A study on the estimation of fuel consumption and emitted emissions from vehicles in Turkey until 2050

Year 2021, , 118 - 125, 09.11.2021

Ahmet Rasim Girişen , Hakan Özcan , Abdülvahap Çakmak , Bilal Genez

https://doi.org/10.18245/ijaet.815450

Cited By: 1

Abstract

In this study, the number of vehicles, fuel consumption, and emissions was estimated in two scenarios by using the logistic model until 2050. The fuel consumption estimations were studied in three groups as diesel, LPG, and gasoline. Also, diesel vehicles were inspected in three groups as passenger cars, light-duty, and heavy-duty vehicles. The annual mileage values of the vehicles and the average fuel consumption per 100 km have been calculated by considering similar studies and statistics. Future estimates of the number of vehicles have been made considering that the percentage of diesel vehicles will decrease in the next few years due to the prohibition of diesel vehicles in specific regions and the increasing demand for electric cars and hybrid vehicles. Emission estimates were made by considering the European Emission Standart and future expectations of Euro norms. The total fuel consumption was estimated as 10347 million liters (ML) gasoline, 50978 ML diesel, 12767 ML LPG, and 9390 ML gasoline, 45171 ML diesel, and 11568 ML LPG, respectively by the normal and optimistic scenario for 2050. The results also show that the total fuel consumption in 2050 will increase by 2.75 and 2.46 times, respectively, for the normal and optimistic scenario compared with fuel consumption in 2018. When emission estimates are examined, the increase in the number of electric/hybrid vehicles will reduce the emission amounts. If the two scenarios are compared for 2050, it is seen that the amount of CO2 in scenario B is 11.39% less than the amount in scenario A. In this case, the presence of 9.6% electric/hybrid vehicles in the market in the next 30 years brings about 11% less greenhouse gas emissions from vehicles.

Keywords

Emisions, Vehicle numbers, Fuel Consumptions, Predictions, Logistic Model

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