Thirteenth International Conference on Climate Change: Impacts & Responses

Abstract

In democratic societies, efforts to mitigate climate change must first undergo the legislative process. Campaign contributions from both pro-environment and anti-environment groups have been known to affect the way members of the United States Congress vote on climate and environment-related bills. We use a random forest model to predict the environmental voting records of members of Congress from the donations they received from relevant organizations. We use campaign finance data from The Center for Responsive Politics and assess voting records with The League of Conservation Voters’ National Environmental Scorecard. Our work proposes a novel machine learning method to approach this problem. Our research seeks to gain insight into the impact of political contributions on the ability of Congress to pass environmental legislation of all types, particularly that directly related to confronting climate change.