Principal Component Regression to Study the Impact of Economic Factors on Disadvantaged Communities

Abstract

The Council on Environmental Quality’s Climate and Economic Justice Screening Tool defines “disadvantaged communities” (DAC) in the USA, highlighting census tracts where benefits of climate and energy investments are not accruing. We use a principal component generalized linear model (PCGLM), which addresses the intertwined nature of economic factors, income and employment and model their relationship to DAC status. Our study (1) identifies the most significant income groups and employment industries that impact DAC status (2) provides the probability of DAC status across census tracts and compares the predictive accuracy with widely used machine learning (ML) approaches, (3) obtains historical predictions of the probability of DAC status, (4) obtains spatial downscaling of DAC status across block groups. Our study provides valuable insights for policymakers and stakeholders to develop strategies that promote sustainable development and address inequities in climate and energy investments in the USA.

Keywords

• disadvantaged communities
• justice40
• principal component generalized linear model
• socio-economic challenges
• spatial downscaling
• temporal trend