Progress in the researches on the Economics of Ecosystems and Biodiversity (TEEB)
Bibliographic record
Abstract
The Economics of Ecosystems and Biodiversity (TEEB), which provides new insight and approaches for biodiversity conservation and sustainable use, is an integrated approach to assess, demonstrate, and apply policy for biodiversity and ecosystem value.TEEB was firstly proposed in 2007, and has been supported by United Nations Environment Programme (UNEP) since 2008.Ecosystem services include supply services, regulating services, cultural services, and habitat services based on the TEEB framework.The value evaluation methods generally include the direct market value method, revealed preference method and stated preference method.We also summarized the measures to mainstream biodiversity at the global, regional, national and local levels.Presently, more than 30 countries have undertaken studies on TEEB and have produced positive impacts on policy-making and further application of TEEB.For example, at the country level, it can be used to green economy, sustainable development and corporate green management.At the international level, it can support the implementation of the Convention of Biological Diversity and other relevant international action.For the future, this paper suggested TEEB's focuses: (1) At the international level, it is needed to enhance cross-sector and inter-regional cooperation in biodiversity and promote findings at the science-policy interface; (2) In China, it is needed to build TEEB methodology from the sub-levels (ecosystem, species and gene) and sub-scales (national, provincial and local), and explore the application of TEEB concepts in local development assessment, cadre performance appraisal, paying utilization of natural resources, ecological compensation and other policies in order to promote regional equity and sustainable use of natural resources.•综述•
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".