Inclusion of forestry offsets in emission trading schemes: insights from global experts
Bibliographic record
Abstract
Abstract Emissions trading schemes (ETSs) have been a central component of international climate change policies, as a carbon pricing tool to achieve emissions reduction targets. Forest carbon offset credits have been leveraged in many ETSs to efficiently meet emission reduction targets, yet there is little knowledge about the perceptions, experiences, and challenges associated with the forest carbon offsetting in existing and pilot ETS. Given that the future inclusion of forest carbon offset in ETS management activities and policies will require strong support and acceptability among the institutions and experts involved in ETS, this study explores the experiences and lessons learned with 16 globally engaging experts representing major existing ETSs (North America, Europe, and New Zealand) and Chinese pilot ETSs towards the inclusion of forestry offsets, major concerns and challenges with existing implementation models. Findings revealed that many respondents particularly from North America, New Zealand, and Chinese pilot systems portrayed positive attitudes toward the inclusion of forestry carbon offsets and its role in contributing to a viable ETS, while European experts were not supportive. Respondents cited leakage, permanence, additionality, and monitoring design features as the major challenges and concerns that inhibit the expansion and inclusion of forest carbon offsetting. Respondents from Chinese pilot schemes referenced a unique set of challenges related to implementation, including the increasing cost of afforestation and reforestation projects, the uncertainty in the future supply and demand for their national Certified Emissions Reduction (CER) scheme and landowner engagement. Existing and future ETSs should learn from and address the challenges experienced by global experts and carbon pricing mechanisms to design, evaluate, or enhance their forest carbon offset programs for an effective and viable system that successfully contributes to GHG mitigation practices globally. We recommend inclusion of forest carbon offsets at the early stages of ETS improves the perceptions and experience of policy makers and practitioners toward the success and potential of forestry offsets in ETS ensuring familiarity and confidence in the mechanism.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".