A review of soil C accounting initiatives implemented in EU and extra-EU countries
Bibliographic record
Abstract
Soil is an ecological system and a phylogenetic organism that evolved in response to stimuli and changes. It is a precious, fragile, limited and non-renewable resource, since it takes 100 to 1,000 years to produce one centimetre of fertile soil. Soil constitutes the largest existing organic carbon store and, consequently, plays a central role in the global carbon cycle and in the fight against climate change.However, land degradation has progressed markedly around the world. In fact, studies show that about 33% of the world’s soils are moderately or strongly degraded. An estimated annual global loss of 75 billion tons of fertile soil is caused by erosion, pollution, unsustainable agronomic practices, change of use (e.g. deforestation or conversion from pasture to cultivated land) and sealing of land. More than 12.7% of EU soil is subject to moderate to severe erosion and degradation. Stocks of organic carbon in farmland and the extent of wetlands and peatlands are steadily decreasing. Moreover, carbon, in temperate and cold areas of the planet (such as the EU), is stored in greater quantities in the soil rather than in plants’ biomass, while in tropical areas the exact opposite occurs. Therefore, the protection of soil organic carbon is fundamental especially in Europe.To maintain and increase soil C stocks, agroecological practices should be fostered by European policies and financial mechanisms. Carbon credits represent one of these financial mechanisms, being tradable certificates corresponding to 1 ton of CO2eq. The methodologies and standards used for the quantification of soil Carbon stocks, aimed at the issuing of corresponding carbon credits are defined as soil C accounting.In this study, a detailed and critical analysis of soil C accounting initiatives implemented in EU and extra-EU countries has been conducted considering different scales of implementation, C assessment methods and potential barriers.More specifically, this work aims to:- in EU: describe the state of carbon accounting legislation and its level of implementation, case studies, relevant and successful EIP Operational Groups;- identify the critical issues inherent in Carbon Farming (e.g. payment schemes, overlapping of EU public fundings with carbon credits, etc.);- extra EU: report international case studies of carbon accounting and carbon credits schemes (Australia, Alberta in Canada, Brazil and the United States) to inspire proposals for possible implementation in the EU;- provide recommendations for future European policies in order to avoid greenwashing and ensure environmental protection.The outcome is a synthesis of “Lessons Learned” and recommendations for possible transferability of extra-EU initiatives to EU.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".