Carbon Credit Systems in Agriculture: A Review of Literature
Bibliographic record
Abstract
Carbon-credit systems allow agricultural producers to earn an extra revenue through selling their surplus of carbon credits to producers who emit higher amounts of greenhouse gases (GHGs). However, agricultural carbon-credit systems are still at early stage; hence, these benefits cannot be guaranteed due to their uncertain nature and the paucity of scientific evidence about agricultural carbon credits. The objective of this study is to provide a comprehensive literature review to highlight the gaps in existing knowledge related to agricultural carbon credits/offsets. Our particular interest is on Alberta because the province indicates the highest agricultural GHG emissions from 1990 to 2019 and, therefore, developing strategies to reduce the sector’s carbon intensity without compromising its economic contribution to the provincial economy poses a challenge. Literature is evident for promising GHG-mitigation strategies such as adoption of 4R practices (the right source at the right rate, right time and right place) as a package and improved efficiency in cattle farm management. Reduced tillage has been found to be less efficient. Researchers favour the concept of regenerative agriculture, which is more likely to return better outcomes compared to tillage practices. Moreover, ranchers are willing to upgrade their farms with efficient cattle breeds to take advantage of decreased feed costs. Conversely, farmers are reluctant to participate in the Alberta Emission Offset System unless rewarded with incentives. However, carbon-credit markets are still growing; consequently, farmers may have more opportunities in the future. If the Alberta credit price continues to grow with no expected increase in transaction costs, agricultural producers would be more attracted to participate in the Alberta Emission Offset System. Moreover, endorsing farmers for carbon-crediting mechanisms by emphasizing the co-benefits and associated economic incentives is recommended, instead of prioritizing its potential financial gains. Nevertheless, due to the scarcity of published studies, it is too early to project the economic and climate-mitigative potential of carbon-offset–credit markets for Canadian farmers. Literature suggests farmers wait until the carbon market becomes more stable before making a decision. Future research and scientific evidence will be crucial to filling these gaps and to guaranteeing future protocols.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".