Abstract Number:
3778
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Narmadha Mohankumar (1), Milan Jain (2), Heng Wan (1), Sumitrra Ganguli (2), Kyle Wilson (2), David Anderson (2)
Institutions:
(1) Pacific Northwest National Laboratory, Seattle, WA, (2) Pacific Northwest National Laboratory, Richland, WA
Co-Author(s):
Heng Wan
Pacific Northwest National Laboratory
First Author:
Presenting Author:
Abstract Text:
The Council on Environmental Quality's Climate and Economic Justice Screening Tool defines "disadvantaged communities" (DAC) in the USA, highlighting where benefits of climate and energy investments are not accruing. Understanding the impact of economic factors such as income and employment on DAC is crucial for addressing economic well-being and reducing inequalities. However, investigating the individual impacts of income and employment categories is challenging due to their highly intercorrelated nature, influenced by numerous hidden factors. To address this, we employ principal component generalized linear regression to model their relationship to DAC status. We (1) identify the significant income groups and employment industries impacting DAC status, (2) predict DAC distribution spatially across census tracts, comparing predictive accuracy with conventional machine learning methods, (3) project historical DAC probabilities, and (4) spatially downscale DAC 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|Socio-economic challenges|Principal component generalized linear model|Spatial distribution|Spatial downscaling|Temporal trend
Sponsors:
Section on Statistics and the Environment
Tracks:
Environmental Policy and Regulations
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