The True Valuation of Land Use Project in China Considering Ecosystem Services
Bibliographic record
Abstract
Nowadays, most land use projects disregard the impact of their decisions on the biosphere. However, they often directly affect the biodiversity and lead to environmental degradation. Considering this issue, we first select indexes from four dimensions: provisioning, regulating, supporting and culture, and establish a static ecosystem services valuation (SESV) model using equivalent factor method. Then, introducing dynamic adjustment coefficient and current discount rate, we construct a dynamic ecosystem services valuation (DESV) model, which is used to put a value on the environmental cost of land use projects. Moreover, we use entropy method to calculate the weights of the economic benefit indexes we choose and establish an economic benefit (EB) model to determine project benefit. After that, we select Canglong District Development Project in Wuhan and Three Gorges Project in China as a small community-based project and a large national project respectively to perform a cost benefit analysis using the models. As the results, compared to small-scale project, large-scale project can greatly increase the economic benefit, but at the same time, it notably increases the environmental degradation cost. Effectiveness analysis proves that the results are effective and reliable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".