Complex Systems Method Applied to Identify Carbon Dioxide Emission Reductions for Light-Duty Vehicles for the 2020-2025 Timeframe

2012 ◽  
Author(s):  
Anrico F. Casadei ◽  
Henry H. Huang ◽  
Felipe Brandao ◽  
John J. Kasab ◽  
Wayne A. Thelen ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Complex Systems ◽  
Carbon Dioxide Emission ◽  
Emission Reductions ◽  
Light Duty ◽  
Light Duty Vehicles

Are carbon dioxide emission reductions compatible with sustainable well-being?

2019 ◽  
Vol 242 ◽  
pp. 1-11 ◽  
Author(s):  
Yogi Sugiawan ◽  
Robi Kurniawan ◽  
Shunsuke Managi
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emission ◽  
Well Being ◽  
Emission Reductions

scholarly journals Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

2022 ◽  
Vol 12 (2) ◽  
pp. 803
Author(s):  
Ngo Le Huy Hien ◽  
Ah-Lian Kor
Keyword(s):  
Carbon Dioxide ◽  
Fuel Consumption ◽  
Carbon Dioxide Emissions ◽  
Polynomial Regression ◽  
Control Strategies ◽  
Future Research ◽  
Light Duty ◽  
The Government ◽  
Government Of Canada ◽  
Light Duty Vehicles

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.

The potential for electricity conservation and carbon dioxide emission reductions in the household sector of Brazil

2008 ◽  
Vol 2 (2) ◽  
pp. 165-178 ◽  
Author(s):  
Roberto Schaeffer ◽  
Claude Cohen ◽  
Anna Cecília J. de Aguiar ◽  
Glaucio V. R. Faria
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emission ◽  
Emission Reductions ◽  
Household Sector ◽  
Electricity Conservation
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