An Efficient Artificial Intelligence- and Deep Learning-Based Smart Sustainability in Net Zero

Bremananth Ramachandran and Raed Atef

Chapter 5 in The Role of Technology and Innovation in Achieving Sustainability:Assessing Benefits and Limitations, 2026, pp 103-135 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The pursuit of a Net Zero society, where human activities do not contribute to the net accumulation of greenhouse gases, presents an urgent challenge to global sustainability efforts. The transition to a Net Zero society is critical in addressing the challenges of climate change and environmental degradation. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions to accelerate sustainability efforts, enabling efficient resource management, decarbonization, and optimized energy systems. This book chapter explores how AI and ML can drive smart sustainability initiatives by enhancing energy efficiency, decarbonizing industries, and enabling data-driven decision-making for sustainable practices. Key applications include smart grid management, renewable energy integration, precision agriculture, waste reduction through circular economy principles, and the development of sustainable urban infrastructures. AI-driven solutions also play a critical role in integrating renewable energy sources, improving climate modeling and risk assessment, and promoting sustainable consumer behaviors. In addition, AI and ML are integral to improving consumer behavior, optimizing supply chains, and providing real-time climate predictions. Through data-driven insights, real-time optimization, and predictive capabilities, AI and ML can enhance efficiency, reduce environmental impact, and foster more sustainable practices. By harnessing these technologies, AI and ML can contribute to a more sustainable and resilient future, aligning economic growth with environmental stewardship. This chapter presents an overview of current AI- and ML-driven innovations in sustainability and discusses their potential to catalyze the achievement of Net Zero targets globally. Furthermore, the integration of Imaging, Computer Vision, AI, and Deep ML (DML) techniques, such as deep belief networks (DBNs), alongside Decision Trees and Random Forest Classifiers, offers powerful tools for advancing smart sustainability efforts in the pursuit of a Net Zero society. This methodology explores how these technologies can be combined to enable more efficient resource use, optimize processes, reduce waste, and ultimately help in achieving Net Zero carbon emissions goals across various industries.

Keywords: Sustainability; Technology; Innovation; Social Responsibility; CSR; AI; Management; Fintech; Public Health; SDGs (Sustainable Development Goals); Industry 5.0; Blockchain; Epidemic Management Global Security; Emerging Markets (search for similar items in EconPapers)
JEL-codes: M14 O31 Q01 Q55 Q56 (search for similar items in EconPapers)
Date: 2026
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