Synergies and differences between the China National Biodiversity Conservation Strategy and Action Plan (2023‒2030) and the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Background & Aims: Developing a National Biodiversity Strategy and Action Plan (NBSAP) is an important implementation mechanism under the United Nations Convention on Biological Diversity.To maintain consistency with the global targets of the Kunming-Montreal Global Biodiversity Framework (KMGBF) and provide an updated guidance to domestic conservation and sustainable use of biodiversity, China officially released the China National Biodiversity •保护与治理对策• 昆蒙框架如何在中国体制下成为主流工作目标专题 姜雪原等: 《中国生物多样性保护战略与行动计划(2023-2030年)》与《昆蒙框架》的协同与差异 2025年 | 33卷 | 3期 | 24575 | 第2页Conservation Strategy and Action Plan (2023-2030) (China's updated NBSAP) in January 2024, which is one of the first parties to submit an update in accordance with the KMGBF.We compared the action-oriented targets of China's updated NBSAP and the KMGBF to understand their synergies and differences.Based on the core elements of global targets, the degree of synergy between the two is divided into four categories, according to the coverage of core elements.Findings: This paper reveals that the China's updated NBSAP aligns strategically with the KMGBF, however China has adapted its approach by proposing phased objectives and guiding principles tailored to its national context.At the action level, both documents demonstrate high overall synergy in their targets, though differences emerge in quantitative benchmarks, priority-setting, action pathways, and core elements.The alignment between the China's updated NBSAP and the KMGBF can enhance China's biodiversity governance system and foster international convergence in biodiversity practices.While their disparities highlight gaps in China's current governance framework, they also offer globally relevant insights.Notably, the "ecological product value realization" initiative exemplifies China's innovative approach to reconciling conservation and development.By establishing a system to quantify and trade the diverse services and products derived from biodiversity, this mechanism incentivizes sustainable natural resource use by markets and businesses, channels funding toward conservation, and advances biodiversity mainstreaming in economic systems-presenting a unique solution.Building on this analysis, the paper proposes recommendations to optimize the implementation of China's updated NBSAP, aiming to support both domestic execution and the global review process of the KMGBF's progress.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".