Polarisation of Climate and Environmental Attitudes in the United States, 1973-2022
Bibliographic record
Abstract
Abstract Since the early 1990s, increasing political polarisation is among the greatest determinants of individual-level environmental and climate change attitudes in the United States. But several patterns remain unclear: are historical patterns of polarisation largely symmetrical (equal) or is rather asymmetrical (where one set of partisans shifts more than others)? How have polarisation patterns have changed over time? How generalizable are polarization patterns across different environmental and climate change attitudes? We harmonised four unique sets of historical, pooled cross-sectional survey data from the past 50 years to investigate shifts across seven distinct measures of citizen environmental and climate change attitudes. We find that contemporary attitudes are polarised symmetrically, with Democrats (higher) and Republicans (lower) attitudes are equidistant from the median. But the historical trends in polarisation differ by attitudes and beliefs. In particular, we find evidence of two distinct historical patterns of asymmetric polarisation within environmental and climate change attitudes: first, with Republicans becoming less pro-environmental, beginning in the early 1990s, and second, a more recent greening of Democratic environmental attitudes since the mid-2010s. Notably, recent increases in pro-environmental attitudes within Democrats is a potentially optimistic finding, providing opportunities towards overcoming decades-long inertia in climate action. These findings provide a foundation for further research avenues into the factors shaping increased pro-environmental attitudes within Democrats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".