Abstract

The economic and health impacts resulting from the greenhouse effect is a major concern in many countries. The transportation sector is one of the major contributors to greenhouse gas (GHG) emissions worldwide. Almost 15 percent of the global GHG and over 20 percent of energy-related CO2 emissions are produced by the transportation sector. Quantifying GHG emissions from the road transport sector assists in assessing the existing vehicles’ energy consumptions and in proposing technological interventions for enhancing vehicle efficiency and reducing energy-supply greenhouse gas intensity. This paper aims to develop a model for the projection of GHG emissions from the road transport sector. We consider the Vehicle-Kilometre by Mode (VKM) to Number of Transportation Vehicles (NTV) ratio for the six different modes of transportation. These modes include motorcycles, passenger cars, tractors, single-unit trucks, buses and light trucks data from the North American Transportation Statistics (NATS) online database over a period of 22 years. We use multivariate regression and double exponential approaches to model the projection of GHG emissions. The results indicate that the VKM to NTV ratio for the different transportation modes has a significant effect on GHG emissions, with the coefficient of determination adjusted R2 and R2 values of 89.46% and 91.8%, respectively. This shows that VKM and NTV are the main factors influencing GHG emission growth. The developed model is used to examine various scenarios for introducing plug-in hybrid electric vehicles and battery electric vehicles in the future. If there will be a switch to battery electric vehicles, a 62.2 % reduction in CO2 emissions would occur. The results of this paper will be useful in developing appropriate planning, policies, and strategies to reduce GHG emissions from the road transport sector.

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The different versions of the original document can be found in:

https://doaj.org/toc/2071-1050
https://www.mdpi.com/2071-1050/12/21/9152/pdf,
https://ideas.repec.org/a/gam/jsusta/v12y2020i21p9152-d439546.html,
https://academic.microsoft.com/#/detail/3096740167
http://dx.doi.org/10.3390/su12219152 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.3390/su12219152
Licence: Other

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