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    Is there enough land on Earth to fight climate change and feed the world?

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    Capping global warming at 1.5 degrees Celsius is a tall order. Achieving that goal will not only require a massive reduction in greenhouse gas emissions from human activities, but also a substantial reallocation of land to support that effort and sustain the biosphere, including humans. More land will be needed to accommodate a growing demand for bioenergy and nature-based carbon sequestration while ensuring sufficient acreage for food production and ecological sustainability.The expanding role of land in a 1.5 C world will be twofold — to remove carbon dioxide from the atmosphere and to produce clean energy. Land-based carbon dioxide removal strategies include bioenergy with carbon capture and storage; direct air capture; and afforestation/reforestation and other nature-based solutions. Land-based clean energy production includes wind and solar farms and sustainable bioenergy cropland. Any decision to allocate more land for climate mitigation must also address competing needs for long-term food security and ecosystem health.Land-based climate mitigation choices vary in terms of costs — amount of land required, implications for food security, impact on biodiversity and other ecosystem services — and benefits — potential for sequestering greenhouse gases and producing clean energy.Now a study in the journal Frontiers in Environmental Science provides the most comprehensive analysis to date of competing land-use and technology options to limit global warming to 1.5 C. Led by researchers at the MIT Center for Sustainability Science and Strategy (CS3), the study applies the MIT Integrated Global System Modeling (IGSM) framework to evaluate costs and benefits of different land-based climate mitigation options in Sky2050, a 1.5 C climate-stabilization scenario developed by Shell.Under this scenario, demand for bioenergy and natural carbon sinks increase along with the need for sustainable farming and food production. To determine if there’s enough land to meet all these growing demands, the research team uses the global hectare (gha) — an area of 10,000 square meters, or 2.471 acres — as the standard unit of measurement, and current estimates of the Earth’s total habitable land area (about 10 gha) and land area used for food production and bioenergy (5 gha).The team finds that with transformative changes in policy, land management practices, and consumption patterns, global land is sufficient to provide a sustainable supply of food and ecosystem services throughout this century while also reducing greenhouse gas emissions in alignment with the 1.5 C goal. These transformative changes include policies to protect natural ecosystems; stop deforestation and accelerate reforestation and afforestation; promote advances in sustainable agriculture technology and practice; reduce agricultural and food waste; and incentivize consumers to purchase sustainably produced goods.If such changes are implemented, 2.5–3.5 gha of land would be used for NBS practices to sequester 3–6 gigatonnes (Gt) of CO2 per year, and 0.4–0.6 gha of land would be allocated for energy production — 0.2–0.3 gha for bioenergy and 0.2–0.35 gha for wind and solar power generation.“Our scenario shows that there is enough land to support a 1.5 degree C future as long as effective policies at national and global levels are in place,” says CS3 Principal Research Scientist Angelo Gurgel, the study’s lead author. “These policies must not only promote efficient use of land for food, energy, and nature, but also be supported by long-term commitments from government and industry decision-makers.” More

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      in Security

      To design better water filters, MIT engineers look to manta rays

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      Filter feeders are everywhere in the animal world, from tiny crustaceans and certain types of coral and krill, to various molluscs, barnacles, and even massive basking sharks and baleen whales. Now, MIT engineers have found that one filter feeder has evolved to sift food in ways that could improve the design of industrial water filters.In a paper appearing this week in the Proceedings of the National Academy of Sciences, the team characterizes the filter-feeding mechanism of the mobula ray — a family of aquatic rays that includes two manta species and seven devil rays. Mobula rays feed by swimming open-mouthed through plankton-rich regions of the ocean and filtering plankton particles into their gullet as water streams into their mouths and out through their gills.The floor of the mobula ray’s mouth is lined on either side with parallel, comb-like structures, called plates, that siphon water into the ray’s gills. The MIT team has shown that the dimensions of these plates may allow for incoming plankton to bounce all the way across the plates and further into the ray’s cavity, rather than out through the gills. What’s more, the ray’s gills absorb oxygen from the outflowing water, helping the ray to simultaneously breathe while feeding.“We show that the mobula ray has evolved the geometry of these plates to be the perfect size to balance feeding and breathing,” says study author Anette “Peko” Hosoi, the Pappalardo Professor of Mechanical Engineering at MIT.The engineers fabricated a simple water filter modeled after the mobula ray’s plankton-filtering features. They studied how water flowed through the filter when it was fitted with 3D-printed plate-like structures. The team took the results of these experiments and drew up a blueprint, which they say designers can use to optimize industrial cross-flow filters, which are broadly similar in configuration to that of the mobula ray.“We want to expand the design space of traditional cross-flow filtration with new knowledge from the manta ray,” says lead author and MIT postdoc Xinyu Mao PhD ’24. “People can choose a parameter regime of the mobula ray so they could potentially improve overall filter performance.”Hosoi and Mao co-authored the new study with Irmgard Bischofberger, associate professor of mechanical engineering at MIT.A better trade-offThe new study grew out of the group’s focus on filtration during the height of the Covid pandemic, when the researchers were designing face masks to filter out the virus. Since then, Mao has shifted focus to study filtration in animals and how certain filter-feeding mechanisms might improve filters used in industry, such as in water treatment plants.Mao observed that any industrial filter must strike a balance between permeability (how easily fluid can flow through a filter), and selectivity (how successful a filter is at keeping out particles of a target size). For instance, a membrane that is studded with large holes might be highly permeable, meaning a lot of water can be pumped through using very little energy. However, the membrane’s large holes would let many particles through, making it very low in selectivity. Likewise, a membrane with much smaller pores would be more selective yet also require more energy to pump the water through the smaller openings.“We asked ourselves, how do we do better with this tradeoff between permeability and selectivity?” Hosoi says.As Mao looked into filter-feeding animals, he found that the mobula ray has struck an ideal balance between permeability and selectivity: The ray is highly permeable, in that it can let water into its mouth and out through its gills quickly enough to capture oxygen to breathe. At the same time, it is highly selective, filtering and feeding on plankton rather than letting the particles stream out through the gills.The researchers realized that the ray’s filtering features are broadly similar to that of industrial cross-flow filters. These filters are designed such that fluid flows across a permeable membrane that lets through most of the fluid, while any polluting particles continue flowing across the membrane and eventually out into a reservoir of waste.The team wondered whether the mobula ray might inspire design improvements to industrial cross-flow filters. For that, they took a deeper dive into the dynamics of mobula ray filtration.A vortex keyAs part of their new study, the team fabricated a simple filter inspired by the mobula ray. The filter’s design is what engineers refer to as a “leaky channel” — effectively, a pipe with holes along its sides. In this case, the team’s “channel” consists of two flat, transparent acrylic plates that are glued together at the edges, with a slight opening between the plates through which fluid can be pumped. At one end of the channel, the researchers inserted 3D-printed structures resembling the grooved plates that run along the floor of the mobula ray’s mouth.The team then pumped water through the channel at various rates, along with colored dye to visualize the flow. They took images across the channel and observed an interesting transition: At slow pumping rates, the flow was “very peaceful,” and fluid easily slipped through the grooves in the printed plates and out into a reservoir. When the researchers increased the pumping rate, the faster-flowing fluid did not slip through, but appeared to swirl at the mouth of each groove, creating a vortex, similar to a small knot of hair between the tips of a comb’s teeth.“This vortex is not blocking water, but it is blocking particles,” Hosoi explains. “Whereas in a slower flow, particles go through the filter with the water, at higher flow rates, particles try to get through the filter but are blocked by this vortex and are shot down the channel instead. The vortex is helpful because it prevents particles from flowing out.”The team surmised that vortices are the key to mobula rays’ filter-feeding ability. The ray is able to swim at just the right speed that water, streaming into its mouth, can form vortices between the grooved plates. These vortices effectively block any plankton particles — even those that are smaller than the space between plates. The particles then bounce across the plates and head further into the ray’s cavity, while the rest of the water can still flow between the plates and out through the gills.The researchers used the results of their experiments, along with dimensions of the filtering features of mobula rays, to develop a blueprint for cross-flow filtration.“We have provided practical guidance on how to actually filter as the mobula ray does,” Mao offers.“You want to design a filter such that you’re in the regime where you generate vortices,” Hosoi says. “Our guidelines tell you: If you want your plant to pump at a certain rate, then your filter has to have a particular pore diameter and spacing to generate vortices that will filter out particles of this size. The mobula ray is giving us a really nice rule of thumb for rational design.”This work was supported, in part, by the U.S. National Institutes of Health, and the Harvey P. Greenspan Fellowship Fund.  More

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      in Environment

      New AI tool generates realistic satellite images of future flooding

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      Visualizing the potential impacts of a hurricane on people’s homes before it hits can help residents prepare and decide whether to evacuate.MIT scientists have developed a method that generates satellite imagery from the future to depict how a region would look after a potential flooding event. The method combines a generative artificial intelligence model with a physics-based flood model to create realistic, birds-eye-view images of a region, showing where flooding is likely to occur given the strength of an oncoming storm.As a test case, the team applied the method to Houston and generated satellite images depicting what certain locations around the city would look like after a storm comparable to Hurricane Harvey, which hit the region in 2017. The team compared these generated images with actual satellite images taken of the same regions after Harvey hit. They also compared AI-generated images that did not include a physics-based flood model.The team’s physics-reinforced method generated satellite images of future flooding that were more realistic and accurate. The AI-only method, in contrast, generated images of flooding in places where flooding is not physically possible.The team’s method is a proof-of-concept, meant to demonstrate a case in which generative AI models can generate realistic, trustworthy content when paired with a physics-based model. In order to apply the method to other regions to depict flooding from future storms, it will need to be trained on many more satellite images to learn how flooding would look in other regions.“The idea is: One day, we could use this before a hurricane, where it provides an additional visualization layer for the public,” says Björn Lütjens, a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences, who led the research while he was a doctoral student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the biggest challenges is encouraging people to evacuate when they are at risk. Maybe this could be another visualization to help increase that readiness.”To illustrate the potential of the new method, which they have dubbed the “Earth Intelligence Engine,” the team has made it available as an online resource for others to try.The researchers report their results today in the journal IEEE Transactions on Geoscience and Remote Sensing. The study’s MIT co-authors include Brandon Leshchinskiy; Aruna Sankaranarayanan; and Dava Newman, professor of AeroAstro and director of the MIT Media Lab; along with collaborators from multiple institutions.Generative adversarial imagesThe new study is an extension of the team’s efforts to apply generative AI tools to visualize future climate scenarios.“Providing a hyper-local perspective of climate seems to be the most effective way to communicate our scientific results,” says Newman, the study’s senior author. “People relate to their own zip code, their local environment where their family and friends live. Providing local climate simulations becomes intuitive, personal, and relatable.”For this study, the authors use a conditional generative adversarial network, or GAN, a type of machine learning method that can generate realistic images using two competing, or “adversarial,” neural networks. The first “generator” network is trained on pairs of real data, such as satellite images before and after a hurricane. The second “discriminator” network is then trained to distinguish between the real satellite imagery and the one synthesized by the first network.Each network automatically improves its performance based on feedback from the other network. The idea, then, is that such an adversarial push and pull should ultimately produce synthetic images that are indistinguishable from the real thing. Nevertheless, GANs can still produce “hallucinations,” or factually incorrect features in an otherwise realistic image that shouldn’t be there.“Hallucinations can mislead viewers,” says Lütjens, who began to wonder whether such hallucinations could be avoided, such that generative AI tools can be trusted to help inform people, particularly in risk-sensitive scenarios. “We were thinking: How can we use these generative AI models in a climate-impact setting, where having trusted data sources is so important?”Flood hallucinationsIn their new work, the researchers considered a risk-sensitive scenario in which generative AI is tasked with creating satellite images of future flooding that could be trustworthy enough to inform decisions of how to prepare and potentially evacuate people out of harm’s way.Typically, policymakers can get an idea of where flooding might occur based on visualizations in the form of color-coded maps. These maps are the final product of a pipeline of physical models that usually begins with a hurricane track model, which then feeds into a wind model that simulates the pattern and strength of winds over a local region. This is combined with a flood or storm surge model that forecasts how wind might push any nearby body of water onto land. A hydraulic model then maps out where flooding will occur based on the local flood infrastructure and generates a visual, color-coded map of flood elevations over a particular region.“The question is: Can visualizations of satellite imagery add another level to this, that is a bit more tangible and emotionally engaging than a color-coded map of reds, yellows, and blues, while still being trustworthy?” Lütjens says.The team first tested how generative AI alone would produce satellite images of future flooding. They trained a GAN on actual satellite images taken by satellites as they passed over Houston before and after Hurricane Harvey. When they tasked the generator to produce new flood images of the same regions, they found that the images resembled typical satellite imagery, but a closer look revealed hallucinations in some images, in the form of floods where flooding should not be possible (for instance, in locations at higher elevation).To reduce hallucinations and increase the trustworthiness of the AI-generated images, the team paired the GAN with a physics-based flood model that incorporates real, physical parameters and phenomena, such as an approaching hurricane’s trajectory, storm surge, and flood patterns. With this physics-reinforced method, the team generated satellite images around Houston that depict the same flood extent, pixel by pixel, as forecasted by the flood model.“We show a tangible way to combine machine learning with physics for a use case that’s risk-sensitive, which requires us to analyze the complexity of Earth’s systems and project future actions and possible scenarios to keep people out of harm’s way,” Newman says. “We can’t wait to get our generative AI tools into the hands of decision-makers at the local community level, which could make a significant difference and perhaps save lives.”The research was supported, in part, by the MIT Portugal Program, the DAF-MIT Artificial Intelligence Accelerator, NASA, and Google Cloud. 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      in Environment

      Advancing urban tree monitoring with AI-powered digital twins

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      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 breezeWhile 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.  More

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      in Energy

      Reality check on technologies to remove carbon dioxide from the air

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      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 realityIncluding 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 upWhen 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 requirementGiven 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: SitingSome 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: CostConsidering 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 lineIn 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.” More

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      in Environment

      J-PAL North America announces new evaluation incubator collaborators from state and local governments

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      J-PAL North America recently selected government partners for the 2024-25 Leveraging Evaluation and Evidence for Equitable Recovery (LEVER) Evaluation Incubator cohort. Selected collaborators will receive funding and technical assistance to develop or launch a randomized evaluation for one of their programs. These collaborations represent jurisdictions across the United States and demonstrate the growing enthusiasm for evidence-based policymaking.Launched in 2023, LEVER is a joint venture between J-PAL North America and Results for America. Through the Evaluation Incubator, trainings, and other program offerings, LEVER seeks to address the barriers many state and local governments face around finding and generating evidence to inform program design. LEVER offers government leaders the opportunity to learn best practices for policy evaluations and how to integrate evidence into decision-making. Since the program’s inception, more than 80 government jurisdictions have participated in LEVER offerings.J-PAL North America’s Evaluation Incubator helps collaborators turn policy-relevant research questions into well-designed randomized evaluations, generating rigorous evidence to inform pressing programmatic and policy decisions. The program also aims to build a culture of evidence use and give government partners the tools to continue generating and utilizing evidence in their day-to-day operations.In addition to funding and technical assistance, the selected state and local government collaborators will be connected with researchers from J-PAL’s network to help advance their evaluation ideas. Evaluation support will also be centered on community-engaged research practices, which emphasize collaborating with and learning from the groups most affected by the program being evaluated.Evaluation Incubator selected projectsPierce County Human Services (PCHS) in the state of Washington will evaluate two programs as part of the Evaluation Incubator. The first will examine how extending stays in a fentanyl detox program affects the successful completion of inpatient treatment and hospital utilization for individuals. “PCHS is interested in evaluating longer fentanyl detox stays to inform our funding decisions, streamline our resource utilization, and encourage additional financial commitments to address the unmet needs of individuals dealing with opioid use disorder,” says Trish Crocker, grant coordinator.The second PCHS program will evaluate the impact of providing medication and outreach services via a mobile distribution unit to individuals with opioid use disorders on program take-up and substance usage. Margo Burnison, a behavioral health manager with PCHS, says that the team is “thrilled to be partnering with J-PAL North America to dive deep into the data to inform our elected leaders on the best way to utilize available resources.”The City of Los Angeles Youth Development Department (YDD) seeks to evaluate a research-informed program: Student Engagement, Exploration, and Development in STEM (SEEDS). This intergenerational STEM mentorship program supports underrepresented middle school and college students in STEM by providing culturally responsive mentorship. The program seeks to foster these students’ STEM identity and degree attainment in higher education. YDD has been working with researchers at the University of Southern California to measure the SEEDS program’s impact, but is interested in developing a randomized evaluation to generate further evidence. Darnell Cole, professor and co-director of the Research Center for Education, Identity and Social Justice, shares his excitement about the collaboration with J-PAL: “We welcome the opportunity to measure the impact of the SEEDS program on our students’ educational experience. Rigorously testing the SEEDS program will help us improve support for STEM students, ultimately enhancing their persistence and success.”The Fort Wayne Police Department’s Hope and Recovery Team in Indiana will evaluate the impact of two programs that connect social workers with people who have experienced an overdose, or who have a mental health illness, to treatment and resources. “We believe we are on the right track in the work we are doing with the crisis intervention social worker and the recovery coach, but having an outside evaluation of both programs would be extremely helpful in understanding whether and what aspects of these programs are most effective,” says Police Captain Kevin Hunter.The County of San Diego’s Office of Evaluation, Performance and Analytics, and Planning & Development Services will engage with J-PAL staff to explore evaluation opportunities for two programs that are a part of the county’s Climate Action Plan. The Equity-Driven Tree Planting Program seeks to increase tree canopy coverage, and the Climate Smart Land Stewardship Program will encourage climate-smart agricultural practices. Ricardo Basurto-Davila, chief evaluation officer, says that “the county is dedicated to evidence-based policymaking and taking decisive action against climate change. The work with J-PAL will support us in combining these commitments to maximize the effectiveness in decreasing emissions through these programs.”J-PAL North America looks forward to working with the selected collaborators in the coming months to learn more about these promising programs, clarify our partner’s evidence goals, and design randomized evaluations to measure their impact. More

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      in Environment

      MIT engineers make converting CO2 into useful products more practical

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      As the world struggles to reduce greenhouse gas emissions, researchers are seeking practical, economical ways to capture carbon dioxide and convert it into useful products, such as transportation fuels, chemical feedstocks, or even building materials. But so far, such attempts have struggled to reach economic viability.New research by engineers at MIT could lead to rapid improvements in a variety of electrochemical systems that are under development to convert carbon dioxide into a valuable commodity. The team developed a new design for the electrodes used in these systems, which increases the efficiency of the conversion process.The findings are reported today in the journal Nature Communications, in a paper by MIT doctoral student Simon Rufer, professor of mechanical engineering Kripa Varanasi, and three others.“The CO2 problem is a big challenge for our times, and we are using all kinds of levers to solve and address this problem,” Varanasi says. It will be essential to find practical ways of removing the gas, he says, either from sources such as power plant emissions, or straight out of the air or the oceans. But then, once the CO2 has been removed, it has to go somewhere.A wide variety of systems have been developed for converting that captured gas into a useful chemical product, Varanasi says. “It’s not that we can’t do it — we can do it. But the question is how can we make this efficient? How can we make this cost-effective?”In the new study, the team focused on the electrochemical conversion of CO2 to ethylene, a widely used chemical that can be made into a variety of plastics as well as fuels, and which today is made from petroleum. But the approach they developed could also be applied to producing other high-value chemical products as well, including methane, methanol, carbon monoxide, and others, the researchers say.Currently, ethylene sells for about $1,000 per ton, so the goal is to be able to meet or beat that price. The electrochemical process that converts CO2 into ethylene involves a water-based solution and a catalyst material, which come into contact along with an electric current in a device called a gas diffusion electrode.There are two competing characteristics of the gas diffusion electrode materials that affect their performance: They must be good electrical conductors so that the current that drives the process doesn’t get wasted through resistance heating, but they must also be “hydrophobic,” or water repelling, so the water-based electrolyte solution doesn’t leak through and interfere with the reactions taking place at the electrode surface.Unfortunately, it’s a tradeoff. Improving the conductivity reduces the hydrophobicity, and vice versa. Varanasi and his team set out to see if they could find a way around that conflict, and after many months of trying, they did just that.The solution, devised by Rufer and Varanasi, is elegant in its simplicity. They used a plastic material, PTFE (essentially Teflon), that has been known to have good hydrophobic properties. However, PTFE’s lack of conductivity means that electrons must travel through a very thin catalyst layer, leading to significant voltage drop with distance. To overcome this limitation, the researchers wove a series of conductive copper wires through the very thin sheet of the PTFE.“This work really addressed this challenge, as we can now get both conductivity and hydrophobicity,” Varanasi says.Research on potential carbon conversion systems tends to be done on very small, lab-scale samples, typically less than 1-inch (2.5-centimeter) squares. To demonstrate the potential for scaling up, Varanasi’s team produced a sheet 10 times larger in area and demonstrated its effective performance.To get to that point, they had to do some basic tests that had apparently never been done before, running tests under identical conditions but using electrodes of different sizes to analyze the relationship between conductivity and electrode size. They found that conductivity dropped off dramatically with size, which would mean much more energy, and thus cost, would be needed to drive the reaction.“That’s exactly what we would expect, but it was something that nobody had really dedicatedly investigated before,” Rufer says. In addition, the larger sizes produced more unwanted chemical byproducts besides the intended ethylene.Real-world industrial applications would require electrodes that are perhaps 100 times larger than the lab versions, so adding the conductive wires will be necessary for making such systems practical, the researchers say. They also developed a model which captures the spatial variability in voltage and product distribution on electrodes due to ohmic losses. The model along with the experimental data they collected enabled them to calculate the optimal spacing for conductive wires to counteract the drop off in conductivity.In effect, by weaving the wire through the material, the material is divided into smaller subsections determined by the spacing of the wires. “We split it into a bunch of little subsegments, each of which is effectively a smaller electrode,” Rufer says. “And as we’ve seen, small electrodes can work really well.”Because the copper wire is so much more conductive than the PTFE material, it acts as a kind of superhighway for electrons passing through, bridging the areas where they are confined to the substrate and face greater resistance.To demonstrate that their system is robust, the researchers ran a test electrode for 75 hours continuously, with little change in performance. Overall, Rufer says, their system “is the first PTFE-based electrode which has gone beyond the lab scale on the order of 5 centimeters or smaller. It’s the first work that has progressed into a much larger scale and has done so without sacrificing efficiency.”The weaving process for incorporating the wire can be easily integrated into existing manufacturing processes, even in a large-scale roll-to-roll process, he adds.“Our approach is very powerful because it doesn’t have anything to do with the actual catalyst being used,” Rufer says. “You can sew this micrometric copper wire into any gas diffusion electrode you want, independent of catalyst morphology or chemistry. So, this approach can be used to scale anybody’s electrode.”“Given that we will need to process gigatons of CO2 annually to combat the CO2 challenge, we really need to think about solutions that can scale,” Varanasi says. “Starting with this mindset enables us to identify critical bottlenecks and develop innovative approaches that can make a meaningful impact in solving the problem. Our hierarchically conductive electrode is a result of such thinking.”The research team included MIT graduate students Michael Nitzsche and Sanjay Garimella,  as well as Jack Lake PhD ’23. The work was supported by Shell, through the MIT Energy Initiative. More

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      in Environment

      Tackling the energy revolution, one sector at a time

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      As a major contributor to global carbon dioxide (CO2) emissions, the transportation sector has immense potential to advance decarbonization. However, a zero-emissions global supply chain requires re-imagining reliance on a heavy-duty trucking industry that emits 810,000 tons of CO2, or 6 percent of the United States’ greenhouse gas emissions, and consumes 29 billion gallons of diesel annually in the U.S. alone.A new study by MIT researchers, presented at the recent American Society of Mechanical Engineers 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, quantifies the impact of a zero-emission truck’s design range on its energy storage requirements and operational revenue. The multivariable model outlined in the paper allows fleet owners and operators to better understand the design choices that impact the economic feasibility of battery-electric and hydrogen fuel cell heavy-duty trucks for commercial application, equipping stakeholders to make informed fleet transition decisions.“The whole issue [of decarbonizing trucking] is like a very big, messy pie. One of the things we can do, from an academic standpoint, is quantify some of those pieces of pie with modeling, based on information and experience we’ve learned from industry stakeholders,” says ZhiYi Liang, PhD student on the renewable hydrogen team at the MIT K. Lisa Yang Global Engineering and Research Center (GEAR) and lead author of the study. Co-authored by Bryony Dupont, visiting scholar at GEAR, and Amos Winter, the Germeshausen Professor in the MIT Department of Mechanical Engineering, the paper elucidates operational and socioeconomic factors that need to be considered in efforts to decarbonize heavy-duty vehicles (HDVs).Operational and infrastructure challengesThe team’s model shows that a technical challenge lies in the amount of energy that needs to be stored on the truck to meet the range and towing performance needs of commercial trucking applications. Due to the high energy density and low cost of diesel, existing diesel drivetrains remain more competitive than alternative lithium battery-electric vehicle (Li-BEV) and hydrogen fuel-cell-electric vehicle (H2 FCEV) drivetrains. Although Li-BEV drivetrains have the highest energy efficiency of all three, they are limited to short-to-medium range routes (under 500 miles) with low freight capacity, due to the weight and volume of the onboard energy storage needed. In addition, the authors note that existing electric grid infrastructure will need significant upgrades to support large-scale deployment of Li-BEV HDVs.While the hydrogen-powered drivetrain has a significant weight advantage that enables higher cargo capacity and routes over 750 miles, the current state of hydrogen fuel networks limits economic viability, especially once operational cost and projected revenue are taken into account. Deployment will most likely require government intervention in the form of incentives and subsidies to reduce the price of hydrogen by more than half, as well as continued investment by corporations to ensure a stable supply. Also, as H2-FCEVs are still a relatively new technology, the ongoing design of conformal onboard hydrogen storage systems — one of which is the subject of Liang’s PhD — is crucial to successful adoption into the HDV market.The current efficiency of diesel systems is a result of technological developments and manufacturing processes established over many decades, a precedent that suggests similar strides can be made with alternative drivetrains. However, interactions with fleet owners, automotive manufacturers, and refueling network providers reveal another major hurdle in the way that each “slice of the pie” is interrelated — issues must be addressed simultaneously because of how they affect each other, from renewable fuel infrastructure to technological readiness and capital cost of new fleets, among other considerations. And first steps into an uncertain future, where no one sector is fully in control of potential outcomes, is inherently risky. “Besides infrastructure limitations, we only have prototypes [of alternative HDVs] for fleet operator use, so the cost of procuring them is high, which means there isn’t demand for automakers to build manufacturing lines up to a scale that would make them economical to produce,” says Liang, describing just one step of a vicious cycle that is difficult to disrupt, especially for industry stakeholders trying to be competitive in a free market. Quantifying a path to feasibility“Folks in the industry know that some kind of energy transition needs to happen, but they may not necessarily know for certain what the most viable path forward is,” says Liang. Although there is no singular avenue to zero emissions, the new model provides a way to further quantify and assess at least one slice of pie to aid decision-making.Other MIT-led efforts aimed at helping industry stakeholders navigate decarbonization include an interactive mapping tool developed by Danika MacDonell, Impact Fellow at the MIT Climate and Sustainability Consortium (MCSC); alongside Florian Allroggen, executive director of MITs Zero Impact Aviation Alliance; and undergraduate researchers Micah Borrero, Helena De Figueiredo Valente, and Brooke Bao. The MCSC’s Geospatial Decision Support Tool supports strategic decision-making for fleet operators by allowing them to visualize regional freight flow densities, costs, emissions, planned and available infrastructure, and relevant regulations and incentives by region.While current limitations reveal the need for joint problem-solving across sectors, the authors believe that stakeholders are motivated and ready to tackle climate problems together. Once-competing businesses already appear to be embracing a culture shift toward collaboration, with the recent agreement between General Motors and Hyundai to explore “future collaboration across key strategic areas,” including clean energy. Liang believes that transitioning the transportation sector to zero emissions is just one part of an “energy revolution” that will require all sectors to work together, because “everything is connected. In order for the whole thing to make sense, we need to consider ourselves part of that pie, and the entire system needs to change,” says Liang. “You can’t make a revolution succeed by yourself.” The authors acknowledge the MIT Climate and Sustainability Consortium for connecting them with industry members in the HDV ecosystem; and the MIT K. Lisa Yang Global Engineering and Research Center and MIT Morningside Academy for Design for financial support. More

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      in Energy

      Nanoscale transistors could enable more efficient electronics

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      Silicon transistors, which are used to amplify and switch signals, are a critical component in most electronic devices, from smartphones to automobiles. But silicon semiconductor technology is held back by a fundamental physical limit that prevents transistors from operating below a certain voltage.This limit, known as “Boltzmann tyranny,” hinders the energy efficiency of computers and other electronics, especially with the rapid development of artificial intelligence technologies that demand faster computation.In an effort to overcome this fundamental limit of silicon, MIT researchers fabricated a different type of three-dimensional transistor using a unique set of ultrathin semiconductor materials.Their devices, featuring vertical nanowires only a few nanometers wide, can deliver performance comparable to state-of-the-art silicon transistors while operating efficiently at much lower voltages than conventional devices.“This is a technology with the potential to replace silicon, so you could use it with all the functions that silicon currently has, but with much better energy efficiency,” says Yanjie Shao, an MIT postdoc and lead author of a paper on the new transistors.The transistors leverage quantum mechanical properties to simultaneously achieve low-voltage operation and high performance within an area of just a few square nanometers. Their extremely small size would enable more of these 3D transistors to be packed onto a computer chip, resulting in fast, powerful electronics that are also more energy-efficient.“With conventional physics, there is only so far you can go. The work of Yanjie shows that we can do better than that, but we have to use different physics. There are many challenges yet to be overcome for this approach to be commercial in the future, but conceptually, it really is a breakthrough,” says senior author Jesús del Alamo, the Donner Professor of Engineering in the MIT Department of Electrical Engineering and Computer Science (EECS).They are joined on the paper by Ju Li, the Tokyo Electric Power Company Professor in Nuclear Engineering and professor of materials science and engineering at MIT; EECS graduate student Hao Tang; MIT postdoc Baoming Wang; and professors Marco Pala and David Esseni of the University of Udine in Italy. The research appears today in Nature Electronics.Surpassing siliconIn electronic devices, silicon transistors often operate as switches. Applying a voltage to the transistor causes electrons to move over an energy barrier from one side to the other, switching the transistor from “off” to “on.” By switching, transistors represent binary digits to perform computation.A transistor’s switching slope reflects the sharpness of the “off” to “on” transition. The steeper the slope, the less voltage is needed to turn on the transistor and the greater its energy efficiency.But because of how electrons move across an energy barrier, Boltzmann tyranny requires a certain minimum voltage to switch the transistor at room temperature.To overcome the physical limit of silicon, the MIT researchers used a different set of semiconductor materials — gallium antimonide and indium arsenide — and designed their devices to leverage a unique phenomenon in quantum mechanics called quantum tunneling.Quantum tunneling is the ability of electrons to penetrate barriers. The researchers fabricated tunneling transistors, which leverage this property to encourage electrons to push through the energy barrier rather than going over it.“Now, you can turn the device on and off very easily,” Shao says.But while tunneling transistors can enable sharp switching slopes, they typically operate with low current, which hampers the performance of an electronic device. Higher current is necessary to create powerful transistor switches for demanding applications.Fine-grained fabricationUsing tools at MIT.nano, MIT’s state-of-the-art facility for nanoscale research, the engineers were able to carefully control the 3D geometry of their transistors, creating vertical nanowire heterostructures with a diameter of only 6 nanometers. They believe these are the smallest 3D transistors reported to date.Such precise engineering enabled them to achieve a sharp switching slope and high current simultaneously. This is possible because of a phenomenon called quantum confinement.Quantum confinement occurs when an electron is confined to a space that is so small that it can’t move around. When this happens, the effective mass of the electron and the properties of the material change, enabling stronger tunneling of the electron through a barrier.Because the transistors are so small, the researchers can engineer a very strong quantum confinement effect while also fabricating an extremely thin barrier.“We have a lot of flexibility to design these material heterostructures so we can achieve a very thin tunneling barrier, which enables us to get very high current,” Shao says.Precisely fabricating devices that were small enough to accomplish this was a major challenge.“We are really into single-nanometer dimensions with this work. Very few groups in the world can make good transistors in that range. Yanjie is extraordinarily capable to craft such well-functioning transistors that are so extremely small,” says del Alamo.When the researchers tested their devices, the sharpness of the switching slope was below the fundamental limit that can be achieved with conventional silicon transistors. Their devices also performed about 20 times better than similar tunneling transistors.“This is the first time we have been able to achieve such sharp switching steepness with this design,” Shao adds.The researchers are now striving to enhance their fabrication methods to make transistors more uniform across an entire chip. With such small devices, even a 1-nanometer variance can change the behavior of the electrons and affect device operation. They are also exploring vertical fin-shaped structures, in addition to vertical nanowire transistors, which could potentially improve the uniformity of devices on a chip.“This work definitively steps in the right direction, significantly improving the broken-gap tunnel field effect transistor (TFET) performance. It demonstrates steep-slope together with a record drive-current. It highlights the importance of small dimensions, extreme confinement, and low-defectivity materials and interfaces in the fabricated broken-gap TFET. These features have been realized through a well-mastered and nanometer-size-controlled process,” says Aryan Afzalian, a principal member of the technical staff at the nanoelectronics research organization imec, who was not involved with this work.This research is funded, in part, by Intel Corporation. More