Climate change

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Helping power-system planners prepare for an unknown future

Artificial Intelligence, Climate change, Computer modeling, Electric grid, Emissions, Energy, Energy efficiency, infrastructure, Invention, MIT Energy Initiative, MIT Sloan School of Management, Open Source, Policy, Research, Technology and policy

A new computer modeling tool developed by an MIT Energy Initiative (MITEI) research team will help infrastructure planners working in the electricity and other energy-intensive sectors better predict and prepare for future needs and conditions as they develop plans for power generation capacity, transmission lines, and other necessary infrastructure. The tool could reduce the amount

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MIT Energy Initiative launches Data Center Power Forum

Artificial Intelligence, Cleaner industry, Climate change, Electric grid, Emissions, Energy, Energy efficiency, MIT Energy Initiative, Research, Sustainability, Technology and policy

With global power demand from data centers expected to more than double by 2030, the MIT Energy Initiative (MITEI) in September launched an effort that brings together MIT researchers and industry experts to explore innovative solutions for powering the data-driven future. At its annual research conference, MITEI announced the Data Center Power Forum, a targeted research effort for

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MIT Energy Initiative conference spotlights research priorities amidst a changing energy landscape

Artificial Intelligence, Batteries, Carbon dioxide, Chemistry, Cleaner industry, Climate change, Collaboration, DMSE, Electric grid, Energy, Energy storage, Faculty, government, infrastructure, Innovation and Entrepreneurship (I&E), Investments, Mechanical engineering, MIT Energy Initiative, Policy, Renewable energy, School of Engineering, School of Science, Special events and guest speakers, Startups, Students, Sustainability, The Engine, Transportation

“We’re here to talk about really substantive changes, and we want you to be a participant in that,” said Desirée Plata, the School of Engineering Distinguished Professor of Climate and Energy in MIT’s Department of Civil and Environmental Engineering, at Energizing@MIT: the MIT Energy Initiative’s (MITEI) Annual Research Conference that was held on Sept. 9-10.Plata’s

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Fighting for the health of the planet with AI

Algorithms, Artificial Intelligence, Climate change, Computer science and technology, Electric grid, Electrical engineering and computer science (EECS), Energy, Faculty, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Profile, Renewable energy, School of Engineering, Sustainability, Technology and policy

For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the MIT Department of Electrical Engineering and

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Responding to the climate impact of generative AI

Algorithms, Artificial Intelligence, Climate change, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Emissions, Energy, Energy storage, infrastructure, Lincoln Laboratory, Machine Learning, MIT Energy Initiative, MIT Schwarzman College of Computing, MIT Sloan School of Management, Research, School of Engineering, Supercomputing, Sustainable computing

In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.The energy demands of generative AI are expected to continue increasing dramatically over the next decade.For instance, an April 2025 report from the International Energy Agency predicts that the global

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Simpler models can outperform deep learning at climate prediction

Algorithms, Artificial Intelligence, Climate change, Computer modeling, data, EAPS, Earth and atmospheric sciences, Emissions, IDSS, Machine Learning, MIT Schwarzman College of Computing, Pollution, Public health, Research, School of Science, Sustainability

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.Their analysis also

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