A Causal Analysis of CO2 Reduction Strategies in Electricity Markets Through Machine Learning-Driven Metalearners

arXiv:2403.15499v1 Announce Type: new
Abstract: 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 …

abstract analysis analyze arxiv carbon carbon dioxide causal causality co2 cs.ai cs.cy cs.lg effects electricity influence machine machine learning markets policies pricing sector statistical statistical method stat.me strategies study through treatment type