RESEARCH HIGHLIGHT - Act now to prevent uncontrolled rise in carbon footprint of computational science
Writing in Nature Computational Science, researchers from the Department of Public Health and Primary Care at the University of Cambridge argue that the scientific community needs to act now if it is to prevent a potentially uncontrolled rise in the carbon footprint of computational science as data science and algorithms increase in usage.
"Science has transformed our understanding of the world around us and has led to great benefits to society. But this has come with a not-insignificant – and not always well understood – impact on the environment.
As scientists – as with people working in every sector – it’s important that we do what we can to reduce the carbon footprint of our work to ensure that the benefits of our discoveries are not outweighed by their environmental costs," Dr Loïc Lannelongue, Dept of Public Health and Primary Care
One aspect of research that often gets overlooked – and which can have a substantial environmental impact: high performance and cloud computing. In 2020, the Information and Communication Technologies sector was estimated to have made up between 1.8% and 2.8% of global greenhouse gas emissions – more than aviation (1.9%). In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centres’ water usage and land footprint.
"While the environmental impact of experimental ‘wet’ labs is more immediately obvious, the impact of algorithms is less clear and often underestimated.
While new hardware, lower-energy data centres and more efficient high performance computing systems can help reduce their impact, the increasing ubiquity of artificial intelligence and data science more generally means their carbon footprint could grow exponentially in coming years if we don’t act now." Professor Michael Inouye, Dept of Public Health and Primary Care
To help address this issue, the team has developed GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research), a set of principles to allow the computational science community to lead the way in sustainable research practices, maximising computational science’s benefit to both humanity and the environment. The analysis includes:
1. Governance and Responsibility
2. Estimate and report the energy consumption of algorithms
Estimating and monitoring the carbon footprint of computations identifies inefficiencies and opportunities for improvement.
User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. The financial cost of running computations is often negligible, particularly in academia, and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.
3. Tackling Energy and embodied impacts through New collaborations
Minimising carbon intensity – that is, the carbon footprint of producing electricity – is one of the most immediately impactful ways to reduce greenhouse gas emissions. This could involve relocating computations to low-carbon settings and countries, but this needs to be done with equity in mind. Carbon intensities can differ by as much as three orders of magnitude between the top and bottom performing high-income countries (from 0.10 gCO2e/kWh in Iceland to 770 gCO2e/kWh in Australia).
The footprint of user devices is also a factor: one estimate found that almost three-quarters (72%) of the energy footprint of streaming a video to a laptop is from the laptop, with 23% used in transmission and a mere 5% at the data centre.
Another key consideration is data storage. The carbon footprint of storing data depends on numerous factors, but the life cycle footprint of storing one terabyte of data for a year is of the order of 10 kg CO2e. This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Large (hyperscale) data centres are expected to be more energy efficient, but they may also encourage unnecessary increases in the scale of computing (the ‘rebound effect’).
4. Education and Research
Read the full University of Cambridge article
Lannelongue, L et al. GREENER principles for environmentally sustainable computational science. Nat Comp Sci; 26 June; DOI: 10.1038/s43588-023-00461-y