IMPLICATIONS OF A MARKET FOR CARBON ON TIMBER AND NON-TIMBER VALUES IN AN UNCERTAIN WORLD
Bibliographic record
Abstract
Despite considerable interest in the potential for forests to sequester carbon, the impact of carbon management on the provision of timber and non-timber resources has received relatively little attention in the literature. The introduction of value for stored carbon may result in modifications to traditional forest management objectives, generating trade-offs with other forest resources depending on the incentives provided by carbon markets. This paper investigates these issues by examining the impact of a particular form of carbon market on timber and non-timber values in a managed forest. An integrated modeling framework, developed for the incorporation of carbon management into operational timber management modeling tools, is also described. There is still substantial debate over how to properly credit carbon sequestered in forests. To date, there has been little research on how the form of a carbon market will impact the operations and objectives of forestry firms. Alternative market structures could produce very different responses in terms of rotation age, net present value and harvest policy. Here, a specific form of carbon market, the specified level contract, is investigated. Forestry firms are assumed to reach contracts with carbon-seeking agents which "guarantee" that a specified level of carbon stock will be maintained over a defined time period. Optimal forest management decisions are examined by implementing an optimization model for a specific land base in Alberta. The Woodstock forest modeling package is used for optimization. Analysis of trade-offs is based on the work of Armstrong et al. (1999, 2003) which assess non-timber resources using the natural disturbance approach to forest management. The analysis is then expanded to include a more rigorous, and realistic, depiction of carbon and carbon stock changes. Using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), carbon yield curves are developed which are integrated directly into the Woodstock forest management ii model. These carbon yields capture dynamics specific to separate biomass and dead organic matter (DOM) carbon pools and are represented for individual forest cover types. Interestingly, the inclusion of DOM carbon generates unexpected relationships between non-timber resources and incentives to sequester carbon. Results show that the presence of co-benefits will depend upon forest cover type, the harvest flow regulation faced by the managing firm and the incentives for timber supply provided by the market. Furthermore, firms that agree to enter contracts for carbon sequestration appear to do so at the expense of a decline in timber supply, with estimates of the opportunity cost of carbon management falling within the range of those found in recent literature.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".