An Ecological Compensation Model for Liuxi River Basin Based on Emission Rights
Bibliographic record
Abstract
Researches on standards of ecological compensation will raise awareness of protecting the ecological environment in the basin and maintain the economy’s and society’s sustainable development. This paper constructed an ecological compensation model on the Liuxi River Basin in Guangzhou, China. Data and statistics were acquired from the statistical yearbook in Guangzhou Statistical Information Network, including the total GDP, total wastewater discharge amount and total population of Guangzhou for 13 years (1995, 2000, 2005–2015). Then, SPSS was used to fit the scatter plot of volume of wastewater discharged per capita and GDP per capita, and the most accurate regression equation was selected. Most importantly, an ecological compensation model was constructed based on emission rights and it was then used to calculate the annual eco-compensation fee for Conghua, where the river’s upper part is located. The results showed that the amount of ecological compensation in 2017 was 33.821 billion Chinese Yuan, which should be used to compensate Conghua for the emission rights it had given up for protecting water quality of the Liuxi River. This study provided an effective reference to the government’s decision on continuous improvement of the “Guangzhou Ecological Compensation Plan for Liuxi River Basin”.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".