Purpose This study aims to examine how climate change could be mitigated by the adoption of electric vehicle (EV) and the use of renewable energy sources. Design/methodology/approach A dynamic panel model with a Generalized Methods Moments technique is used to analyze panel data from 30 countries between 2010 and 2022 to account for endogeneity issues. The study further used quantile regression to analyze the distributional effect based on low medium and high emitting countries. the percentage of energy derived from fossil fuels and CO2 emissions per capita are proxies for climate change to capture energy consumption and emissions patterns. Findings According to the findings, both EV adoption and renewable energy consumption influence negatively greenhouse gas emission and per capita CO2 emission. However, the distributional effect using quantile regression suggest that the effect of EV adoption on climate change is negative and significant only in the category of countries with high CO2 emissions, while in low-emission countries, the effect is positive, meaning that it could lead to an increase in emissions. As for renewable energy consumption, its effect remains negative and significant in all conditional distributions of per capita CO2 emissions. Research limitations/implications The findings lead us to recommend legislative modifications that will promote the continued development of renewable energy and the adoption of electric cars for the purpose of mitigating climate change. Policy design has to be location-specific. For high-emitting countries, support for EV deployment can be extremely effective, especially if backed by renewable electricity. In low-emitting countries, however, the marginal climate value of EVs is not so evident, and policy focus needs to shift toward electrifying the public transport sector, promoting energy efficiency and avoiding a dirty grid lock-in. Originality/value This study demonstrates how combining the adoption of EVs with renewable energy sources can mitigate the negative effects of climate change. It also demonstrates the distributional effect based on emissions level using quantile regression.
Hamadou et al. (Tue,) studied this question.