INTERSECTIONS OF THE PARIS AGREEMENT AND CARBON OFFSETTING LEGAL AND FUNCTIONAL CONSIDERATIONS
Bibliographic record
Abstract
As the global community debates the viability of approaches to climate change mitigation and adaptation, carbon offsetting is quickly becoming an avenue of choice. Following the adoption of the Paris Agreement at the Twenty first Conference of the Parties (COP21) to the United Nations Framework Convention on Climate Change (UNFCCC), and looking forward to the potential outcomes of COP22 in Marrakesh, carbon offsetting is gaining increased emphasis, in particular in the context of ongoing discussions at ICAO relating to aviation-based carbon emissions. This policy brief explores the intersection of the Paris Agreement and carbon offsetting and summarizes the legal and functional considerations. Carbon offsetting is explained, with particular emphasis on outlining the legal framework under the UNFCCC, including the Clean Development Mechanism (CDM) and the Paris Agreement of 2015, followed by a brief summary of project types, criteria, and standards used to determine the quality of carbon offsets. As offsetting continues to grow in popularity and application, increased scrutiny must be placed on the quality of offset credits as carbon credits are inherently unequal.
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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".