Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index

TL;DR

This study uses machine learning to analyze how socio-economic factors influence the adoption of alternative fuel vehicles and their impact on air quality, aiming to inform equitable policy development.

Contribution

It introduces a machine learning framework linking socio-economic status, vehicle adoption, and air quality, providing insights for policy-making to promote sustainable transportation.

Findings

Positive correlation between socio-economic status and alternative fuel vehicle adoption

Predictive model of air quality index based on vehicle adoption and socio-economic data

Artificial intelligence application for social good in environmental policy

Abstract

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.