Biodiversity conservation and ecosystem restoration to meet the Kunming-Montreal Global Biodiversity Framework and satisfy climate goals of the Paris Agreement
Bibliographic record
Abstract
The remaining carbon budget for curbing climate change according to the Paris Agreement can be depleted within only less than a decade or at most a few decades if the current emissions trend continues. Many ideas and policies have been proposed to reduce carbon emissions. However, the importance of biodiversity conservation and ecosystem restoration has not been properly empirically underscored.According to the latest Living Planet Report, between 1970 and 2018, the average abundance of 31,821 populations of 5,230 species monitored worldwide declined by 69% (63–75%). When living organisms die and decompose, they can only increase CO₂ in the atmosphere or further acidify the oceans. Therefore, maintaining an abundance of living organisms can help global climate action.And while plants are the largest reservoirs of carbon (450 billion tonnes), they are not the only ones. For example, bacteria (70 billion tonnes) and fungi (12 billion tonnes) contain far more carbon than the entire animal kingdom (2 billion tonnes). If we can increase or at least conserve the biomass of living organisms, we can maintain living carbon stocks, avoiding additional carbon emissions from local extinctions or, in the long run, extinction of the species itself. That’s why biodiversity conservation and ecosystem restoration could fill in the last blank on achieving carbon neutrality.In this regard, this study investigates how environmental degradation as well as changing land and ocean use disrupt the global carbon cycle from the conservation biology perspective.Using the levels of success to meet the goals and targets of the Kunming-Montreal Global Biodiversity Framework (GBF) adopted by 196 parties of the Convention on Biological Diversity in December 2022, this study estimates the expected carbon storage gains. Policy implications in relation to the GBF and the Enhanced Transparency Framework of the Paris Agreement are also discussed.Acknowledgement:This research was supported by the Core Research Institute Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A10045235).
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".