From fabrication to consolidation of China's political blue‐sky: How can environmental regulations shape sustainable air pollution governance?
Bibliographic record
Abstract
Abstract Political blue sky is unsustainable because conflicting interests in China's fragmented authoritarianism (FA) lead to the failure of air pollution regulations. This study explores how to transform China's political blue sky from short‐term fabrication to long‐term consolidation by effective environmental regulations. A two‐pronged mechanism design in the non‐cooperative tripartite evolutionary game is employed to model the dynamic cost–benefit strategy interactions among the central government, local governments, and polluting enterprises for sustainable air pollution control. Policy simulations are conducted to examine the effectiveness of several environmental regulation instruments for leading to the ideal outcome. In baseline scenarios, proper coordination of environmental inspection, vertical and horizontal transfers, and environmental taxation can impel the game to converge to the desired evolutionary equilibrium. In extended scenarios, three long‐term oriented inspirations—double‐dividend effect of environmental tax revenues, public monitoring, and the Porter hypothesis effect of environmental regulations—can improve the efficiency of environmental regulations compared to baseline scenarios. From a methodology perspective, policy simulation in an evolutionary game framework provides a novel addition to the research toolkit for addressing FA.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".