Research and Optimization of the Coupling and Coordination of Environmental Regulation, Technological Innovation, and Green Development
Bibliographic record
Abstract
Environmental regulation and technological innovation play important strategic roles in green growth, and the three systems interact and influence each other. Herein, we used a comprehensive development level evaluation model for calculating the environmental regulation and technological innovation indices for 17 cities in Shandong Province. We used the slack-based measure-data envelopment analysis (SBM-DEA) model to measure green development efficiency. The coupling coordination degree model was used to determine the coordination of environmental regulation, technological innovation, and green development; we divided the cities into three systems: green economy lagging, environmental regulation lagging, and technological innovation lagging. We used grey correlation analysis to explore the factors affecting system development. Eastern coastal cities were better developed in the three systems and the degree of coupling and coordination, like Qingdao and Weihai, and the observed level of technological innovation development, a critical factor in the coordinated development of cities, was lowest in Shandong Province. The grey correlation analysis illustrated that the level of economic development and the level of foreign economic development impacts cities labeled green economy lagging; the impact of pollutant emissions is greater than pollution control expenditure in environmental regulation lagging cities; and the government’s attention and the cultivation and attraction of talent are foundational for technological innovation in lagging cities. Considering these factors, we make recommendations for the optimal development of cities and coordinated development of regions.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".