All Electric cars Will be Cheaper than Regular Vehicles by 2022
By 2022, it is believed that it will be cheaper to own an electric car than a petrol one. Battery prices are dropping at a good rate, and this will enhance the market according to Bloomberg New Energy Finance.
By 2022, it is believed that it will be cheaper to own an electric car than a petrol one. Battery prices are dropping at a good rate, and this will enhance the market according to Bloomberg New Energy Finance.
The increase in electric vehicles is seen as a boost to the environment as a result of cutting back air pollution and cutting back on carbon emissions. Even though a lot of countries subsidies the use of electric cars, they are still at present the more expensive option, with only around 1% of new vehicles being run this way.
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The report published by BNEF will still show it to be cheaper to drive an electric car even if petrol and diesel cut cost by 3.5% every year. It is the projected oil prices that have led to the calculation of these figures and even though BNEF senior analyst Salim Morsy expects conventional car ownership to be cheaper for the next few years; the 2020/s will see significant changes.
Looking ahead to the 2040s; it is thought that 25% of cars with being all electric vehicles, and this would have a major effect on oil use and while past views have overestimated the growth of EV’s these figures are based on much more extensive data. There is also evidence that UK drivers are looking the same way as a survey from BMW showed that 20% of 2000 drivers would look at EV for their next purchase.
Story Via; The Guardian
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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