Work

Green metrics for AI: A hybrid strategy for environmental impact assessment

Year: 2025

Type: article

Abstract: Accurately assessing the environmental impact of Artificial Intelligence (AI) systems is essential to promoting sustainable development within the field. However, existing carbon emission tracking tools often rely on divergent assumptions, limited hardware compatibility, and inconsistent measurement granularity, leading to fragmented and often incomparable results. In this paper, we propose a hybrid methodology that integrates a weighted aggregation of ten widely-used emission estimation tools with a dynamic model-to-tool matching procedure. The weighted strategy assigns normalized importance scores to each tool based on six criteria. In parallel, the dynamic matching component identifies the most context-appropriate estimation tool for a given AI model. We evaluate our methodology on multiple architectures, including MobileNet, DenseNet, and GPT-3, deployed in different computing environments. Results demonstrate that our combined approach enhances both estimation accuracy and interpretability, outperforming single-tool assessments and offering a scalable solution for transparent carbon accounting in AI research.

Source: Array

DOI: 10.1016/j.array.2025.100528

Authors: Salvatore Borraccia, Elio Masciari, Enea Vincenzo Napolitano

Institution: University of Naples Federico II

Language: English

Topic: Green IT and Sustainability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,680