Statistical Model for Estimating Carbon Dioxide Emissions from a Light-Duty Gasoline Vehicle

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.

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Introducing a New Emissions Certification Procedure for European Light-Duty Vehicles: Monte Carlo Simulation of the Potential Effect on Fleet Carbon Dioxide Emissions

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2572-08 ◽

2016 ◽

Vol 2572 (1) ◽

pp. 66-77 ◽

Cited By ~ 6

Author(s):

Stefanos Tsiakmakis ◽

Biagio Ciuffo ◽

Georgios Fontaras ◽

Konstantinos Anagnostopoulos ◽

Vincenzo Arcidiacono ◽

...

Keyword(s):

Carbon Dioxide ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Carbon Dioxide Emissions ◽

Potential Effect ◽

Light Duty ◽

Certification Procedure ◽

Light Duty Vehicles

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Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

Environmental and Climate Technologies ◽

10.2478/rtuect-2018-0007 ◽

2018 ◽

Vol 22 (1) ◽

pp. 107-117 ◽

Cited By ~ 3

Author(s):

Pruethsan Sutthichaimethee ◽

Danupon Ariyasajjakorn

Keyword(s):

Carbon Dioxide ◽

Energy Consumption ◽

Population Growth ◽

Statistical Model ◽

Data Model ◽

Carbon Dioxide Emissions ◽

Gdp Growth ◽

Input Output ◽

Minor Error ◽

Industry Sectors

Abstract The aim of this research is to forecast CO2emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate CO2emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in CO2emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.

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