Sustainable Information Retrieval and Green Artificial Intelligence: A Solution for Environmentally-Friendly Computing

Mozafari, Niloofar; Shariati, Seyyed Yasin

doi:10.22034/jipm.2025.2052948.1937

Sustainable Information Retrieval and Green Artificial Intelligence: A Solution for Environmentally-Friendly Computing

Document Type : Original Article

Authors

Niloofar Mozafari ¹

Seyyed Yasin shariati ²

¹ Assistant Professor; Islamic World Science & Technology Monitoring and Citation Institute (ISC); Shiraz; Iran

² Undergraduate Student in Computer Engineering; Shiraz University; Shiraz; Iran.

10.22034/jipm.2025.2052948.1937

Abstract

Information retrieval plays a fundamental role in organizing and searching data across various information systems, including digital libraries, search engines, recommender systems, and online platforms. These systems are widely used to facilitate access to information across different scales, including in scientific, commercial, and social domains. However, the use of complex models, such as deep neural networks and large language models, which require high computational power and rely on powerful hardware, has introduced new challenges related to energy consumption and environmental impact.
This paper addresses these challenges and explains the concept of green artificial intelligence. Green AI is presented as an approach to developing intelligent algorithms and systems while considering the limitations of energy resources and their environmental impact. Given the recent advancements in AI technologies, particularly in natural language processing and deep learning, it is essential to focus on the environmental impacts of these models. Specifically, the increased energy consumption and greenhouse gas emissions during the training and inference processes of complex models necessitate innovative approaches to mitigate these effects. The paper also introduces the concept of sustainable information retrieval and presents methods for measuring greenhouse gas emissions and strategies to reduce these emissions in information retrieval processes.
The primary objective of this paper is to raise awareness within the scientific community about the importance of minimizing environmental impacts when designing and implementing information retrieval systems. The paper emphasizes that researchers and system designers should consider environmental effects alongside system performance and accuracy. To achieve sustainable information retrieval, energy consumption and costs can be reduced by optimizing algorithms, reusing pre-trained models, and leveraging cloud computing. These approaches not only reduce costs and improve system efficiency but also contribute to environmental sustainability. Furthermore, the paper encourages researchers to consider environmental impacts as a critical parameter in their research processes.

Keywords

Green Artificial Intelligence

Sustainable Information Retrieval

Greenhouse Gas Emissions

Red Artificial Intelligence

Neural Networks

Subjects

Information Storage and Retrieval

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