Digital Waste Mitigation in AI and Cloud Computing: A Comprehensive Framework for Environmental Sustainability

Durga Rao Manchikanti

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 1357-1366

Abstract: This article examines the growing environmental impact of digital waste generated by artificial intelligence systems and cloud computing infrastructure. It presents a comprehensive article analysis of the environmental footprint associated with data centers, AI model training, and cloud-based operations. The article introduces a framework for identifying and measuring digital waste across computing environments, followed by an evaluation of current mitigation strategies including smart data management, deduplication techniques, and energy-efficient algorithm design. Through case studies and empirical analysis, the article demonstrates the effectiveness of combining technical solutions with organizational policies to reduce digital waste. The article highlights the crucial role of edge computing and renewable energy adoption in minimizing environmental impact. Additionally, the article proposes a set of best practices for organizations to integrate sustainability into their digital transformation initiatives. This article contributes to the growing body of literature on sustainable computing by providing actionable strategies for balancing technological advancement with environmental preservation. The conclusions emphasize the importance of industry-wide collaboration in establishing standards for measuring and reducing digital waste, while highlighting areas for future research in green computing technologies.

Keywords: digital waste; artificial intelligence; cloud computing; environmental sustainability; green computing (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112147
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:797

DOI: 10.32628/CSEIT251112147

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