# Unraveling the Hidden Environmental Impacts of AI Solutions for Environment

arXiv: https://arxiv.org/abs/2110.11822 · HTML: https://mlsanity.com/paper/2110.11822/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles](https://mlsanity.com/paper/1901.10581/) (2019)
- [Green Algorithms: Quantifying the carbon footprint of computation](https://mlsanity.com/paper/2007.07610/) (2020)

## Built on by (most cited first, 30 of 32)

- [DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining](https://mlsanity.com/paper/2305.10429/) (2023)
- [Data Selection for Language Models via Importance Resampling](https://mlsanity.com/paper/2302.03169/) (2023)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [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)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series](https://mlsanity.com/paper/2510.25785/) (2025)
- [Watt For What: Rethinking Deep Learning's Energy-Performance Relationship](https://mlsanity.com/paper/2310.06522/) (2023)
- [Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI](https://mlsanity.com/paper/2309.02065/) (2023)
- [EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inference](https://mlsanity.com/paper/2502.04700/) (2025)
- [What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text](https://mlsanity.com/paper/2503.04804/) (2025)
- [LACONIC: A 3D Layout Adapter for Controllable Image Creation](https://mlsanity.com/paper/2507.03257/) (2025)
- [On the Ethical Considerations of Generative Agents](https://mlsanity.com/paper/2411.19211/) (2024)
- [AI persuading AI vs AI persuading Humans: LLMs' Differential Effectiveness in Promoting Pro-Environmental Behavior](https://mlsanity.com/paper/2503.02067/) (2025)
- [Environmental and Social Sustainability of Creative-Ai](https://mlsanity.com/paper/2209.12879/) (2022)
- [A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption](https://mlsanity.com/paper/2504.19179/) (2025)
- [Shared LoRA Subspaces for almost Strict Continual Learning](https://mlsanity.com/paper/2602.06043/) (2026)
- [How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena](https://mlsanity.com/paper/2402.13208/) (2024)
- [Explainable Sustainability for AI in the Arts](https://mlsanity.com/paper/2309.14877/) (2023)
- [Green Runner: A tool for efficient deep learning component selection](https://mlsanity.com/paper/2401.15810/) (2024)
- [Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production](https://mlsanity.com/paper/2402.09328/) (2024)
- [Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture](https://mlsanity.com/paper/2509.12247/) (2025)
- [Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts](https://mlsanity.com/paper/2511.07090/) (2025)
- [Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs](https://mlsanity.com/paper/2603.27376/) (2026)
- [Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model](https://mlsanity.com/paper/2604.11154/) (2026)
- [CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification](https://mlsanity.com/paper/2605.16401/) (2026)

## Abstract (arXiv)

In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG). At the same time the deep learning community began to realize that training models with more and more parameters requires a lot of energy and as a consequence GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green), and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts, then we present the different methodologies used to assess those impacts, and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service, and point out the limitations of existing work in AI for Green.