Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector | Science and Technology for Energy Transition (STET)

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Table 2

Input parameters and their effects on the output parameters, along with data source used in this study.

Item	Effects on the output parameters	Source
Year	In light of historical data, it is understood that parameters such as energy demand and the number of vehicles will increase for countries.	–
Vehicle kilometer¹	This parameter depends on the number of registered vehicles in the country. This value has increased over the years. For this reason, it is predicted that both energy consumption and CO₂ emissions will increase, depending on the number of vehicles in the transportation sector.	[52]
Population	It is a well-known fact that with the increase in population, the demand of countries for energy will increase. Therefore, it can be stated that, as a result of the increase in population, there will be an increase in the number of vehicles in the transportation sector and, accordingly, in the emission level.	[50]
Gross domestic product per capita	It is predicted that there is a high correlation between the CO₂ emissions originating from the transportation sector of the countries and the amount of production. It is also known that changes in the GDP parameter have a very significant effect on emissions.	[50]
Transportation energy consumption/demand	The amount of energy used in the transportation sector means the fuel used in vehicles. The increase in this value is directly related to the CO₂ emissions that occur as a result of combustion in the engines. The increase in the amount of energy originating from the transportation sector over the years means an increase in CO₂ emissions.	[50]

Trucks (tankers and road tractors are also included), small trucks, buses, minibusses, passenger cars, and motorcycles are included. Administrative records collected from the odometers of vehicle inspection stations are computed.

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