Harvard Students Are Working Hard to Find Solutions To Climate Change
Undergraduate students at Harvard have been creating a calorimeter with just an empty soda can and an already used styrofoam cup. Just a week earlier, they had extracted biodiesel from the oil fryer that was located in the Annenberg Dining Hall.
Undergraduate students at Harvard have been creating a calorimeter with just an empty soda can and an already used styrofoam cup. Just a week earlier, they had extracted biodiesel from the oil fryer that was located in the Annenberg Dining Hall. Their hope was to work out the efficiency and energy density of the biodiesel.
Students are working hard to try and find solutions to climate change, and institutional action along with scholarship is being factored in. Working with James Stock of the Harvard Kennedy School faculty, they have benefited from the grant he has for research into climate change from the Climate Change Solutions Fund. This is to allow him to carry out research into the markets level of impediment when it comes to biofuel penetration.
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The students are investigating the decision made by the University in 2004 to use biodiesel to fuel its shuttles as it was seen as an alternative source of fuel for cars as there are fewer greenhouse gases and fewer emissions released which meant less pollution.
Victor Agbafe stated that the lesson made the manner of using fuel on campus more personal, and as Harvard was always looking for ways to save energy, it was not always the case that the results were tangible and could be seen in action on a regular basis. The area around Harvard is often used as a living laboratory and as a result there has been an increase in the level of resources provided by the Office of Sustainability.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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