A Causal Analysis of CO2 Reduction Strategies in Electricity Markets
  Through Machine Learning-Driven Metalearners
Iman Emtiazi Naeini Zahra Saberi Khadijeh Hassanzadeh
This study employs the Causal Machine Learning (CausalML) statistical method
to analyze the influence of electricity pricing policies on carbon dioxide
(CO2) levels in the household sector. Investigating the causality between
potential outcomes and treatment effects, where changes in pricing policies are
the treatment, our analysis challenges the conventional wisdom surrounding
incentive-based electricity pricing. The study's findings suggest that adopting
such policies may inadvertently increase CO2 intensity. Additionally, we
integrate a machine learning-based meta-algorithm, reflecting a contemporary
statistical approach, to enhance the depth of our causal analysis. The study
conducts a comparative analysis of learners X, T, S, and R to ascertain the
optimal methods based on the defined question's specified goals and contextual
nuances. This research contributes valuable insights to the ongoing dialogue on
sustainable development practices, emphasizing the importance of considering
unintended consequences in policy formulation.