A systematic review of artificial intelligence applications in reducing the carbon footprint of computer systems

Abstract

This research aimed to analyze, through a systematic literature review, the applications of Artificial Intelligence (AI) geared towards reducing the carbon footprint of computing systems. To this end, a methodology based on the PRISMA guidelines was applied, conducting searches in high- impact academic databases such as Scopus, considering articles published between 2021 and 2025 related to AI, sustainability, energy efficiency, green software, and data centers. After the identification, screening, and eligibility process, 31 articles were selected that met the inclusion criteria defined by the PICO model. The results showed that AI is applied in multiple areas with significant improvements in energy efficiency and emissions reduction. In data centers, it is used to optimize energy consumption and thermal management, resource allocation, and real-time electricity consumption. In sustainable software, optimization algorithms and machine learning models are applied to reduce computational demand. In sectors such as industry, agriculture, and logistics, predictive models and hybrid techniques show significant reductions in CO2 emissions and improvements in operational efficiency. Likewise, the growing adoption of digital twins, deep learning, and approaches based on dynamic optimization was identified. Despite this progress, significant gaps remain, as most research focuses on isolated sectoral applications, and a consolidated analysis of AI's contribution to computational systems specifically aimed at reducing carbon footprints is still lacking. In conclusion, these findings support the need for new strategic guidelines that incorporate dispersed contributions, synthesize trends, and establish clear and concise guidelines for developing more efficient computational architectures.