{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T03:06:26Z","timestamp":1775012786650,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MIT Portugal Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/su14137720","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"7720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Analysis of Renewable Energy Policies through Decision Trees"],"prefix":"10.3390","volume":"14","author":[{"given":"Dania","family":"Ortiz","sequence":"first","affiliation":[{"name":"MIT Portugal Program, Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal"}]},{"given":"Vera","family":"Migueis","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8396-2907","authenticated-orcid":false,"given":"Vitor","family":"Leal","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0378-5811","authenticated-orcid":false,"given":"Janelle","family":"Knox-Hayes","sequence":"additional","affiliation":[{"name":"Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Avenue 9-424, Cambridge, MA 02139, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3399-4852","authenticated-orcid":false,"given":"Jungwoo","family":"Chun","sequence":"additional","affiliation":[{"name":"Department of Urban Studies and Planning, Massachusetts Institute of Technology, 105 Massachusetts Avenue 9-366, Cambridge, MA 02139, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","unstructured":"(2022, May 02). EU Overachieves 2020 Renewable Energy Target\u2014Products Eurostat News\u2014Eurostat. Available online: https:\/\/ec.europa.eu\/eurostat\/web\/products-eurostat-news\/-\/ddn-20220119-1."},{"key":"ref_2","unstructured":"Nitze, I., Schulthess, U., and Asche, H. (2012, January 7\u20139). Comparison of machine learning algorithms random forest, artificial neuronal network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th Conference on GEographic Object-Based Image Analysis\u2014GEOBIA 2012, Salzburg, Austria."},{"key":"ref_3","unstructured":"The Royal Society (2017). Machine Learning: The Power and Promise of Computers that Learn by Example, The Royal Society."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.eneco.2019.05.006","article-title":"Machine learning in energy economics and finance: A review","volume":"81","author":"Ghoddusi","year":"2019","journal-title":"Energy Econ."},{"key":"ref_5","first-page":"94","article-title":"Prosumer bidding and scheduling in electricity markets","volume":"2016","author":"Ottesen","year":"2016","journal-title":"Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.apenergy.2015.10.002","article-title":"Decision tree aided planning and energy balancing of planned community microgrids","volume":"161","author":"Moutis","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.apenergy.2016.11.040","article-title":"Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment","volume":"187","author":"Kamali","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_8","first-page":"9","article-title":"Optimal operation of electric railways with renewable energy and electric storage systems","volume":"2018","author":"Aguado","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Salimi, M., Ardabili, S.F., Rabczuk, T., Shamshirband, S., and Varkonyi-Koczy, A.R. (2019). State of the art of machine learning models in energy systems, a systematic review. Energies, 12.","DOI":"10.3390\/en12071301"},{"key":"ref_10","first-page":"44","article-title":"Decision Trees","volume":"1897","author":"Rokach","year":"2005","journal-title":"Tel. Aviv. Univ."},{"key":"ref_11","unstructured":"Steuer, F. (2018). Machine Learning for Public Policy Making How to Use Data-Driven Predictive Modeling for the Social Good, Institut Batcelona."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10994-013-5425-9","article-title":"Machine learning for science and society","volume":"95","author":"Rudin","year":"2014","journal-title":"Mach Learn."},{"key":"ref_13","unstructured":"Bush, V. (2022, May 02). Science, the Endless Frontier. United States Government Printing Office, Available online: https:\/\/www.nsf.gov\/about\/history\/EndlessFrontier_w.pdf?linkId=81927236."},{"key":"ref_14","unstructured":"Baesens, B. (2014). Analytics in a Big Data World. Wiley & SAS Business Series, John Wiley & Sons."},{"key":"ref_15","first-page":"1","article-title":"Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities","volume":"2020","author":"Zhang","year":"2020","journal-title":"Hinday Complex."},{"key":"ref_16","first-page":"130","article-title":"Decision tree methods: Applications for classification and prediction","volume":"27","author":"Song","year":"2015","journal-title":"Shanghai Arch. Psychiatry"},{"key":"ref_17","unstructured":"ADEME (2020, July 07). Aides \u00e0 la R\u00e9novation|Agir Pour la Transition Ecologique|ADEME. Available online: https:\/\/particuliers.ademe.fr\/finances\/aides-la-renovation."},{"key":"ref_18","unstructured":"Buchanan, B., and Miller, T. (2017). Machine Learning for Policymakers, Harvard Kennedy School."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, E.S., Choi, Y., and Byun, J. (2020). Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs. Sustainability, 12.","DOI":"10.3390\/su12010202"},{"key":"ref_20","unstructured":"Developments for Sustainability (2008). Deliverable n D3. 2\u2014RS 2b \u201cFinal Set of Sustainability Criteria and Indicators for Assessment of Electricity Supply Options\u201d, Paul Scherrer Institut."},{"key":"ref_21","unstructured":"Cambridge Dictionary (2018, September 12). Effectiveness. Available online: https:\/\/dictionary.cambridge.org\/es-LA\/dictionary\/essential-british-english\/effectiveness."},{"key":"ref_22","unstructured":"Eurostat (2020). Energy, Transport and Environment Indicators, Eurostat Pocketbook."},{"key":"ref_23","unstructured":"European Commission (2020). EUROPE 2020: A Strategy for Smart, Sustainable and Inclusive Growth, European Commission."},{"key":"ref_24","unstructured":"European Commission (2020, September 09). Commission Awards 2003 Prizes to the Best European Projects in Renewable Energy. Available online: https:\/\/ec.europa.eu\/commission\/presscorner\/detail\/en\/IP_04_76\/."},{"key":"ref_25","unstructured":"Cambridge Dictionary (2018, September 12). Efficacy. Available online: https:\/\/dictionary.cambridge.org\/es-LA\/dictionary\/essential-british-english\/efficacy."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ortiz, D., and Leal, V. (2020). Energy Policy Concerns, Objectives and Indicators: A Review towards a Framework for Effectiveness Assessment. Energies, 13.","DOI":"10.3390\/en13246533"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1214\/aoms\/1177732676","article-title":"The Method of Path Coefficients","volume":"5","author":"Wright","year":"1934","journal-title":"Ann. Math. Stat."},{"key":"ref_28","unstructured":"Lucertini, G. (2012). Evaluating Public Policies Normative Models Beyond Cost Benefit Analysis, Universita degli Studi di Padova."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rottmann, M., Maag, K., Chan, R., Huger, F., Schlicht, P., and Gottschalk, H. (2020). Detection of False Positive and False Negative Samples in Semantic Segmentation, IEEE.","DOI":"10.23919\/DATE48585.2020.9116288"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101704","DOI":"10.1016\/j.erss.2020.101704","article-title":"Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research","volume":"69","author":"Debnath","year":"2020","journal-title":"Energy Res. Soc. Sci."},{"key":"ref_31","unstructured":"AEA Group (2009). Quantification of the Effects on Greenhouse Gas Emissions of Policies and Measures Final Report a Final Report to the European Commission AEA Technology, AEA Group."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112829","DOI":"10.1016\/j.enpol.2022.112829","article-title":"Are abundant energy resources and Chinese business a solution to environmental prosperity in Africa?","volume":"163","author":"Zakari","year":"2022","journal-title":"Energy Policy"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zakari, A., Khan, I., Tawiah, V., and Alvarado, R. (2022). Reviewing the ecological footprints of Africa top carbon consumer: A quantile on quantile analysis. Int. J. Environ. Sci. Technol.","DOI":"10.1007\/s13762-021-03904-z"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1016\/j.apenergy.2018.07.084","article-title":"A survey of artificial neural network in wind energy systems","volume":"228","author":"Perez","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/20964471.2018.1526057","article-title":"Machine learning for energy-water nexus: Challenges and opportunities","volume":"2","author":"Zaidi","year":"2018","journal-title":"Big Earth Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.anucene.2017.11.014","article-title":"Machine learning based system performance prediction model for reactor control","volume":"113","author":"Zeng","year":"2018","journal-title":"Ann. Nucl. Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1016\/j.apenergy.2018.06.051","article-title":"Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review","volume":"228","author":"Zendehboudi","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.petrol.2017.01.024","article-title":"Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization","volume":"151","author":"Anifowose","year":"2017","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.renene.2016.12.095","article-title":"Machine learning methods for solar radiation forecasting: A review","volume":"105","author":"Voyant","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.renene.2015.11.073","article-title":"Machine learning ensembles for wind power prediction","volume":"89","author":"Heinermann","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23","DOI":"10.2118\/174784-PA","article-title":"Machine learning as a reliable technology for evaluating time\/rate performance of unconventional wells","volume":"8","author":"Fulford","year":"2016","journal-title":"SPE Econ. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1109\/TIE.2014.2361493","article-title":"Support-vector-machine-based proactive cascade prediction in smart grid using probabilistic framework","volume":"62","author":"Gupta","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_43","first-page":"141","article-title":"Decision tree-based security dispatch application in integrated electric power and natural-gas networks","volume":"2016","author":"Costa","year":"2016","journal-title":"Electr. Power Syst. Res."}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/14\/13\/7720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:24Z","timestamp":1760139564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/14\/13\/7720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":43,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["su14137720"],"URL":"https:\/\/doi.org\/10.3390\/su14137720","relation":{},"ISSN":["2071-1050"],"issn-type":[{"value":"2071-1050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,24]]}}}