was not involved with this research. “This research shows that deforestation can also result in substantial mercury emissions and needs to be considered both in terms of global mercury models and land management policies. It therefore has the potential to advance our field scientifically as well as to promote policies that reduce mercury emissions via deforestation.

      This work was funded, in part, by the U.S. National Science Foundation, the Swiss National Science Foundation, and Swiss Federal Institute of Aquatic Science and Technology. More

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      in Environment

      Forging climate connections across the Institute

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      Climate change is the ultimate cross-cutting issue: Not limited to any one discipline, it ranges across science, technology, policy, culture, human behavior, and well beyond. The response to it likewise requires an all-of-MIT effort.

      Now, to strengthen such an effort, a new grant program spearheaded by the Climate Nucleus, the faculty committee charged with the oversight and implementation of Fast Forward: MIT’s Climate Action Plan for the Decade, aims to build up MIT’s climate leadership capacity while also supporting innovative scholarship on diverse climate-related topics and forging new connections across the Institute.

      Called the Fast Forward Faculty Fund (F^4 for short), the program has named its first cohort of six faculty members after issuing its inaugural call for proposals in April 2023. The cohort will come together throughout the year for climate leadership development programming and networking. The program provides financial support for graduate students who will work with the faculty members on the projects — the students will also participate in leadership-building activities — as well as $50,000 in flexible, discretionary funding to be used to support related activities. 

      “Climate change is a crisis that truly touches every single person on the planet,” says Noelle Selin, co-chair of the nucleus and interim director of the Institute for Data, Systems, and Society. “It’s therefore essential that we build capacity for every member of the MIT community to make sense of the problem and help address it. Through the Fast Forward Faculty Fund, our aim is to have a cohort of climate ambassadors who can embed climate everywhere at the Institute.”

      F^4 supports both faculty who would like to begin doing climate-related work, as well as faculty members who are interested in deepening their work on climate. The program has the core goal of developing cohorts of F^4 faculty and graduate students who, in addition to conducting their own research, will become climate leaders at MIT, proactively looking for ways to forge new climate connections across schools, departments, and disciplines.

      One of the projects, “Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies,” led by Professor Siqi Zheng of the MIT Center for Real Estate in collaboration with colleagues from the MIT Sloan School of Management, focuses on the roughly 40 percent of carbon dioxide emissions that come from the buildings and real estate sector. Zheng notes that this sector has been slow to respond to climate change, but says that is starting to change, thanks in part to the rising awareness of climate risks and new local regulations aimed at reducing emissions from buildings.

      Using a data-driven approach, the project seeks to understand the efficient and equitable market incentives, technology solutions, and public policies that are most effective at transforming the real estate industry. Johnattan Ontiveros, a graduate student in the Technology and Policy Program, is working with Zheng on the project.

      “We were thrilled at the incredible response we received from the MIT faculty to our call for proposals, which speaks volumes about the depth and breadth of interest in climate at MIT,” says Anne White, nucleus co-chair and vice provost and associate vice president for research. “This program makes good on key commitments of the Fast Forward plan, supporting cutting-edge new work by faculty and graduate students while helping to deepen the bench of climate leaders at MIT.”

      During the 2023-24 academic year, the F^4 faculty and graduate student cohorts will come together to discuss their projects, explore opportunities for collaboration, participate in climate leadership development, and think proactively about how to deepen interdisciplinary connections among MIT community members interested in climate change.

      The six inaugural F^4 awardees are:

      Professor Tristan Brown, History Section: Humanistic Approaches to the Climate Crisis  

      With this project, Brown aims to create a new community of practice around narrative-centric approaches to environmental and climate issues. Part of a broader humanities initiative at MIT, it brings together a global working group of interdisciplinary scholars, including Serguei Saavedra (Department of Civil and Environmental Engineering) and Or Porath (Tel Aviv University; Religion), collectively focused on examining the historical and present links between sacred places and biodiversity for the purposes of helping governments and nongovernmental organizations formulate better sustainability goals. Boyd Ruamcharoen, a PhD student in the History, Anthropology, and Science, Technology, and Society (HASTS) program, will work with Brown on this project.

      Professor Kerri Cahoy, departments of Aeronautics and Astronautics and Earth, Atmospheric, and Planetary Sciences (AeroAstro): Onboard Autonomous AI-driven Satellite Sensor Fusion for Coastal Region Monitoring

      The motivation for this project is the need for much better data collection from satellites, where technology can be “20 years behind,” says Cahoy. As part of this project, Cahoy will pursue research in the area of autonomous artificial intelligence-enabled rapid sensor fusion (which combines data from different sensors, such as radar and cameras) onboard satellites to improve understanding of the impacts of climate change, specifically sea-level rise and hurricanes and flooding in coastal regions. Graduate students Madeline Anderson, a PhD student in electrical engineering and computer science (EECS), and Mary Dahl, a PhD student in AeroAstro, will work with Cahoy on this project.

      Professor Priya Donti, Department of Electrical Engineering and Computer Science: Robust Reinforcement Learning for High-Renewables Power Grids 

      With renewables like wind and solar making up a growing share of electricity generation on power grids, Donti’s project focuses on improving control methods for these distributed sources of electricity. The research will aim to create a realistic representation of the characteristics of power grid operations, and eventually inform scalable operational improvements in power systems. It will “give power systems operators faith that, OK, this conceptually is good, but it also actually works on this grid,” says Donti. PhD candidate Ana Rivera from EECS is the F^4 graduate student on the project.

      Professor Jason Jackson, Department of Urban Studies and Planning (DUSP): Political Economy of the Climate Crisis: Institutions, Power and Global Governance

      This project takes a political economy approach to the climate crisis, offering a distinct lens to examine, first, the political governance challenge of mobilizing climate action and designing new institutional mechanisms to address the global and intergenerational distributional aspects of climate change; second, the economic challenge of devising new institutional approaches to equitably finance climate action; and third, the cultural challenge — and opportunity — of empowering an adaptive socio-cultural ecology through traditional knowledge and local-level social networks to achieve environmental resilience. Graduate students Chen Chu and Mrinalini Penumaka, both PhD students in DUSP, are working with Jackson on the project.

      Professor Haruko Wainwright, departments of Nuclear Science and Engineering (NSE) and Civil and Environmental Engineering: Low-cost Environmental Monitoring Network Technologies in Rural Communities for Addressing Climate Justice 

      This project will establish a community-based climate and environmental monitoring network in addition to a data visualization and analysis infrastructure in rural marginalized communities to better understand and address climate justice issues. The project team plans to work with rural communities in Alaska to install low-cost air and water quality, weather, and soil sensors. Graduate students Kay Whiteaker, an MS candidate in NSE, and Amandeep Singh, and MS candidate in System Design and Management at Sloan, are working with Wainwright on the project, as is David McGee, professor in earth, atmospheric, and planetary sciences.

      Professor Siqi Zheng, MIT Center for Real Estate and DUSP: Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies 

      See the text above for the details on this project. More

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      in Environment

      Improving US air quality, equitably

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      Decarbonization of national economies will be key to achieving global net-zero emissions by 2050, a major stepping stone to the Paris Agreement’s long-term goal of keeping global warming well below 2 degrees Celsius (and ideally 1.5 C), and thereby averting the worst consequences of climate change. Toward that end, the United States has pledged to reduce its greenhouse gas emissions by 50-52 percent from 2005 levels by 2030, backed by its implementation of the 2022 Inflation Reduction Act. This strategy is consistent with a 50-percent reduction in carbon dioxide (CO2) by the end of the decade.

      If U.S. federal carbon policy is successful, the nation’s overall air quality will also improve. Cutting CO2 emissions reduces atmospheric concentrations of air pollutants that lead to the formation of fine particulate matter (PM2.5), which causes more than 200,000 premature deaths in the United States each year. But an average nationwide improvement in air quality will not be felt equally; air pollution exposure disproportionately harms people of color and lower-income populations.

      How effective are current federal decarbonization policies in reducing U.S. racial and economic disparities in PM2.5 exposure, and what changes will be needed to improve their performance? To answer that question, researchers at MIT and Stanford University recently evaluated a range of policies which, like current U.S. federal carbon policies, reduce economy-wide CO2 emissions by 40-60 percent from 2005 levels by 2030. Their findings appear in an open-access article in the journal Nature Communications.

      First, they show that a carbon-pricing policy, while effective in reducing PM2.5 exposure for all racial/ethnic groups, does not significantly mitigate relative disparities in exposure. On average, the white population undergoes far less exposure than Black, Hispanic, and Asian populations. This policy does little to reduce exposure disparities because the CO2 emissions reductions that it achieves primarily occur in the coal-fired electricity sector. Other sectors, such as industry and heavy-duty diesel transportation, contribute far more PM2.5-related emissions.

      The researchers then examine thousands of different reduction options through an optimization approach to identify whether any possible combination of carbon dioxide reductions in the range of 40-60 percent can mitigate disparities. They find that that no policy scenario aligned with current U.S. carbon dioxide emissions targets is likely to significantly reduce current PM2.5 exposure disparities.

      “Policies that address only about 50 percent of CO2 emissions leave many polluting sources in place, and those that prioritize reductions for minorities tend to benefit the entire population,” says Noelle Selin, supervising author of the study and a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences. “This means that a large range of policies that reduce CO2 can improve air quality overall, but can’t address long-standing inequities in air pollution exposure.”

      So if climate policy alone cannot adequately achieve equitable air quality results, what viable options remain? The researchers suggest that more ambitious carbon policies could narrow racial and economic PM2.5 exposure disparities in the long term, but not within the next decade. To make a near-term difference, they recommend interventions designed to reduce PM2.5 emissions resulting from non-CO2 sources, ideally at the economic sector or community level.

      “Achieving improved PM2.5 exposure for populations that are disproportionately exposed across the United States will require thinking that goes beyond current CO2 policy strategies, most likely involving large-scale structural changes,” says Selin. “This could involve changes in local and regional transportation and housing planning, together with accelerated efforts towards decarbonization.” More

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      in Energy

      Supporting sustainability, digital health, and the future of work

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      The MIT and Accenture Convergence Initiative for Industry and Technology has selected three new research projects that will receive support from the initiative. The research projects aim to accelerate progress in meeting complex societal needs through new business convergence insights in technology and innovation.

      Established in MIT’s School of Engineering and now in its third year, the MIT and Accenture Convergence Initiative is furthering its mission to bring together technological experts from across business and academia to share insights and learn from one another. Recently, Thomas W. Malone, the Patrick J. McGovern (1959) Professor of Management, joined the initiative as its first-ever faculty lead. The research projects relate to three of the initiative’s key focus areas: sustainability, digital health, and the future of work.

      “The solutions these research teams are developing have the potential to have tremendous impact,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “They embody the initiative’s focus on advancing data-driven research that addresses technology and industry convergence.”

      “The convergence of science and technology driven by advancements in generative AI, digital twins, quantum computing, and other technologies makes this an especially exciting time for Accenture and MIT to be undertaking this joint research,” says Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences. “Our three new research projects focusing on sustainability, digital health, and the future of work have the potential to help guide and shape future innovations that will benefit the way we work and live.”

      The MIT and Accenture Convergence Initiative charter project researchers are described below.

      Accelerating the journey to net zero with industrial clusters

      Jessika Trancik is a professor at the Institute for Data, Systems, and Society (IDSS). Trancik’s research examines the dynamic costs, performance, and environmental impacts of energy systems to inform climate policy and accelerate beneficial and equitable technology innovation. Trancik’s project aims to identify how industrial clusters can enable companies to derive greater value from decarbonization, potentially making companies more willing to invest in the clean energy transition.

      To meet the ambitious climate goals that have been set by countries around the world, rising greenhouse gas emissions trends must be rapidly reversed. Industrial clusters — geographically co-located or otherwise-aligned groups of companies representing one or more industries — account for a significant portion of greenhouse gas emissions globally. With major energy consumers “clustered” in proximity, industrial clusters provide a potential platform to scale low-carbon solutions by enabling the aggregation of demand and the coordinated investment in physical energy supply infrastructure.

      In addition to Trancik, the research team working on this project will include Aliza Khurram, a postdoc in IDSS; Micah Ziegler, an IDSS research scientist; Melissa Stark, global energy transition services lead at Accenture; Laura Sanderfer, strategy consulting manager at Accenture; and Maria De Miguel, strategy senior analyst at Accenture.

      Eliminating childhood obesity

      Anette “Peko” Hosoi is the Neil and Jane Pappalardo Professor of Mechanical Engineering. A common theme in her work is the fundamental study of shape, kinematic, and rheological optimization of biological systems with applications to the emergent field of soft robotics. Her project will use both data from existing studies and synthetic data to create a return-on-investment (ROI) calculator for childhood obesity interventions so that companies can identify earlier returns on their investment beyond reduced health-care costs.

      Childhood obesity is too prevalent to be solved by a single company, industry, drug, application, or program. In addition to the physical and emotional impact on children, society bears a cost through excess health care spending, lost workforce productivity, poor school performance, and increased family trauma. Meaningful solutions require multiple organizations, representing different parts of society, working together with a common understanding of the problem, the economic benefits, and the return on investment. ROI is particularly difficult to defend for any single organization because investment and return can be separated by many years and involve asymmetric investments, returns, and allocation of risk. Hosoi’s project will consider the incentives for a particular entity to invest in programs in order to reduce childhood obesity.

      Hosoi will be joined by graduate students Pragya Neupane and Rachael Kha, both of IDSS, as well a team from Accenture that includes Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences; Kaveh Safavi, senior managing director in Accenture Health Industry; and Elizabeth Naik, global health and public service research lead.

      Generating innovative organizational configurations and algorithms for dealing with the problem of post-pandemic employment

      Thomas Malone is the Patrick J. McGovern (1959) Professor of Management at the MIT Sloan School of Management and the founding director of the MIT Center for Collective Intelligence. His research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology. Malone will be joined in this project by John Horton, the Richard S. Leghorn (1939) Career Development Professor at the MIT Sloan School of Management, whose research focuses on the intersection of labor economics, market design, and information systems. Malone and Horton’s project will look to reshape the future of work with the help of lessons learned in the wake of the pandemic.

      The Covid-19 pandemic has been a major disrupter of work and employment, and it is not at all obvious how governments, businesses, and other organizations should manage the transition to a desirable state of employment as the pandemic recedes. Using natural language processing algorithms such as GPT-4, this project will look to identify new ways that companies can use AI to better match applicants to necessary jobs, create new types of jobs, assess skill training needed, and identify interventions to help include women and other groups whose employment was disproportionately affected by the pandemic.

      In addition to Malone and Horton, the research team will include Rob Laubacher, associate director and research scientist at the MIT Center for Collective Intelligence, and Kathleen Kennedy, executive director at the MIT Center for Collective Intelligence and senior director at MIT Horizon. The team will also include Nitu Nivedita, managing director of artificial intelligence at Accenture, and Thomas Hancock, data science senior manager at Accenture. More

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      in Energy

      To improve solar and other clean energy tech, look beyond hardware

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      To continue reducing the costs of solar energy and other clean energy technologies, scientists and engineers will likely need to focus, at least in part, on improving technology features that are not based on hardware, according to MIT researchers. They describe this finding and the mechanisms behind it today in Nature Energy.

      While the cost of installing a solar energy system has dropped by more than 99 percent since 1980, this new analysis shows that “soft technology” features, such as the codified permitting practices, supply chain management techniques, and system design processes that go into deploying a solar energy plant, contributed only 10 to 15 percent of total cost declines. Improvements to hardware features were responsible for the lion’s share.

      But because soft technology is increasingly dominating the total costs of installing solar energy systems, this trend threatens to slow future cost savings and hamper the global transition to clean energy, says the study’s senior author, Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society (IDSS).

      Trancik’s co-authors include lead author Magdalena M. Klemun, a former IDSS graduate student and postdoc who is now an assistant professor at the Hong Kong University of Science and Technology; Goksin Kavlak, a former IDSS graduate student and postdoc who is now an associate at the Brattle Group; and James McNerney, a former IDSS postdoc and now senior research fellow at the Harvard Kennedy School.

      The team created a quantitative model to analyze the cost evolution of solar energy systems, which captures the contributions of both hardware technology features and soft technology features.

      The framework shows that soft technology hasn’t improved much over time — and that soft technology features contributed even less to overall cost declines than previously estimated.

      Their findings indicate that to reverse this trend and accelerate cost declines, engineers could look at making solar energy systems less reliant on soft technology to begin with, or they could tackle the problem directly by improving inefficient deployment processes.  

      “Really understanding where the efficiencies and inefficiencies are, and how to address those inefficiencies, is critical in supporting the clean energy transition. We are making huge investments of public dollars into this, and soft technology is going to be absolutely essential to making those funds count,” says Trancik.

      “However,” Klemun adds, “we haven’t been thinking about soft technology design as systematically as we have for hardware. That needs to change.”

      The hard truth about soft costs

      Researchers have observed that the so-called “soft costs” of building a solar power plant — the costs of designing and installing the plant — are becoming a much larger share of total costs. In fact, the share of soft costs now typically ranges from 35 to 64 percent.

      “We wanted to take a closer look at where these soft costs were coming from and why they weren’t coming down over time as quickly as the hardware costs,” Trancik says.

      In the past, scientists have modeled the change in solar energy costs by dividing total costs into additive components — hardware components and nonhardware components — and then tracking how these components changed over time.

      “But if you really want to understand where those rates of change are coming from, you need to go one level deeper to look at the technology features. Then things split out differently,” Trancik says.

      The researchers developed a quantitative approach that models the change in solar energy costs over time by assigning contributions to the individual technology features, including both hardware features and soft technology features.

      For instance, their framework would capture how much of the decline in system installation costs — a soft cost — is due to standardized practices of certified installers — a soft technology feature. It would also capture how that same soft cost is affected by increased photovoltaic module efficiency — a hardware technology feature.

      With this approach, the researchers saw that improvements in hardware had the greatest impacts on driving down soft costs in solar energy systems. For example, the efficiency of photovoltaic modules doubled between 1980 and 2017, reducing overall system costs by 17 percent. But about 40 percent of that overall decline could be attributed to reductions in soft costs tied to improved module efficiency.

      The framework shows that, while hardware technology features tend to improve many cost components, soft technology features affect only a few.

      “You can see this structural difference even before you collect data on how the technologies have changed over time. That’s why mapping out a technology’s network of cost dependencies is a useful first step to identify levers of change, for solar PV and for other technologies as well,” Klemun notes.  

      Static soft technology

      The researchers used their model to study several countries, since soft costs can vary widely around the world. For instance, solar energy soft costs in Germany are about 50 percent less than those in the U.S.

      The fact that hardware technology improvements are often shared globally led to dramatic declines in costs over the past few decades across locations, the analysis showed. Soft technology innovations typically aren’t shared across borders. Moreover, the team found that countries with better soft technology performance 20 years ago still have better performance today, while those with worse performance didn’t see much improvement.

      This country-by-country difference could be driven by regulation and permitting processes, cultural factors, or by market dynamics such as how firms interact with each other, Trancik says.

      “But not all soft technology variables are ones that you would want to change in a cost-reducing direction, like lower wages. So, there are other considerations, beyond just bringing the cost of the technology down, that we need to think about when interpreting these results,” she says.

      Their analysis points to two strategies for reducing soft costs. For one, scientists could focus on developing hardware improvements that make soft costs more dependent on hardware technology variables and less on soft technology variables, such as by creating simpler, more standardized equipment that could reduce on-site installation time.

      Or researchers could directly target soft technology features without changing hardware, perhaps by creating more efficient workflows for system installation or automated permitting platforms.

      “In practice, engineers will often pursue both approaches, but separating the two in a formal model makes it easier to target innovation efforts by leveraging specific relationships between technology characteristics and costs,” Klemun says.

      “Often, when we think about information processing, we are leaving out processes that still happen in a very low-tech way through people communicating with one another. But it is just as important to think about that as a technology as it is to design fancy software,” Trancik notes.

      In the future, she and her collaborators want to apply their quantitative model to study the soft costs related to other technologies, such as electrical vehicle charging and nuclear fission. They are also interested in better understanding the limits of soft technology improvement, and how one could design better soft technology from the outset.

      This research is funded by the U.S. Department of Energy Solar Energy Technologies Office. More

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      in Environment

      The curse of variety in transportation systems

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      Cathy Wu has always delighted in systems that run smoothly. In high school, she designed a project to optimize the best route for getting to class on time. Her research interests and career track are evidence of a propensity for organizing and optimizing, coupled with a strong sense of responsibility to contribute to society instilled by her parents at a young age.

      As an undergraduate at MIT, Wu explored domains like agriculture, energy, and education, eventually homing in on transportation. “Transportation touches each of our lives,” she says. “Every day, we experience the inefficiencies and safety issues as well as the environmental harms associated with our transportation systems. I believe we can and should do better.”

      But doing so is complicated. Consider the long-standing issue of traffic systems control. Wu explains that it is not one problem, but more accurately a family of control problems impacted by variables like time of day, weather, and vehicle type — not to mention the types of sensing and communication technologies used to measure roadway information. Every differentiating factor introduces an exponentially larger set of control problems. There are thousands of control-problem variations and hundreds, if not thousands, of studies and papers dedicated to each problem. Wu refers to the sheer number of variations as the curse of variety — and it is hindering innovation.

      Play video

      “To prove that a new control strategy can be safely deployed on our streets can take years. As time lags, we lose opportunities to improve safety and equity while mitigating environmental impacts. Accelerating this process has huge potential,” says Wu.  

      Which is why she and her group in the MIT Laboratory for Information and Decision Systems are devising machine learning-based methods to solve not just a single control problem or a single optimization problem, but families of control and optimization problems at scale. “In our case, we’re examining emerging transportation problems that people have spent decades trying to solve with classical approaches. It seems to me that we need a different approach.”

      Optimizing intersections

      Currently, Wu’s largest research endeavor is called Project Greenwave. There are many sectors that directly contribute to climate change, but transportation is responsible for the largest share of greenhouse gas emissions — 29 percent, of which 81 percent is due to land transportation. And while much of the conversation around mitigating environmental impacts related to mobility is focused on electric vehicles (EVs), electrification has its drawbacks. EV fleet turnover is time-consuming (“on the order of decades,” says Wu), and limited global access to the technology presents a significant barrier to widespread adoption.

      Wu’s research, on the other hand, addresses traffic control problems by leveraging deep reinforcement learning. Specifically, she is looking at traffic intersections — and for good reason. In the United States alone, there are more than 300,000 signalized intersections where vehicles must stop or slow down before re-accelerating. And every re-acceleration burns fossil fuels and contributes to greenhouse gas emissions.

      Highlighting the magnitude of the issue, Wu says, “We have done preliminary analysis indicating that up to 15 percent of land transportation CO2 is wasted through energy spent idling and re-accelerating at intersections.”

      To date, she and her group have modeled 30,000 different intersections across 10 major metropolitan areas in the United States. That is 30,000 different configurations, roadway topologies (e.g., grade of road or elevation), different weather conditions, and variations in travel demand and fuel mix. Each intersection and its corresponding scenarios represents a unique multi-agent control problem.

      Wu and her team are devising techniques that can solve not just one, but a whole family of problems comprised of tens of thousands of scenarios. Put simply, the idea is to coordinate the timing of vehicles so they arrive at intersections when traffic lights are green, thereby eliminating the start, stop, re-accelerate conundrum. Along the way, they are building an ecosystem of tools, datasets, and methods to enable roadway interventions and impact assessments of strategies to significantly reduce carbon-intense urban driving.

      Play video

      Their collaborator on the project is the Utah Department of Transportation, which Wu says has played an essential role, in part by sharing data and practical knowledge that she and her group otherwise would not have been able to access publicly.

      “I appreciate industry and public sector collaborations,” says Wu. “When it comes to important societal problems, one really needs grounding with practitioners. One needs to be able to hear the perspectives in the field. My interactions with practitioners expand my horizons and help ground my research. You never know when you’ll hear the perspective that is the key to the solution, or perhaps the key to understanding the problem.”

      Finding the best routes

      In a similar vein, she and her research group are tackling large coordination problems. For example, vehicle routing. “Every day, delivery trucks route more than a hundred thousand packages for the city of Boston alone,” says Wu. Accomplishing the task requires, among other things, figuring out which trucks to use, which packages to deliver, and the order in which to deliver them as efficiently as possible. If and when the trucks are electrified, they will need to be charged, adding another wrinkle to the process and further complicating route optimization.

      The vehicle routing problem, and therefore the scope of Wu’s work, extends beyond truck routing for package delivery. Ride-hailing cars may need to pick up objects as well as drop them off; and what if delivery is done by bicycle or drone? In partnership with Amazon, for example, Wu and her team addressed routing and path planning for hundreds of robots (up to 800) in their warehouses.

      Every variation requires custom heuristics that are expensive and time-consuming to develop. Again, this is really a family of problems — each one complicated, time-consuming, and currently unsolved by classical techniques — and they are all variations of a central routing problem. The curse of variety meets operations and logistics.

      By combining classical approaches with modern deep-learning methods, Wu is looking for a way to automatically identify heuristics that can effectively solve all of these vehicle routing problems. So far, her approach has proved successful.

      “We’ve contributed hybrid learning approaches that take existing solution methods for small problems and incorporate them into our learning framework to scale and accelerate that existing solver for large problems. And we’re able to do this in a way that can automatically identify heuristics for specialized variations of the vehicle routing problem.” The next step, says Wu, is applying a similar approach to multi-agent robotics problems in automated warehouses.

      Wu and her group are making big strides, in part due to their dedication to use-inspired basic research. Rather than applying known methods or science to a problem, they develop new methods, new science, to address problems. The methods she and her team employ are necessitated by societal problems with practical implications. The inspiration for the approach? None other than Louis Pasteur, who described his research style in a now-famous article titled “Pasteur’s Quadrant.” Anthrax was decimating the sheep population, and Pasteur wanted to better understand why and what could be done about it. The tools of the time could not solve the problem, so he invented a new field, microbiology, not out of curiosity but out of necessity. More

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      in Environment

      Q&A: Are far-reaching fires the new normal?

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      Where there’s smoke, there is fire. But with climate change, larger and longer-burning wildfires are sending smoke farther from their source, often to places that are unaccustomed to the exposure. That’s been the case this week, as smoke continues to drift south from massive wildfires in Canada, prompting warnings of hazardous air quality, and poor visibility in states across New England, the mid-Atlantic, and the Midwest.

      As wildfire season is just getting going, many may be wondering: Are the air-polluting effects of wildfires a new normal?

      MIT News spoke with Professor Colette Heald of the Department of Civil and Environmental Engineering and the Department of Earth, Atmospheric and Planetary Sciences, and Professor Noelle Selin of the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences. Heald specializes in atmospheric chemistry and has studied the climate and health effects associated with recent wildfires, while Selin works with atmospheric models to track air pollutants around the world, which she uses to inform policy decisions on mitigating  pollution and climate change. The researchers shared some of their insights on the immediate impacts of Canada’s current wildfires and what downwind regions may expect in the coming months, as the wildfire season stretches into summer.  

      Q: What role has climate change and human activity played in the wildfires we’ve seen so far this year?

      Heald: Unusually warm and dry conditions have dramatically increased fire susceptibility in Canada this year. Human-induced climate change makes such dry and warm conditions more likely. Smoke from fires in Alberta and Nova Scotia in May, and Quebec in early June, has led to some of the worst air quality conditions measured locally in Canada. This same smoke has been transported into the United States and degraded air quality here as well. Local officials have determined that ignitions have been associated with lightning strikes, but human activity has also played a role igniting some of the fires in Alberta.

      Q: What can we expect for the coming months in terms of the pattern of wildfires and their associated air pollution across the United States?

      Heald: The Government of Canada is projecting higher-than-normal fire activity throughout the 2023 fire season. Fire susceptibility will continue to respond to changing weather conditions, and whether the U.S. is impacted will depend on the winds and how air is transported across those regions. So far, the fire season in the United States has been below average, but fire risk is expected to increase modestly through the summer, so we may see local smoke influences as well.

      Q: How has air pollution from wildfires affected human health in the U.S. this year so far?

      Selin: The pollutant of most concern in wildfire smoke is fine particulate matter (PM2.5) – fine particles in the atmosphere that can be inhaled deep into the lungs, causing health damages. Exposure to PM2.5 causes respiratory and cardiovascular damage, including heart attacks and premature deaths. It can also cause symptoms like coughing and difficulty breathing. In New England this week, people have been breathing much higher concentrations of PM2.5 than usual. People who are particularly vulnerable to the effects are likely experiencing more severe impacts, such as older people and people with underlying conditions. But PM2.5 affects everyone. While the number and impact of wildfires varies from year to year, the associated air pollution from them generally lead to tens of thousands of premature deaths in the U.S. overall annually. There is also some evidence that PM2.5 from fires could be particularly damaging to health.

      While we in New England usually have relatively lower levels of pollution, it’s important also to note that some cities around the globe experience very high PM2.5 on a regular basis, not only from wildfires, but other sources such as power plants and industry. So, while we’re feeling the effects over the past few days, we should remember the broader importance of reducing PM2.5 levels overall for human health everywhere.

      Q: While firefighters battle fires directly this wildfire season, what can we do to reduce the effects of associated air pollution? And what can we do in the long-term, to prevent or reduce wildfire impacts?

      Selin: In the short term, protecting yourself from the impacts of PM2.5 is important. Limiting time outdoors, avoiding outdoor exercise, and wearing a high-quality mask are some strategies that can minimize exposure. Air filters can help reduce the concentrations of particles in indoor air. Taking measures to avoid exposure is particularly important for vulnerable groups. It’s also important to note that these strategies aren’t equally possible for everyone (for example, people who work outside) — stressing the importance of developing new strategies to address the underlying causes of increasing wildfires.

      Over the long term, mitigating climate change is important — because warm and dry conditions lead to wildfires, warming increases fire risk. Preventing the fires that are ignited by people or human activities can help.  Another way that damages can be mitigated in the longer term is by exploring land management strategies that could help manage fire intensity. More