Impact of Biogenic Carbon Neutrality Assumption for Achieving a Net-Zero Emission Target: Insights from a Techno-Economic Analysis
Bibliographic record
Abstract
Global pathways limiting warming to 2 °C or below require deep carbon dioxide removal through a large-scale transformation of the land surface, an increase in forest cover, and the deployment of negative emission technologies (NETs). Government initiatives endorse bioenergy as an alternative, carbon-neutral energy source for fossil fuels. However, this carbon neutral assumption is increasingly being questioned, with several studies indicating that it may result in accounting errors and biased decision-making. To address this growing issue, we use a carbon budget model combined with an energy system model. We show that including forest sequestration in the energy system model alleviates the decarbonization effort. We discuss how a forest management strategy with a high sequestration capacity reduces the need for expensive negative emission technologies. This study indicates the necessity of establishing the most promising forest management strategy before investing in bioenergy with carbon capture and storage. Finally, we describe how a carbon neutrality assumption may lead to biased decision-making because it allows the model to use more biomass without being constrained by biogenic CO 2 emissions. The risk of biased decision-making is higher for regions that have lower forest coverage, since available forest sequestration cannot sink biogenic emissions in the short term, and importing bioenergy could worsen the situation.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".