Clean Development Mechanism Afforestation and Reforestation projects: implications for local agriculture.
Bibliographic record
Abstract
Abstract The potential of Clean Development Mechanism Afforestation and Reforestation (CDM A/R) projects to contribute to climate change mitigation and sustainable development is widely recognized. Yet, problems with the design and implementation of CDM A/R projects have limited analyses of project outcomes. In fact, of the nearly 1400 registered CDM projects in early January 2009, there was only one A/R project. Yet, as of May 2010, the number of registered CDM A/R projects had rapidly grown to 14 with 41 more CDM A/R projects in the pipeline. This rapid increase in A/R activities may provide some early indications of whether CDM A/R projects are successfully meeting their potential to contribute to sustainable development goals. This review specifically examines the literature that documents the positive and negative impacts of CDM A/R projects on local agriculture. It finds that while half of the current CDM A/R projects are credited with generating carbon offsets from 2007 or earlier, there is little published evidence of their specific impacts on local agriculture or sustainable development. This review recommends that future research should focus on (1) developing field surveys with criteria and indicators that evaluate the performance of individual CDM A/R projects in meeting stipulated outcomes, (2) increasing critical scrutiny of CDM A/R project validation documentation and procedures and (3) developing criteria and indicators to analyse the impacts of all CDM A/R projects on broad issues (such as tenure security and institutional capacity) and specific demographic groups, geographic regions or livelihoods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".