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.

研究替代燃料车的潜在广泛使用，将其与相应消费者的社会经济地位以及对空气质量指数的影响联系起来，旨在利用机器学习技术促进适当的政策推广替代燃料车，如电动汽车，并公正对待不同人口群体。该研究通过使用Pearson相关系数模型分析了社会经济数据、空气质量指数和替代燃料车的数据之间的关系。基于社会经济因素，采用线性回归对替代燃料车的采用情况进行预测建模，以此对空气质量指数进行预测建模。这项工作是为了社会福祉而展示了人工智能的优秀应用。

将替代燃料车辆采用与社会经济地位和空气质量指数相联系