Analysis of Renewable Energy Policies through Decision Trees
Name
sustainability-14-07720-v3.pdf
Size
2.41 MB
Format
Adobe PDF
Checksum (MD5)
b04420516b8a437c2cb9d486b788d886
Author(s)
Ortiz, Dania
Migueis, Vera
Leal, Vitor
Knox-Hayes, Janelle
Chun, Jungwoo
Date Issued
June 24, 2022
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sustainability 14 (13): 7720 (2022)
Version
Final published version
Abstract
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.
MIT Department
MIT-Portugal Program
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/su14137720
