Carbon Footprint Evaluation of Code Generation through LLM as a Service

2025-03-30

AI Summary

As AI code generation is increasingly deployed in high-reliability domains such as automotive systems, quantifying its embodied carbon (from development) and operational carbon (from execution) has become critically urgent. Method: This paper introduces the first code-level, full-lifecycle carbon footprint assessment framework tailored for LLM-based coding services—exemplified by GitHub Copilot—integrating hardware-aware and software-aware carbon modeling with established software sustainability metrics to yield a reproducible empirical evaluation pipeline. Contribution/Results: We demonstrate that carbon impact varies significantly across usage scenarios; moreover, green coding strategies substantially reduce functional carbon intensity (e.g., gCO₂e per feature or per executed line). This work delivers the first measurable, verifiable carbon assessment methodology for AI-generated code in safety-critical domains, enabling evidence-based green AI development practices and informing sustainable AI policy formulation.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Efficient ML / Green AI
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Evaluating carbon footprint of AI-generated code in data centers
• Measuring sustainability impact of LLM services like GitHub Copilot
• Assessing embodied and operational carbon in auto-generated software

Innovation

• LLM as a service for code generation
• Sustainability metrics for AI models
• Quantify embodied and operational carbon