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Author = {{Bruce}, A.~R.~W. and {Ruff}, L. and {Kelloway}, J. and {MacMillan}, F. and {Rogers}, A.},
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Title = {Carbon intensity methodology regional carbon intensity},
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@article{GrealeyLannelongue2022, title={The Carbon Footprint of Bioinformatics}, rights={All rights reserved}, ISSN={0737-4038, 1537-1719}, DOI={10.1093/molbev/msac034}, abstractNote={ABSTRACT
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Bioinformatic research relies on large-scale computational infrastructures which have a non-zero carbon footprint but so far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this work, we estimate the carbon footprint of bioinformatics (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org). We assessed (i) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics and molecular simulations, as well as (ii) computation strategies, such as parallelisation, CPU (central processing unit) vs GPU (graphics processing unit), cloud vs. local computing infrastructure and geography. In particular, we found that biobank-scale GWAS emitted substantial kgCO2e and simple software upgrades could make it greener, e.g. upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Moreover, switching from the average data centre to a more efficient one can reduce carbon footprint by ∼34%. Memory over-allocation can also be a substantial contributor to an algorithm’s greenhouse gas (GHG) emissions. The use of faster processors or greater parallelisation reduces running time but can lead to greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimise kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.}, journal={Molecular Biology and Evolution}, author={Grealey, Jason and Lannelongue, Loïc and Saw, Woei-Yuh and Marten, Jonathan and Méric, Guillaume and Ruiz-Carmona, Sergio and Inouye, Michael}, year={2022}, month=feb, pages={msac034}, language={en} }
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@article{Weber_2024, title={The Heidelberg Agreement on Environmental Sustainability in Research Funding}, rights={Creative Commons Attribution 4.0 International}, url={https://zenodo.org/doi/10.5281/zenodo.13938809}, DOI={10.5281/ZENODO.13938809}, publisher={Zenodo}, author={Weber, Philipp M. and Bendiscioli, Sandra and Wallon, Gerlind and von Ahsen, Uwe and de Beaufort, Anne Marie and Boland, Marion and Dekkers, Florijn and Dunon-Bluteau, Dominique and Farley, Martin and Fox, Alyson and Guillot, Stéphane and Lannelongue, Loïc and Łazarowicz-Kowalik, Marta and Rouse, Brendan and Samuel, Gabrielle and Sanchis, Teresa and Simon, Susan and Tata, Mathew and van der Goot, Gisou and Watt, Fiona M.}, year={2024}, month=oct, language={en} }
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@article{Weber_2024, title={The Heidelberg Agreement on Environmental Sustainability in Research Funding},
author={Weber, Philipp M. and Bendiscioli, Sandra and Wallon, Gerlind and von Ahsen, Uwe and de Beaufort, Anne Marie and Boland, Marion and Dekkers, Florijn and Dunon-Bluteau, Dominique and Farley, Martin and Fox, Alyson and Guillot, Stéphane and Lannelongue, Loïc and Łazarowicz-Kowalik, Marta and Rouse, Brendan and Samuel, Gabrielle and Sanchis, Teresa and Simon, Susan and Tata, Mathew and van der Goot, Gisou and Watt, Fiona M.}, year={2024}, month=oct, language={en} }
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@article{Lannelongue_Fropier_Matencio_2025, title={How to include environmental sustainability criteria in national AI funding schemes? Reflecting on the example of France and the Green Algorithms tool.}, rights={Creative Commons Attribution 4.0 International}, url={https://zenodo.org/doi/10.5281/zenodo.14607021}, DOI={10.5281/ZENODO.14607021}, abstractNote={Artificial intelligence (AI) is in the media spotlight for its potential to transform the economic and research sectors, among others. This drives funding bodies to support AI-based innovation, with for example the Horizon Europe and Digital Europe programmes run by the European Union, or France’s investment strategy France 2030 (national strategy for AI). On the other hand, the environmental impacts of AI are now better understood, and we cannot ignore the role of AI on electricity and water usage, mineral resource depletion, and greenhouse gas emissions1,2. To bring together innovation and sustainability, the French Department for the Environment (Ministère en charge de la Transition Écologique) has decided to require the use of the Green Algorithms tool for funding applications on the topic of AI and climate change. Applicants now have to include estimates of the carbon footprint and energy usage of the different development phases of the proposed AI solution. This was tested on a first funding call “Demonstrators of frugal AI for sustainable development of local communities”. The first applications were received in December 2023, with positive feedback from the different stakeholders. Applicants in particular approved of this new criterion, as they understood its necessity, found the tool easy to use, and did not consider this to slow down innovation. Following this successful implementation in a first funding call, it was decided to include the Green Algorithms tool more systematically in the application guidelines of other AI-related funding calls run by the Department. The goal of this piece is to reflect on the inclusion of environmental criteria in AI funding calls and share the lessons learned with other funding bodies internationally to promote similar initiatives across the AI ecosystem.}, publisher={Zenodo}, author={Lannelongue, Loïc and Fropier, Juliette and Matencio, Even}, year={2025}, month=jan, language={en} }
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@article{Lannelongue_Fropier_Matencio_2025,
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title={How to include environmental sustainability criteria in national AI funding schemes? Reflecting on the example of France and the Green Algorithms tool.},
abstractNote={Artificial intelligence (AI) is in the media spotlight for its potential to transform the economic and research sectors, among others. This drives funding bodies to support AI-based innovation, with for example the Horizon Europe and Digital Europe programmes run by the European Union, or France’s investment strategy France 2030 (national strategy for AI). On the other hand, the environmental impacts of AI are now better understood, and we cannot ignore the role of AI on electricity and water usage, mineral resource depletion, and greenhouse gas emissions1,2. To bring together innovation and sustainability, the French Department for the Environment (Ministère en charge de la Transition Écologique) has decided to require the use of the Green Algorithms tool for funding applications on the topic of AI and climate change. Applicants now have to include estimates of the carbon footprint and energy usage of the different development phases of the proposed AI solution. This was tested on a first funding call “Demonstrators of frugal AI for sustainable development of local communities”. The first applications were received in December 2023, with positive feedback from the different stakeholders. Applicants in particular approved of this new criterion, as they understood its necessity, found the tool easy to use, and did not consider this to slow down innovation. Following this successful implementation in a first funding call, it was decided to include the Green Algorithms tool more systematically in the application guidelines of other AI-related funding calls run by the Department. The goal of this piece is to reflect on the inclusion of environmental criteria in AI funding calls and share the lessons learned with other funding bodies internationally to promote similar initiatives across the AI ecosystem.}, publisher={Zenodo}, author={Lannelongue, Loïc and Fropier, Juliette and Matencio, Even}, year={2025}, month=jan, language={en} }
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@misc{Norsecode, author={{University of York}}, year={2024}, url={https://www.york.ac.uk/news-and-events/news/2024/community/viking-computer-cluster/}, abstractNote={The University of York has located a new high performance computer facility dubbed “Viking 2” at a renewable data centre in Sweden.}, journal={University of York}, language={en} }
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misc{Norsecode, author={{University of York}}, year={2024}, url={https://www.york.ac.uk/news-and-events/news/2024/community/viking-computer-cluster/}, abstractNote={The University of York has located a new high performance computer facility dubbed “Viking 2” at a renewable data centre in Sweden.}, journal={University of York}, language={en} }
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@inproceedings{Dodge2022, address={Seoul Republic of Korea}, title={Measuring the Carbon Intensity of AI in Cloud Instances}, ISBN={978-1-4503-9352-2}, url={https://dl.acm.org/doi/10.1145/3531146.3533234}, DOI={10.1145/3531146.3533234}, booktitle={2022 ACM Conference on Fairness, Accountability, and Transparency}, publisher={ACM}, author={Dodge, Jesse and Prewitt, Taylor and Tachet des Combes, Remi and Odmark, Erika and Schwartz, Roy and Strubell, Emma and Luccioni, Alexandra Sasha and Smith, Noah A. and DeCario, Nicole and Buchanan, Will}, year={2022}, month=jun, pages={1877–1894}, language={en} }
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@misc{ElectricityMaps, author={ElectricityMaps}, year={2025}, url={https://www.electricitymaps.com/}, abstractNote={The Electricity Maps API provides worldwide access to 24/7 grid carbon intensity historically, in real time, and as a forecast for the next 24 hours.}, language={en} }
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misc{ElectricityMaps, author={ElectricityMaps}, year={2025}, url={https://www.electricitymaps.com/}, abstractNote={The Electricity Maps API provides worldwide access to 24/7 grid carbon intensity historically, in real time, and as a forecast for the next 24 hours.}, language={en} }
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@book{Lannelongue_Vegad_Dorn_2024, title={Environmental Impacts of the UK Digital Sector - Data Driven Report}, url={https://zenodo.org/records/14831758}, DOI={10.17863/CAM.114743}, abstractNote={The digital sector has been and continues to be a catalyst for new capabilities and efficiencies, transforming the way society operates and interacts. The environmental impacts of this sector, such as greenhouse gas (GHG) emissions and resources depletion, are significant and require careful consideration. The first essential step to inform future policy making is to quantify these impacts through a detailed inventory for the UK. This is something that had not been conducted before. Commissioned by the Department for Culture, Media and Sport (DCMS) and the Department for Science, Innovation and Technology (DSIT), this report provides a robust analysis of the electricity consumption, GHG emissions and wider environmental impacts associated with the UK’s digital sector. It also investigates future trends through case studies and scenario projections. The research was conducted in partnership with Frazer Nash Consultancy, the University of Cambridge, and the University of Bristol.}, author={Lannelongue, Loïc and Vegad, Vinesh and Dorn, Emily}, year={2024}, month=may, language={eng} }
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