Publication Date
1-1-2022
Document Type
Contribution to a Book
Publication Title
Machine Learning for Societal Improvement, Modernization, and Progress
Editor
Vishnu Pendyala
DOI
10.4018/978-1-6684-4045-2.ch001
First Page
1
Last Page
26
Abstract
Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions.
Department
Applied Data Science
Recommended Citation
Vishnu S. Pendyala and Saritha Podali. "An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress" Machine Learning for Societal Improvement, Modernization, and Progress (2022): 1-26. https://doi.org/10.4018/978-1-6684-4045-2.ch001
Comments
This is the Version of Record, and has been used with the permission of IGI Global, under their Fair Use Policy.