Consortium led by MIT, Harvard University, and Mass General Brigham spurs development of 408 MW of renewable energy

MIT is co-leading an effort to enable the development of two new large-scale renewable energy projects in regions with carbon-intensive electrical grids: Big Elm Solar in Bell County, Texas, came online this year, and the Bowman Wind Project in Bowman County, North Dakota, is expected to be operational in 2026. Together, they will add a combined 408 megawatts (MW) of new renewable energy capacity to the power grid. This work is a critical part of MIT’s strategy to achieve its goal of net-zero carbon emissions by 2026.

The Consortium for Climate Solutions, which includes MIT and 10 other Massachusetts organizations, seeks to eliminate close to 1 million metric tons of greenhouse gases each year — more than five times the annual direct emissions from MIT’s campus — by committing to purchase an estimated 1.3-million-megawatt hours of new solar and wind electricity generation annually.

“MIT has mobilized on multiple fronts to expedite solutions to climate change,” says Glen Shor, executive vice president and treasurer. “Catalyzing these large-scale renewable projects is an important part of our comprehensive efforts to reduce carbon emissions from generating energy. We are pleased to work in partnership with other local enterprises and organizations to amplify the impact we could achieve individually.”

The two new projects complement MIT’s existing 25-year power purchase agreement established with Summit Farms in 2016, which enabled the construction of a roughly 650-acre, 60 MW solar farm on farmland in North Carolina, leading to the early retirement of a coal-fired plant nearby. Its success has inspired other institutions to implement similar aggregation models.

A collective approach to enable global impact

MIT, Harvard University, and Mass General Brigham formed the consortium in 2020 to provide a structure to accelerate global emissions reductions through the development of large-scale renewable energy projects — accelerating and expanding the impact of each institution’s greenhouse gas reduction initiatives. As the project’s anchors, they collectively procured the largest volume of energy through the aggregation.  

The consortium engaged with PowerOptions, a nonprofit energy-buying consortium, which offered its members the opportunity to participate in the projects. The City of Cambridge, Beth Israel Lahey, Boston Children’s Hospital, Dana-Farber Cancer Institute, Tufts University, the Mass Convention Center Authority, the Museum of Fine Arts, and GBH later joined the consortium through PowerOptions. 
 
The consortium vetted over 125 potential projects against its rigorous project evaluation criteria. With faculty and MIT stakeholder input on a short list of the highest-ranking projects, it ultimately chose Bowman Wind and Big Elm Solar. Collectively, these two projects will achieve large greenhouse gas emissions reductions in two of the most carbon-intensive electrical grid regions in the United States and create clean energy generation sources to reduce negative health impacts.

“Enabling these projects in regions where the grids are most carbon-intensive allows them to have the greatest impact. We anticipate these projects will prevent two times more emissions per unit of generated electricity than would a similar-scale project in New England,” explains Vice President for Campus Services and Stewardship Joe Higgins.

By all consortium institutions making significant 15-to-20-year financial commitments to buy electricity, the developer was able to obtain critical external project financing to build the projects. Owned and operated by Apex Clean Energy, the projects will add new renewable electricity to the grid equivalent to powering 130,000 households annually, displacing over 950,000 metric tons of greenhouse gas emissions each year from highly carbon-intensive power plants in the region. 

Complementary decarbonization work underway 

In addition to investing in offsite renewable energy projects, many consortium members have developed strategies to reduce and eliminate their own direct emissions. At MIT, accomplishing this requires transformative change in how energy is generated, distributed, and used on campus. Efforts underway include the installation of solar panels on campus rooftops that will increase renewable energy generation four-fold by 2026; continuing to transition our heat distribution infrastructure from steam-based to hot water-based; utilizing design and construction that minimizes emissions and increases energy efficiency; employing AI-enabled sensors to optimize temperature set points and reduce energy use in buildings; and converting MIT’s vehicle fleet to all-electric vehicles while adding more electric car charging stations.

The Institute has also upgraded the Central Utilities Plant, which uses advanced co-generation technology to produce power that is up to 20 percent less carbon-intensive than that from the regional power grid. MIT is charting the course toward a next-generation district energy system, with a comprehensive planning initiative to revolutionize its campus energy infrastructure. The effort is exploring leading-edge technology, including industrial-scale heat pumps, geothermal exchange, micro-reactors, bio-based fuels, and green hydrogen derived from renewable sources as solutions to achieve full decarbonization of campus operations by 2050.

“At MIT, we are focused on decarbonizing our own campus as well as the role we can play in solving climate at the largest of scales, including supporting a cleaner grid in line with the call to triple renewables globally by 2030. By enabling these large-scale renewable projects, we can have an immediate and significant impact of reducing emissions through the urgently needed decarbonization of regional power grids,” says Julie Newman, MIT’s director of sustainability.  

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A vision for U.S. science success

White House science advisor Arati Prabhakar expressed confidence in U.S. science and technology capacities during a talk on Wednesday about major issues the country must tackle.

“Let me start with the purpose of science and technology and innovation, which is to open possibilities so that we can achieve our great aspirations,” said Prabhakar, who is the director of the Office of Science and Technology Policy (OSTP) and a co-chair of the President’s Council of Advisors on Science and Technology (PCAST). 

“The aspirations that we have as a country today are as great as they have ever been,” she added.

Much of Prabhakar’s talk focused on three major issues in science and technology development: cancer prevention, climate change, and AI. In the process, she also emphasized the necessity for the U.S. to sustain its global leadership in research across domains of science and technology, which she called “one of America’s long-time strengths.”

“Ever since the end of the Second World War, we said we’re going in on basic research, we’re going to build our universities’ capacity to do it, we have an unparalleled basic research capacity, and we should always have that,” said Prabhakar.

“We have gotten better, I think, in recent years at commercializing technology from our basic research,” Prabhakar added, noting, “Capital moves when you can see profit and growth.” The Biden administration, she said, has invested in a variety of new ways for the public and private sector to work together to massively accelerate the movement of technology into the market.

Wednesday’s talk drew a capacity audience of nearly 300 people in MIT’s Wong Auditorium and was hosted by the Manufacturing@MIT Working Group. The event included introductory remarks by Suzanne Berger, an Institute Professor and a longtime expert on the innovation economy, and Nergis Mavalvala, dean of the School of Science and an astrophysicist and leader in gravitational-wave detection.

Introducing Mavalvala, Berger said the 2015 announcement of the discovery of gravitational waves “was the day I felt proudest and most elated to be a member of the MIT community,” and noted that U.S. government support helped make the research possible. Mavalvala, in turn, said MIT was “especially honored” to hear Prabhakar discuss leading-edge research and acknowledge the role of universities in strengthening the country’s science and technology sectors.

Prabhakar has extensive experience in both government and the private sector. She has been OSTP director and co-chair of PCAST since October of 2022. She served as director of the Defense Advanced Research Projects Agency (DARPA) from 2012 to 2017 and director of the National Institute of Standards and Technology (NIST) from 1993 to 1997.

She has also held executive positions at Raychem and Interval Research, and spent a decade at the investment firm U.S. Venture Partners. An engineer by training, Prabhakar earned a BS in electrical engineering from Texas Tech University in 1979, an MA in electrical engineering from Caltech in 1980, and a PhD in applied physics from Caltech in 1984.

Among other remarks about medicine, Prabhakar touted the Biden administration’s “Cancer Moonshot” program, which aims to cut the cancer death rate in half over the next 25 years through multiple approaches, from better health care provision and cancer detection to limiting public exposure to carcinogens. We should be striving, Prabhakar said, for “a future in which people take good health for granted and can get on with their lives.”

On AI, she heralded both the promise and concerns about technology, saying, “I think it’s time for active steps to get on a path to where it actually allows people to do more and earn more.”

When it comes to climate change, Prabhakar said, “We all understand that the climate is going to change. But it’s in our hands how severe those changes get. And it’s possible that we can build a better future.” She noted the bipartisan infrastructure bill signed into law in 2021 and the Biden administration’s Inflation Reduction Act as important steps forward in this fight.

“Together those are making the single biggest investment anyone anywhere on the planet has ever made in the clean energy transition,” she said. “I used to feel hopeless about our ability to do that, and it gives me tremendous hope.”

After her talk, Prabhakar was joined onstage for a group discussion with the three co-presidents of the MIT Energy and Climate Club: Laurentiu Anton, a doctoral candidate in electrical engineering and computer science; Rosie Keller, an MBA candidate at the MIT Sloan School of Management; and Thomas Lee, a doctoral candidate in MIT’s Institute for Data, Systems, and Society.

Asked about the seemingly sagging public confidence in science today, Prabhakar offered a few thoughts.

“The first thing I would say is, don’t take it personally,” Prabhakar said, noting that any dip in public regard for science is less severe than the diminished public confidence in other institutions.

Adding some levity, she observed that in polling about which occupations are regarded as being desirable for a marriage partner to have, “scientist” still ranks highly.

“Scientists still do really well on that front, we’ve got that going for us,” she quipped.

More seriously, Prabhakar observed, rather than “preaching” at the public, scientists should recognize that “part of the job for us is to continue to be clear about what we know are the facts, and to present them clearly but humbly, and to be clear that we’re going to continue working to learn more.” At the same time, she continued, scientists can always reinforce that “oh, by the way, facts are helpful things that can actually help you make better choices about how the future turns out. I think that would be better in my view.”

Prabhakar said that her White House work had been guided, in part, by one of the overarching themes that President Biden has often reinforced.

“He thinks about America as a nation that can be described in a single word, and that word is ‘possibilities,’” she said. “And that idea, that is such a big idea, it lights me up. I think of what we do in the world of science and technology and innovation as really part and parcel of creating those possibilities.”

Ultimately, Prabhakar said, at all times and all points in American history, scientists and technologists must continue “to prove once more that when people come together and do this work … we do it in a way that builds opportunity and expands opportunity for everyone in our country. I think this is the great privilege we all have in the work we do, and it’s also our responsibility.”

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Catherine Wolfram: High-energy scholar

In the mid 2000s, Catherine Wolfram PhD ’96 reached what she calls “an inflection point” in her career. After about a decade of studying U.S. electricity markets, she had come to recognize that “you couldn’t study the energy industries without thinking about climate mitigation,” as she puts it.

At the same time, Wolfram understood that the trajectory of energy use in the developing world was a massively important part of the climate picture. To get a comprehensive grasp on global dynamics, she says, “I realized I needed to start thinking about the rest of the world.”

An accomplished scholar and policy expert, Wolfram has been on the faculty at Harvard University, the University of California at Berkeley — and now MIT, where she is the William Barton Rogers Professor in Energy. She has also served as deputy assistant secretary for climate and energy economics at the U.S. Treasury.

Yet even leading experts want to keep learning. So, when she hit that inflection point, Wolfram started carving out a new phase of her research career.

“One of the things I love about being an academic is, I could just decide to do that,” Wolfram says. “I didn’t need to check with a boss. I could just pivot my career to being more focused to thinking about energy in the developing world.”

Over the last decade, Wolfram has published a wide array of original studies about energy consumption in the developing world. From Kenya to Mexico to South Asia, she has shed light on the dynamics of economics growth and energy consumption — while spending some of that time serving the government too. Last year, Wolfram joined the faculty of the MIT Sloan School of Management, where her work bolsters the Institute’s growing effort to combat climate change.

Studying at MIT

Wolfram largely grew up in Minnesota, where her father was a legal scholar, although he moved to Cornell University around the time she started high school. As an undergraduate, she majored in economics at Harvard University, and after graduation she worked first for a consultant, then for the Massachusetts Department of Public Utilities, the agency regulating energy rates. 

In the latter job, Wolfram kept noticing that people were often citing the research of an MIT scholar named Paul Joskow (who is now the Elizabeth and James Killian Professor of Economics Emeritus in MIT’s Department of Economics) and Richard Schmalensee (a former dean of the MIT Sloan School of Management and now the Howard W. Johnson Professor of Management Emeritus). Seeing how consequential economics research could be for policymaking, Wolfram decided to get a PhD in the field and was accepted into MIT’s doctoral program.

“I went into graduate school with an unusually specific view of what I wanted to do,” Wolfram says. “I wanted to work with Paul Joskow and Dick Schmalensee on electricity markets, and that’s how I wound up here.”

At MIT, Wolfram also ended up working extensively with Nancy Rose, the Charles P. Kindleberger Professor of Applied Economics and a former head of the Department of Economics, who helped oversee Wolfram’s thesis; Rose has extensively studied market regulation as well.

Wolfram’s dissertation research largely focused on price-setting behavior in the U.K.’s newly deregulated electricity markets, which, it turned out, applied handily to the U.S., where a similar process was taking place. “I was fortunate because this was around the time California was thinking about restructuring, as it was known,” Wolfram says. She spent four years on the faculty at Harvard, then moved to UC Berkeley. Wolfram’s studies have shown that deregulation has had some medium-term benefits, for instance in making power plants operate more efficiently.

Turning on the AC

By around 2010, though, Wolfram began shifting her scholarly focus in earnest, conducting innovative studies about energy in the developing world. One strand of her research has centered on Kenya, to better understand how more energy access for people without electricity might fit into growth in the developing world.

In this case, Wolfram’s perhaps surprising conclusion is that electrification itself is not a magic ticket to prosperity; people without electricity are more eager to adopt it when they have a practical economic need for it. Meanwhile, they have other essential needs that are not necessarily being addressed.

“The 800 million people in the world who don’t have electricity also don’t have access to good health care or running water,” Wolfram says. “Giving them better housing infrastructure is important, and harder to tackle. It’s not clear that bringing people electricity alone is the single most useful thing from a development perspective. Although electricity is a super-important component of modern living.”

Wolfram has even delved into topics such as air conditioner use in the developing world — an important driver of energy use. As her research shows, many countries, with a combined population far bigger than the U.S., are among the fastest-growing adopters of air conditioners and have an even greater need for them, based on their climates. Adoption of air conditioning within those countries also is characterized by marked economic inequality.

From early 2021 until late 2022, Wolfram also served in the administration of President Joe Biden, where her work also centered on global energy issues. Among other things, Wolfram was part of the team working out a price-cap policy for Russian oil exports, a concept that she thinks could be applied to many other products globally. Although, she notes, working with countries heavily dependent on exporting energy materials will always require careful engagement.

“We need to be mindful of that dependence and importance as we go through this massive effort to decarbonize the energy sector and shift it to a whole new paradigm,” Wolfram says.

At MIT again

Still, she notes, the world does need a whole new energy paradigm, and fast. Her arrival at MIT overlaps with the emergence of a new Institute-wide effort, the Climate Project at MIT, that aims to accelerate and scale climate solutions and good climate policy, including through the new Climate Policy Center at MIT Sloan. That kind of effort, Wolfram says, matters to her.

“It’s part of why I’ve come to MIT,” Wolfram says. “Technology will be one part of the climate solution, but I do think an innovative mindset, how can we think about doing things better, can be productively applied to climate policy.” On being at MIT, she adds: “It’s great, it’s awesome. One of the things that pleasantly surprised me is how tight-knit and friendly the MIT faculty all are, and how many interactions I’ve had with people from other departments.”

Wolfram has also been enjoying her teaching at MIT, and will be offering a large class in spring 2025, 15.016 (Climate and Energy in the Global Economy), that she debuted this past academic year.

“It’s super fun to have students from around the world, who have personal stories and knowledge of energy systems in their countries and can contribute to our discussions,” she says.

When it comes to tackling climate change, many things seem daunting. But there is still a world of knowledge to be acquired while we try to keep the planet from overheating, and Wolfram has a can-do attitude about learning more and applying those lessons.

“We’ve made a lot of progress,” Wolfram says. “But we still have a lot more to do.”

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Advancing urban tree monitoring with AI-powered digital twins

The Irish philosopher George Berkely, best known for his theory of immaterialism, once famously mused, “If a tree falls in a forest and no one is around to hear it, does it make a sound?”

What about AI-generated trees? They probably wouldn’t make a sound, but they will be critical nonetheless for applications such as adaptation of urban flora to climate change. To that end, the novel “Tree-D Fusion” system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Google, and Purdue University merges AI and tree-growth models with Google's Auto Arborist data to create accurate 3D models of existing urban trees. The project has produced the first-ever large-scale database of 600,000 environmentally aware, simulation-ready tree models across North America.

“We’re bridging decades of forestry science with modern AI capabilities,” says Sara Beery, MIT electrical engineering and computer science (EECS) assistant professor, MIT CSAIL principal investigator, and a co-author on a new paper about Tree-D Fusion. “This allows us to not just identify trees in cities, but to predict how they’ll grow and impact their surroundings over time. We’re not ignoring the past 30 years of work in understanding how to build these 3D synthetic models; instead, we’re using AI to make this existing knowledge more useful across a broader set of individual trees in cities around North America, and eventually the globe.”

Tree-D Fusion builds on previous urban forest monitoring efforts that used Google Street View data, but branches it forward by generating complete 3D models from single images. While earlier attempts at tree modeling were limited to specific neighborhoods, or struggled with accuracy at scale, Tree-D Fusion can create detailed models that include typically hidden features, such as the back side of trees that aren’t visible in street-view photos.

The technology’s practical applications extend far beyond mere observation. City planners could use Tree-D Fusion to one day peer into the future, anticipating where growing branches might tangle with power lines, or identifying neighborhoods where strategic tree placement could maximize cooling effects and air quality improvements. These predictive capabilities, the team says, could change urban forest management from reactive maintenance to proactive planning.

A tree grows in Brooklyn (and many other places)

The researchers took a hybrid approach to their method, using deep learning to create a 3D envelope of each tree’s shape, then using traditional procedural models to simulate realistic branch and leaf patterns based on the tree’s genus. This combo helped the model predict how trees would grow under different environmental conditions and climate scenarios, such as different possible local temperatures and varying access to groundwater.

Now, as cities worldwide grapple with rising temperatures, this research offers a new window into the future of urban forests. In a collaboration with MIT’s Senseable City Lab, the Purdue University and Google team is embarking on a global study that re-imagines trees as living climate shields. Their digital modeling system captures the intricate dance of shade patterns throughout the seasons, revealing how strategic urban forestry could hopefully change sweltering city blocks into more naturally cooled neighborhoods.

“Every time a street mapping vehicle passes through a city now, we’re not just taking snapshots — we’re watching these urban forests evolve in real-time,” says Beery. “This continuous monitoring creates a living digital forest that mirrors its physical counterpart, offering cities a powerful lens to observe how environmental stresses shape tree health and growth patterns across their urban landscape.”

AI-based tree modeling has emerged as an ally in the quest for environmental justice: By mapping urban tree canopy in unprecedented detail, a sister project from the Google AI for Nature team has helped uncover disparities in green space access across different socioeconomic areas. “We’re not just studying urban forests — we’re trying to cultivate more equity,” says Beery. The team is now working closely with ecologists and tree health experts to refine these models, ensuring that as cities expand their green canopies, the benefits branch out to all residents equally.

It’s a breeze

While Tree-D fusion marks some major “growth” in the field, trees can be uniquely challenging for computer vision systems. Unlike the rigid structures of buildings or vehicles that current 3D modeling techniques handle well, trees are nature’s shape-shifters — swaying in the wind, interweaving branches with neighbors, and constantly changing their form as they grow. The Tree-D fusion models are “simulation-ready” in that they can estimate the shape of the trees in the future, depending on the environmental conditions.

“What makes this work exciting is how it pushes us to rethink fundamental assumptions in computer vision,” says Beery. “While 3D scene understanding techniques like photogrammetry or NeRF [neural radiance fields] excel at capturing static objects, trees demand new approaches that can account for their dynamic nature, where even a gentle breeze can dramatically alter their structure from moment to moment.”

The team’s approach of creating rough structural envelopes that approximate each tree’s form has proven remarkably effective, but certain issues remain unsolved. Perhaps the most vexing is the “entangled tree problem;” when neighboring trees grow into each other, their intertwined branches create a puzzle that no current AI system can fully unravel.

The scientists see their dataset as a springboard for future innovations in computer vision, and they’re already exploring applications beyond street view imagery, looking to extend their approach to platforms like iNaturalist and wildlife camera traps.

“This marks just the beginning for Tree-D Fusion,” says Jae Joong Lee, a Purdue University PhD student who developed, implemented and deployed the Tree-D-Fusion algorithm. “Together with my collaborators, I envision expanding the platform’s capabilities to a planetary scale. Our goal is to use AI-driven insights in service of natural ecosystems — supporting biodiversity, promoting global sustainability, and ultimately, benefiting the health of our entire planet.”

Beery and Lee’s co-authors are Jonathan Huang, Scaled Foundations head of AI (formerly of Google); and four others from Purdue University: PhD students Jae Joong Lee and Bosheng Li, Professor and Dean's Chair of Remote Sensing Songlin Fei, Assistant Professor Raymond Yeh, and Professor and Associate Head of Computer Science Bedrich Benes. Their work is based on efforts supported by the United States Department of Agriculture’s (USDA) Natural Resources Conservation Service and is directly supported by the USDA’s National Institute of Food and Agriculture. The researchers presented their findings at the European Conference on Computer Vision this month. 

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Your child, the sophisticated language learner

As young children, how do we build our vocabulary? Even by age 1, many infants seem to think that if they hear a new word, it means something different from the words they already know. But why they think so has remained subject to inquiry among scholars for the last 40 years.

A new study carried out at the MIT Language Acquisition Lab offers a novel insight into the matter: Sentences contain subtle hints in their grammar that tell young children about the meaning of new words. The finding, based on experiments with 2-year-olds, suggests that even very young kids are capable of absorbing grammatical cues from language and leveraging that information to acquire new words.

“Even at a surprisingly young age, kids have sophisticated knowledge of the grammar of sentences and can use that to learn the meanings of new words,” says Athulya Aravind, an associate professor of linguistics at MIT.

The new insight stands in contrast to a prior explanation for how children build vocabulary: that they rely on the concept of “mutual exclusivity,” meaning they treat each new word as corresponding to a new object or category. Instead, the new research shows how extensively children respond directly to grammatical information when interpreting words.

“For us it’s very exciting because it’s a very simple idea that explains so much about how children understand language,” says Gabor Brody, a postdoc at Brown University, who is the first author of the paper.

The paper is titled, “Why Do Children Think Words Are Mutually Exclusive?” It is published in advance online form in Psychological Science. The authors are Brody; Roman Feiman, the Thomas J. and Alice M. Tisch Assistant Professor of Cognitive and Psychological Sciences and Linguistics at Brown; and Aravind, the Alfred Henry and Jean Morrison Hayes Career Development Associate Professor in MIT’s Department of Linguistics and Philosophy.

Focusing on focus

Many scholars have thought that young children, when learning new words, have an innate bias toward mutual exclusivity, which could explain how children learn some of their new words. However, the concept of mutual exclusivity has never been airtight: Words like “bat” refer to multiple kinds of objects, while any object can be described using countlessly many words. For instance a rabbit can be called not only a “rabbit” or a “bunny,” but also an “animal,” or a “beauty,” and in some contexts even a “delicacy.” Despite this lack of perfect one-to-one mapping between words and objects, mutual exclusivity has still been posited as a strong tendency in children’s word learning.

What Aravind, Brody, and Fieman propose is that children have no such tendency, and instead rely on so-called “focus” signals to decide what a new word means. Linguists use the term “focus” to refer to the way we emphasize or stress certain words to signal some kind of contrast. Depending on what is focused, the same sentence can have different implications. “Carlos gave Lewis a Ferrari” implies contrast with other possible cars — he could have given Lewis a Mercedes. But “Carlos gave Lewis a Ferrari” implies contrast with other people — he could have given Alexandra a Ferrari.

The researchers’ experiments manipulated focus in three experiments with a total of 106 children. The participants watched videos of a cartoon fox who asked them to point to different objects.

The first experiment established how focus influences kids’ choice between two objects when they hear a label, like “toy,” that could, in principle, correspond to either of the two. After giving a name to one of the two objects (“Look, I am pointing to the blicket”), the fox told the child, “Now you point to the toy!” Children were divided into two groups. One group heard “toy” without emphasis, while the other heard it with emphasis.

In the first version, “blicket” and “toy” plausibly refer to the same object. But in the second version, the added focus, through intonation, implies that “toy” contrasts with the previously discussed “blicket.” Without focus, only 24 percent of the respondents thought the words were mutually exclusive, whereas with the focus created by emphasizing “toy,” 89 percent of participants thought “blicket” and “toy” referred to different objects.

The second and third experiments showed that focus is not just key when it comes to words like “toy,” but it also affects the interpretation of new words children have never encountered before, like “wug” or “dax.” If a new word was said without focus, children thought the word meant the previously named object 71 percent of the time. But when hearing the new word spoken with focus, they thought it must refer to a new object 87 percent of the time.

“Even though they know nothing about this new word, when it was focused, that still told them something: Focus communicated to children the presence of a contrasting alternative, and they correspondingly understood the noun to refer to an object that had not previously been labeled,” Aravind explains.

She adds: “The particular claim we’re making is that there is no inherent bias in children toward mutual exclusivity. The only reason we make the corresponding inference is because focus tells you that the word means something different from another word. When focus goes away, children don’t draw those exclusivity inferences any more.”

The researchers believe the full set of experiments sheds new light on the issue.

“Earlier explanations of mutual exclusivity introduced a whole new problem,” Feiman says. “If kids assume words are mutually exclusive, how do they learn words that are not? After all, you can call the same animal either a rabbit or a bunny, and kids have to learn both of those at some point. Our finding explains why this isn't actually a problem. Kids won’t think the new word is mutually exclusive with the old word by default, unless adults tell them that it is — all adults have to do if the new word is not mutually exclusive is just say it without focusing it, and they’ll naturally do that if they're thinking about it as compatible.”

Learning language from language

The experiment, the researchers note, is the result of interdisciplinary research bridging psychology and linguistics — in this case, mobilizing the linguistics concept of focus to address an issue of interest in both fields.

“We are hopeful this will be a paper that shows that small, simple theories have a place in psychology,” Brody says. “It is a very small theory, not a huge model of the mind, but it completely flips the switch on some phenomena we thought we understood.”

If the new hypothesis is correct, the researchers may have developed a more robust explanation about how children correctly apply new words.

“An influential idea in language development is that children can use their existing knowledge of language to learn more language,” Aravind says. “We’re in a sense building on that idea, and saying that even in the simplest cases, aspects of language that children already know, in this case an understanding of focus, help them grasp the meanings of unknown words.”

The scholars acknowledge that more studies could further advance our knowledge about the issue. Future research, they note in the paper, could reexamine prior studies about mutual exclusivity, record and study naturalistic interactions between parents and children to see how focus is used, and examine the issue in other languages, especially those marking focus in alternate ways, such as word order.

The research was supported, in part, by a Jacobs Foundation Fellowship awarded to Feiman.

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3 Questions: Claire Wang on training the brain for memory sports

On Nov. 10, some of the country’s top memorizers converged on MIT’s Kresge Auditorium to compete in a “Tournament of Memory Champions” in front of a live audience.

The competition was split into four events: long-term memory, words-to-remember, auditory memory, and double-deck of cards, in which competitors must memorize the exact order of two decks of cards. In between the events, MIT faculty who are experts in the science of memory provided short talks and demos about memory and how to improve it. Among the competitors was MIT’s own Claire Wang, a sophomore majoring in electrical engineering and computer science. Wang has competed in memory sports for years, a hobby that has taken her around the world to learn from some of the best memorists on the planet. At the tournament, she tied for first place in the words-to-remember competition.

The event commemorated the 25th anniversary of the USA Memory Championship Organization (USAMC). USAMC sponsored the event in partnership with MIT’s McGovern Institute for Brain Research, the Department of Brain and Cognitive Sciences, the MIT Quest for Intelligence, and the company Lumosity.

MIT News sat down with Wang to learn more about her experience with memory competitions — and see if she had any advice for those of us with less-than-amazing memory skills.

Q: How did you come to get involved in memory competitions?

A: When I was in middle school, I read the book “Moonwalking with Einstein,” which is about a journalist’s journey from average memory to being named memory champion in 2006. My parents were also obsessed with this TV show where people were memorizing decks of cards and performing other feats of memory. I had already known about the concept of “memory palaces,” so I was inspired to explore memory sports. Somehow, I convinced my parents to let me take a gap year after seventh grade, and I travelled the world going to competitions and learning from memory grandmasters. I got to know the community in that time and I got to build my memory system, which was really fun. I did a lot less of those competitions after that year and some subsequent competitions with the USA memory competition, but it’s still fun to have this ability.

Q: What was the Tournament of Memory Champions like?

A: USAMC invited a lot of winners from previous years to compete, which was really cool. It was nice seeing a lot of people I haven’t seen in years. I didn’t compete in every event because I was too busy to do the long-term memory, which takes you two weeks of memorization work. But it was a really cool experience. I helped a bit with the brainstorming beforehand because I know one of the professors running it. We thought about how to give the talks and structure the event.

Then I competed in the words event, which is when they give you 300 words over 15 minutes, and the competitors have to recall each one in order in a round robin competition. You got two strikes. A lot of other competitions just make you write the words down. The round robin makes it more fun for people to watch. I tied with someone else — I made a dumb mistake — so I was kind of sad in hindsight, but being tied for first is still great.

Since I hadn't done this in a while (and I was coming back from a trip where I didn’t get much sleep), I was a bit nervous that my brain wouldn’t be able to remember anything, and I was pleasantly surprised I didn’t just blank on stage. Also, since I hadn’t done this in a while, a lot of my loci and memory palaces were forgotten, so I had to speed-review them before the competition. The words event doesn’t get easier over time — it’s just 300 random words (which could range from “disappointment” to “chair”) and you just have to remember the order.

Q: What is your approach to improving memory?

A: The whole idea is that we memorize images, feelings, and emotions much better than numbers or random words. The way it works in practice is we make an ordered set of locations in a “memory palace.” The palace could be anything. It could be a campus or a classroom or a part of a room, but you imagine yourself walking through this space, so there’s a specific order to it, and in every location I place certain information. This is information related to what I’m trying to remember. I have pictures I associate with words and I have specific images I correlate with numbers. Once you have a correlated image system, all you need to remember is a story, and then when you recall, you translate that back to the original information.

Doing memory sports really helps you with visualization, and being able to visualize things faster and better helps you remember things better. You start remembering with spaced repetition that you can talk yourself through. Allowing things to have an emotional connection is also important, because you remember emotions better. Doing memory competitions made me want to study neuroscience and computer science at MIT.

The specific memory sports techniques are not as useful in everyday life as you’d think, because a lot of the information we learn is more operative and requires intuitive understanding, but I do think they help in some ways. First, sometimes you have to initially remember things before you can develop a strong intuition later. Also, since I have to get really good at telling a lot of stories over time, I have gotten great at visualization and manipulating objects in my mind, which helps a lot. 

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Reality check on technologies to remove carbon dioxide from the air

In 2015, 195 nations plus the European Union signed the Paris Agreement and pledged to undertake plans designed to limit the global temperature increase to 1.5 degrees Celsius. Yet in 2023, the world exceeded that target for most, if not all of, the year — calling into question the long-term feasibility of achieving that target.

To do so, the world must reduce the levels of greenhouse gases in the atmosphere, and strategies for achieving levels that will “stabilize the climate” have been both proposed and adopted. Many of those strategies combine dramatic cuts in carbon dioxide (CO₂) emissions with the use of direct air capture (DAC), a technology that removes CO₂ from the ambient air. As a reality check, a team of researchers in the MIT Energy Initiative (MITEI) examined those strategies, and what they found was alarming: The strategies rely on overly optimistic — indeed, unrealistic — assumptions about how much CO₂ could be removed by DAC. As a result, the strategies won’t perform as predicted. Nevertheless, the MITEI team recommends that work to develop the DAC technology continue so that it’s ready to help with the energy transition — even if it’s not the silver bullet that solves the world’s decarbonization challenge.

DAC: The promise and the reality

Including DAC in plans to stabilize the climate makes sense. Much work is now under way to develop DAC systems, and the technology looks promising. While companies may never run their own DAC systems, they can already buy “carbon credits” based on DAC. Today, a multibillion-dollar market exists on which entities or individuals that face high costs or excessive disruptions to reduce their own carbon emissions can pay others to take emissions-reducing actions on their behalf. Those actions can involve undertaking new renewable energy projects or “carbon-removal” initiatives such as DAC or afforestation/reforestation (planting trees in areas that have never been forested or that were forested in the past). 

DAC-based credits are especially appealing for several reasons, explains Howard Herzog, a senior research engineer at MITEI. With DAC, measuring and verifying the amount of carbon removed is straightforward; the removal is immediate, unlike with planting forests, which may take decades to have an impact; and when DAC is coupled with CO₂ storage in geologic formations, the CO₂ is kept out of the atmosphere essentially permanently — in contrast to, for example, sequestering it in trees, which may one day burn and release the stored CO₂.

Will current plans that rely on DAC be effective in stabilizing the climate in the coming years? To find out, Herzog and his colleagues Jennifer Morris and Angelo Gurgel, both MITEI principal research scientists, and Sergey Paltsev, a MITEI senior research scientist — all affiliated with the MIT Center for Sustainability Science and Strategy (CS3) — took a close look at the modeling studies on which those plans are based.

Their investigation identified three unavoidable engineering challenges that together lead to a fourth challenge — high costs for removing a single ton of CO₂ from the atmosphere. The details of their findings are reported in a paper published in the journal One Earth on Sept. 20.

Challenge 1: Scaling up

When it comes to removing CO₂ from the air, nature presents “a major, non-negotiable challenge,” notes the MITEI team: The concentration of CO₂ in the air is extremely low — just 420 parts per million, or roughly 0.04 percent. In contrast, the CO₂ concentration in flue gases emitted by power plants and industrial processes ranges from 3 percent to 20 percent. Companies now use various carbon capture and sequestration (CCS) technologies to capture CO₂ from their flue gases, but capturing CO₂ from the air is much more difficult. To explain, the researchers offer the following analogy: “The difference is akin to needing to find 10 red marbles in a jar of 25,000 marbles of which 24,990 are blue [the task representing DAC] versus needing to find about 10 red marbles in a jar of 100 marbles of which 90 are blue [the task for CCS].”

Given that low concentration, removing a single metric ton (tonne) of CO₂ from air requires processing about 1.8 million cubic meters of air, which is roughly equivalent to the volume of 720 Olympic-sized swimming pools. And all that air must be moved across a CO₂-capturing sorbent — a feat requiring large equipment. For example, one recently proposed design for capturing 1 million tonnes of CO₂ per year would require an “air contactor” equivalent in size to a structure about three stories high and three miles long.

Recent modeling studies project DAC deployment on the scale of 5 to 40 gigatonnes of CO₂ removed per year. (A gigatonne equals 1 billion metric tonnes.) But in their paper, the researchers conclude that the likelihood of deploying DAC at the gigatonne scale is “highly uncertain.”

Challenge 2: Energy requirement

Given the low concentration of CO₂ in the air and the need to move large quantities of air to capture it, it’s no surprise that even the best DAC processes proposed today would consume large amounts of energy — energy that’s generally supplied by a combination of electricity and heat. Including the energy needed to compress the captured CO₂ for transportation and storage, most proposed processes require an equivalent of at least 1.2 megawatt-hours of electricity for each tonne of CO₂ removed.

The source of that electricity is critical. For example, using coal-based electricity to drive an all-electric DAC process would generate 1.2 tonnes of CO₂ for each tonne of CO₂ captured. The result would be a net increase in emissions, defeating the whole purpose of the DAC. So clearly, the energy requirement must be satisfied using either low-carbon electricity or electricity generated using fossil fuels with CCS. All-electric DAC deployed at large scale — say, 10 gigatonnes of CO₂ removed annually — would require 12,000 terawatt-hours of electricity, which is more than 40 percent of total global electricity generation today.

Electricity consumption is expected to grow due to increasing overall electrification of the world economy, so low-carbon electricity will be in high demand for many competing uses — for example, in power generation, transportation, industry, and building operations. Using clean electricity for DAC instead of for reducing CO₂ emissions in other critical areas raises concerns about the best uses of clean electricity.

Many studies assume that a DAC unit could also get energy from “waste heat” generated by some industrial process or facility nearby. In the MITEI researchers’ opinion, “that may be more wishful thinking than reality.” The heat source would need to be within a few miles of the DAC plant for transporting the heat to be economical; given its high capital cost, the DAC plant would need to run nonstop, requiring constant heat delivery; and heat at the temperature required by the DAC plant would have competing uses, for example, for heating buildings. Finally, if DAC is deployed at the gigatonne per year scale, waste heat will likely be able to provide only a small fraction of the needed energy.

Challenge 3: Siting

Some analysts have asserted that, because air is everywhere, DAC units can be located anywhere. But in reality, siting a DAC plant involves many complex issues. As noted above, DAC plants require significant amounts of energy, so having access to enough low-carbon energy is critical. Likewise, having nearby options for storing the removed CO₂ is also critical. If storage sites or pipelines to such sites don’t exist, major new infrastructure will need to be built, and building new infrastructure of any kind is expensive and complicated, involving issues related to permitting, environmental justice, and public acceptability — issues that are, in the words of the researchers, “commonly underestimated in the real world and neglected in models.”

Two more siting needs must be considered. First, meteorological conditions must be acceptable. By definition, any DAC unit will be exposed to the elements, and factors like temperature and humidity will affect process performance and process availability. And second, a DAC plant will require some dedicated land — though how much is unclear, as the optimal spacing of units is as yet unresolved. Like wind turbines, DAC units need to be properly spaced to ensure maximum performance such that one unit is not sucking in CO₂-depleted air from another unit.

Challenge 4: Cost

Considering the first three challenges, the final challenge is clear: the cost per tonne of CO₂ removed is inevitably high. Recent modeling studies assume DAC costs as low as $100 to $200 per ton of CO₂ removed. But the researchers found evidence suggesting far higher costs.

To start, they cite typical costs for power plants and industrial sites that now use CCS to remove CO₂ from their flue gases. The cost of CCS in such applications is estimated to be in the range of $50 to $150 per ton of CO₂ removed. As explained above, the far lower concentration of CO₂ in the air will lead to substantially higher costs.

As explained under Challenge 1, the DAC units needed to capture the required amount of air are massive. The capital cost of building them will be high, given labor, materials, permitting costs, and so on. Some estimates in the literature exceed $5,000 per tonne captured per year.

Then there are the ongoing costs of energy. As noted under Challenge 2, removing 1 tonne of CO₂ requires the equivalent of 1.2 megawatt-hours of electricity. If that electricity costs $0.10 per kilowatt-hour, the cost of just the electricity needed to remove 1 tonne of CO₂ is $120. The researchers point out that assuming such a low price is “questionable,” given the expected increase in electricity demand, future competition for clean energy, and higher costs on a system dominated by renewable — but intermittent — energy sources.

Then there’s the cost of storage, which is ignored in many DAC cost estimates.

Clearly, many considerations show that prices of $100 to $200 per tonne are unrealistic, and assuming such low prices will distort assessments of strategies, leading them to underperform going forward.

The bottom line

In their paper, the MITEI team calls DAC a “very seductive concept.” Using DAC to suck CO₂ out of the air and generate high-quality carbon-removal credits can offset reduction requirements for industries that have hard-to-abate emissions. By doing so, DAC would minimize disruptions to key parts of the world’s economy, including air travel, certain carbon-intensive industries, and agriculture. However, the world would need to generate billions of tonnes of CO₂ credits at an affordable price. That prospect doesn’t look likely. The largest DAC plant in operation today removes just 4,000 tonnes of CO₂ per year, and the price to buy the company’s carbon-removal credits on the market today is $1,500 per tonne.

The researchers recognize that there is room for energy efficiency improvements in the future, but DAC units will always be subject to higher work requirements than CCS applied to power plant or industrial flue gases, and there is not a clear pathway to reducing work requirements much below the levels of current DAC technologies.

Nevertheless, the researchers recommend that work to develop DAC continue “because it may be needed for meeting net-zero emissions goals, especially given the current pace of emissions.” But their paper concludes with this warning: “Given the high stakes of climate change, it is foolhardy to rely on DAC to be the hero that comes to our rescue.”

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