Analysis of Renewable Energy Policies through Decision Trees

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Ortiz, D (Author) Other; Vera L. Miguéis (Author) FEUP; Vítor Leal (Author) FEUP; Knox Hayes, J (Author) Other; Chun, J (Author) Other

Journal

Title: Sustainability

Vol. 359

Final page: 7720

ISSN: 2071-1050

Publisher: MDPI

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00W-SN9

DOI: 10.3390/su14137720

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 31