# Energy Usage Reports: Environmental awareness as part of algorithmic accountability

arXiv: https://arxiv.org/abs/1911.08354 · HTML: https://mlsanity.com/paper/1911.08354/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 30 of 44)

- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Calibrating Large Language Models Using Their Generations Only](https://mlsanity.com/paper/2403.05973/) (2024)
- [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023)
- [How to estimate carbon footprint when training deep learning models? A guide and review](https://mlsanity.com/paper/2306.08323/) (2023)
- [Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity](https://mlsanity.com/paper/2210.15452/) (2022)
- [Non-Exchangeable Conformal Language Generation with Nearest Neighbors](https://mlsanity.com/paper/2402.00707/) (2024)
- [Towards Climate Awareness in NLP Research](https://mlsanity.com/paper/2205.05071/) (2022)
- [Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle](https://mlsanity.com/paper/2303.01980/) (2023)
- [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://mlsanity.com/paper/2401.16640/) (2024)
- [Tucano: Advancing Neural Text Generation for Portuguese](https://mlsanity.com/paper/2411.07854/) (2024)
- [Hyperparameter Importance Analysis for Multi-Objective AutoML](https://mlsanity.com/paper/2405.07640/) (2024)
- [Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning](https://mlsanity.com/paper/2107.06946/) (2021)
- [NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands](https://mlsanity.com/paper/2103.02523/) (2021)
- [EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models](https://mlsanity.com/paper/2103.16435/) (2021)
- [Position: Tensor Networks are a Valuable Asset for Green AI](https://mlsanity.com/paper/2205.12961/) (2022)
- [Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks](https://mlsanity.com/paper/2404.01965/) (2024)
- [Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments](https://mlsanity.com/paper/2408.15128/) (2024)
- [SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version](https://mlsanity.com/paper/2508.15478/) (2025)
- [Benchmarking Energy Efficiency of Large Language Models Using vLLM](https://mlsanity.com/paper/2509.08867/) (2025)
- [Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems](https://mlsanity.com/paper/2306.13686/) (2023)
- [Do Large Language Models have Shared Weaknesses in Medical Question Answering?](https://mlsanity.com/paper/2310.07225/) (2023)
- [La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America](https://mlsanity.com/paper/2507.00999/) (2025)
- [The Environmental Cost of LLMs in AIED: Reporting and Practices](https://mlsanity.com/paper/2606.11215/) (2026)
- [Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning Models](https://mlsanity.com/paper/2308.02022/) (2023)
- [An energy-based comparative analysis of common approaches to text classification in the Legal domain](https://mlsanity.com/paper/2311.01256/) (2023)
- [Tensor-Aware Energy Accounting](https://mlsanity.com/paper/2311.11424/) (2023)
- [An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning](https://mlsanity.com/paper/2403.15150/) (2024)
- [OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Models](https://mlsanity.com/paper/2405.12843/) (2024)
- [Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks](https://mlsanity.com/paper/2505.15631/) (2025)
- [AI2: The next leap toward native language based and explainable machine learning framework](https://mlsanity.com/paper/2301.03391/) (2023)

## Abstract (arXiv)

The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also include comparisons with energy mixtures employed in electrical grids around the world. We propose including these automatically-generated Energy Usage Reports as part of standard algorithmic accountability practices, and demonstrate the use of these reports as part of model-choice in a machine learning context.