Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.01.14.426384

An open-source tool to assess the carbon footprint of research

Abstract

The scrutiny over the carbon footprint of academics has increased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies - on which these estimates rely - unfortunately prevents from any direct comparison because of the sensitivity of carbon footprint assessments to variations in the scope and to key parameters such as emission factors. In an effort to enabling a robust comparison of research carbon footprints across institutions, contexts or disciplines, we present an open-source web application, GES 1point5 designed to estimate the carbon footprint of a department, research lab or team in any country of the world with a transparent and common methodology. The current version of GES 1point5, open-source and freely available, takes into account the most common and often predominant emission sources in research labs: buildings, digital devices, commuting, and professional travel. GES 1point5 is developed by an interdisciplinary team of scientists from several public research institutions in France as part of the Labos 1point5 project. GES 1point5 is therefore presently tailored for the French context but can be adjusted to any national contexts by adjusting the values of emission factors. The versatility and usability of the software have been empirically validated by its adoption by several hundred research labs in France over the last 18 months. In addition to enabling the estimation and monitoring of greenhouse gas (GHG) emissions at the scale of a research lab, GES 1point5 is designed to aggregate the data entered by the labs and the corresponding GHG emissions estimates into a comprehensive database. GES 1point5 can therefore allow to (i) identify robust determinants of the carbon footprint of research activities across a network of research labs (ii) estimate the carbon footprint of research at the national scale. A preliminary analysis of the carbon footprint of more than one hundred laboratories is presented to illustrate the potential of the approach. While assessments of carbon footprints are often externalized onto extension services and proprietary softwares, GES 1point5 is designed as a hands-on, pedagogic and transparent tool for research labs to monitor and reduce their own carbon footprint. This internalization has strong positive co-benefits for academics in terms of awareness and empowerment. We further expect that international dissemination of GES 1point5 will contribute to establishing a global understanding of the drivers of the research carbon footprint worldwide and an identification of the levers to decrease it. Availability and implementationGES 1point5 is available online at http://labos1point5.org/ges-1point5 and its source code can be downloaded from the GitLab platform at https://framagit.org/labos1point5/l1p5-vuejs.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mariette, J., Blanchard, O., Berne, O., Ben Ari, T.. 2021-01-16. An open-source tool to assess the carbon footprint of research. https://doi.org/10.1101/2021.01.14.426384

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

Welfare concerns for mounted load carrying by working donkeys in Pakistan

Working donkeys (Equus asinus) are vital to peoples livelihoods. They are essential for carrying goods, however globally, overloading is one of the primary welfare concerns of working donkeys. We studied mounted load carrying by donkeys and associated factors in Pakistan. A cross-sectional study of donkey owners (n = 332) was conducted, and interviews were undertaken based on a questionnaire. Owners estimated that the median weight of their donkeys was 110kg (interquartile range (IQR) 100-120kg), and that they carried a median mounted load of 81.5kg (IQR 63-99kg). We found that 87.4% of donkeys carried a load above 50% of their bodyweight ratio (BWR), the median BWR carried was 77.1% (IQR 54.5-90.7%), and 25.3% of donkeys carried above 90% BWR. Donkeys that were loaded at more than 50% BWR were more likely to sit, compared to donkeys loaded with less weight (p=0.01). Donkeys working in peri-urban and urban areas were more likely to carry a greater BWR than donkeys working in rural areas (P<0.001), as were those carrying construction materials or bricks, compared to agricultural materials (p=0.004). Age (p=0.03) and breed (p=0.01) were also associated with carrying a higher weight. Overloading based on current recommendations (50% BWR) was common, with the majority (87.4%) of donkeys reported to carry more than the recommended 50% limit. This survey provides evidence of on-the-ground working practices and factors associated with mounted load carrying, which is critical for developing evidence-based recommendations for loading, in order to improve the welfare of working donkeys.

scientific communication and education↗