Practical Mechanism Design and Strategic Choice of China’s Industry Green Transformation: Base on the Perspective of Game Theory
Bibliographic record
Abstract
Nowadays, green economy has become the trend of world, and an industrial revolution as the core of green development has emerged. China’s industry experiences 30 years development, the resources and environment problems highlight increasingly, so it needs to transform into green development. However, both academic and practice do not have perfect practical mechanism of green transformation, and is also lack of strategic support system. Given this, Firstly, the paper researches the interactive relationship among main bodies in china’s industry green transformation by using the game theory; Secondly, on basis of the game results, focusing on different bodies’ main responsibilities and interests, we design the practical industry green transformation mechanism which taking the government as leading body, the industry circle & industrial enterprises & public as executing body, and the financial institution & transaction center & scientific research institute etc. as supporting body; Finally, in order to establish the policy framework, we propose “six in one” strategic choice of industry green transformation from government, region, industry, enterprise, society and individual’s view.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".