Estimation for Reduction Potential Evaluation of CO 2 Emissions from Individual Private Passenger Cars Using Telematics

Masahiro Mae (), Ziyang Wang, Shoma Nishimura and Ryuji Matsuhashi
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Masahiro Mae: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan
Ziyang Wang: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan
Shoma Nishimura: Department of Digital Business Design, Aioi Nissay Dowa Insurance Co., Ltd., Tokyo 150-8488, Japan
Ryuji Matsuhashi: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan

Energies, 2024, vol. 18, issue 1, 1-17

Abstract: CO 2 emissions from gas-powered cars have a large impact on global warming. The aim of this paper is to develop an accurate estimation method of CO 2 emissions from individual private passenger cars by using actual driving data obtained by telematics. CO 2 emissions from gas-powered cars vary depending on various factors such as car models and driving behavior. The developed approach uses actual monthly driving data from telematics and vehicle features based on drag force. Machine learning based on random forest regression enables better estimation performance of CO 2 emissions compared to conventional multiple linear regression. CO 2 emissions from individual private passenger cars in 24 car models are estimated by the machine learning model based on random forest regression using data from telematics, and the coefficient of determination for all 24 car models is R 2 = 0.981 . The estimation performance for interpolation and extrapolation of car models is also evaluated, and it keeps enough estimation accuracy with slight performance degradation. The case study with actual telematics data is conducted to analyze the relationship between driving behavior and monthly CO 2 emissions in similar driving record conditions. The result shows the possibility of reducing CO 2 emissions by eco-driving. The accurate estimation of the reduced amount of CO 2 estimated by the machine learning model enables valuing it as carbon credits to motivate the eco-driving of individual drivers.

Keywords: CO 2 emissions; car fuel consumption; estimation; machine learning; driving data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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