Analysis of Renewable Energy Policies through Decision Trees

Dania Ortiz, Vera Migueis, Vitor Leal, Janelle Knox-Hayes and Jungwoo Chun
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Dania Ortiz: MIT Portugal Program, Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
Vera Migueis: Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), 4200-465 Porto, Portugal
Vitor Leal: Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
Janelle Knox-Hayes: Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Avenue 9-424, Cambridge, MA 02139, USA
Jungwoo Chun: Department of Urban Studies and Planning, Massachusetts Institute of Technology, 105 Massachusetts Avenue 9-366, Cambridge, MA 02139, USA

Sustainability, 2022, vol. 14, issue 13, 1-31

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.

Keywords: energy policy; policy effectiveness; policy efficacy; decision trees; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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