THE GREEN ALGORITHM: ARTIFICIAL INTELLIGENCE AS A BRIDGE BETWEEN LEAN OPERATIONS AND ENVIRONMENTAL SUSTAINABILITY

Abstract

Currently, industries around the world are under growing pressure to meet the United Nations Sustainable Development Goals (SDGs). Creating a reliable way to measure the environmental benefits of digital transformation has become essential for industries everywhere. This study builds a framework based on the Resource-Based View (RBV) and dynamic capabilities theory and aims to test the cause-and-effect link between AI-driven operational integration and environmental sustainability performance. Using a detailed study of leading companies—Unilever, Amazon, and Tesla—the research looks at how Resource Optimization plays a role in this connection. The results show that the independent factor has a strong effect on the dependent factor through several clear ways. For example, predictive load pooling helps cut logistics-related CO₂ emissions by 64%, supporting SDG 13. Also, custom hardware designs reduce computing energy use by 29%, matching SDG 9, while computer vision-assisted manufacturing recycling recovers 92% of metals, contributing to SDG 12. However, comparing cases reveals an important limit called the AI-Sustainability Paradox. This means the large energy needed to run the independent variable can cause a digital version of the Jevons Paradox, which might cancel out the environmental gains in the dependent variable. The study finds that while AI is essential to separate industrial growth from carbon emissions, its success relies on breaking down technology barriers between sectors and creating decentralized data systems to remove isolated Scope 3 reporting.