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    Climate Action Learning Lab helps state and local leaders identify and implement effective climate mitigation strategies

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    This spring, J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — launched its first ever Learning Lab, centered on climate action. The Learning Lab convened a cohort of government leaders who are enacting a broad range of policies and programs to support the transition to a low-carbon economy. Through the Learning Lab, participants explored how to embed randomized evaluation into promising solutions to determine how to maximize changes in behavior — a strategy that can help advance decarbonization in the most cost-effective ways to benefit all communities. The inaugural cohort included more than 25 participants from state agencies and cities, including the Massachusetts Clean Energy Center, the Minnesota Housing Finance Agency, and the cities of Lincoln, Nebraska; Newport News, Virginia; Orlando, Florida; and Philadelphia.“State and local governments have demonstrated tremendous leadership in designing and implementing decarbonization policies and climate action plans over the past few years,” said Peter Christensen, scientific advisor of the J-PAL North America Environment, Energy, and Climate Change Sector. “And while these are informed by scientific projections on which programs and technologies may effectively and equitably reduce emissions, the projection methods involve a lot of assumptions. It can be challenging for governments to determine whether their programs are actually achieving the expected level of emissions reductions that we desperately need. The Climate Action Learning Lab was designed to support state and local governments in addressing this need — helping them to rigorously evaluate their programs to detect their true impact.”From May to July, the Learning Lab offered a suite of resources for participants to leverage rigorous evaluation to identify effective and equitable climate mitigation solutions. Offerings included training lectures, one-on-one strategy sessions, peer learning engagements, and researcher collaboration. State and local leaders built skills and knowledge in evidence generation and use, reviewed and applied research insights to their own programmatic areas, and identified priority research questions to guide evidence-building and decision-making practices. Programs prioritized for evaluation covered topics such as compliance with building energy benchmarking policies, take-up rates of energy-efficient home improvement programs such as heat pumps and Solar for All, and scoring criteria for affordable housing development programs.“We appreciated the chance to learn about randomized evaluation methodology, and how this impact assessment tool could be utilized in our ongoing climate action planning. With so many potential initiatives to pursue, this approach will help us prioritize our time and resources on the most effective solutions,” said Anna Shugoll, program manager at the City of Philadelphia’s Office of Sustainability.This phase of the Learning Lab was possible thanks to grant funding from J-PAL North America’s longtime supporter and collaborator Arnold Ventures. The work culminated in an in-person summit in Cambridge, Massachusetts, on July 23, where Learning Lab participants delivered a presentation on their jurisdiction’s priority research questions and strategic evaluation plans. They also connected with researchers in the J-PAL network to further explore impact evaluation opportunities for promising decarbonization programs.“The Climate Action Learning Lab has helped us identify research questions for some of the City of Orlando’s deep decarbonization goals. J-PAL staff, along with researchers in the J-PAL network, worked hard to bridge the gap between behavior change theory and the applied, tangible benefits that we achieve through rigorous evaluation of our programs,” said Brittany Sellers, assistant director for sustainability, resilience and future-ready for Orlando. “Whether we’re discussing an energy-efficiency policy for some of the biggest buildings in the City of Orlando or expanding [electric vehicle] adoption across the city, it’s been very easy to communicate some of these high-level research concepts and what they can help us do to actually pursue our decarbonization goals.”The next phase of the Climate Action Learning Lab will center on building partnerships between jurisdictions and researchers in the J-PAL network to explore the launch of randomized evaluations, deepening the community of practice among current cohort members, and cultivating a broad culture of evidence building and use in the climate space. “The Climate Action Learning Lab provided a critical space for our city to collaborate with other cities and states seeking to implement similar decarbonization programs, as well as with researchers in the J-PAL network to help rigorously evaluate these programs,” said Daniel Collins, innovation team director at the City of Newport News. “We look forward to further collaboration and opportunities to learn from evaluations of our mitigation efforts so we, as a city, can better allocate resources to the most effective solutions.”The Climate Action Learning Lab is one of several offerings under the J-PAL North America Evidence for Climate Action Project. The project’s goal is to convene an influential network of researchers, policymakers, and practitioners to generate rigorous evidence to identify and advance equitable, high-impact policy solutions to climate change in the United States. In addition to the Learning Lab, J-PAL North America will launch a climate special topic request for proposals this fall to fund research on climate mitigation and adaptation initiatives. J-PAL will welcome applications from both research partnerships formed through the Learning Lab as well as other eligible applicants.Local government leaders, researchers, potential partners, or funders committed to advancing climate solutions that work, and who want to learn more about the Evidence for Climate Action Project, may email na_eecc@povertyactionlab.org or subscribe to the J-PAL North America Climate Action newsletter. More

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      New self-assembling material could be the key to recyclable EV batteries

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      Today’s electric vehicle boom is tomorrow’s mountain of electronic waste. And while myriad efforts are underway to improve battery recycling, many EV batteries still end up in landfills.A research team from MIT wants to help change that with a new kind of self-assembling battery material that quickly breaks apart when submerged in a simple organic liquid. In a new paper published in Nature Chemistry, the researchers showed the material can work as the electrolyte in a functioning, solid-state battery cell and then revert back to its original molecular components in minutes.The approach offers an alternative to shredding the battery into a mixed, hard-to-recycle mass. Instead, because the electrolyte serves as the battery’s connecting layer, when the new material returns to its original molecular form, the entire battery disassembles to accelerate the recycling process.“So far in the battery industry, we’ve focused on high-performing materials and designs, and only later tried to figure out how to recycle batteries made with complex structures and hard-to-recycle materials,” says the paper’s first author Yukio Cho PhD ’23. “Our approach is to start with easily recyclable materials and figure out how to make them battery-compatible. Designing batteries for recyclability from the beginning is a new approach.”Joining Cho on the paper are PhD candidate Cole Fincher, Ty Christoff-Tempesta PhD ’22, Kyocera Professor of Ceramics Yet-Ming Chiang, Visiting Associate Professor Julia Ortony, Xiaobing Zuo, and Guillaume Lamour.Better batteriesThere’s a scene in one of the “Harry Potter” films where Professor Dumbledore cleans a dilapidated home with the flick of the wrist and a spell. Cho says that image stuck with him as a kid. (What better way to clean your room?) When he saw a talk by Ortony on engineering molecules so that they could assemble into complex structures and then revert back to their original form, he wondered if it could be used to make battery recycling work like magic.That would be a paradigm shift for the battery industry. Today, batteries require harsh chemicals, high heat, and complex processing to recycle. There are three main parts of a battery: the positively charged cathode, the negatively charged electrode, and the electrolyte that shuttles lithium ions between them. The electrolytes in most lithium-ion batteries are highly flammable and degrade over time into toxic byproducts that require specialized handling.To simplify the recycling process, the researchers decided to make a more sustainable electrolyte. For that, they turned to a class of molecules that self-assemble in water, named aramid amphiphiles (AAs), whose chemical structures and stability mimic that of Kevlar. The researchers further designed the AAs to contain polyethylene glycol (PEG), which can conduct lithium ions, on one end of each molecule. When the molecules are exposed to water, they spontaneously form nanoribbons with ion-conducting PEG surfaces and bases that imitate the robustness of Kevlar through tight hydrogen bonding. The result is a mechanically stable nanoribbon structure that conducts ions across its surface.“The material is composed of two parts,” Cho explains. “The first part is this flexible chain that gives us a nest, or host, for lithium ions to jump around. The second part is this strong organic material component that is used in the Kevlar, which is a bulletproof material. Those make the whole structure stable.”When added to water, the nanoribbons self-assemble to form millions of nanoribbons that can be hot-pressed into a solid-state material.“Within five minutes of being added to water, the solution becomes gel-like, indicating there are so many nanofibers formed in the liquid that they start to entangle each other,” Cho says. “What’s exciting is we can make this material at scale because of the self-assembly behavior.”The team tested the material’s strength and toughness, finding it could endure the stresses associated with making and running the battery. They also constructed a solid-state battery cell that used lithium iron phosphate for the cathode and lithium titanium oxide as the anode, both common materials in today’s batteries. The nanoribbons moved lithium ions successfully between the electrodes, but a side-effect known as polarization limited the movement of lithium ions into the battery’s electrodes during fast bouts of charging and discharging, hampering its performance compared to today’s gold-standard commercial batteries.“The lithium ions moved along the nanofiber all right, but getting the lithium ion from the nanofibers to the metal oxide seems to be the most sluggish point of the process,” Cho says.When they immersed the battery cell into organic solvents, the material immediately dissolved, with each part of the battery falling away for easier recycling. Cho compared the materials’ reaction to cotton candy being submerged in water.“The electrolyte holds the two battery electrodes together and provides the lithium-ion pathways,” Cho says. “So, when you want to recycle the battery, the entire electrolyte layer can fall off naturally and you can recycle the electrodes separately.”Validating a new approachCho says the material is a proof of concept that demonstrates the recycle-first approach.“We don’t want to say we solved all the problems with this material,” Cho says. “Our battery performance was not fantastic because we used only this material as the entire electrolyte for the paper, but what we’re picturing is using this material as one layer in the battery electrolyte. It doesn’t have to be the entire electrolyte to kick off the recycling process.”Cho also sees a lot of room for optimizing the material’s performance with further experiments.Now, the researchers are exploring ways to integrate these kinds of materials into existing battery designs as well as implementing the ideas into new battery chemistries.“It’s very challenging to convince existing vendors to do something very differently,” Cho says. “But with new battery materials that may come out in five or 10 years, it could be easier to integrate this into new designs in the beginning.”Cho also believes the approach could help reshore lithium supplies by reusing materials from batteries that are already in the U.S.“People are starting to realize how important this is,” Cho says. “If we can start to recycle lithium-ion batteries from battery waste at scale, it’ll have the same effect as opening lithium mines in the U.S. Also, each battery requires a certain amount of lithium, so extrapolating out the growth of electric vehicles, we need to reuse this material to avoid massive lithium price spikes.”The work was supported, in part, by the National Science Foundation and the U.S. Department of Energy. 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      in Energy

      New method could monitor corrosion and cracking in a nuclear reactor

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      MIT researchers have developed a technique that enables real-time, 3D monitoring of corrosion, cracking, and other material failure processes inside a nuclear reactor environment.This could allow engineers and scientists to design safer nuclear reactors that also deliver higher performance for applications like electricity generation and naval vessel propulsion.During their experiments, the researchers utilized extremely powerful X-rays to mimic the behavior of neutrons interacting with a material inside a nuclear reactor.They found that adding a buffer layer of silicon dioxide between the material and its substrate, and keeping the material under the X-ray beam for a longer period of time, improves the stability of the sample. This allows for real-time monitoring of material failure processes.By reconstructing 3D image data on the structure of a material as it fails, researchers could design more resilient materials that can better withstand the stress caused by irradiation inside a nuclear reactor.“If we can improve materials for a nuclear reactor, it means we can extend the life of that reactor. It also means the materials will take longer to fail, so we can get more use out of a nuclear reactor than we do now. The technique we’ve demonstrated here allows to push the boundary in understanding how materials fail in real-time,” says Ericmoore Jossou, who has shared appointments in the Department of Nuclear Science and Engineering (NSE), where he is the John Clark Hardwick Professor, and the Department of Electrical Engineering and Computer Science (EECS), and the MIT Schwarzman College of Computing.Jossou, senior author of a study on this technique, is joined on the paper by lead author David Simonne, an NSE postdoc; Riley Hultquist, a graduate student in NSE; Jiangtao Zhao, of the European Synchrotron; and Andrea Resta, of Synchrotron SOLEIL. The research was published Tuesday by the journal Scripta Materiala.“Only with this technique can we measure strain with a nanoscale resolution during corrosion processes. Our goal is to bring such novel ideas to the nuclear science community while using synchrotrons both as an X-ray probe and radiation source,” adds Simonne.Real-time imagingStudying real-time failure of materials used in advanced nuclear reactors has long been a goal of Jossou’s research group.Usually, researchers can only learn about such material failures after the fact, by removing the material from its environment and imaging it with a high-resolution instrument.“We are interested in watching the process as it happens. If we can do that, we can follow the material from beginning to end and see when and how it fails. That helps us understand a material much better,” he says.They simulate the process by firing an extremely focused X-ray beam at a sample to mimic the environment inside a nuclear reactor. The researchers must use a special type of high-intensity X-ray, which is only found in a handful of experimental facilities worldwide.For these experiments they studied nickel, a material incorporated into alloys that are commonly used in advanced nuclear reactors. But before they could start the X-ray equipment, they had to prepare a sample.To do this, the researchers used a process called solid state dewetting, which involves putting a thin film of the material onto a substrate and heating it to an extremely high temperature in a furnace until it transforms into single crystals.“We thought making the samples was going to be a walk in the park, but it wasn’t,” Jossou says.As the nickel heated up, it interacted with the silicon substrate and formed a new chemical compound, essentially derailing the entire experiment. After much trial-and-error, the researchers found that adding a thin layer of silicon dioxide between the nickel and substrate prevented this reaction.But when crystals formed on top of the buffer layer, they were highly strained. This means the individual atoms had moved slightly to new positions, causing distortions in the crystal structure.Phase retrieval algorithms can typically recover the 3D size and shape of a crystal in real-time, but if there is too much strain in the material, the algorithms will fail.However, the team was surprised to find that keeping the X-ray beam trained on the sample for a longer period of time caused the strain to slowly relax, due to the silicon buffer layer. After a few extra minutes of X-rays, the sample was stable enough that they could utilize phase retrieval algorithms to accurately recover the 3D shape and size of the crystal.“No one had been able to do that before. Now that we can make this crystal, we can image electrochemical processes like corrosion in real time, watching the crystal fail in 3D under conditions that are very similar to inside a nuclear reactor. This has far-reaching impacts,” he says.They experimented with a different substrate, such as niobium doped strontium titanate, and found that only a silicon dioxide buffered silicon wafer created this unique effect.An unexpected resultAs they fine-tuned the experiment, the researchers discovered something else.They could also use the X-ray beam to precisely control the amount of strain in the material, which could have implications for the development of microelectronics.In the microelectronics community, engineers often introduce strain to deform a material’s crystal structure in a way that boosts its electrical or optical properties.“With our technique, engineers can use X-rays to tune the strain in microelectronics while they are manufacturing them. While this was not our goal with these experiments, it is like getting two results for the price of one,” he adds.In the future, the researchers want to apply this technique to more complex materials like steel and other metal alloys used in nuclear reactors and aerospace applications. They also want to see how changing the thickness of the silicon dioxide buffer layer impacts their ability to control the strain in a crystal sample.“This discovery is significant for two reasons. First, it provides fundamental insight into how nanoscale materials respond to radiation — a question of growing importance for energy technologies, microelectronics, and quantum materials. Second, it highlights the critical role of the substrate in strain relaxation, showing that the supporting surface can determine whether particles retain or release strain when exposed to focused X-ray beams,” says Edwin Fohtung, an associate professor at the Rensselaer Polytechnic Institute, who was not involved with this work.This work was funded, in part, by the MIT Faculty Startup Fund and the U.S. Department of Energy. The sample preparation was carried out, in part, at the MIT.nano facilities. More

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      in Environment

      Simpler models can outperform deep learning at climate prediction

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

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      in Environment

      Eco-driving measures could significantly reduce vehicle emissions

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

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

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

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      in Energy

      MIT-Africa launches new collaboration with Angola

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      The MIT Center for International Studies announced the launch of a new pilot initiative with Angola, to be implemented through its MIT-Africa Program.The new initiative marks a significant collaboration between MIT-Africa, Sonangol (Angola’s national energy company), and the Instituto Superior Politécnico de Tecnologias e Ciências (ISPTEC). The collaboration was formalized at a signing ceremony on MIT’s campus in June with key stakeholders from all three institutions present, including Diamantino Pedro Azevedo, the Angolan minister of mineral resources, petroleum, and gas, and Sonangol CEO Gaspar Martins.“This partnership marks a pivotal step in the Angolan government’s commitment to leveraging knowledge as the cornerstone of the country’s economic transformation,” says Azevedo. “By connecting the oil and gas sector with science, innovation, and world-class training, we are equipping future generations to lead Angola into a more technological, sustainable, and globally competitive era.”The sentiment is shared by the MIT-Africa Program leaders. “This initiative reflects MIT’s deep commitment to fostering meaningful, long-term relationships across the African continent,” says Mai Hassan, faculty director of the MIT-Africa Program. “It supports our mission of advancing knowledge and educating students in ways that are globally informed, and it provides a platform for mutual learning. By working with Angolan partners, we gain new perspectives and opportunities for innovation that benefit both MIT and our collaborators.”In addition to its new collaboration with MIT-Africa, Sonangol has joined MIT’s Industrial Liaison Program (ILP), breaking new ground as its first corporate member based in sub-Saharan Africa. ILP enables companies worldwide to harness MIT resources to address current challenges and to anticipate future needs. As an ILP member, Sonangol seeks to facilitate collaboration in key sectors such as natural resources and mining, energy, construction, and infrastructure.The MIT-Africa Program manages a portfolio of research, teaching, and learning initiatives that emphasize two-way value — offering impactful experiences to MIT students and faculty while collaborating closely with institutions and communities across Africa. The new Angola collaboration is aligned with this ethos, and will launch with two core activities during the upcoming academic year:Global Classroom: An MIT course on geo-spatial technologies for environmental monitoring, taught by an MIT faculty member, will be brought directly to the ISPTEC campus, offering Angolan students and MIT participants a collaborative, in-country learning experience.Global Teaching Labs: MIT students will travel to ISPTEC to teach science, technology, engineering, arts, and mathematics subjects on renewable energy technologies, engaging Angolan students through hands-on instruction.“This is not a traditional development project,” says Ari Jacobovits, managing director of MIT-Africa. “This is about building genuine partnerships rooted in academic rigor, innovation, and shared curiosity. The collaboration has been designed from the ground up with our partners at ISPTEC and Sonangol. We’re coming in with a readiness to learn as much as we teach.”The pilot marks an important first step in establishing a long-term collaboration with Angola. By investing in collaborative education and innovation, the new initiative aims to spark novel approaches to global challenges and strengthen academic institutions on both sides.These agreements with MIT-Africa and ILP “not only enhance our innovation and technological capabilities, but also create opportunities for sustainable development and operational excellence,” says Gaspar. “They advance our mission to be a leading force in the African energy sector.”“The vision behind this initiative is bold,” says Hassan. “It’s about co-creating knowledge and building capacity that lasts.” More

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      in Environment

      School of Architecture and Planning welcomes new faculty for 2025

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

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      in Environment

      Study shows how a common fertilizer ingredient benefits plants

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      Lanthanides are a class of rare earth elements that in many countries are added to fertilizer as micronutrients to stimulate plant growth. But little is known about how they are absorbed by plants or influence photosynthesis, potentially leaving their benefits untapped.Now, researchers from MIT have shed light on how lanthanides move through and operate within plants. These insights could help farmers optimize their use to grow some of the world’s most popular crops.Published today in the Journal of the American Chemical Society, the study shows that a single nanoscale dose of lanthanides applied to seeds can make some of the world’s most common crops more resilient to UV stress. The researchers also uncovered the chemical processes by which lanthanides interact with the chlorophyll pigments that drive photosynthesis, showing that different lanthanide elements strengthen chlorophyll by replacing the magnesium at its center.“This is a first step to better understand how these elements work in plants, and to provide an example of how they could be better delivered to plants, compared to simply applying them in the soil,” says Associate Professor Benedetto Marelli, who conducted the research with postdoc Giorgio Rizzo. “This is the first example of a thorough study showing the effects of lanthanides on chlorophyll, and their beneficial effects to protect plants from UV stress.”Inside plant connectionsCertain lanthanides are used as contrast agents in MRI and for applications including light-emitting diodes, solar cells, and lasers. Over the last 50 years, lanthanides have become increasingly used in agriculture to enhance crop yields, with China alone applying lanthanide-based fertilizers to nearly 4 million hectares of land each year.“Lanthanides have been considered for a long time to be biologically irrelevant, but that’s changed in agriculture, especially in China,” says Rizzo, the paper’s first author. “But we largely don’t know how lanthanides work to benefit plants — nor do we understand their uptake mechanisms from plant tissues.”Recent studies have shown that low concentrations of lanthanides can promote plant growth, root elongation, hormone synthesis, and stress tolerance, but higher doses can cause harm to plants. Striking the right balance has been hard because of our lack of understanding around how lanthanides are absorbed by plants or how they interact with root soil.For the study, the researchers leveraged seed coating and treatment technologies they previously developed to investigate the way the plant pigment chlorophyll interacts with lanthanides, both inside and outside of plants. Up until now, researchers haven’t been sure whether chlorophyll interacts with lanthanide ions at all.Chlorophyll drives photosynthesis, but the pigments lose their ability to efficiently absorb light when the magnesium ion at their core is removed. The researchers discovered that lanthanides can fill that void, helping chlorophyll pigments partially recover some of their optical properties in a process known as re-greening.“We found that lanthanides can boost several parameters of plant health,” Marelli says. “They mostly accumulate in the roots, but a small amount also makes its way to the leaves, and some of the new chlorophyll molecules made in leaves have lanthanides incorporated in their structure.”This study also offers the first experimental evidence that lanthanides can increase plant resilience to UV stress, something the researchers say was completely unexpected.“Chlorophylls are very sensitive pigments,” Rizzo says. “They can convert light to energy in plants, but when they are isolated from the cell structure, they rapidly hydrolyze and degrade. However, in the form with lanthanides at their center, they are pretty stable, even after extracting them from plant cells.”The researchers, using different spectroscopic techniques, found the benefits held across a range of staple crops, including chickpea, barley, corn, and soybeans.The findings could be used to boost crop yield and increase the resilience of some of the world’s most popular crops to extreme weather.“As we move into an environment where extreme heat and extreme climate events are more common, and particularly where we can have prolonged periods of sun in the field, we want to provide new ways to protect our plants,” Marelli says. “There are existing agrochemicals that can be applied to leaves for protecting plants from stressors such as UV, but they can be toxic, increase microplastics, and can require multiple applications. This could be a complementary way to protect plants from UV stress.”Identifying new applicationsThe researchers also found that larger lanthanide elements like lanthanum were more effective at strengthening chlorophyll pigments than smaller ones. Lanthanum is considered a low-value byproduct of rare earths mining, and can become a burden to the rare earth element (REE) supply chain due to the need to separate it from more desirable rare earths. Increasing the demand for lanthanum could diversify the economics of REEs and improve the stability of their supply chain, the scientists suggest.“This study shows what we could do with these lower-value metals,” Marelli says. “We know lanthanides are extremely useful in electronics, magnets, and energy. In the U.S., there’s a big push to recycle them. That’s why for the plant studies, we focused on lanthanum, being the most abundant, cheapest lanthanide ion.”Moving forward, the team plans to explore how lanthanides work with other biological molecules, including proteins in the human body.In agriculture, the team hopes to scale up its research to include field and greenhouse studies to continue testing the results of UV resilience on different crop types and in experimental farm conditions.“Lanthanides are already widely used in agriculture,” Rizzo says. “We hope this study provides evidence that allows more conscious use of them and also a new way to apply them through seed treatments.”The research was supported by the MIT Climate Grand Challenge and the Office for Naval Research. More

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      in Energy

      Confronting the AI/energy conundrum

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