Green AI Strategy Compass: A Comparative Framework for Sustainable AI Design

Abstract

The rapid expansion of artificial intelligence (AI) has led to significant environmental concerns, prompting the emergence of Green AI as a sustainable alternative. While numerous techniques such as pruning, quantization, neural architecture search, and hardware substitution have been proposed to reduce energy consumption, the field lacks a unified framework for comparing these strategies across diverse contexts. This paper introduces the Green AI Strategy Compass, a purely theoretical decision-support model that evaluates techniques across five dimensions: algorithmic efficiency, hardware adaptability, policy alignment, scalability, and accessibility. By synthesizing insights from recent literature, the framework enables contextaware selection of sustainable AI 
methods without requiring empirical validation. A comparative scoring system and decision logic are used to rank strategies, offering transparency and adaptability for researchers, educators, and policymakers. The results demonstrate how theoretical evaluation can guide sustainable AI adoption across academic, enterprise, and edge environments.