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Record W4402778143 · doi:10.1111/ejss.13577

Lessons learned from existing carbon removal methodologies for agricultural soils to drive European Union policies

2024· article· en· W4402778143 on OpenAlex
Irene Criscuoli, Andrea Martelli, Ilaria Falconi, Francesco Galioto, Maria Valentina Lasorella, Stefania Maurino, Avion Phillips, Guido Bonati, Giovanni Dara Guccione

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean Journal of Soil Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
FundersEuropean Commission
KeywordsEuropean unionAgricultureSoil waterEnvironmental scienceCarbon fibersEnvironmental protectionBusinessNatural resource economicsEnvironmental planningEnvironmental resource managementSoil scienceComputer scienceEconomicsInternational tradeGeographyArchaeology

Abstract

fetched live from OpenAlex

Abstract Soil plays a central role in the global carbon (C) cycle and the fight against climate change as it contains the largest existing organic C stock on earth. Natural processes exacerbated by climate change and unsustainable agricultural soil management practices are contributing to the steady decrease in organic C stocks in farmland. Carbon farming practices, underpinned by various incentives, can be used to maintain and increase C stocks in agricultural soils. Carbon credit mechanisms, that is, tradable credits each corresponding to one tonne of CO 2 eq, are one such incentive. Carbon credits are issued upon the demonstration of increased soil C stocks over time through the application of C accounting methodologies for each agroecosystem and farming practice. This study presents a detailed and critical analysis of carbon credit methodologies, focusing on agricultural soil C in temperate zones, by comparing the European Commission proposal for a regulation on carbon removals with relevant certification frameworks implemented in extra‐European Union industrialized countries (Australia, Alberta in Canada, United States). Based on this, we recommend strengthening the European Commission proposal by (i) expanding the list of eligible agricultural practices, (ii) setting a minimum maintenance time frame for each agricultural practice and incentivizing longer duration, (iii) setting the Good Agricultural and Environmental Conditions of the European Common Agricultural Policy (CAP) as a regulatory baseline, (iv) beyond the regulatory baseline, defining a farm level baseline in terms of carbon farming practices applied that can be monitored through the Integrated Administration and Control System of the CAP, (v) clarifying the interaction between the European Commission proposal of regulation and the CAP, the Soil Monitoring Law, and Land Use/Cover Area Frame Survey inventory, (vi) retaining a portion of unsold carbon credits as a buffer against the risk of reversal and (vii) applying a default discount to account for leakage risk if yield reductions are observed. We propose these recommendations to guarantee effective environmental protection, technical and bureaucratic feasibility as well as economic affordability for farmers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.182
GPT teacher head0.334
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it