Promoting the carbon market in agroforestry systems: the role of global, national and sectoral initiatives
Bibliographic record
Abstract
Climate change, driven by increasing greenhouse gas emissions, manifests through recurrent heatwaves, wildfires, and erratic rainfall, posing significant environmental and socio-economic challenges. Mitigating these effects necessitates the reduction or offsetting of emissions, with the carbon market playing a critical role in achieving these goals. Agroforestry systems, which integrate trees with agricultural practices, offer a promising solution for carbon sequestration and climate change mitigation. The primary objective of this paper is to examine how agroforestry systems can be incorporated into the carbon market framework, with a focus on existing initiatives at global, national, and sectoral levels. A systematic review of empirical research was conducted using three academic databases: Google Scholar, Scopus, and Web of Science (WoS). Relevant papers published between 2000 and 2024 were selected based on their focus on carbon markets and agroforestry systems. The review included 95 empirical studies that were analyzed through thematic analysis. Key themes explored include the effectiveness of carbon market initiatives, the challenges and opportunities for agroforestry systems in carbon sequestration, and the alignment of these practices with carbon market mechanisms. Findings indicated that, while much of the focus has been on forests and wetlands, agroforestry systems have significant potential within carbon markets, offering environmental and socio-economic benefits, including enhanced climate adaptation and diversified income for smallholders. Carbon markets are divided into compliance markets and voluntary carbon markets (VCMs), each with distinct challenges. VCMs, in particular, face issues such as weak regulation and the absence of sustainability standards. Notable initiatives, such as the ACORN Platform, Amazonia Connect Initiative, and Dream Fund Program, are working to enhance smallholder access to carbon markets. Best practices for integrating agroforestry into carbon markets include baseline assessments, community engagement, agroforestry system design, and robust monitoring and verification systems. The integration of agroforestry into carbon markets presents a promising pathway for mitigating climate change while promoting sustainable land-use practices. However, challenges such as lack of infrastructure, technical knowledge, and financing barriers hinder smallholder participation. To overcome these obstacles, policy recommendations include investing in rural infrastructure, providing capacity-building programs, developing innovative financing models, simplifying certification processes, and ensuring policy stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".