A Causal Analysis of CO2 Reduction Strategies in Electricity Markets Through Machine Learning-Driven Metalearners
- URL: http://arxiv.org/abs/2403.15499v1
- Date: Thu, 21 Mar 2024 18:55:05 GMT
- Title: A Causal Analysis of CO2 Reduction Strategies in Electricity Markets Through Machine Learning-Driven Metalearners
- Authors: Iman Emtiazi Naeini, Zahra Saberi, Khadijeh Hassanzadeh,
- Abstract summary: 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.
The study's findings suggest that adopting such policies may inadvertently increase CO2 intensity.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
Related papers
- Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies [5.923818043882103]
Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret.
We combine RL policy model checking and co-activation graph analysis.
This combination lets us interpret the RL policy's inner workings for safe decision-making.
arXiv Detail & Related papers (2025-01-06T17:07:44Z) - A Graphical Approach to State Variable Selection in Off-policy Learning [0.0]
We provide a set of graphical identification criteria in general decision processes.
We discuss how our results relate to the often implicit causal assumptions made in the dynamic treatment regimes and offline reinforcement learning literatures.
We present a realistic simulation study for the dynamic pricing problem encountered in container logistics.
arXiv Detail & Related papers (2025-01-01T14:37:35Z) - From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations [0.0]
This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations.
The proposed framework offers solutions that support data-driven policy-making and strategic decision-making in climate-related contexts.
arXiv Detail & Related papers (2024-12-21T16:33:07Z) - Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
In practice, causal researchers do not have a single outcome in mind a priori.
In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty.
We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
arXiv Detail & Related papers (2024-04-29T08:16:30Z) - Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories [0.0]
This research examines the temporal dynamics associated with attaining a 5 percent rise in purchasing power parity-adjusted GDP per capita over a period of 120 months (2013-2022).
A comparative investigation reveals that DeepSurv is proficient at capturing non-linear interactions, although standard models exhibit comparable performance under certain circumstances.
arXiv Detail & Related papers (2024-04-03T07:27:59Z) - A Comparative Study of Machine Learning Algorithms for Anomaly Detection
in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
This study seeks to address the demands of high-performance machine learning models with environmental sustainability.
Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance.
However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
arXiv Detail & Related papers (2023-07-01T15:18:00Z) - Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators [0.9463895540925061]
This paper presents an approach to analyzing industry trends leveraging real-time stock market data and generative small language models (SLMs)
One of the key challenges lies in the inherent noise in raw data, which can compromise the precision of statistical analyses.
We propose a dual-pronged approach to industry trend analysis: explicit and implicit analysis.
arXiv Detail & Related papers (2023-03-05T16:17:56Z) - Offline Reinforcement Learning with Instrumental Variables in Confounded
Markov Decision Processes [93.61202366677526]
We study the offline reinforcement learning (RL) in the face of unmeasured confounders.
We propose various policy learning methods with the finite-sample suboptimality guarantee of finding the optimal in-class policy.
arXiv Detail & Related papers (2022-09-18T22:03:55Z) - Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity.
We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class.
We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
arXiv Detail & Related papers (2022-01-31T20:58:47Z) - Reliable Off-policy Evaluation for Reinforcement Learning [53.486680020852724]
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy.
We propose a novel framework that provides robust and optimistic cumulative reward estimates using one or multiple logged data.
arXiv Detail & Related papers (2020-11-08T23:16:19Z) - Reinforcement Learning via Fenchel-Rockafellar Duality [97.86417365464068]
We review basic concepts of convex duality, focusing on the very general and supremely useful Fenchel-Rockafellar duality.
We summarize how this duality may be applied to a variety of reinforcement learning settings, including policy evaluation or optimization, online or offline learning, and discounted or undiscounted rewards.
arXiv Detail & Related papers (2020-01-07T02:59:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.