# Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI

arXiv: https://arxiv.org/abs/2309.02065 · HTML: https://mlsanity.com/paper/2309.02065/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 104 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 30 of 45)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [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)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [The Evolved Transformer](https://mlsanity.com/paper/1901.11117/) (2019)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (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)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)

## Built on by (most cited first, 12 of 12)

- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance Minimization](https://mlsanity.com/paper/2309.11856/) (2023)
- [PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/2403.12562/) (2024)
- [BMRS: Bayesian Model Reduction for Structured Pruning](https://mlsanity.com/paper/2406.01345/) (2024)
- [Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race](https://mlsanity.com/paper/2502.20016/) (2025)
- [Is Adversarial Training with Compressed Datasets Effective?](https://mlsanity.com/paper/2402.05675/) (2024)
- [Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations](https://mlsanity.com/paper/2503.20126/) (2025)
- [How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI](https://mlsanity.com/paper/2602.00056/) (2026)
- [The HCI GenAI CO2ST Calculator: A Tool for Calculating the Carbon Footprint of Generative AI Use in Human-Computer Interaction Research](https://mlsanity.com/paper/2504.00692/) (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)
- [Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI](https://mlsanity.com/paper/2602.19789/) (2026)
- [Position: AI Must Become Planet-Centered, Not Just Human-Centered](https://mlsanity.com/paper/2606.13704/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning which have accelerated progress on many tasks thought to be out of reach of AI. These recent ML methods are often compute hungry, energy intensive, and result in significant green house gas emissions, a known driver of anthropogenic climate change. Additionally, the platforms on which ML systems run are associated with environmental impacts that go beyond the energy consumption driven carbon emissions. The primary solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the compute and energy efficiency with which ML systems operate. In this perspective, we argue that it is time to look beyond efficiency in order to make ML more environmentally sustainable. We present three high-level discrepancies between the many variables that influence the efficiency of ML and the environmental sustainability of ML. Firstly, we discuss how compute efficiency does not imply energy efficiency or carbon efficiency. Second, we present the unexpected effects of efficiency on operational emissions throughout the ML model life cycle. And, finally, we explore the broader environmental impacts that are not accounted by efficiency. These discrepancies show as to why efficiency alone is not enough to remedy the adverse environmental impacts of ML. Instead, we argue for systems thinking as the next step towards holistically improving the environmental sustainability of ML.