Mitigation deterrence and unrealistic expectations: the future costs of forest carbon offsets
Bibliographic record
Abstract
• Forest offsets decrease carbon prices but deter renewables, carbon capture, and innovation. • Forest-sink losses raise mitigation costs up to 0.5 percentage points versus no offsets. • Low-income regions face up to two percent higher costs when forest offsets fail. • Offset reliance creates moral hazard and undermines fair paths to net zero. This study examines the economic and societal impacts of using Forest Carbon Offsets (FCO) as a negative emissions technology in climate mitigation strategies. FCO includes afforestation, reforestation, and reduced emissions from deforestation and degradation (REDD) initiatives aimed at achieving global climate targets, such as limiting temperature rise to 2 °C by 2100. Despite their potential, challenges such as the impermanence of carbon storage, overestimation of carbon removal, and mitigation deterrence—where reliance on FCO reduces other climate actions—persist. Using the WITCH integrated assessment model, this study analyzes the effects of FCO on energy sector investments, carbon pricing, and mitigation costs under scenarios with perfect foresight, myopic behavior, and varying degrees of forest carbon loss (FCL). Results indicate that heavy reliance on FCO leads to mitigation deterrence, with renewable and carbon capture investments decreasing by 8.6 % and 31 %, respectively, while fossil fuel investments increase by 1 %. Scenarios with 100 % FCL by 2045 could increase global GDP loss by 0.5 percentage points, surpassing the costs of not using FCO. Non-OECD countries, more vulnerable with lower economic resilience, could face mitigation costs up to 1.7 percentage points higher than OECD countries in similar FCL scenarios, raising equity concerns in climate policy. This research underscores the need for careful FCO management, accurate carbon sequestration estimates, and equitable policy frameworks to prevent moral hazards and ensure effective climate action. Clear definitions of which emissions can be offset versus those requiring direct reduction are essential to prevent over-reliance on offsets and maintain a balanced mitigation approach.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".