The Economics of Forest Carbon Sequestration Revisited: A Challenge for Emissions Offset Trading
Bibliographic record
Abstract
This paper provides an overview of the role that forestry activities play in mitigating climate change. The emphasis is on a comparison of carbon offset credits and a carbon tax/subsidy scheme for incentivizing reductions in the release of CO2 emissions and increase in sequestration of atmospheric CO2 through forestry. In addition to traditional issues related to additionality, leakages, and the transaction costs of determining and verifying how many carbon offsets are created, we investigate the importance of good governance and contracts. There are three options available to a public or private forestland owner for creating carbon offsets once tree reach maturity: (1) avoid or delay harvest; (2) harvest timber and use sawmill, logging and other residuals to generate electricity; and (3) sustainably manage the forest and carbon fluxes (i.e., post-harvest wood product carbon pools and avoided emissions from substituting wood for non-wood in construction or wood bioenergy for fossil fuels) to maximize net revenues. Delaying harvests or avoiding deforestation are considered important but outside the domain of a tax/subsidy or cap-and-trade scheme. With respect to bioenergy, the analysis suggests that, if there is a carbon dividend, it is likely to be small even if the life cycle of carbon is appropriately taken into account. Further, if there is some urgency to mitigate climate change, the use of wood bioenergy is more likely to result in a carbon debt, even with respect to coal, because of the need to weight CO2 according to when it is released to and removed from the atmosphere. Only holistic commercial forest management that is sustainable and incentivizes sequestration of carbon assures efficient mitigation of climate change. We demonstrate this by investigating carbon fluxes derived from an integrated forest management model and confirm this result more generally on the basis of a Faustmann rotation age model that explicitly includes benefits of storing carbon.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".