Effects of the Implementation Intensity of Ecological Engineering on Ecosystem Service Tradeoffs in Qinghai Province, China
Bibliographic record
Abstract
Ecological engineering (EE) has a profound impact on land-use dynamics, leading to alterations in ecosystem services (ESs). However, an appropriate EE implementation intensity that can balance the tradeoffs associated with altered ESs well has always been a concern for researchers and policymakers. In this study, we set the transition probability of farmland, bare land, and desertification land to forest and natural shrub, with 2010–2020 as the natural implementation scenario, as 10% for the low-intensity implementation scenario (LIS), 30% for the medium-intensity scenario, and 50% for the high-intensity scenario. The patch-generating land-use simulation (PLUS) model was used to project land-use patterns and the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model was used to simulate changes in the quality of ESs under four EE implementation intensities in 2030. We then performed a quantitative tradeoff analysis on the dominant ESs under four scenarios and used the production possibility frontier (PPF) curve to identify the optimal EE implementation intensity scenario. Our results indicated that an increase in EE implementation intensity would lead to an increase in soil retention, water purification, habitat quality, and carbon storage, but also to a decrease in water yield, aggravating the tradeoffs between water yield and other ESs. In all EE implementation intensity scenarios, the LIS had the lowest tradeoff intensity index and balanced ESs well, and thus was the optimal EE implementation scenario in Qinghai province. Our results provide knowledge to help decision makers select the appropriate EE intensity to maintain sustainable development. The integrated methodology can also be applied in other conservation regions to carry out practical land management.
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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 | 0.000 | 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 | 0.000 |
| 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".