Emerging new global soil governance structure in agrifood systems: Taking the “4 per 1,000” initiative as an example
Bibliographic record
Abstract
Food systems emit 21%-37% of the global greenhouse gases (GHGs). Soil degradation, accelerated by global warming, poses a threat to over 40% of the land surfaces, threatening food security. Keeping soils alive and healthy could not only play a part in food security, but also in sequestrating GHGs for climate mitigation. In 2015, the “4 per 1,000” Initiative was launched in Paris COP21, indicating that a “4‰” annual growth rate of the soil organic carbon sequestration could hold the temperature increase within 1.5°C−2°C. However, major GHG emitting countries haven't signed the 4‰ Initiative at national level. Political willingness need to be encouraged though institutional innovations in the global soil governance (GSG). This article conducts a comprehensive policy review for the 4‰ Initiative and attempts to develop the concept of global soil governance from an aspect of New Common But Differentiated Responsibility. The SOC sink targets reveal that countries like China, India, the UK, the US, and France take more pains than those like Australia, Russia, and Canada. A new “soil carbon rich” and “soil carbon poor” divide is perceived, which needs to be taken into the GSG as a restructuring motivation for setting a more practical and integrated framework. In that sense, some developed countries face similar challenges as the developing countries do, but could contribute more in finance and technology. Bandwagon of applying sustainable agricultural land management (SALM) methodology in carbon markets implies that soil-climate co-benefits get greater practical momentum with quantified trading platforms, which may stimulate potentiality if embodied in Article 6 of the Paris Agreement.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".