Trends and dynamics of philanthropic funding for biodiversity conservation in China
Bibliographic record
Abstract
Abstract Implementation and funding mechanisms to reverse biodiversity loss formed the core of the discussion focusing on the Post‐2020 Global Biodiversity Framework (“the Framework”), at the 15th Conference of Parties at the Convention on Biological Diversity hosted by China. Before financial support emerged from the private sector in China, biodiversity conservation had primarily been financed by the government. By the end of the 20th‐century international nongovernmental organizations and China's local philanthropists began to launch pilot programs in the country. In the past 5 years, biodiversity conservation across China has received CNY 1.757 billion (approximately $279 million) from the philanthropy sector. It represents the largest‐ and fastest‐growing share (69%) of environmental philanthropic funding; however, it accounted for <1% of all the philanthropic in all sectors nationwide. We suggested Foundations and NGOs review and adjust their strategies to align with the Kunming‐Montreal global biodiversity framework. Proactive connection and engagement with the philanthropies is required to expand its contributions while providing better pathways and support mechanisms for philanthropic funding for biodiversity conservation. Despite the philanthropic funding provided has been relatively modest over the past few decades, the philanthropic organizations have achieved significant positive results for biodiversity conservation in China. However, the funding for biodiversity conservation falls far short of what is needed to achieve the goals under the Kunming‐Montreal global biodiversity framework. This study provides a comprehensive overview of biodiversity philanthropic funding in China. Based on the collection of data related to environmentally relevant grants provided by companies, foundations, and individuals, we conducted a visualization analysis to reveal China's philanthropic funding flows between 2016 and 2020 in China. The profiles of donors and the receipts of the funding have been described.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".