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Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

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arxiv 2403.03344 v1 pith:TC4KR5GI submitted 2024-03-05 cs.SE cs.AI

Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Tina Vartziotis , Ippolyti Dellatolas , George Dasoulas , Maximilian Schmidt , Florian Schneider , Tim Hoffmann , Sotirios Kotsopoulos , Michael Keckeisen This is my paper
classification cs.SE cs.AI
keywords codemodelsgreensustainabilitycapacitydevelopmentawarenesscoding
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation

    cs.SE 2026-04 unverdicted novelty 7.0

    Chain-of-Thought prompting balances high accuracy with low energy use in small language models for code generation, while multi-sampling strategies add high energy costs for small accuracy gains.

  2. Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

    cs.LG 2026-07 conditional novelty 6.5

    Simulation-in-the-loop GRPO on energy-labeled C++ pairs yields 12.63% CARET and beats human energy efficiency on 58.4% of valid held-out outputs, while IPC misranks energy on 67.8% of problems.

  3. An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code

    cs.LG 2026-03 unverdicted novelty 5.0

    Contrastive Prompt Tuning raises code accuracy on two of three tested models but produces inconsistent energy-efficiency gains that depend on model, language, and task.

  4. Sustainable Code Generation Using Large Language Models: A Systematic Literature Review

    cs.SE 2026-03 unverdicted novelty 3.0

    A systematic review finds research on the sustainability of LLM-generated code to be limited, fragmented, and without accepted frameworks for measurement or benchmarking.