  This is a study on the potential widespread usage of alternative fuel
vehicles, linking them with the socio-economic status of the respective
consumers as well as the impact on the resulting air quality index. Research in
this area aims to leverage machine learning techniques in order to promote
appropriate policies for the proliferation of alternative fuel vehicles such as
electric vehicles with due justice to different population groups. Pearson
correlation coefficient is deployed in the modeling the relationships between
socio-economic data, air quality index and data on alternative fuel vehicles.
Linear regression is used to conduct predictive modeling on air quality index
as per the adoption of alternative fuel vehicles, based on socio-economic
factors. This work exemplifies artificial intelligence for social good.
