Clean Development Mechanism Afforestation and Reforestation projects: implications for local agriculture.
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.
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.
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 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 | 0.001 | 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 it