Applying Machine Learning to Estimate the Impact of Carbon Pricing on Company Financials

Abstract

Implementing carbon pricing policies, such as carbon taxes and cap-and-trade systems, carries substantial financial consequences for companies operating in diverse industries. The precise evaluation of the effects of these policies is essential for businesses and investors in mitigating risks and making wellinformed choices effectively. The present white paper investigates the utilization of machine learning methodologies to estimate the financial ramifications of carbon pricing on corporations across diverse sectors. The development and evaluation of machine learning models that capture industry-specific factors and dynamics are achieved through company financial data, emissions data, and scenario variables. The paper examines the difficulties and constraints associated with these models, such as data accessibility, the interpretability of the models, and their ability to be applied to different sectors. Additionally, we offer suggestions for potential improvements and the incorporation of other risk assessment tools in the future. Our study’s results showcase machine learning’s capacity to facilitate well-informed decision-making regarding carbon pricing and climate risk handling.