The Environmental Price of Intelligence: Evaluating the Social Cost of Carbon in Machine Learning

The rapid expansion of machine learning (ML) technologies has brought with it an increasing concern over the carbon emissions associated with their hardware and computational demands. This study aims to quantify the environmental impact of ML through the lens of the social cost of carbon (SCC), a metric that translates carbon emissions into future economic impacts. Our approach involves a detailed calculation of the SCC for the entire development lifecycle of ML models, offering a monetary perspective on their environmental footprint. By mapping these costs to a sustainable framework, we aim to foster greater awareness and encourage responsible practices in ML development. This paper not only quantifies the often-overlooked environmental costs of ML but also proposes a pathway towards more sustainable model development, navigating the complex balance between technological advancement and environmental responsibility.