Does the assumption of biogenic carbon neutrality affect decarbonization pathways? Lessons learned from a techno-economic analysis
Bibliographic record
Abstract
Net-zero emission targets require transitioning to low carbon energy sources (including bioenergy) and large-scale carbon dioxide removal. Aside from direct air capture (DAC), bioenergy with carbon capture and storage (BECCS) and terrestrial carbon removal and sequestration are two available negative emission technologies (NETs) ready for large-scale deployment. Nationally determined contributions endorse bioenergy as an alternative carbon neutral energy source for fossil fuels. However, this carbon neutral assumption is disputed with several studies indicating that it may lead to accounting errors and biased decision-making.Bottom-up techno-economic energy system models such as the TIMES framework are used to identify and analyze potential decarbonization pathways for countries or regions. However, these models do not include biogenic carbon flows. Biogenic carbon refers to the carbon contained in biomass. One could assume that biogenic carbon is neutral since the amount of carbon emitted into the atmosphere through biomass combustion and the amount of carbon sequestered by plants during their lifetimes are equal. This assumption may be acceptable when the biomass rotation length is short, as in annual crops, and the balance between emissions and uptakes is indeed neutral. The assumption, however, may not remain valid when the sequestration period is lengthy, as in the case of forest trees. This study combines an aspatial, stand- and landscape-level modeling framework (CBM-CFS3) with a bottom-up techno-economic energy system model (NATEM). We use the CBM-CFS3 output to model various forest management strategies that would result in different biomass availability for bioenergy as well as net forest carbon stocks and emissions. This allows us to determine whether and how this biomass will be used in the energy system over time. Besides, we model several forest-based bioenergy and BECCS technologies to allow the energy system to use the available biomass. This is the first time biogenic CO2 flows are being modeled in a thorough energy system model such as TIMES. We show how the assumption of carbon neutrality results in biased decision-making (using different sets of NETs and resources). We demonstrate that the decarbonization effort could be reduced by integrating forest sequestration into the energy system model. We explore how a high sequestration capacity forest management strategy may minimize the need for expensive NETs such as DAC. Moreover, this research highlights the need to adopt the most promising forest management strategy before investing in BECCS.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".