Storage capacity estimates and site conditions of potential locations for offshore-wind powered carbon dioxide removal and carbon sequestration in ocean basalt
Bibliographic record
Abstract
Negative emission technologies (NETs) are considered essential to keep global warming below 2 °C. Situating wind-powered carbon dioxide removal (CDR) devices offshore and injecting carbon dioxide (CO2) into deep-water sub-seafloor basalt aquifers has the potential to offer large CO2 removal capacity. It also avoids land and water-use competition and provides additional low-risk protections against post-injection leakage compared to terrestrial CO2 storage. This paper seeks to identify locations where offshore wind and potential basalt storage locations exist within close proximity to one another around the globe. A global mean wind power density map at 150 m height was computed using 30 years (1986–2016) of ERA5 hourly wind speed reanalysis data. Offshore regions with mean wind speed greater than 8 m/s were identified. Offshore regions with basalt aquifers along seismic or aseismic ridges which provide potential CO2 storage sites were identified and selected based on sediment thickness, age, and distance from plate boundaries. Four scenarios were constructed to capture a range of constraints with implications for technical, economic and regulatory difficulties. For each scenario, eligible regions for CO2 injection were filled by regularly spaced grid points and the distance to the nearest eligible wind resource was calculated for each point to identify the most promising configurations. Total available storage capacity within reach of wind resources was estimated to be between 4,300Gt and 196,000Gt depending on both uncertainties in porosity and other imposed constraints; even the most conservative estimates represent enormous capacity compared to global targets for negative emissions technologies. Typically, the best areas were found close to the poles due to the greater prevalence of good wind resources in those areas. Site-specific properties such as water depth and distance from shore are computed for the identified locations in order to characterize the conditions in which such locations are typically found.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".