mlsanityData to 14 Oct 2026

Carbon Footprint Evaluation of Code Generation through LLM as a Service

arXiv 2504.01036 ↗First posted 30 Mar 2025cs.CY cs.LG cs.SEcited by 4

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Abstract (arXiv)

Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI models grow and become major components of daily working routines. To reduce the environmental impact of software development, green (sustainable) coding and claims that AI models can improve energy efficiency have grown in popularity. Furthermore, in the automotive industry, where software increasingly governs vehicle performance, safety, and user experience, the principles of green coding and AI-driven efficiency could significantly contribute to reducing the sector's environmental footprint. We present an overview of green coding and metrics to measure AI model sustainability awareness. This study introduces LLM as a service and uses a generative commercial AI language model, GitHub Copilot, to auto-generate code. Using sustainability metrics to quantify these AI models' sustainability awareness, we define the code's embodied and operational carbon.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 30 Mar 2025.

Builds on

15 of 28 references.

References · 15

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  4. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  5. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  6. 2019Green AIcited by 627
  7. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  8. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  9. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
  10. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  11. 2023Using an LLM to Help With Code Understandingcited by 228
  12. 2019The Evolved Transformercited by 180
3 more
  1. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
  2. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 72
  3. 2024Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2026From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUscited by 3
  2. 2025Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programmingcited by 2
  3. 2026MoEITS: A Green AI approach for simplifying MoE-LLMscited by 1
  4. 2025EnviroLLM: Resource Tracking and Optimization for Local AIcited by 0