Progress in the researches on the Economics of Ecosystems and Biodiversity (TEEB)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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•
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it