Evaluating ecosystem services and disservices of bamboo forest using the emergy-based method
Bibliographic record
Abstract
Bamboo forests are widely distributed in southern China and have expanded rapidly in recent years. However, uncertainties remain in estimating ecosystem services (ESs) and disservices due to a lack of standardized accounting frameworks. This study introduces a non-monetary evaluation method for the ESs of bamboo forest and categorizes the integrated valuation framework into four components: growing costs, ESs, needed costs for human health and biodiversity damage, and disservices. In the case of the bamboo forest ecosystem in different cities in Sichuan, three types of bamboo forests are selected for service/disservice valuation, including the intercropped bamboo forest (IBF), grain-for-green bamboo forest (GFGB), and natural bamboo forest (NBF). In the same way, the relationships among the three key component flows in bamboo forest ecosystems (input costs, ESs, and related disservices) are compared through a ternary diagram. Our study reveal that: (i) the estimated ESs is ∼4.97 E+23 sej yr −1 , with the IBF and GFGB contributing ∼86.09% of the total service value; (ii) the top ten cities in Sichuan in terms of ESs per unit area of bamboo forest are Meishan, Zigong, Yibin, Guangan, Neijiang, Luzhou, Ziyang, Leshan, Chengdu, and Suining which together contribute 90.00% of the total ESs; and (iii) the IBF has the highest ESs, followed by the NBF and GFGB. Our findings will deliver valuable guidance for policymakers, especially about climate change mitigation and sustainable forest management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".