Scenario Storylines for Carbon Dioxide Removal in Germany: Drawing From Regional Perspectives
Bibliographic record
Abstract
ABSTRACT Carbon dioxide removal (CDR) is indispensable for reaching the German climate neutrality target as a complementary strategy alongside reducing and avoiding greenhouse gas emissions. Biomass can be used in various ways to deliver bio‐based CDR, including Bioenergy with Carbon Capture and Storage (BECCS), natural sink enhancement, and biomass‐based construction materials. By focusing on bio‐based solutions, actions can be streamlined to achieve both CDR and a range of co‐benefits; for example, in terms of ecosystem services. The ramp‐up of bio‐based CDR in Germany is driven by a diverse set of factors. In this study, scenarios were developed that allow for exploring these factors in a set of narratives. The selection of key drivers followed the PESTEL approach (Policy, Environmental, Social, Technological, Economic, and Legal aspects), to which the Biomass category was added. Desirable net‐zero futures and drivers identified in stakeholder surveys, interviews, and workshops were translated into consistent scenario storylines. These represent diverse bio‐based CDR portfolios that differ in the implementation level of single concepts and in the overall contribution to negative emissions for Germany in 2045, considering the national potentials for different CDR options. The scenarios encompass (1) a focus on cost efficiency, (2) prioritizing decentralized options and natural sinks, (3) larger amounts of bio‐based CDR (skyrocketing), and (4) little support for bio‐based CDR (roadblock). The scenario storylines and drivers can inform modeling for cost‐optimized implementation and paint a picture of potential developments for stakeholders. They can also serve as a basis for compiling bio‐based value chains with maximum removal capacities that deliver a series of additional system benefits.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".