Big data’s hidden cost: The carbon footprint of computational science
As the climate emergency and cost-of-living crisis focus our minds on how to reduce energy, a group of scientists have highlighted the hidden environmental cost behind some of our major breakthroughs.
High performance computing has transformed how research works and our ability to make previously unthinkable discoveries. We’re able to model our future climate with unprecedented accuracy. We’re able to predict what a protein looks like from its genetic code. We even know what a black hole 55 million light-years away looks like.
But while few people would argue against such progress, it comes with a cost.
In 15 years of writing about medical research, I have found myself writing countless stories about genome-wide association studies, where researchers compare the DNA of potentially hundreds of thousands of people—patients and healthy ‘controls’—to look for genetic variants that increase our risk of developing a particular disease. Never once did I find myself considering the environmental impact of such studies.
It turns out that it can be quite staggering.
Early this year, a team from Cambridge, together with colleagues at the Baker Institute in Melbourne, Australia, published research showing that a genome-wide association study (GWAS) trawling data from 500,000 participants registered to a biobank database would create a carbon footprint of 17.3kg of CO2e (carbon dioxide equivalent) for each genetic trait being studied.
But in fact, researchers would commonly look at thousands of traits. The same GWAS run for 1,000 traits would generate 17.3 metrics tons of CO2e. That’s equivalent to 346 flights between Paris and London. (The researchers point out that upgrading the software used to the latest version would reduce this by three-quarters.)
At the start of 2020, Loic Lannelongue was in the middle of a Ph.D. in health data science at Cambridge’s Department of Public Health and Primary Care. He was a computational biologist, using machine learning to predict how proteins interact in the human body. One of his collaborators was Jason Grealey, an academic based at University of Melbourne, Australia. Lannelongue was watching on the news—and hearing first hand from Grealey—about the bushfires tearing through Australia. This made him reflect on the climate emergency and the part we all play.
A few months earlier, Lannelongue had read about a study that equated training artificial intelligence (AI) to the carbon footprint of five cars over their lifetimes. He began to wonder what the impact of his own work was, and together with Grealey decided to work it out, expecting to find an online calculator that they could just plug their numbers into.
“We started thinking it would be a two week project, a nice break from our Ph.D. research,” says Lannelongue, “just figuring out what the carbon footprint of what we were doing was to get a number and probably tweeting about it. Except there was nothing out there. We realized that there was a massive gap, that computational scientists weren’t really thinking about their carbon footprint yet.”
Since then, with the support of his supervisor, Dr. Michael Inouye, Lannelongue has been spending half of his time working on this project, leading to the development of Green Algorithms, a simple online calculator that allows researchers to work out the carbon footprint of their computing work.
This is not the first time the research community has turned the spotlight on its own practices. Some in the community have already been asking questions about the impact of flying across the globe to present their findings at scientific conferences, for example.
Others have raised the issue of plastic and chemical waste and energy requirements from so-called ‘wet labs’—that is, laboratories where experimental work takes place. Computer labs also have a significant impact: equipment needs updating and replacing every few years at a minimum, while even data storage itself requires energy.
