Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms

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dc.contributor.authorAğbulut, Ümit
dc.date.accessioned2021-12-01T18:47:33Z
dc.date.available2021-12-01T18:47:33Z
dc.date.issued2022
dc.departmentDÜ, Teknoloji Fakültesi, Makine ve İmalat Mühendisliği Bölümüen_US
dc.description.abstractAdverse impacts of the transportation sector on not only air quality but also economic growth of a country are nowadays well-noticed, particularly by developing countries. Today, the transportation sector is powered by burning the fossil-based fuels at more than 99% and approximately 6.5 million deaths annually occur due to air-pollution-related diseases worldwide. Therefore, knowledge of both energy demand and CO2 emission of a country is a very significant issue in order to revise its future energy investments and policies. In this framework, three machine learning algorithms (deep learning (DL), support vector machine (SVM), and artificial neural network (ANN)) are used to forecast the transportation-based-CO2 emission and energy demand in Turkey. The gross domestic product per capita (GDP), population, vehicle kilometer, and year are used as input parameters in the study. It is noticed that there is a very high correlation among year, economic indicators, population, vehicle kilometer, transportation-based energy demand, and CO2 emissions. To present a better comparison, the results of these algorithms are discussed with six frequently used statistical metrics (R-2, RMSE, MAPE, MBE, rRMSE, and MABE). For all machine learning algorithms, R-2 values are varying between 0.8639 and 0.9235, and RMSE is smaller than 5 x 10(6) tons for CO2 emission and 2 Mtoe for energy demand. According to the classifications in the literature, the forecast results are generally categorized as excellent for rRMSE metric (<10%), and high prediction accuracy for MAPE metric (<10%). On the other hand, with two mathematical models, future energy demand and CO2 emission arising from the transportation sector in Turkey are forecasted by the year 2050. In the results, it is fore-casted that the annual growth rate for transportation-related energy demand and CO2 emission in Turkey cumulatively rise by 3.7% and 3.65%, respectively. Both energy demand and CO2 emissions from the transportation sector in Turkey will increase nearly 3.4 times higher in the year 2050 than those of today. In conclusion, the paper clearly reports that the future energy investments of the country should be revised, and various policies, regulations, norms, restrictions, legislations, and challenges on both energy consumption and emission mitigation from the transportation sector should be established by the policy-makers. (C) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.spc.2021.10.001
dc.identifier.endpage157en_US
dc.identifier.issn2352-5509
dc.identifier.scopus2-s2.0-85120321423en_US
dc.identifier.startpage141en_US
dc.identifier.urihttps://doi.org/10.1016/j.spc.2021.10.001
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10305
dc.identifier.volume29en_US
dc.identifier.wosWOS:000711675500007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorAgbulut, Umit
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Production And Consumptionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTransportation sectoren_US
dc.subjectCarbon footprintsen_US
dc.subjectEnergy demanden_US
dc.subjectGHG emissionsen_US
dc.subjectCO2 emissionsen_US
dc.subjectSupport Vector Machineen_US
dc.subjectGlobal Solar-Radiationen_US
dc.subjectSwarm Intelligenceen_US
dc.subjectEmpirical-Modelsen_US
dc.subjectPredictionen_US
dc.subjectConsumptionen_US
dc.subjectRegressionen_US
dc.subjectRegionsen_US
dc.subjectImpacten_US
dc.titleForecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithmsen_US
dc.typeArticleen_US

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