# Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation arXiv: https://arxiv.org/abs/2403.03344 ยท HTML: https://mlsanity.com/paper/2403.03344/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 66 references that are in the map. - First posted on arXiv 5 Mar 2024. ## Builds on (most cited first, 30 of 31) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017) - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021) - [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015) - [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015) - [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020) - [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019) - [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018) - [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) - [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020) - [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022) - [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022) - [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 Evolved Transformer](https://mlsanity.com/paper/1901.11117/) (2019) - [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://mlsanity.com/paper/2006.03236/) (2020) - [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016) - [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022) - [Jigsaw: Large Language Models meet Program Synthesis](https://mlsanity.com/paper/2112.02969/) (2021) - [Write, Execute, Assess: Program Synthesis with a REPL](https://mlsanity.com/paper/1906.04604/) (2019) - [Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT](https://mlsanity.com/paper/2304.10778/) (2023) - [Constructing Fast Network through Deconstruction of Convolution](https://mlsanity.com/paper/1806.07370/) (2018) - [Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1906.04585/) (2019) - [A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques](https://mlsanity.com/paper/1907.10863/) (2019) - [The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment](https://mlsanity.com/paper/2302.06117/) (2023) - [Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions](https://mlsanity.com/paper/1803.09926/) (2018) ## Built on by (most cited first, 7 of 7) - [Sociotechnical Implications of Generative Artificial Intelligence for Information Access](https://mlsanity.com/paper/2405.11612/) (2024) - [Green My LLM: Studying the key factors affecting the energy consumption of code assistants](https://mlsanity.com/paper/2411.11892/) (2024) - [Carbon Footprint Evaluation of Code Generation through LLM as a Service](https://mlsanity.com/paper/2504.01036/) (2025) - [Evaluating the Energy-Efficiency of the Code Generated by LLMs](https://mlsanity.com/paper/2505.20324/) (2025) - [Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming](https://mlsanity.com/paper/2508.08332/) (2025) - [Addressing the sustainable AI trilemma: a case study on LLM agents and RAG](https://mlsanity.com/paper/2501.08262/) (2025) - [Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning](https://mlsanity.com/paper/2607.04577/) (2026) ## Abstract (arXiv) The increasing use of information technology has led to a significant share of energy consumption and carbon emissions from data centers. These contributions are expected to rise with the growing demand for big data analytics, increasing digitization, and the development of large artificial intelligence (AI) models. The need to address the environmental impact of software development has led to increased interest in green (sustainable) coding and claims that the use of AI models can lead to energy efficiency gains. Here, we provide an empirical study on green code and an overview of green coding practices, as well as metrics used to quantify the sustainability awareness of AI models. In this framework, we evaluate the sustainability of auto-generated code. The auto-generate codes considered in this study are produced by generative commercial AI language models, GitHub Copilot, OpenAI ChatGPT-3, and Amazon CodeWhisperer. Within our methodology, in order to quantify the sustainability awareness of these AI models, we propose a definition of the code's "green capacity", based on certain sustainability metrics. We compare the performance and green capacity of human-generated code and code generated by the three AI language models in response to easy-to-hard problem statements. Our findings shed light on the current capacity of AI models to contribute to sustainable software development.