Effect of Battery Electric Vehicles on Greenhouse Gas Emissions in 29 European Union Countries

José Alberto Fuinhas,Matheus Koengkan,Stefania Relva,Chinazaekpere Nwani,Drielli Peyerl,Nuno Carlos Leitão,Gizem Uzuner,Fatemeh Dehdar

doi:10.3390/su132413611

Abstract

This analysis explored the effect of battery electric vehicles (BEVs) on greenhouse gas emissions (GHGs) in a panel of twenty-nine countries from the European Union (EU) from 2010 to 2020. The method of moments quantile regression (MM-QR) was used, and the ordinary least squares with fixed effects (OLSfe) was used to verify the robustness of the results. The MM-QR support that in all three quantiles, economic growth causes a positive impact on GHGs. In the 50th and 75th quantiles, energy consumption causes a positive effect on GHGs. BEVs in the 25th, 50th, and 75th quantiles have a negative impact on GHGs. The OLSfe reveals that economic growth has a negative effect on GHGs, which contradicts the results from MM-QR. Energy consumption positively impacts GHGs. BEVs negatively impacts GHGs. Although the EU has supported a more sustainable transport system, accelerating the adoption of BEVs still requires effective political planning to achieve net-zero emissions. Thus, BEVs are an important technology to reduce GHGs to achieve the EU targets of decarbonising the energy sector. This research topic can open policy discussion between industry, government, and researchers, towards ensuring that BEVs provide a climate change mitigation pathway in the EU region.

Highlights

The variable LnBEVs in the 25th, 50th, and 75th quantiles result in a negative impact on the variable LnGHGs, meaning that the battery electric vehicles are capable of mitigating greenhouse gas emissions (GHGs) emissions
The estimation results applying the ordinary least squares (OLS) model with fixed effects indicated that the variable LnGDP has a negative impact on the variable LnGHGs; it is possible to conclude that economic development mitigates the emissions of GHGs
The moments quantile regression (MM-QR) was used as the main model, while the OLS with fixed effects was used to verify the robustness of the results

Summary

Introduction

The arguments for climate protection have never been so convincing as now. Climate change is endangering the lives of millions of people and threatening many aspects of the human economy [1]. The transport sector is expected to be a crucial part of the solution: a sector that can help reduce greenhouse gas (GHG) emissions, including carbon dioxide (CO2 ) and non-CO2 gases such as methane (CH4 ), nitrous oxide (N2 O), partially fluorinated hydrocarbons (HFC), perfluorinated hydrocarbons (PFC), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3). To a greater or lesser extent, these gases are harmful to the environment as they trap heat in the atmosphere, causing global warming [2]

Methods

Results

Conclusion