Big Data and Machine Learning for Sustainable Waste Reduction

Abstract

Purpose: Climate change, rising resource costs, and global policy frameworks like the United Nations Sustainable Development Goals (SDGs) are putting more and more pressure on businesses to adopt sustainable practices. This paper examines the integration of Big Data, data analytics, machine learning (ML), and waste-management strategies to mitigate waste and enhance sustainability across various industries.Methodology: A systematic literature review (SLR) of 50 peer-reviewed studies published between 2015 and 2024 was conducted using databases such as Scopus, Web of Science, and IEEE Xplore. The review combines real-world evidence, finds patterns, and looks at how data-driven technologies affect sustainability. A conceptual framework is suggested that connects Big Data, ML, and analytics to how waste is handled.Findings: Data-driven methods cut waste by 20–45% compared to traditional methods, improve recycling accuracy to over 90%, and save businesses 15–25% on costs each year. Adoption rates differ by industry, with retail and logistics leading the way and agriculture and small-to-medium businesses (SMEs) falling behind.Research limitations: Research remains fragmented, exhibiting constrained cross-sector frameworks, nascent real-time machine learning applications, and inadequate investigation of return on investment (ROI).Originality: This paper presents a comprehensive framework for the integration of Big Data and Machine Learning into sustainable business practices, addressing deficiencies in current research and providing practical strategies for organizations and policymakers.