Electoral appeal of climate policies: The Green New Deal and the 2020 U.S. House of Representatives elections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate issues widely feature in policy discussions, but it is not clear if voters reward politicians who champion climate policies. In some countries, candidates and parties with an explicit climate agenda have done well in elections (Switzerland and Germany being recent examples) while in other cases, voters have either ignored climate issues or punished candidates/parties for their climate positions (Australia, the U.K., and Canada). Focusing on the U.S. as a case study, we examine the electoral appeal of the Green New Deal (GND) legislative proposal which outlined a vision for a sustainable and equitable economy. Different versions of the GND policy idea have been adopted across the world. The GND was introduced in the US Congress in 2019 and was endorsed by 102 of the 232 House Democrats, but not by a single Republican. Our analysis finds an association between Democrats’ endorsement of the GND and a 2.01 percentage point increase in their vote share, even after controlling for the 2018 vote share. Unlike most western democracies, the U.S. is a laggard on climate issues. Yet, we find that U.S. voters reward legislators who advocate an ambitious climate policy agenda.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it