Research on resource allocation optimization based on multilevel model in green economy driven by data elements
Bibliographic record
Abstract
Green economy is an important factor to measure the quality of economic development.In order to explore the current green economy resource allocation, this paper embeds methods such as DEA-Malmquist model and Tobit regression model into the study of green economy resource allocation, explores the green economy resource allocation efficiency of 30 provinces in China by constructing a multilevel model of green economy resource allocation, and analyzes China's green economy during the period of 2021-2023 through the results of the measurement of the Static, dynamic and level changes of resource allocation efficiency.Tobit regression analysis of the influencing factors of green economy resource allocation efficiency is carried out to optimize the current resource allocation based on the influencing factors.The green economy resource allocation efficiency increases year by year in 2021-2023, and the resource allocation effect improves continuously, with the mean value of the comprehensive efficiency of 0.712, 0.762, and 0.809, respectively.The green economy resource allocation efficiency in Beijing, Shanghai, Jiangsu, and Zhejiang is the highest, and the allocation structure is the most reasonable.Chongqing, Gansu, Qinghai, Ningxia and Xinjiang are less efficient in green economy resource allocation.The per capita GDP and the ratio of education expenditure to GDP have a positive impact on the effect of green economy resource allocation, with an impact of 1.246 and 0.489, respectively.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".