Bibliography

[1]

Jesse Dodge, Taylor Prewitt, Remi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A. Smith, Nicole DeCario, and Will Buchanan. Measuring the carbon intensity of ai in cloud instances. In 2022 ACM Conference on Fairness, Accountability, and Transparency, 1877–1894. Seoul Republic of Korea, June 2022. ACM. URL: https://dl.acm.org/doi/10.1145/3531146.3533234, doi:10.1145/3531146.3533234.

[2]

ElectricityMaps. 2025. URL: https://www.electricitymaps.com/.

[3]

Adrian Jackson, Alan Simpson, and Andrew Turner. Emissions and energy efficiency on large-scale high performance computing facilities: archer2 uk national supercomputing service case study. In Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W '23, 1866–1870. New York, NY, USA, 2023. Association for Computing Machinery. URL: https://doi.org/10.1145/3624062.3624269, doi:10.1145/3624062.3624269.

[4]

Loïc Lannelongue, Jason Grealey, and Michael Inouye. Green algorithms: quantifying the carbon footprint of computation. Advanced Science, 8(12):2100707, 2021. doi:10.1002/advs.202100707.

[5]

A. R. W. Bruce, L. Ruff, J. Kelloway, F. MacMillan, and A. Rogers. Carbon intensity forecast methodology. National Grid ESO report, 2021. URL: https://github.com/carbon-intensity/methodology/raw/master/Carbon%20Intensity%20Forecast%20Methodology.pdf.

[6]

A. R. W. Bruce, L. Ruff, J. Kelloway, F. MacMillan, and A. Rogers. Carbon intensity methodology regional carbon intensity. National Grid ESO report, 2021. URL: https://github.com/carbon-intensity/methodology/raw/master/Regional%20Carbon%20Intensity%20Forecast%20Methodology.pdf.