Study: Heavy snowfall and rain may contribute to some earthquakes
The results suggest that climate may influence seismic activity.
The results suggest that climate may influence seismic activity.
Surprising “photomolecular effect” discovered by MIT researchers could affect calculations of climate change and may lead to improved desalination and drying processes.
The new approach “nudges” existing climate simulations closer to future reality.
This measure, developed by MIT researchers, reflects direct effects on people’s quality of life — and reveals significant global disparities.
In field tests, MIT spinoff AgZen demonstrated that its feedback-optimized spraying system could halve the pesticide needs of farms and improve crop yields.
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
The team used machine learning to analyze satellite and roadside images of areas where small farms predominate and agricultural data are sparse.
Using New York as a test case, the model predicts flooding at the level experienced during Hurricane Sandy will occur roughly every 30 years by the end of this century.
Five multimedia projects communicating climate futures selected for 2023 WORLDING program, online and at MIT.
2023 Global Change Outlook from the MIT Joint Program on the Science and Policy of Global Change quantifies benefits of policies that cap global warming at 1.5 C.
An accordion-textured clay called smectite efficiently traps organic carbon and could help buffer global warming over millions of years.
A newly identified process could explain a variety of natural phenomena and enable new approaches to desalination.
MIT engineers and collaborators developed a solar-powered device that avoids salt-clogging issues of other designs.
Plata’s expertise in academics and industry will help advance the mission of the consortium and propel implementable climate solutions forward.
A cross-departmental team is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings.