There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.

For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.

On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.

Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.

Controlling real-time operations

Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.

But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”

The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.

Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.

Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.

In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.

Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”

Using AI to help plan investments in infrastructure for the future

Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.

One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.

And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.

Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.

Harnessing AI to discover and exploit advanced materials needed for the energy transition

“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.

First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.

And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”

He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”

The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.

“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” 

MITEI’s contributions

At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.

Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.

In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”

© Image: Igor Borisenko/iStock

As states continue to curb health insurers’ use of artificial intelligence, patients and doctors are arming themselves with AI tools to fight claims denials, prior authorizations and soaring medical bills. (Photo by Anna Claire Vollers/Stateline)

As states strive to curb health insurers’ use of artificial intelligence, patients and doctors are arming themselves with AI tools to fight claims denials, prior authorizations and soaring medical bills.

Several businesses and nonprofits have launched AI-powered tools to help patients get their insurance claims paid and navigate byzantine medical bills, creating a robotic tug-of-war over who gets care and who foots the bill for it.

Sheer Health, a three-year-old company that helps patients and providers navigate health insurance and billing, now has an app that allows consumers to connect their health insurance account, upload medical bills and claims, and ask questions about deductibles, copays and covered benefits.

“You would think there would be some sort of technology that could explain in real English why I’m getting a bill for $1,500,” said cofounder Jeff Witten. The program uses both AI and humans to provide the answers for free, he said. Patients who want extra support in challenging a denied claim or dealing with out-of-network reimbursements can pay Sheer Health to handle those for them.

In North Carolina, the nonprofit Counterforce Health designed an AI assistant to help patients appeal their denied health insurance claims and fight large medical bills. The free service uses AI models to analyze a patient’s denial letter, then look through the patient’s policy and outside medical research to draft a customized appeal letter.

Other consumer-focused services use AI to catch billing errors or parse medical jargon. Some patients are even turning to AI chatbots like Grok for help.

A quarter of adults under age 30 said they used an AI chatbot at least once a month for health information or advice, according to a poll the health care research nonprofit KFF published in August 2024. But most adults said they were not confident that the health information is accurate.

State legislators on both sides of the aisle, meanwhile, are scrambling to keep pace, passing new regulations that govern how insurers, physicians and others use AI in health care. Already this year, more than a dozen states have passed laws regulating AI in health care, according to Manatt, a consulting firm.

“It doesn’t feel like a satisfying outcome to just have two robots argue back and forth over whether a patient should access a particular type of care,” said Carmel Shachar, assistant clinical professor of law and the faculty director of the Health Law and Policy Clinic at Harvard Law School.

“We don’t want to get on an AI-enabled treadmill that just speeds up.”

A black box

Health care can feel like a black box. If your doctor says you need surgery, for example, the cost depends on a dizzying number of factors, including your health insurance provider, your specific health plan, its copayment requirements, your deductible, where you live, the facility where the surgery will be performed, whether that facility and your doctor are in-network and your specific diagnosis.

Some insurers may require prior authorization before a surgery is approved. That can entail extensive medical documentation. After a surgery, the resulting bill can be difficult to parse.

Witten, of Sheer Health, said his company has seen thousands of instances of patients whose doctors recommend a certain procedure, like surgery, and then a few days before the surgery the patient learns insurance didn’t approve it.

In recent years, as more health insurance companies have turned to AI to automate claims processing and prior authorizations, the share of denied claims has risen. This year, 41% of physicians and other providers said their claims are denied more than 10% of the time, up from 30% of providers who said that three years ago, according to a September report from credit reporting company Experian.

Insurers on Affordable Care Act marketplaces denied nearly 1 in 5 in-network claims in 2023, up from 17% in 2021, and more than a third of out-of-network claims, according to the most recently available data from KFF.

Insurance giant UnitedHealth Group has come under fire in the media and from federal lawmakers for using algorithms to systematically deny care to seniors, while Humana and other insurers face lawsuits and regulatory investigations that allege they’ve used sophisticated algorithms to block or deny coverage for medical procedures.

Insurers say AI tools can improve efficiency and reduce costs by automating tasks that can involve analyzing vast amounts of data. And companies say they’re monitoring their AI to identify potential problems. A UnitedHealth representative pointed Stateline to the company’s AI Review Board, a team of clinicians, scientists and other experts that reviews its AI models for accuracy and fairness.

“Health plans are committed to responsibly using artificial intelligence to create a more seamless, real-time customer experience and to make claims management faster and more effective for patients and providers,” a spokesperson for America’s Health Insurance Plans, the national trade group representing health insurers, told Stateline.

But states are stepping up oversight.

Arizona, Maryland, Nebraska and Texas, for example, have banned insurance companies from using AI as the sole decisionmaker in prior authorization or medical necessity denials.

Dr. Arvind Venkat is an emergency room physician in the Pittsburgh area. He’s also a Democratic Pennsylvania state representative and the lead sponsor of a bipartisan bill to regulate the use of AI in health care.

He’s seen new technologies reshape health care during his 25 years in medicine, but AI feels wholly different, he said. It’s an “active player” in people’s care in a way that other technologies haven’t been.

“If we’re able to harness this technology to improve the delivery and efficiency of clinical care, that is a huge win,” said Venkat. But he’s worried about AI use without guardrails.

His legislation would force insurers and health care providers in Pennsylvania to be more transparent about how they use AI; require a human to make the final decision any time AI is used; and mandate that they show evidence of minimizing bias in their use of AI.

“In health care, where it’s so personal and the stakes are so high, we need to make sure we’re mandating in every patient’s case that we’re applying artificial intelligence in a way that looks at the individual patient,” Venkat said.

Patient supervision

Historically, consumers rarely challenge denied claims: A KFF analysis found fewer than 1% of health coverage denials are appealed. And even when they are, patients lose more than half of those appeals.

New consumer-focused AI tools could shift that dynamic by making appeals easier to file and the process easier to understand. But there are limits; without human oversight, experts say, the AI is vulnerable to mistakes.

“It can be difficult for a layperson to understand when AI is doing good work and when it is hallucinating or giving something that isn’t quite accurate,” said Shachar, of Harvard Law School.

For example, an AI tool might draft an appeals letter that a patient thinks looks impressive. But because most patients aren’t medical experts, they may not recognize if the AI misstates medical information, derailing an appeal, she said.

“The challenge is, if the patient is the one driving the process, are they going to be able to properly supervise the AI?” she said.

Earlier this year, Mathew Evins learned just 48 hours before his scheduled back surgery that his insurer wouldn’t cover it. Evins, a 68-year-old public relations executive who lives in Florida, worked with his physician to appeal, but got nowhere. He used an AI chatbot to draft a letter to his insurer, but that failed, too.

On his son’s recommendation, Evins turned to Sheer Health. He said Sheer identified a coding error in his medical records and handled communications with his insurer. The surgery was approved about three weeks later.

“It’s unfortunate that the public health system is so broken that it needs a third party to intervene on the patient’s behalf,” Evins told Stateline. But he’s grateful the technology made it possible to get life-changing surgery.

“AI in and of itself isn’t an answer,” he said. “AI, when used by a professional that understands the issues and ramifications of a particular problem, that’s a different story. Then you’ve got an effective tool.”

Most experts and lawmakers agree a human is needed to keep the robots in check.

AI has made it possible for insurance companies to rapidly assess cases and make decisions about whether to authorize surgeries or cover certain medical care. But that ability to make lightning-fast determinations should be tempered with a human, Venkat said.

“It’s why we need government regulation and why we need to make sure we mandate an individualized assessment with a human decisionmaker.”

Witten said there are situations in which AI works well, such as when it sifts through an insurance policy — which is essentially a contract between the company and the consumer — and connects the dots between the policy’s coverage and a corresponding insurance claim.

But, he said, “there are complicated cases out there AI just can’t resolve.” That’s when a human is needed to review.

“I think there’s a huge opportunity for AI to improve the patient experience and overall provider experience,” Witten said. “Where I worry is when you have insurance companies or other players using AI to completely replace customer support and human interaction.”

Furthermore, a growing body of research has found AI can reinforce bias that’s found elsewhere in medicine, discriminating against women, ethnic and racial minorities, and those with public insurance.

“The conclusions from artificial intelligence can reinforce discriminatory patterns and violate privacy in ways that we have already legislated against,” Venkat said.

Stateline reporter Anna Claire Vollers can be reached at avollers@stateline.org.

FRISCO, Texas — Betsey Helfrich said school district polices never trump the Individuals with Disabilities Education Act or Section 504 of the Rehabilitation Act. “There is always an exception for a child with a disability,” said the special education legal expert during her keynote address, Avoiding the Bumps & Legal Hazards in Student Transportation, Saturday during the Transporting Students with Disabilities (TSD) and Special Needs Conference.

Helfrich, who practices special education law in Missouri and Kansas, provided an overview of legal updates, court cases and compliance practices in student transportation. She focused on students with disabilities under IDEA and Section 504. The session emphasized how transportation decisions intersect with legal requirements, equity and student safety, urging districts to train staff, document decisions and avoid blanket policies.

Despite current events on the federal level, such as the proposed closing the U.S. Department of Education’s Office for Civil Rights (OCR) and funding cuts, IDEA and Section 504 remain fully in effect. Congress would need to vote to disband the U.S. Department of Transportation as well as where IDEA and Section 504 oversight would move to. Funding shifts do not change the underlying rights, she said.

She provided brief overview of each law, noting that attendees in the room should go back to their school districts and teach their school bus drivers the same thing, so they understand the importance of federal requirements.

IDEA is a funded law requiring Individualized Education Programs (IEPs). Transportation can be a “related service” if necessary for a student to benefit from an free and appropriate public education, or FAPE, in the least restrictive environment, or LRE. Section 504 is a civil rights law focused on equal access and nondiscrimination. It is broader, older and less specific than IDEA, and not tied to any monetary gains. She said Section 504 has not been updated since it was written in 1977.

Typically, Helfrich said, students should not have both an IEP and a 504 plan, as everything in the IEP is essentially a contract. She advised being cautious with automatic decisions like “door-to-door” transport, noting that the IEP team must determine needs on a case-by-case basis.

She provided court case examples, citing instances in which parents won and others which districts won, depending on the request and circumstances. She particularly stressed the importance of avoiding discrimination on field trips, extracurricular activities and other events.

For districts that rely on policy, she said they are opening themselves up a lawsuit, as “we don’t do that here” is not a legal defense.

An attendee told School Transportation News following the keynote that Helfrich is very knowledgeable and was able to speak globally on transporting students with disabilities. Even though she touched on different states, the attendee said the rules are the same, because the laws are the same.

The attendee from Maryland said she will be involved in a case next week. She noted that while her school district policy says one thing, it doesn’t mean it meets the needs of the student and federal law. “That was a huge eye-opening moment for me,” she said, noting that they shouldn’t be saying some things as it not legally true.

Helfrich said IEPs should specify supports like wheelchair lifts, on-board attendents or aides, and climate-controlled buses, but parents cannot dictate who drives the student and the type of vehicle used, unless it is pertinent to the child’s disability.

She reminded attendees to inform contractors of relevant IEP details, as they are part of the need-to-know under the Family Educational Rights and Privacy Act, or FERPA, that protects student records. It is different from HIPAA, or the Health Insurance Portability and Accountability Act, which protects personal health information.

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Students with disabilities also have additional rights regarding behavior and discipline. However, school bus suspensions over 10 days will trigger a Manifestation Determination Review, where the behavior will be evaluated to determine if it is related or not to a student’s disability.

She said school bus drivers should be trained on Behavior Intervention Plans (BIPs), including triggers and calming strategies. Plus, Helfrich said when a child needs to be searched due to reasonable suspicion of having a weapon, she advised having policies and procedures in place. For instance, does the school bus driver search the child or call for assistance?

While Section 504 provides for the reasonable accommodation of service animals and protects students from being discriminated against for using them, she cautioned the attendees to know the difference between service animals and emotional support animals. Only trained service animals performing tasks are protected under the broader ADA. Emotional support animals are not.

In conclusion, Helfrich advised attendees to train all staff, especially school bus drivers, on IDEA, Section 504 and district procedures. Document all staff participation and policy adherence. She underscored the importance of collaboration with special education and IEP teams before making unilateral changes to the IEP in terms of transportation. She noted the importance of reviewing and updating polices to avoid blanket decisions or discrimination risks and to plan for staff absences and service disruptions.

The post Legal Keynote Opens Attendees’ Eyes to Federal Special Needs Transportation Laws appeared first on School Transportation News.

A demonstrator stands outside the Wisconsin State Capitol in Madison, Wisc., in 2022 to commemorate missing and murdered Indigenous women and girls. Researchers have launched a new survey to determine the prevalence of brain injuries in Native survivors of sexual assault and domestic violence. (Photo by Stacy Revere/Getty Images)

Abigail Echo-Hawk, director of the Urban Indian Health Institute, recalled a Native mother in her 30s who started having memory loss and other dementia-like symptoms.

The woman had suffered multiple blows to her head and falls at the hands of her husband over the years. He had wanted to disable her, to make it more difficult for her to keep her children if she tried to leave him, Echo-Hawk said.

Many Native women have traumatic brain injury symptoms as a direct result of abuse, Echo-Hawk said. Tribal health advocates and groups serving survivors have long been aware of the problem, she said, but there has been little national research documenting the extent of it.

“It’s a very difficult thing to see,” said Echo-Hawk, of the Pawnee Nation of Oklahoma. “This is a pressing concern.”

The Urban Indian Health Institute, an Indigenous health research group, this month launched a first-of-its-kind national survey of American Indian, Alaska Native and Native Hawaiian women to determine the prevalence of brain injuries in Native survivors of sexual assault and domestic violence. The goal is to illuminate the extent of the problem, guide clinicians, raise public awareness and direct resources.

A 2015 study in Arizona found a higher incidence of traumatic brain injuries in Native women in that state, but the new survey is the first national, Indigenous-led study of its kind, according to the institute.

It comes as domestic violence groups across the nation are struggling with federal funding delays caused by the government shutdown. As the impasse continues, the Trump administration has furloughed grant workers at the Office on Violence Against Women, which is part of the U.S. Department of Justice.

Traumatic brain injuries can cause memory loss, confusion and long-term behavioral changes and raise the risk of dementia. Some abusers intentionally inflict traumatic brain injuries on their victims because it doesn’t leave visible bruises, according to the Brain Injury Association of America.

The link between domestic violence and traumatic brain injuries has been documented in women generally, and the effects of such injuries have been studied in former football players and veterans. But research on Native communities is lacking. Even when victims show up in ERs, their cases can go underreported.

In a previous survey of survivors, some Native women reported broken teeth, evidence of blows to the head, Echo-Hawk said. But pushing and strangulation also can cause traumatic brain injuries.

Violence is a public health crisis among American Indian, Alaska Native and Native Hawaiian women, who are overrepresented in intimate partner violence statistics. Fifty-five percent report experiencing intimate partner violence, and a disproportionate number of Native women and girls are murdered or go missing.

In a 2020 survey by the federal Centers for Disease Control and Prevention, nearly 44% of American Indian and Alaska Native women reported being raped in their lifetime.

“People are losing their children because of memory loss and dementia,” Echo-Hawk said. “When people are experiencing intimate partner violence, they end up in ERs. Their children suffer. The whole community suffers as a direct result. And the same with the crisis of missing and murdered Indigenous women and girls.”

Doctors and other hospital staff should receive more training on brain injuries and should know which communities are most likely to experience violence, said Nikki Cristobal, policy and research specialist for Pouhana ʻO Nā Wāhine, a nonprofit domestic violence resource center for Native Hawaiians.

Cristobal said one survivor told her clinicians hadn’t performed a brain scan or traumatic brain injury assessment on her, despite her ongoing psychological and cognitive symptoms. “It never occurred to anybody,” she said.

“We have to talk more about it,” said Cristobal, who worked with Echo-Hawk on developing the survey and is the principal investigator for the Missing and Murdered Native Hawaiian Women, Girls and Mahu state task force.

Native communities, including Native Hawaiians, have endured long-term, intergenerational traumas during colonization and forced assimilation that can’t be ignored when targeting the disproportionate rates of violence, Cristobal said.

“It’s the undercurrent,” Cristobal said. “It’s the precursor.”

Stateline reporter Nada Hassanein can be reached at nhassanein@stateline.org.

Around 80 percent of global energy production today comes from the combustion of fossil fuels. Combustion, or the process of converting stored chemical energy into thermal energy through burning, is vital for a variety of common activities including electricity generation, transportation, and domestic uses like heating and cooking — but it also yields a host of environmental consequences, contributing to air pollution and greenhouse gas emissions.

Sili Deng, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT, is leading research to drive the transition from the heavy dependence on fossil fuels to renewable energy with storage.

“I was first introduced to flame synthesis in my junior year in college,” Deng says. “I realized you can actually burn things to make things, [and] that was really fascinating.”

Deng says she ultimately picked combustion as a focus of her work because she likes the intellectual challenge the concept offers. “In combustion you have chemistry, and you have fluid mechanics. Each subject is very rich in science. This also has very strong engineering implications and applications.”

Deng’s research group targets three areas: building up fundamental knowledge on combustion processes and emissions; developing alternative fuels and metal combustion to replace fossil fuels; and synthesizing flame-based materials for catalysis and energy storage, which can bring down the cost of manufacturing battery materials.

One focus of the team has been on low-cost, low-emission manufacturing of cathode materials for lithium-ion batteries. Lithium-ion batteries play an increasingly critical role in transportation electrification (e.g., batteries for electric vehicles) and grid energy storage for electricity that is generated from renewable energy sources like wind and solar. Deng’s team has developed a technology they call flame-assisted spray pyrolysis, or FASP, which can help reduce the high manufacturing costs associated with cathode materials.

FASP is based on flame synthesis, a technology that dates back nearly 3,000 years. In ancient China, this was the primary way black ink materials were made. “[People burned] vegetables or woods, such that afterwards they can collect the solidified smoke,” Deng explains. “For our battery applications, we can try to fit in the same formula, but of course with new tweaks.”

The team is also interested in developing alternative fuels, including looking at the use of metals like aluminum to power rockets. “We’re interested in utilizing aluminum as a fuel for civil applications,” Deng says, because aluminum is abundant in the earth, cheap, and it’s available globally. “What we are trying to do is to understand [aluminum combustion] and be able to tailor its ignition and propagation properties.”

Among other accolades, Deng is a 2025 recipient of the Hiroshi Tsuji Early Career Researcher Award from the Combustion Institute, an award that recognizes excellence in fundamental or applied combustion science research.

© Photo: John Freidah/MIT MechE

Researchers have long known that the COVID-19 vaccine protects individuals against severe illness. But a study analyzing data from the pandemic finds that the vaccine actually reduces the spread of the disease between vaccinated people and their close contacts.

The post Wisconsin researcher finds COVID-19 vaccine offers stronger protection than once thought appeared first on WPR.

Stephanie Johnson, whose husband died after living with Parkinson's Disease for 13 years, speaks at a news conference Wednesday, Oct. 1, about legislation to create a state Parkinson's registry. (Photo by Erik Gunn/Wisconsin Examiner)

A bipartisan group of Wisconsin lawmakers announced legislation Wednesday to  create a statewide registry for Parkinson’s Disease.

Parkinson’s, a neurological condition that is characterized by tremors, but also by a variety of other symptoms, has been increasing disproportionately, according to Dr. Brian Nagle, a movement disorder specialist.

“It’s the second most common neurological disease after Alzheimer’s disease, but it’s the fastest growing,” Nagle said in an interview Wednesday.

The speed with which Parkinson’s diagnoses are increasing is outpacing the aging of the population, “which suggests that it’s not just due to our population getting older, but that there may be some sort of risk factor that is causing it to grow more rapidly,” Nagle said.

A statewide registry of Parkinson’s patients could help provide clues about factors, such as environmental conditions, that may be at the root of the illness, he said.

State Sen. Rachael Cabral-Guevara (R-Fox Crossing) and state Rep. Lisa Subeck (D-Madison) began circulating a draft bill Wednesday to create the proposed state registry.

“Right now, when patients and their doctors are looking for answers, we struggle a little bit,” said Cabral-Guevara, who is a nurse practitioner, at a press conference to announce the legislation.

“We simply don’t have the data that we need,” she said. “We don’t know who is infected. Where the disease is hitting the hardest. Are there environmental factors that impact this, that cause this, that make it progress even faster? That lack of the clear picture of this is a barrier.”

The legislation was the brainchild of Stephanie Johnson, director of the Parkinson’s Disease Alliance of Wisconsin. Johnson told reporters Wednesday that her husband, Rick, was diagnosed with Parkinson’s 15 years ago when he was 61. After living with the illness for 13 years, he died in December 2023.

“I think we typically think of Parkinson’s as tremors or shuffling,” Johnson said, “but Rick had dangerously low blood pressure that would cause him to pass out. He had cognitive changes that made it very, very challenging for him to communicate. And he had visual hallucinations and many other non-motor symptoms.”

Johnson said she was also diagnosed with Parkinson’s three months after her husband’s death — a finding that astonished her. Then she learned that in the neighborhood where they had previously lived for 20 years, they were two of six residents who developed Parkinson’s disease, she said.

“And I thought, this can’t be a coincidence,” Johnson said, “And I wondered, is this a disease cluster? I didn’t know.”

“We don’t have a systematic way of tracking the incidence and prevalence of Parkinson’s in Wisconsin,” Johnson said.

The proposed legislation is aimed at filling that gap. The bill’s authors have named it in memory of Johnson’s husband at her request.

Fourteen U.S. states have some form of registry for Parkinson’s Disease, with some tracking other conditions as well, according to the Michael J. Fox foundation, a national research nonprofit named for the TV actor who was diagnosed with Parkinson’s disease when he was 30.

The draft legislation calls for the establishment of a registry at the Department of Population Health Sciences at the University of Wisconsin-Madison School of Medicine and Public Health. The registry would include a website with annual reports on the incidence and prevalence of Parkinson’s Disease in Wisconsin.

Health care providers would file information with the directory about patients they treat for Parkinson’s or closely related conditions. If patients don’t consent for their information to be shared, the incidence would be reported and nothing else, according to the bill.

Parkinson’s Disease is the subject of “a lot of mysteries,” Subeck said. “The reality is we are not going to get closer to curing Parkinson’s unless we do the research, unless we collect the data, and unless we enable that data to be used in meaningful ways.”

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Before batteries lose power, fail suddenly, or burst into flames, they tend to produce faint sounds over time that provide a signature of the degradation processes going on within their structure. But until now, nobody had figured out how to interpret exactly what those sounds meant, and how to distinguish between ordinary background noise and significant signs of possible trouble.

Now, a team of researchers at MIT’s Department of Chemical Engineering have done a detailed analysis of the sounds emanating from lithium ion batteries, and has been able to correlate particular sound patterns with specific degradation processes taking place inside the cells. The new findings could provide the basis for relatively simple, totally passive and nondestructive devices that could continuously monitor the health of battery systems, for example in electric vehicles or grid-scale storage facilities, to provide ways of predicting useful operating lifetimes and forecasting failures before they occur.

The findings were reported Sept. 5 in the journal Joule, in a paper by MIT graduate students Yash Samantaray and Alexander Cohen, former MIT research scientist Daniel Cogswell PhD ’10, and Chevron Professor of Chemical Engineering and professor of mathematics Martin Z. Bazant.

“In this study, through some careful scientific work, our team has managed to decode the acoustic emissions,” Bazant says. “We were able to classify them as coming from gas bubbles that are generated by side reactions, or by fractures from the expansion and contraction of the active material, and to find signatures of those signals even in noisy data.”

Samantaray explains that, “I think the core of this work is to look at a way to investigate internal battery mechanisms while they’re still charging and discharging, and to do this nondestructively.” He adds, “Out there in the world now, there are a few methods that exist, but most are very expensive and not really conducive to batteries in their normal format.”

To carry out their analysis, the team coupled electrochemical testing with recording of the acoustic emissions, under real-world charging and discharging conditions, using detailed signal processing to correlate the electrical and acoustic data. By doing so, he says, “we were able to come up with a very cost-effective and efficient method of actually understanding gas generation and fracture of materials.”

Gas generation and fracturing are two primary mechanisms of degradation and failure in batteries, so being able to detect and distinguish those processes, just by monitoring the sounds produced by the batteries, could be a significant tool for those managing battery systems.

Previous approaches have simply monitored the sounds and recorded times when the overall sound level exceeded some threshold. But in this work, by simultaneously monitoring the voltage and current as well as the sound characteristics, Bazant says, “We know that [sound] emissions happen at a certain potential [voltage], and that helps us identify what the process might be that is causing that emission.”

After these tests, they would then take the batteries apart and study them under an electron microscope to detect fracturing of the materials.

In addition, they took a wavelet transform — essentially, a way of encoding the frequency and duration of each signal that is captured, providing distinct signatures that can then be more easily extracted from background noise. “No one had done that before,” Bazant says, “so that was another breakthrough.”

Acoustic emissions are widely used in engineering, he points out, for example to monitor structures such as bridges for signs of incipient failure. “It’s a great way to monitor a system,” he says, “because those emissions are happening whether you’re listening to them or not,” so by listening, you can learn something about internal processes that would otherwise be invisible.

With batteries, he says, “we often have a hard time interpreting the voltage and current information as precisely as we’d like, to know what’s happening inside a cell. And so this offers another window into the cell’s state of health, including its remaining useful life, and safety, too.” In a related paper with Oak Ridge National Laboratory researchers, the team has shown that acoustic emissions can provide an early warning of thermal runaway, a situation that can lead to fires if not caught. The new study suggests that these sounds can be used to detect gas generation prior to combustion, “like seeing the first tiny bubbles in a pot of heated water, long before it boils,” says Bazant.

The next step will be to take this new knowledge of how certain sounds relate to specific conditions, and develop a practical, inexpensive monitoring system based on this understanding. “Now, we know what to look for, and how to correlate that with lifetime and health and safety,” Bazant says.

One possible application of this new understanding, Samantaray says, is “as a lab tool for groups that are trying to develop new materials or test new environments, so they can actually determine gas generation or active material fracturing without having to open up the battery.”

Bazant adds that the system could also be useful for quality control in battery manufacturing. “The most expensive and rate-limiting process in battery production is often the formation cycling,” he says. This is the process where batteries are cycled through charging and discharging to break them in, and part of that process involves chemical reactions that release some gas. The new system would allow detection of these gas formation signatures, he says, “and by sensing them, it may be easier to isolate well-formed cells from poorly formed cells very early, even before the useful life of the battery, when it’s being made,” he says.

The work was supported by the Toyota Research Institute, the Center for Battery Sustainability, the National Science Foundation, and the Department of Defense, and made use of the facilities of MIT.nano.

© Photo: Alexander Cohen

With 2.2 million Canadian students back in school via the yellow school bus, a new report by the Canadian Electric School Bus Alliance (CESBA) highlights the need for equity of access and funding to make the transition to electric school buses a successful one. ​

Fewer than 4 percent of Canada’s 51,000 school buses, about 2,000 vehicles, are currently electric. But 70 percent of school buses on the road are set to be replaced in the next two to seven years, the report emphasizes.

Embedding Equity in Canada’s Transition to Electric School Buses calls on federal and provincial policymakers to ensure no one is left behind during the country’s move toward zero-emissions school buses. It identifies challenges faced by indigenous communities, students with disabilities and under-resourced areas in accessing ESBs. Adoption remains “significantly lower” in indigenous and remote communities nationwide, due primarily to cost barriers. ​

“We want to make sure that provinces roll out some financial incentive for electric school buses because right now just for the deployment there are absolutely no guidelines that force school bus operators or school districts to prioritize electric school buses in communities where there is more pollution and where they’re actually underserved,” lead author Valerie Tremblay of Green Communities Canada, a co-coordinator of CESBA, told School Transportation News.

The paper notes most ESBs range from $400,000 to $600,000 per bus compared to $125,000 for a diesel model — and related infrastructure, which proves especially challenging for indigenous and remote communities that already have higher transportation costs and barriers to funding. For example, transporting a student in northern Alberta costs $1,279 compared to $363 in urban areas, according to a report on education transportation needs prepared for the Assembly of First Nations, an advocacy group for indigenous people across Canada. ​

School bus contractor Switzer-Carty is a CESBA member company and currently operates two, 2018 model-year, Type C ESBs from the former Lion Electric. Those buses transport general education students, said Rich Bagdonas, vice president of business development for Switzer-Carty. But funding is also at issue.

The federal government targets 35 percent of medium- and heavy-duty vehicles sales to be zero emissions by 2030 and 100 percent by 2040. The Zero Emissions Transit Fund (ZETF) covers capital and planning costs, while the Zero Emissions Vehicle Infrastructure Program funds chargers.

But Bagdonas pointed out that Ontario, where Switzer-Carty mainly operates, does not currently offer provincial funding programs or incentives though the company is exploring other local options.

Tremblay added ESB funding and deployment has so far focused on Montreal and Quebec, where 80 percent or about 1,600 ESBs operate, and other urban cities. Quebec also mandates nearly two-thirds of school bus fleets be electrified by 2030. British Columbia operates about 150 ESBs and also offers incentives, noted Bagdonas, as the province also aligns with California’s mandate that all trucks and buses be electrified by 2036.

Further illustrating the challenge, the report shares that Prince Edward’s Island also has no funding program currently in place despite targeting 100 percent ESBs province-wide by 2030. It had been relying on funding from the Canada Infrastructure Bank Zero-Emissions Bus Initiative, but those funds are now exhausted.

The report recommends revising provincial and federal budgets to cover higher upfront ESB costs and better support small fleet operators.

Tremblay and associate Nicole Roach note that procurement guidelines and safety standards also need updating to ensure universal bus design and a wider range of school bus models that provide accessibility and inclusivity for all. For example, they call for standard wheelchair lifts for students with disabilities.

Tremblay and Roach write that Type A school buses now offer increased range, the prior lack of which had posed “significant challenges,” but supply remains constrained with only a few models available in Canada. The availability of Type C school buses equipped with wheelchair lifts “has the potential to ease some of the equity concerns tied to ESB adoption, especially for smaller operators or school districts,” they write.

Then, there is the obvious reduction in exposure to diesel emissions, which not only improves health but also provides better academic outcomes and school attendance. The report cites findings from the American Journal of Respiratory and Critical Care Medicine and the National Bureau of Economic Research in Massachusetts.

The report also considers the entire lifecycle of electric school buses, from resource extraction to manufacturing, adoption and use to disposal, and calls for intentional planning to ensure the transition benefits all communities, especially those on indigenous lands. Canada is a leading global producer of many critical minerals essential for ESB production, with mining predominantly located in Ontario, Quebec, British Columbia and Alberta.

Meanwhile, the report also notes the need for improved working conditions by increasing wages and operational funding for school transportation staff, “as electric buses provide cleaner and quieter environments but may limit extra income opportunities due to range constraints.” This includes workforce development to expand ESB maintenance training programs that address skill gaps and job losses in the transition. ​

In addition to newly manufactured ESBs, the report recommends funding pilot projects to convert diesel buses to electric, preventing the export of decommissioned buses to countries with weaker safety standards, policies for adopting safe recycling of electric vehicle batteries and strengthening protections in mining practices to respect the rights of indigenous people and address human rights abuses linked to Canadian mining companies. ​

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Related: WRI Research Highlights Monetary Health Benefits of Electric School Buses
Related: Previous Lion Electric School Bus Warranties Voided by Company Sale
Related: Report Finds Challenges to California Vehicle Electrification Plans

The post Report: Inequities in Canadian Electric School Bus Transition Threaten At-risk Populations appeared first on School Transportation News.

New research published by the World Resources Institute (WRI) and Carleton University finds that the U.S. could see an estimated $1.6 billion in societal benefits every year by using electric school buses.

This first-of-its-kind data released Wednesday accounts for the cost of using diesel-burning school buses as compared to using electric school buses, measured by two metrics: Health impacts and climate effects. WRI stated that by “comparing these costs at the local level, the data provides annual societal benefit figures in dollar terms for every county in the contiguous U.S.”

People in every state would experience positive benefits from ESBs, the research suggests, but it would be most pronounced in countries operating the oldest-burning school buses, and in communities with higher proportions of people of color and in countries with larger populations and dense, urban areas.

Still, the research indicates New York, California, Florida and Texas are poised to benefit the most from ESBs. However, nearly every county nationwide stands to benefit by using electric school buses, with the study finding more than $509,000 in average annual benefits per county and some counties seeing more than $30 million in societal benefits each year. Annual benefits vary by county based on school bus fleet size, population density, electricity fuel type mix, and age of the current diesel fleet.

“For years, communities in New York have experienced outsized impacts of diesel pollution,” commented Matt Berlin, CEO of New York City School Bus Umbrella Services. “As this new data from WRI proves, school bus electrification makes sense for New Yorkers. Investing in electric school buses means making the bus ride for kids and bus attendants and drivers on the bus quieter and healthier. Beyond the bus itself, reducing pollution near schools and in the communities where we all live means we all enjoy these benefits.”

WRI stated that the research is among the first to “model and quantify the county-level health and climate impacts of using electric school buses instead of aging diesel-burning school buses.”

When looking at the the factors of population health and climate change, the research notes that about 90 percent of the nearly half-million school buses operating in the U.S. run on diesel fuel and the harmful pollutants in diesel can cause respiratory illness, cognitive impairment and cancer, as recognized by the World Health Organization.

ESBs, however, produce zero tailpipe emissions and have the lowest greenhouse gas footprint of any school bus type at the national level, even when accounting for emissions from the associated electricity generation, the research claims. It examines the effects of diesel-burning school buses in operation, as well as the production and distribution of the fuel used.

The health impacts of diesel-burning school buses were estimated by determining the excess mortality associated with exposure to PM2.5, despite federal regulations over the past 15 years that have reduced diesel emissions by approximately 90 percent. The impacts were calculated into dollar figures based on a sociological metric that reportedly looks at how much society is willing to pay for small reductions to the risk of dying from health conditions that may be caused by environmental pollution. The sociological metric is referred to as the Value of a Statistical Life.

Meanwhile, the climate impacts of diesel-burning school buses were measured by calculating the Social Cost of Carbon, an established metric for the societal damage from extreme heat, sea level rise, food insecurity and other impacts of climate change, from these buses’ carbon dioxide emissions. The study notes that because health impacts were measured only by excess mortality from PM2.5, there are likely more health benefits of electric school buses that aren’t captured in this data, including reduced exposure to ozone pollutants, nitrogen dioxide, nitrogen oxide (NOx) and volatile organic compounds, or VOCs.

Further Studies Needed

 

A technical note acknowledges several research limitations in addition to only studying excess mortality of PM2.5 and recommends additional environmental analysis and higher resolution modeling in urban areas. The study does not address environmental justice or equity benefits of ESBs and disparities in air pollution based on race, ethnicity or income, the latter which the researchers said could reveal additional ESB benefits for marginalized communities. The research also makes assumptions about brake and tire-wear emissions and relies on “not yet mature” ESB operational parameters and emissions based on data from 2020 that does not account for changes in fleet composition changes, vehicle standards and the electricity grid. Additionally, benefits per ton remain consistent between 2016 and 2020 and “may not fully capture changes in atmospheric composition or emissions.”

Once the costs of diesel school bus impacts were determined, the research calculated the same types of impacts for ESBs, including electricity generation, and compared them to that of diesel to provide a dollar figure from each county.

Brian Zepka, research manager for WRI’s Electric School Bus Initiative said the research used a new modeling approach to trace air pollution back to its source, “allowing us to directly attribute which health impacts stem from diesel-burning school buses. While other approaches start with the air pollution source and estimate its impact, this approach, developed in peer-reviewed research funded by the Health Effects Institute, starts with the health impacts, like early deaths from air pollution, and traces that pollution back to its source—in this case, school buses.”
WRI noted the research “uses state-of-the-art models and county-level data to more specifically estimate where electric school buses would provide the most health and climate benefits through reduced emissions. It doesn’t look at the cost to own or operate different types of school buses, instead examining the impact on society from the use of the buses.”

Sue Gander, director of WRI’s Electric School Bus Initiative, said the new research shows “undeniably” that ESBs give kids a cleaner ride to school.

“In every region of the country, North, South, East and West, communities stand to see real, significant benefits from the cleaner air and reduced emissions of electric school buses. And as this research demonstrates, everybody wins when kids get to school on a clean ride, to the tune of $1.6 billion dollars every year in health and climate benefits nationwide,” she said. “Given the outsize benefits of electrifying the most polluting diesel-burning school bus fleets, and the concentration of those buses in low income areas and areas with more people of color, this data reinforces the need to ensure that those most impacted by diesel exhaust pollution are among the first to benefit from electric school buses.”

The 10 percent of diesel-burning school buses that are the most polluting are responsible for nearly 50 percent of the total health impacts of diesel-burning school buses nationwide, the research notes. Breaking that down by per-mile health impacts from diesel school buses, while varying, results to under $10 to nearly $4,000 per 1,000 miles driven, depending on the school bus age and operating location.

While the research only focused on PM2.5-related premature mortality as the primary health end point, diesel-burning school buses also emit large amounts of NOx, which contribute to ozone formation and nitrogen dioxide (NO2) exposure—both are linked to asthma, morbidity and additional premature deaths.

The research does not include the additional health effects or impacts of other diesel pollutants. WRI stated the research is likely underestimating the total benefits of electrification. Incorporating NOx-related outcomes in the future could show greater contrasts between diesel and electric.

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Related: California Doubles Down on Zero-Emission Vehicles with Renewed Affordability, Adoption Priorities
Related: Safety Concerns of the Electric Grid?
Related: Report Highlights Shift in Federal Policy from EVs to Conventional Fuels

The post WRI Research Highlights Monetary Health Benefits of Electric School Buses appeared first on School Transportation News.

Hydrogen has the potential to be a climate-friendly fuel since it doesn’t release carbon dioxide when used as an energy source. Currently, however, most methods for producing hydrogen involve fossil fuels, making hydrogen less of a “green” fuel over its entire life cycle.

A new process developed by MIT engineers could significantly shrink the carbon footprint associated with making hydrogen.

Last year, the team reported that they could produce hydrogen gas by combining seawater, recycled soda cans, and caffeine. The question then was whether the benchtop process could be applied at an industrial scale, and at what environmental cost.

Now, the researchers have carried out a “cradle-to-grave” life cycle assessment, taking into account every step in the process at an industrial scale. For instance, the team calculated the carbon emissions associated with acquiring and processing aluminum, reacting it with seawater to produce hydrogen, and transporting the fuel to gas stations, where drivers could tap into hydrogen tanks to power engines or fuel cell cars. They found that, from end to end, the new process could generate a fraction of the carbon emissions that is associated with conventional hydrogen production.

In a study appearing today in Cell Reports Sustainability, the team reports that for every kilogram of hydrogen produced, the process would generate 1.45 kilograms of carbon dioxide over its entire life cycle. In comparison, fossil-fuel-based processes emit 11 kilograms of carbon dioxide per kilogram of hydrogen generated.

The low-carbon footprint is on par with other proposed “green hydrogen” technologies, such as those powered by solar and wind energy.

“We’re in the ballpark of green hydrogen,” says lead author Aly Kombargi PhD ’25, who graduated this spring from MIT with a doctorate in mechanical engineering. “This work highlights aluminum’s potential as a clean energy source and offers a scalable pathway for low-emission hydrogen deployment in transportation and remote energy systems.”

The study’s MIT co-authors are Brooke Bao, Enoch Ellis, and professor of mechanical engineering Douglas Hart.

Gas bubble

Dropping an aluminum can in water won’t normally cause much of a chemical reaction. That’s because when aluminum is exposed to oxygen, it instantly forms a shield-like layer. Without this layer, aluminum exists in its pure form and can readily react when mixed with water. The reaction that occurs involves aluminum atoms that efficiently break up molecules of water, producing aluminum oxide and pure hydrogen. And it doesn’t take much of the metal to bubble up a significant amount of the gas.

“One of the main benefits of using aluminum is the energy density per unit volume,” Kombargi says. “With a very small amount of aluminum fuel, you can conceivably supply much of the power for a hydrogen-fueled vehicle.”

Last year, he and Hart developed a recipe for aluminum-based hydrogen production. They found they could puncture aluminum’s natural shield by treating it with a small amount of gallium-indium, which is a rare-metal alloy that effectively scrubs aluminum into its pure form. The researchers then mixed pellets of pure aluminum with seawater and observed that the reaction produced pure hydrogen. What’s more, the salt in the water helped to precipitate gallium-indium, which the team could subsequently recover and reuse to generate more hydrogen, in a cost-saving, sustainable cycle.

“We were explaining the science of this process in conferences, and the questions we would get were, ‘How much does this cost?’ and, ‘What’s its carbon footprint?’” Kombargi says. “So we wanted to look at the process in a comprehensive way.”

A sustainable cycle

For their new study, Kombargi and his colleagues carried out a life cycle assessment to estimate the environmental impact of aluminum-based hydrogen production, at every step of the process, from sourcing the aluminum to transporting the hydrogen after production. They set out to calculate the amount of carbon associated with generating 1 kilogram of hydrogen — an amount that they chose as a practical, consumer-level illustration.

“With a hydrogen fuel cell car using 1 kilogram of hydrogen, you can go between 60 to 100 kilometers, depending on the efficiency of the fuel cell,” Kombargi notes.

They performed the analysis using Earthster — an online life cycle assessment tool that draws data from a large repository of products and processes and their associated carbon emissions. The team considered a number of scenarios to produce hydrogen using aluminum, from starting with “primary” aluminum mined from the Earth, versus “secondary” aluminum that is recycled from soda cans and other products, and using various methods to transport the aluminum and hydrogen.

After running life cycle assessments for about a dozen scenarios, the team identified one scenario with the lowest carbon footprint. This scenario centers on recycled aluminum — a source that saves a significant amount of emissions compared with mining aluminum — and seawater — a natural resource that also saves money by recovering gallium-indium. They found that this scenario, from start to finish, would generate about 1.45 kilograms of carbon dioxide for every kilogram of hydrogen produced. The cost of the fuel produced, they calculated, would be about $9 per kilogram, which is comparable to the price of hydrogen that would be generated with other green technologies such as wind and solar energy.

The researchers envision that if the low-carbon process were ramped up to a commercial scale, it would look something like this: The production chain would start with scrap aluminum sourced from a recycling center. The aluminum would be shredded into pellets and treated with gallium-indium. Then, drivers could transport the pretreated pellets as aluminum “fuel,” rather than directly transporting hydrogen, which is potentially volatile. The pellets would be transported to a fuel station that ideally would be situated near a source of seawater, which could then be mixed with the aluminum, on demand, to produce hydrogen. A consumer could then directly pump the gas into a car with either an internal combustion engine or a fuel cell.

The entire process does produce an aluminum-based byproduct, boehmite, which is a mineral that is commonly used in fabricating semiconductors, electronic elements, and a number of industrial products. Kombargi says that if this byproduct were recovered after hydrogen production, it could be sold to manufacturers, further bringing down the cost of the process as a whole.

“There are a lot of things to consider,” Kombargi says. “But the process works, which is the most exciting part. And we show that it can be environmentally sustainable.”

The group is continuing to develop the process. They recently designed a small reactor, about the size of a water bottle, that takes in aluminum pellets and seawater to generate hydrogen, enough to power an electric bike for several hours. They previously demonstrated that the process can produce enough hydrogen to fuel a small car. The team is also exploring underwater applications, and are designing a hydrogen reactor that would take in surrounding seawater to power a small boat or underwater vehicle.

This research was supported, in part, by the MIT Portugal Program.

© Credit: Courtesy of the researchers

There’s a lot of untapped potential in our homes and vehicles that could be harnessed to reinforce local power grids and make them more resilient to unforeseen outages, a new study shows.

In response to a cyber attack or natural disaster, a backup network of decentralized devices — such as residential solar panels, batteries, electric vehicles, heat pumps, and water heaters — could restore electricity or relieve stress on the grid, MIT engineers say.

Such devices are “grid-edge” resources found close to the consumer rather than near central power plants, substations, or transmission lines. Grid-edge devices can independently generate, store, or tune their consumption of power. In their study, the research team shows how such devices could one day be called upon to either pump power into the grid, or rebalance it by dialing down or delaying their power use.

In a paper appearing this week in the Proceedings of the National Academy of Sciences, the engineers present a blueprint for how grid-edge devices could reinforce the power grid through a “local electricity market.” Owners of grid-edge devices could subscribe to a regional market and essentially loan out their device to be part of a microgrid or a local network of on-call energy resources.

In the event that the main power grid is compromised, an algorithm developed by the researchers would kick in for each local electricity market, to quickly determine which devices in the network are trustworthy. The algorithm would then identify the combination of trustworthy devices that would most effectively mitigate the power failure, by either pumping power into the grid or reducing the power they draw from it, by an amount that the algorithm would calculate and communicate to the relevant subscribers. The subscribers could then be compensated through the market, depending on their participation.

The team illustrated this new framework through a number of grid attack scenarios, in which they considered failures at different levels of a power grid, from various sources such as a cyber attack or a natural disaster. Applying their algorithm, they showed that various networks of grid-edge devices were able to dissolve the various attacks.

The results demonstrate that grid-edge devices such as rooftop solar panels, EV chargers, batteries, and smart thermostats (for HVAC devices or heat pumps) could be tapped to stabilize the power grid in the event of an attack.

“All these small devices can do their little bit in terms of adjusting their consumption,” says study co-author Anu Annaswamy, a research scientist in MIT’s Department of Mechanical Engineering. “If we can harness our smart dishwashers, rooftop panels, and EVs, and put our combined shoulders to the wheel, we can really have a resilient grid.”

The study’s MIT co-authors include lead author Vineet Nair and John Williams, along with collaborators from multiple institutions including the Indian Institute of Technology, the National Renewable Energy Laboratory, and elsewhere.

Power boost

The team’s study is an extension of their broader work in adaptive control theory and designing systems to automatically adapt to changing conditions. Annaswamy, who leads the Active-Adaptive Control Laboratory at MIT, explores ways to boost the reliability of renewable energy sources such as solar power.

“These renewables come with a strong temporal signature, in that we know for sure the sun will set every day, so the solar power will go away,” Annaswamy says. “How do you make up for the shortfall?”

The researchers found the answer could lie in the many grid-edge devices that consumers are increasingly installing in their own homes.

“There are lots of distributed energy resources that are coming up now, closer to the customer rather than near large power plants, and it’s mainly because of individual efforts to decarbonize,” Nair says. “So you have all this capability at the grid edge. Surely we should be able to put them to good use.”

While considering ways to deal with drops in energy from the normal operation of renewable sources, the team also began to look into other causes of power dips, such as from cyber attacks. They wondered, in these malicious instances, whether and how the same grid-edge devices could step in to stabilize the grid following an unforeseen, targeted attack.

Attack mode

In their new work, Annaswamy, Nair, and their colleagues developed a framework for incorporating grid-edge devices, and in particular, internet-of-things (IoT) devices, in a way that would support the larger grid in the event of an attack or disruption. IoT devices are physical objects that contain sensors and software that connect to the internet.

For their new framework, named EUREICA (Efficient, Ultra-REsilient, IoT-Coordinated Assets), the researchers start with the assumption that one day, most grid-edge devices will also be IoT devices, enabling rooftop panels, EV chargers, and smart thermostats to wirelessly connect to a larger network of similarly independent and distributed devices. 

The team envisions that for a given region, such as a community of 1,000 homes, there exists a certain number of IoT devices that could potentially be enlisted in the region’s local network, or microgrid. Such a network would be managed by an operator, who would be able to communicate with operators of other nearby microgrids.

If the main power grid is compromised or attacked, operators would run the researchers’ decision-making algorithm to determine trustworthy devices within the network that can pitch in to help mitigate the attack.

The team tested the algorithm on a number of scenarios, such as a cyber attack in which all smart thermostats made by a certain manufacturer are hacked to raise their setpoints simultaneously to a degree that dramatically alters a region’s energy load and destabilizes the grid. The researchers also considered attacks and weather events that would shut off the transmission of energy at various levels and nodes throughout a power grid.

“In our attacks we consider between 5 and 40 percent of the power being lost. We assume some nodes are attacked, and some are still available and have some IoT resources, whether a battery with energy available or an EV or HVAC device that’s controllable,” Nair explains. “So, our algorithm decides which of those houses can step in to either provide extra power generation to inject into the grid or reduce their demand to meet the shortfall.”

In every scenario that they tested, the team found that the algorithm was able to successfully restabilize the grid and mitigate the attack or power failure. They acknowledge that to put in place such a network of grid-edge devices will require buy-in from customers, policymakers, and local officials, as well as innovations such as advanced power inverters that enable EVs to inject power back into the grid.

“This is just the first of many steps that have to happen in quick succession for this idea of local electricity markets to be implemented and expanded upon,” Annaswamy says. “But we believe it’s a good start.”

This work was supported, in part, by the U.S. Department of Energy and the MIT Energy Initiative.

© Credit: Courtesy of the researchers

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

© Credit: Courtesy of Mohamed Elrefaie

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 challenges

The 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.

© Photo: Bob Adams/Flickr

Charging stations for electric vehicles are essential for cleaning up the transportation sector. A new study by MIT researchers suggests they’re good for business, too.

The study found that, in California, opening a charging station boosted annual spending at each nearby business by an average of about $1,500 in 2019 and about $400 between January 2021 and June 2023. The spending bump amounts to thousands of extra dollars annually for nearby businesses, with the increase particularly pronounced for businesses in underresourced areas.

The study’s authors hope the research paints a more holistic picture of the benefits of EV charging stations, beyond environmental factors.

“These increases are equal to a significant chunk of the cost of installing an EV charger, and I hope this study sheds light on these economic benefits,” says lead author Yunhan Zheng MCP ’21, SM ’21, PhD ’24, a postdoc at the Singapore-MIT Alliance for Research and Technology (SMART). “The findings could also diversify the income stream for charger providers and site hosts, and lead to more informed business models for EV charging stations.”

Zheng’s co-authors on the paper, which was published today in Nature Communications, are David Keith, a senior lecturer at the MIT Sloan School of Management; Jinhua Zhao, an MIT professor of cities and transportation; and alumni Shenhao Wang MCP ’17, SM ’17, PhD ’20 and Mi Diao MCP ’06, PhD ’10.

Understanding the EV effect

Increasing the number of electric vehicle charging stations is seen as a key prerequisite for the transition to a cleaner, electrified transportation sector. As such, the 2021 U.S. Infrastructure Investment and Jobs Act committed $7.5 billion to build a national network of public electric vehicle chargers across the U.S.

But a large amount of private investment will also be needed to make charging stations ubiquitous.

“The U.S. is investing a lot in EV chargers and really encouraging EV adoption, but many EV charging providers can’t make enough money at this stage, and getting to profitability is a major challenge,” Zheng says.

EV advocates have long argued that the presence of charging stations brings economic benefits to surrounding communities, but Zheng says previous studies on their impact relied on surveys or were small-scale. Her team of collaborators wanted to make advocates’ claims more empirical.

For their study, the researchers collected data from over 4,000 charging stations in California and 140,000 businesses, relying on anonymized credit and debit card transactions to measure changes in consumer spending. The researchers used data from 2019 through June of 2023, skipping the year 2020 to minimize the impact of the pandemic.

To judge whether charging stations caused customer spending increases, the researchers compared data from businesses within 500 meters of new charging stations before and after their installation. They also analyzed transactions from similar businesses in the same time frame that weren’t near charging stations.

Supercharging nearby businesses

The researchers found that installing a charging station boosted annual spending at nearby establishments by an average of 1.4 percent in 2019 and 0.8 percent from January 2021 to June 2023.

While that might sound like a small amount per business, it amounts to thousands of dollars in overall consumer spending increases. Specifically, those percentages translate to almost $23,000 in cumulative spending increases in 2019 and about $3,400 per year from 2021 through June 2023.

Zheng says the decline in spending increases over the two time periods might be due to a saturation of EV chargers, leading to lower utilization, as well as an overall decrease in spending per business after the Covid-19 pandemic and a reduced number of businesses served by each EV charging station in the second period. Despite this decline, the annual impact of a charging station on all its surrounding businesses would still cover approximately 11.2 percent of the average infrastructure and installation cost of a standard charging station.

Through both time frames, the spending increases were highest for businesses within about a football field’s distance from the new stations. They were also significant for businesses in disadvantaged and low-income areas, as designated by California and the Justice40 Initiative.

“The positive impacts of EV charging stations on businesses are not constrained solely to some high-income neighborhoods,” Wang says. “It highlights the importance for policymakers to develop EV charging stations in marginalized areas, because they not only foster a cleaner environment, but also serve as a catalyst for enhancing economic vitality.”

Zheng believes the findings hold a lesson for charging station developers seeking to improve the profitability of their projects.

“The joint gas station and convenience store business model could also be adopted to EV charging stations,” Zheng says. “Traditionally, many gas stations are affiliated with retail store chains, which enables owners to both sell fuel and attract customers to diversify their revenue stream. EV charging providers could consider a similar approach to internalize the positive impact of EV charging stations.”

Zheng also says the findings could support the creation of new funding models for charging stations, such as multiple businesses sharing the costs of construction so they can all benefit from the added spending.

Those changes could accelerate the creation of charging networks, but Zheng cautions that further research is needed to understand how much the study’s findings can be extrapolated to other areas. She encourages other researchers to study the economic effects of charging stations and hopes future research includes states beyond California and even other countries.

“A huge number of studies have focused on retail sales effects from traditional transportation infrastructure, such as rail and subway stations, bus stops, and street configurations,” Zhao says. “This research provides evidence for an important, emerging piece of transportation infrastructure and shows a consistently positive effect on local businesses, paving the way for future research in this area.”

The research was supported, in part, by the Singapore-MIT Alliance for Research and Technology (SMART) and the Singapore National Research Foundation. Diao was partially supported by the Natural Science Foundation of Shanghai and the Fundamental Research Funds for the Central Universities of China.

© Image: iStock

Although critical to the nation’s food security, farm work is potentially hazardous, farmworkers receive lower wages when compared with nonsupervisory workers outside agriculture, and many hired farm workers lack legal work authorization and access to basic public services. For the United States to remain competitive as a producer of fruit, vegetables, and other labor-intensive commodities both private and government institutions will need to accelerate adaptation to a changing landscape of farm labor.

An upcoming conference on farm labor seeks to strengthen ongoing farm labor research by convening and developing a network of researchers and stakeholders. The Changing Landscape of Farm Labor Conditions in the United States: What the Future Holds and How to Prepare for It conference will take place September 17 to 19, 2024, in Santa Cruz, California.

The conference is presented by the USDA Economic Research Service and Farm Foundation. It will cover four key themes: trends in the farm labor force, including worker migration and the H-2A Temporary Agricultural Program; labor costs, farm worker conditions, and workforce development.

Visit https://farmfoundation.swoogo.com/farmlabor for more information and to register.

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