Meeting Carbon Dioxide Removal Demand in 2030: The Potential of Macroalgae Cultivation and Harvest
Bibliographic record
Abstract
A growing number of countries have announced net-zero and net-negative emissions targets, but only a few countries provide incentives for carbon dioxide removal (CDR). We derive estimates of countries' hypothetical demand for CDR in 2030 based on their emissions reduction targets under the Paris Agreement. The aggregated average global demand for CDR in the compliance year 2030 is 1064 MtCO2, 353 MtCO2, and 124 MtCO2 for the low, medium, and high-cost CDR scenarios, respectively. This demand comes exclusively from countries and regions with relatively high GDP per capita, relatively high abatement costs and a limited supply of removals from afforestation. In a scenario with full international emissions trading, CDR demand until 2030 would drop to zero. Thus, the near-term demand for CDR is primarily driven by fragmented, inefficient climate policies. As there will be no functioning system of international emissions trading in the near future, regions with ambitious climate targets and high abatement costs, such as Canada, Japan, the United Kingdom, and the European Union, will already have significant CDR demand in 2030. Marine CDR methods such as macroalgae cultivation and harvesting could make a small but relevant contribution to meeting this demand. However, given the lead time required to achieve reasonable carbon sequestration efficiencies, a forward-looking climate policy would begin to incentivize and develop such methods now, so that areas within countries' exclusive economic zones can be developed for this purpose.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".