Realizing the values of natural capital for inclusive, sustainable development: Informing China’s new ecological development strategy
Bibliographic record
Abstract
A major challenge in transforming development to inclusive, sustainable pathways is the pervasive and persistent trade-off between provisioning services (e.g., agricultural production) on the one hand and regulating services (e.g., water purification, flood control) and biodiversity conservation on the other. We report on an application of China's new Ecological Development Strategy, now being formally tested and refined for subsequent scaling nationwide, which aims to mitigate and even eliminate these trade-offs. Our focus is the Ecosystem Function Conservation Area of Hainan Island, a rural, tropical region where expansion of rubber plantations has driven extensive loss of natural forest and its vital benefits to people. We explored both the biophysical and the socioeconomic options for achieving simultaneous improvements in product provision, regulating services, biodiversity, and livelihoods. We quantified historic trade-offs between rubber production and vital regulating services, finding that, over the past 20 y (1998-2017), there was a 72.2% increase in rubber plantation area, leading to decreases in soil retention (17.8%), water purification [reduced retention of nitrogen (56.3%) and phosphorus (27.4%)], flood mitigation (21.9%), carbon sequestration (1.7%), and habitat for biodiversity (6.9%). Using scenario analyses, we identified a two-pronged strategy that would significantly reduce these trade-offs, enhancing regulating services and biodiversity, while simultaneously diversifying and increasing product provision and improving livelihoods. This general approach to analyzing product provision, regulating services, biodiversity, and livelihoods has applicability in rural landscapes across China, South and Southeast Asia, and beyond.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".