期刊
SUSTAINABILITY
卷 14, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/su14137720
关键词
energy policy; policy effectiveness; policy efficacy; decision trees; machine learning
资金
- MIT Portugal Program
This paper presents an alternative method using decision trees to predict the effectiveness and efficacy of renewable energy policies. The analysis is data-driven and helps policymakers design policies with a higher likelihood of achieving their goals, emphasizing the importance of data availability.
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.
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