Analysis of Renewable Energy Policies through Decision Trees

This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE tar...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2022-07, Vol.14 (13), p.7720
Main Authors: Ortiz, Dania, Migueis, Vera, Leal, Vitor, Knox-Hayes, Janelle, Chun, Jungwoo
Format: Article
Language:English
Subjects:
Algorithms
Criteria
Datasets
Decision making
Decision trees
Energy
Energy industry
Energy policy
Learning algorithms
Machine learning
Neural networks
Public policy
Renewable resources
Sustainability
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE target will likely be achieved (efficacy) and to what degree (effectiveness), depending on the different criteria, including geographical context, characterizing concerns, and policy characteristics. The results suggest different criteria that could help policymakers in designing policies with a higher propensity to achieve the desired goal. Using this tool, the policy decision-makers can better test/predict whether the target will be achieved and to what degree. The novelty in the present paper is the application of Machine Learning methods (through the Decision Trees) for energy policy analysis. Machine learning methodologies present an alternative way to pilot RE policies before spending lots of time, money, and other resources. We also find that using Machine Learning techniques underscores the importance of data availability. A general summary for policymakers has been included.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14137720