Barriers to achieving additionality in carbon offsets: a regulatory risk perspective
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
The Specified Gas Emitters Regulation (SGER) in Alberta, Canada was the first North American regulation to mandate reductions in greenhouse gas emissions. Regulated entities may use carbon offsets to meet their emissions reduction obligations. Although conceptually sound, the offset market has fallen short of its potential to reduce emissions. By analyzing the policies and operations of the Alberta Emissions Offset System (AEOS), enabled by the SGER, we illustrate how participants are impacted by uncertainty in the Alberta carbon offset development process, using ECB Lethbridge Biogas as a case study. Our analysis shows that existing uncertainty from regulation creates risk for projects, which builds barriers that prevent regulated entities, project developers, and the province of Alberta from reaching the full potential of the regulation. We provide recommendations that will help to achieve additionality within the offset system by encouraging increased participation from high-quality projects, ultimately resulting in greater emission reductions.
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