Farmers, the state's second-largest insurer, pledged to write thousands of new policies in wildfire-prone areas where coverage is scarce.

Amazon also announced plans Monday to invest up to $50 billion to expand data centers and AI technology for federal customers.

The West African nation was among a group of countries that pushed for a deal to reduce fossil fuels.

A report catalogs halting progress since the Oil & Gas Decarbonization Charter was signed at the U.N. climate summit in 2023.

“This was a COP where we were visible but not empowered,” a member of a global Indigenous group said.

TotalEnergies denies it is liable for the damage that Hugues Falys has suffered.

The British government says it will force down bills and emissions by insulating millions of cold, drafty homes. That plan is unraveling.

Scientists warn that a warming climate is intensifying storms and rainfall across Southeast Asia, making floods and landslides increasingly destructive and frequent.

Nature Climate Change, Published online: 25 November 2025; doi:10.1038/s41558-025-02501-z

Research on climate change requires continued support from funding agencies. Nature Climate Change spoke to experts from different organizations across the world to discuss how funding agencies can better promote future climate research and actions regarding interdisciplinary studies, international collaborations, supporting young scholars and more.

Nature Climate Change, Published online: 25 November 2025; doi:10.1038/s41558-025-02506-8

Adaptation to climate change goes beyond the migration–non-migration divide. Families and communities combine mobility with rootedness, drawing on cultural ties, intergenerational learning, and lived knowledge to navigate risks and shape long-term futures.

Nature Climate Change, Published online: 25 November 2025; doi:10.1038/s41558-025-02484-x

It is important to understand the combined effects of multiple changes on the ocean. Here the authors use time of emergence to highlight the increases in impacts of individual and compound changes globally from the surface to the deeper ocean, identifying areas most affected.

At a high level, ammonia seems like a dream fuel: It’s carbon-free, energy-dense, and easier to move and store than hydrogen. Ammonia is also already manufactured and transported at scale, meaning it could transform energy systems using existing infrastructure. But burning ammonia creates dangerous nitrous oxides, and splitting ammonia molecules to create hydrogen fuel typically requires lots of energy and specialized engines.

The startup Amogy, founded by four MIT alumni, believes it has the technology to finally unlock ammonia as a major fuel source. The company has developed a catalyst it says can split — or “crack” — ammonia into hydrogen and nitrogen up to 70 percent more efficiently than state-of-the-art systems today. The company is planning to sell its catalysts as well as modular systems including fuel cells and engines to convert ammonia directly to power. Those systems don’t burn or combust ammonia, and thus bypass the health concerns related to nitrous oxides.

Since Amogy’s founding in 2020, the company has used its ammonia-cracking technology to create the world’s first ammonia-powered drone, tractor, truck, and tugboat. It has also attracted partnerships with industry leaders including Samsung, Saudi Aramco, KBR, and Hyundai, raising more than $300 million along the way.

“No one has showcased that ammonia can be used to power things at the scale of ships and trucks like us,” says CEO Seonghoon Woo PhD ’15, who founded the company with Hyunho Kim PhD ’18, Jongwon Choi PhD ’17, and Young Suk Jo SM ’13, PhD ’16. “We’ve demonstrated this approach works and is scalable.”

Earlier this year, Amogy completed a research and manufacturing facility in Houston and announced a pilot deployment of its catalyst with the global engineering firm JGC Holdings Corporation. Now, with a manufacturing contract secured with Samsung Heavy Industries, Amogy is set to start delivering more of its systems to customers next year. The company will deploy a 1-megawatt ammonia-to-power pilot project with the South Korean city of Pohang in 2026, with plans to scale up to 40 megawatts at that site by 2028 or 2029. Woo says dozens of other projects with multinational corporations are in the works.

Because of the power density advantages of ammonia over renewables and batteries, the company is targeting power-hungry industries like maritime shipping, power generation, construction, and mining for its early systems.

“This is only the beginning,” Woo says. “We’ve worked hard to build the technology and the foundation of our company, but the real value will be generated as we scale. We’ve proved the potential for ammonia to decarbonize heavy industry, and now we really want to accelerate adoption of our technology. We’re thinking long term about the energy transition.”

Unlocking a new fuel source

Woo completed his PhD in MIT’s Department of Materials Science and Engineering before his eventual co-founders, Kim, Choi, and Jo, completed their PhDs in MIT’s Department of Mechanical Engineering. Jo worked on energy science and ran experiments to make engines run more efficiently as part of his PhD.

“The PhD programs at MIT teach you how to think deeply about solving technical problems using systems-based approaches,” Woo says. “You also realize the value in learning from failures, and that mindset of iteration is similar to what you need to do in startups.”

In 2020, Woo was working in the semiconductor industry when he reached out to his eventual co-founders asking if they were working on anything interesting. At that time, Jo was still working on energy systems based on hydrogen and ammonia while Kim was developing new catalysts to create ammonia fuel.

“I wanted to start a company and build a business to do good things for society,” Woo recalls. “People had been talking about hydrogen as a more sustainable fuel source, but it had never come to fruition. We thought there might be a way to improve ammonia catalyst technology and accelerate the hydrogen economy.”

The founders started experimenting with Jo’s technology for ammonia cracking, the process in which ammonia (NH3) molecules split into their nitrogen (N2) and hydrogen (H2) constituent parts. Ammonia cracking to date has been done at huge plants in high-temperature reactors that require large amounts of energy. Those high temperatures limited the catalyst materials that could be used to drive the reaction.

Starting from scratch, the founders were able to identify new material recipes that could be used to miniaturize the catalyst and work at lower temperatures. The proprietary catalyst materials allow the company to create a system that can be deployed in new places at lower costs.

“We really had to redevelop the whole technology, including the catalyst and reformer, and even the integration with the larger system,” Woo says. “One of the most important things is we don’t combust ammonia — we don’t need pilot fuel, and we don’t generate any nitrogen gas or CO2.”

Today Amogy has a portfolio of proprietary catalyst technologies that use base metals along with precious metals. The company has proven the efficiency of its catalysts in demonstrations beginning with the first ammonia-powered drone in 2021. The catalyst can be used to produce hydrogen more efficiently, and by integrating the catalyst with hydrogen fuel cells or engines, Amogy also offers modular ammonia-to-power systems that can scale to meet customer energy demands.

“We’re enabling the decarbonization of heavy industry,” Woo says. “We are targeting transportation, chemical production, manufacturing, and industries that are carbon-heavy and need to decarbonize soon, for example to achieve domestic goals. Our vision in the longer term is to enable ammonia as a fuel in a variety of applications, including power generation, first at microgrids and then eventually full grid-scale.”

Scaling with industry

When Amogy completed its facility in Houston, one of their early visitors was MIT Professor Evelyn Wang, who is also MIT’s vice president for energy and climate. Woo says other people involved in the Climate Project at MIT have been supportive.

Another key partner for Amogy is Samsung Heavy Industries, which announced a multiyear deal to manufacturing Amogy’s ammonia-to-power systems on Nov. 12.

“Our strategy is to partner with the existing big players in heavy industry to accelerate the commercialization of our technology,” Woo says. “We have worked with big oil and gas companies like BHP and Saudi Aramco, companies interested in hydrogen fuel like KBR and Mitsubishi, and many more industrial companies.”

When paired with other clean energy technologies to provide the power for its systems, Woo says Amogy offers a way to completely decarbonize sectors of the economy that can’t electrify on their own.

“In heavy transport, you have to use high-energy density liquid fuel because of the long distances and power requirements,” Woo says. “Batteries can’t meet those requirements. It’s why hydrogen is such an exciting molecule for heavy industry and shipping. But hydrogen needs to be kept super cold, whereas ammonia can be liquid at room temperature. Our job now is to provide that power at scale.”

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.”

The International Association of Cryptologic Research—the academic cryptography association that’s been putting conferences like Crypto (back when “crypto” meant “cryptography”) and Eurocrypt since the 1980s—had to nullify an online election when trustee Moti Yung lost his decryption key.

For this election and in accordance with the bylaws of the IACR, the three members of the IACR 2025 Election Committee acted as independent trustees, each holding a portion of the cryptographic key material required to jointly decrypt the results. This aspect of Helios’ design ensures that no two trustees could collude to determine the outcome of an election or the contents of individual votes on their own: all trustees must provide their decryption shares...

At least 15 coal-fired plants are being kept online to power artificial intelligence as the administration rolls back pollution rules.

The Europeans took a stand but struggled to build a coalition to push for more climate action at this year’s U.N. summit.

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A new study estimates that the deadly 2003 heat wave would cause up to 32,000 deaths each week under 3 degrees Celsius of warming.

John Bel Edwards and the Environmental Defense Fund say lawsuits seeking money to restore the coastline should be heard in state courts.

Annalena Baerbock pointed to droughts and other damage from climate extremes in places such as Chad, Syria and Iraq.