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

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

Inspired by Labov's seminal work on stylistic variation as a function of
social stratification, we develop and compare neural models that predict a
person's presumed socio-economic status, obtained through distant
supervision,from their writing style on social media. The focus of our work is
on identifying the most important stylistic parameters to predict
socio-economic group. In particular, we show the effectiveness of
morpho-syntactic features as stylistic predictors of socio-economic group,in
contrast to lexical features, which are good predictors of topic.

研究社会阶层的影响因素，使用神经模型预测社交媒体上个人的社会经济地位，探讨语言风格在预测社会经济群体方面的作用，发现新颖的词法 - 句法特征是一种有效的风格预测因素。