A researcher surveys wild rice on the Pine River. (Wisconsin SEA Grant)

Through executive orders and the Republican reconciliation bill signed into law in July, the administration of President Donald Trump has cancelled or proposed the cancellation of about $75 million in grants and loans meant for climate-focused projects in Wisconsin, according to data collected by the environmental policy group Atlas Public Policy. 

The cancelled projects include money for the state’s Department of Military Affairs to make infrastructure more resilient to climate change and a grant for the Milwaukee-based water quality non-profit Reflo, Inc. to help children in the city learn about sustainability and the environment. 

Since taking office in January, Trump and congressional Republicans have attacked federal government efforts to address climate change by slashing programs and withholding money. Many of the projects that have lost money in Wisconsin were aimed at marginalized communities such as Native American tribes and Milwaukee’s Black residents — putting them in Trump’s crosshairs because of his aversion to diversity, equity and inclusion efforts. 

Through the 2022 Inflation Reduction Act and the 2021 Infrastructure Investment and Jobs Act, the administration of President Joe Biden targeted billions of dollars to help communities undertake projects meant to help transition to renewable sources of energy, restore local waterways and make homes more energy efficient. 

Under Trump, that money has been clawed back as Republicans have become even more hostile to efforts to address climate change. For example, U.S. Rep. Tom Tiffany, who represents much of northern Wisconsin and is considering running for governor next year, has spent a significant amount of time fighting the construction of solar energy in the state. 

“The loss of this funding represents a profound missed opportunity for Wisconsin, especially for its most vulnerable and disadvantaged communities,” says Jaclyn Lea, an associate at Atlas Public Policy. “These canceled projects would have supported investments in communities building energy efficiency, workforce development, and climate resilience. The impact of these cancellations will be felt across the state, slowing progress on critical environmental and resilience efforts.” 

Milwaukee energy efficiency

Among the projects that have lost their funding is a grant program under the Inflation Reduction Act to help the city of Milwaukee’s Environmental Collaboration Office work with non-profit organizations to help residents of the city’s predominately Black north side and predominately Latino south side connect with programs to make their homes more energy efficient. 

About $200,000 of the $1 million grant would have supported energy audits of 250 homes in the two neighborhoods. Many of the aging homes in the city have problems with old electrical systems, causing energy bills to rise for some of the city’s poorest residents and posing a fire risk. The program would have also helped connect residents with programs to weatherize their homes and remediate lead contamination. 

Erick Shambarger, Milwaukee’s director of environmental sustainability, says the program would have helped the city’s lowest income residents —  who are at the greatest risk of environmental harms —  lower their energy costs while helping the city as a whole cut emissions. 

It was rolled back as part of the tax- and spending-cut mega-bill that congressional Republicans passed this summer and Trump signed into law on July 4. 

“We also have to do a better job of maintaining and improving our existing housing stock,” Shambarger says. “And this was one tool that we had to try to do that, and it got pulled away. And then now you look at all of the other things that the big, ugly bill did in terms of eliminating tax credits for energy efficiency and all of the rest. And so this isn’t just one [decision], there’s a real pullback at the feds to support low- to moderate-income households.” 

He adds that the grant program project highlighted the ways it would help minority communities because that’s what the Biden administration was looking for, but he doesn’t think the program should be controversial. 

“At the end of the day, we are trying to help the people that need the help the most, help them save on their energy bills and help them get their families stabilized, and create better environments for kids, and better environmental conditions for kids to have a better chance in life,” he says. 

Shambarger says that political instability is one of the greatest obstacles to addressing climate change. The instability caused by the Trump administration taking back money the federal government had already promised to deliver makes it more difficult for industries and businesses relying on more predictable government action, he adds. 

“It is just very, very frustrating just to not have the consistency of policy that we need to address the climate crisis,” he says. “It should be frustrating for every American, including our contractors, who have to plan for the future, who have to hire workers with skill sets, and all of that takes time to set up training programs for new industries. It takes time to build partnerships for the financing for all of this.”

Shambarger is particularly frustrated by the federal government canceling contracts in midstream. 

“It’s one thing to say ‘wind down this contract, and maybe you don’t get renewed, and you have time to adjust,’” he says, “but to just terminate stuff without notice, without looking into the particulars of what our program was achieving is really, really disruptive.”

The effects of climate change are here, Shambarger says. Wisconsin and the Midwest have faced days of poor air quality because of wildfires across Canada this summer. Floods have continued to get worse every year. 

Meanwhile, lower-income working Americans are getting less help, “and that’s too bad, because this country, in order for it to really be great, we’ve got to make sure that we are providing really safe and affordable housing that is climate resilient,” he says.

Global warming continues to heighten risks, from wildfire smoke in the Midwest air to floods and wildfires threatening cities, “and all of that threat is not going away,” Shambarger says. “We just appeal to all levels of government to recognize that there is something we can do about this … It’s a benefit to all Americans.”

Brothertown Tribe wild rice restoration 

Another project cancelled by the Trump administration is a $3 million grant meant to help researchers at the University of Wisconsin work with the Brothertown Indian Nation to restore wild rice habitat in the Lake Winnebago watershed and study the effects of that restoration on the lake’s water quality. 

While the project would have helped the tribe connect with a plant that many of the state’s tribes view as sacred, it would also have served as a wetland restoration project on the drinking water source for hundreds of thousands of people in the Fox River valley. Wetland restoration is a major tool for improving water quality because wetlands can serve as a sort of filter to block potentially harmful nutrients such as phosphorus and nitrates from running off of farm fields and into the water system. 

“There’s over 200,000 people that rely on Winnebago alone for drinking water, and we know the positive impact of having better health for that water,” Jessica Ryan, the tribe’s vice chair, says. “And the community has been trying to improve the water quality for a long period of time in that area. But there’s, there’s a long way to go yet. There’s tremendous negative impacts that have happened from prior generations. So we need to keep our foot on the gas.” 

The grant was meant to fund five years of rice seeding and studying to see if the rice population can be increased and if that increase can improve the quality of water, both for drinking and for supporting populations of fish such as sturgeon and waterfowl such as geese and ducks. The grant was designed as a collaboration between indigenous and western methods of science and involved a number of the state’s tribes as well as local groups and farmers.

“We’d like to have the support of all of the state and the federal politicians to support us because we see the similarities in these interests,” Ryan says. “We see how it lifts up the entire community. Regardless of whether we’re American Indian or not, we have this common core value of looking after the land and the water.”

Those values are shared by local farmers and by the large tribal communities in the area — along with the Brothertown, the Oneida, the Stockbridge and the Menominee, she says. 

The Brothertown Indian Nation began in 18th century New England as a community of Christian Native Americans. The tribe later moved west to Wisconsin to avoid the conflicts over land that pushed out most of the East Coast’s native populations. The tribe settled east of Lake Winnebago. 

But in 1838 the federal government then tried to force the tribe out of Wisconsin and into Kansas. Looking for a way to prevent the government from taking their land, the tribe requested the allotment of their land and U.S. citizenship. Members believed that this would allow private ownership of their land and protect the tribe. 

But unbeknownst to the members, this agreement terminated the federal government’s recognition of the tribe — ending its status as a sovereign nation. The tribe continues to work toward once again being recognized by the federal government. But Ryan says that the Trump administration’s cancellation of the Brothertown grant was especially painful because it was another promise to the tribe broken by the U.S. government. 

“The federal government, in my opinion, has an opportunity to make it, to do the right thing, and they have chosen not to do the right thing,” she says. “They’ve chosen to do the opposite. And I don’t know what’s behind that decision making, right? Like, I don’t know if it’s a political decision, if it’s a racial decision, I don’t know what that is, but to us as the recipients who worked diligently, we’ve complied with all that’s been expected of us. We followed the rules, right? And the application process, it was a competitive process. We were selected. And to have the government again unilaterally go back on its word, it’s pretty devastating.” 

Because the tribe isn’t recognized, it has very little resources. All of its budget comes from charitable support, grants and what the tribe can make selling crafts at its store. It can’t cover the work that was supposed to be covered by a $3 million grant. For now, the tribe has kept one person on its payroll to keep collecting data through the project and is hoping for volunteers to help with the additional work. 

“We had so much good in mind that we were going to do with the funds that would benefit far more than just us,” Ryan says. “This was going to have a tremendous positive impact on the entire community within the watershed. It’s not just something that was going to look after our people or a small group of people. This was intended to have a statewide positive impact.”

The research the tribe wants to continue collecting “is something that can be used on a larger model for the entire region,” Ryan says.  “This is a long-term ecological restoration effort, and we are three years into this project, and it’s a really critical, pivotal moment.”

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.

Climate anxiety affects nearly half of young people aged 16-25. Students like second-year Rachel Mohammed find hope and inspiration through her involvement in innovative climate solutions, working alongside peers who share her determination. “I’ve met so many people at MIT who are dedicated to finding climate solutions in ways that I had never imagined, dreamed of, or heard of. That is what keeps me going, and I’m doing my part,” she says.

Hydrogen-fueled engines

Hydrogen offers the potential for zero or near-zero emissions, with the ability to reduce greenhouse gases and pollution by 29 percent. However, the hydrogen industry faces many challenges related to storage solutions and costs.

Mohammed leads the hydrogen team on MIT’s Electric Vehicle Team (EVT), which is dedicated to harnessing hydrogen power to build a cleaner, more sustainable future. EVT is one of several student-led build teams at the Edgerton Center focused on innovative climate solutions. Since its founding in 1992, the Edgerton Center has been a hub for MIT students to bring their ideas to life.

Hydrogen is mostly used in large vehicles like trucks and planes because it requires a lot of storage space. EVT is building their second iteration of a motorcycle based on what Mohammed calls a “goofy hypothesis” that you can use hydrogen to power a small vehicle. The team employs a hydrogen fuel cell system, which generates electricity by combining hydrogen with oxygen. However, the technology faces challenges, particularly in storage, which EVT is tackling with innovative designs for smaller vehicles.

Presenting at the 2024 World Hydrogen Summit reaffirmed Mohammed’s confidence in this project. “I often encounter skepticism, with people saying it’s not practical. Seeing others actively working on similar initiatives made me realize that we can do it too,” Mohammed says.

The team’s first successful track test last October allowed them to evaluate the real-world performance of their hydrogen-powered motorcycle, marking a crucial step in proving the feasibility and efficiency of their design.

MIT’s Sustainable Engine Team (SET), founded by junior Charles Yong, uses the combustion method to generate energy with hydrogen. This is a promising technology route for high-power-density applications, like aviation, but Yong believes it hasn’t received enough attention. Yong explains, “In the hydrogen power industry, startups choose fuel cell routes instead of combustion because gas turbine industry giants are 50 years ahead. However, these giants are moving very slowly toward hydrogen due to its not-yet-fully-developed infrastructure. Working under the Edgerton Center allows us to take risks and explore advanced tech directions to demonstrate that hydrogen combustion can be readily available.”

Both EVT and SET are publishing their research and providing detailed instructions for anyone interested in replicating their results.