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...
| Main Authors: | , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/143639 |
| _version_ | 1811077407664242688 |
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| author | Ortiz, Dania Migueis, Vera Leal, Vitor Knox-Hayes, Janelle Chun, Jungwoo |
| author2 | MIT-Portugal Program |
| author_facet | MIT-Portugal Program Ortiz, Dania Migueis, Vera Leal, Vitor Knox-Hayes, Janelle Chun, Jungwoo |
| author_sort | Ortiz, Dania |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:42:32Z |
| format | Article |
| id | mit-1721.1/143639 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:42:32Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1436392023-04-14T15:06:22Z Analysis of Renewable Energy Policies through Decision Trees Ortiz, Dania Migueis, Vera Leal, Vitor Knox-Hayes, Janelle Chun, Jungwoo MIT-Portugal Program Massachusetts Institute of Technology. Department of Urban Studies and Planning 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. 2022-07-11T14:39:44Z 2022-07-11T14:39:44Z 2022-06-24 2022-07-08T11:54:58Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/143639 Sustainability 14 (13): 7720 (2022) PUBLISHER_CC http://dx.doi.org/10.3390/su14137720 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Ortiz, Dania Migueis, Vera Leal, Vitor Knox-Hayes, Janelle Chun, Jungwoo Analysis of Renewable Energy Policies through Decision Trees |
| title | Analysis of Renewable Energy Policies through Decision Trees |
| title_full | Analysis of Renewable Energy Policies through Decision Trees |
| title_fullStr | Analysis of Renewable Energy Policies through Decision Trees |
| title_full_unstemmed | Analysis of Renewable Energy Policies through Decision Trees |
| title_short | Analysis of Renewable Energy Policies through Decision Trees |
| title_sort | analysis of renewable energy policies through decision trees |
| url | https://hdl.handle.net/1721.1/143639 |
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