2024/03/21 by Iman Emtiazi Naeini, Naeini, Iman Emtiazi, Zahra Saberi +3
Decision Sciences · Economics, Econometrics and Finance · Energy · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Energy Efficiency and Management #Energy, Environment, Economic Growth #FOS: Computer and information sciences #Innovation Diffusion and Forecasting #Machine Learning (cs.LG) #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2403.15499
openalex publication_date 2024/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.