Examining Public Concern about Global Warming and Climate Change in China
Bibliographic record
Abstract
Abstract To what degree are Chinese citizens concerned about the seriousness of global warming and climate change (GWCC) and what are the key factors that shape their concern? Drawing theoretical insights from extant literature and using recent data from a national representative public survey (N = 3,748) and provincial environmental and economic statistics, this study, the first of its kind, examines the variations and determinants of Chinese GWCC concern. Our data show that in China, compared to other countries, average public concern about GWCC is relatively low, and concern varies greatly among Chinese citizens, across different provinces and between coastal and inland areas. Statistical analyses reveal that the levels of Chinese GWCC concern are significantly influenced by individual sociodemographic characteristics, personal post-materialist values, and regional economic dependency on carbon-intensive industries. Specifically, women and younger Chinese with greater post-materialist values are more concerned about GWCC than their counterparts, and citizens from provinces with higher economic dependency on carbon-intensive industries tend to be less concerned about GWCC than people from provinces with lower carbon dependency. We discuss key policy implications and make suggestions for future research in the conclusion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".