Rent Seeking and the Smoke and Mirrors Game in the Creation of Forest Sector Carbon Credits: An Example from British Columbia
Bibliographic record
Abstract
From a cost standpoint and as demonstrated in this paper, it is beneficial to permit forest-sector carbon offsets in lieu of carbon dioxide emissions reduction. Such offsets play a role in voluntary markets and Europe’s Emission Trading System. However, problems related to additionality, leakages, duration and impermanence, high transaction costs, and governance raise important questions about the validity of most carbon offset credits from forestry. Using data for a forest estate in south-eastern British Columbia owned by the Natural Conservancy of Canada (NCC), we construct a forest management model to demonstrate that the planned NCC management program yields questionable forest carbon offsets. NCC management results in slightly less annual carbon sequestration than leaving the forest as wilderness, but sustainable commercial management of the site sequesters between 8 and 270 thousand tonnes of CO2 more per year than NCC management. Because commercial exploitation was the counterfactual used to justify the NCC carbon offsets, offsets were subsequently sold to non-arms-length buyers, and numbers of carbon offsets are highly sensitive to assumptions, one can only conclude that the carbon offsets generated by this (and probably many other) forest conservation projects are simply spurious.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".