Exploring Generative AI as a Catalyst for Sustainability:  Strategies for Waste and Energy Reduction

Shweta Jaiswal; Shivani Pandey

doi:10.21844/DOI: https://doi.org/10.21844/mijia.20.1.2

Shweta Jaiswal Research scholar, Department of Commerce, CMP Degree College, University of Allahabad, Prayagraj, India
Shivani Pandey esearch scholar, Department of Commerce, CMP Degree College, University of Allahabad, Prayagraj, India

DOI: https://doi.org/10.21844/DOI:%20https://doi.org/10.21844/mijia.20.1.2

Keywords: Generative AI, Sustainability, waste and energy reduction, environment

Abstract

The study explores the potential of generative AI as a transformative tool for advancing sustainability, focusing on waste and energy
reduction strategies. Generative AI technologies offer innovative solutions for optimizing resource use, minimizing material waste, and
improving energy efficiency across various industries. By leveraging AI for predictive analytics, process automation, and real-time
decision-making, businesses can significantly reduce their environmental impact. However, the integration of AI into sustainability
efforts also raises important policy considerations, particularly regarding energy consumption during AI model development and
deployment. The study aims to provide a comprehensive analysis of the opportunities and challenges associated with using generative AI
to drive sustainable practices, offering strategic recommendations for policymakers and organizations alike. Generative AI can model
complex systems, simulate environmental impacts, and optimize production processes, leading to reduced material waste, lower energy
consumption, and more efficient resource allocation. Industries such as manufacturing, energy, and logistics stand to benefit immensely
from AI-driven innovations, which can refine processes, predict maintenance needs, and optimize supply chains. Nevertheless, the
widespread implementation of generative AI comes with challenges, especially the high energy demands of AI training models, which
could offset some sustainability gains. As a result, the paper underscores the need for balanced policy frameworks that encourage AI
adoption while promoting sustainable AI development practices, such as the use of energy-efficient hardware and renewable energy
sources. The study also highlights the potential for generative AI to influence global sustainability targets, driving a shift towards greener,
smarter technologies and providing a pathway for industries to achieve net-zero emissions.