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At New York Climate Week 2026, MIT connects with a global network

Abdul Latif Jameel Poverty Action Lab (J-PAL), Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), ai, AI (Artificial Intelligence), Artificial Intelligence, Center for Transportation and Logistics, Civil and environmental engineering, Cleaner industry, Climate, Climate change, Computer science and technology, data, DMSE, Emissions, Energy, Environment, Food, Innovation and Entrepreneurship (I&E), Lincoln Laboratory, Logistics, Mechanical engineering, MIT Schwarzman College of Computing, MIT Sloan School of Management, Office of Open Learning, Policy, Pollution, School of Architecture and Planning, School of Engineering, School of Science, Special events and guest speakers, Supply chains, Sustainability, Urban studies and planning, Water

Each year, Climate Week NYC brings together more than 100,000 people in more than 1,000 events across the city to discuss climate challenges, explore solutions, and spur action. Participants come from industry, academia, philanthropy, nonprofits, government, and finance and venture capital. And this year, the mix included a strong, cross-campus presence from MIT.The MIT community

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State of Supply Chain Sustainability Report finds corporate commitment at record high, but governance lags behind

ai, AI (Artificial Intelligence), Artificial Intelligence, Center for Transportation and Logistics, Cleaner industry, Climate change, Emissions, Environment, government, Pollution, Research, School of Engineering, Supply chains, Sustainability, Technology and policy, Trade, Transportation

When corporate leaders discuss climate strategy, the message that still dominates is that, despite competing priorities and external pressures, sustainability matters. According to the 2026 State of Supply Chain Sustainability report from the MIT Sustainable Supply Chain Lab, which surveyed 1,810 supply chain professionals across 91 countries, 83 percent rate sustainability as important or very important

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Paving the way for greener ammonia production

Agriculture, ai, AI (Artificial Intelligence), Artificial Intelligence, Chemistry, Cleaner industry, Computer modeling, DMSE, Emissions, Food, Industry, Machine Learning, Materials science and engineering, Nuclear science and engineering, Pollution, Research, School of Engineering, School of Science, Sustainability

Ammonia is one of the most important chemicals produced in the world, ranking second only to sulfuric acid in the total volume produced each year. It is used mostly to make fertilizer, which is essential to feeding the world’s population. Yet its production accounts for up to 2 percent of the world’s energy consumption and

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Parking-aware navigation system could prevent frustration and emissions

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Cities, Civil and environmental engineering, Climate change, Computer modeling, Computer science and technology, Electrical engineering and computer science (EECS), Emissions, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Energy Initiative, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Sustainability, Transportation

It happens every day — a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.Most popular navigation

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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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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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