Industry perspectives on carbon-offset programs in Canada and the United States
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
Carbon offsetting is often put forward as a possible mitigation strategy for climate change. This study examines carbon-offset businesses in Canada and the United States to better understand their standards, project types, and project locations and to determine their perspectives regarding the challenges of the carbon-offset industry. Twenty companies (a 40% response rate) agreed to a structured interview, although many were reluctant to share some information. Several salient themes emerged and are discussed in more detail: involvement of the hospitality and tourism industry, financial commitment, confusion in the marketplace, transparency, and needs for education. Implementation of three recommendations—covering standardization, education, and further engagement among the industry, its customers, and researchers—could reduce confusion and increase the transparency of carbon offsetting. Yet these changes might not help business since customers might decide that purchasing carbon offsets does little to address climate change.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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