Clouding the Future: Innovating Towards Net-Zero Emissions
DOI:
https://doi.org/10.47941/ijce.2127Keywords:
Cloud Migration, Sustainability, Generative AI, Serverless Automation, Energy EfficiencyAbstract
Purpose: This paper explores the intersection of cloud computing, generative artificial intelligence (AI), and sustainability, focusing on how these technologies can drive the transition towards net-zero emissions. The aim is to assess how integrating cloud-based solutions and AI can enhance energy efficiency and support environmental goals.
Methodology: The study employs a comprehensive review of recent literature, industry reports, and case studies from major cloud providers and technology companies. It analyzes the sustainability commitments of leading cloud providers, evaluates the role of generative AI in optimizing resource utilization, and examines the benefits of serverless automation in reducing carbon footprints.
Findings: The research finds that cloud providers are making significant strides in sustainability through ambitious climate commitments and energy-efficient technologies. Generative AI is shown to improve decision-making and resource management, while serverless automation optimizes resource use and minimizes energy consumption. These advancements collectively contribute to achieving long-term sustainability goals and enhancing operational efficiency.
Unique Contribution to Theory, Practice, and Policy: This paper provides a novel perspective on leveraging generative AI and serverless automation for sustainability. It offers actionable recommendations for organizations to integrate these technologies into their cloud strategies, emphasizing the importance of aligning with ESG criteria and optimizing data center operations. The findings support the development of policies that encourage technological innovation in the pursuit of environmental stewardship and carbon neutrality.
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References
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Copyright (c) 2024 Sridhar Mahadevan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.