Opportunities for early Carbon Capture Utilisation and \nStorage development in China: \nStrategies for harnessing cost-effective integrated Carbon Capture \nUtilisation and Storage (CCUS) project potential in Shaanxi Province
Bibliographic record
Abstract
Carbon Capture Utilisation and Storage (CCUS) is a key technology to reduce China's carbon emissions, while satisfying its increasing demand for electricity and chemical products, and its continuous reliance on coal. Preliminary work on CCUS in China has focused on CCUS in the power sector. However, capture in the power sector is technically challenging, energy-intensive and expensive. Capture can be implemented at lower cost at large point sources of concentrated CO2, such as in ammonia and methanol plants, coal-to-liquids facilities and hydrogen production processes. China has a large industrial base in these sectors, resulting in a significant CO2 emission reduction potential through CCUS. In recent years China has seen the development of Enhanced Oil Recovery (EOR) activities. EOR injects CO2 in oil reservoirs to enhance production and prolong the life of the reservoir. EOR is widely applied in the United States and Canada and is in development in the Middle East. China has a large EOR potential and an EOR industry is emerging. CO2 from nearby high-concentration point sources has a value for EOR operations. This value can be used to develop early cost-effective CCUS projects involving industries where capture costs are relatively low. To date a number of separate preliminary pilots for the capture and storage of CO2 have been and are being undertaken in China. However, none of these pilots succeed in cost-effectively establishing a fully integrated CCUS chain. Early demonstration of cost-effective CCUS potential in selected sectors can significantly advance CCUS development in China in selected industries, in time crossing over into other sectors, including power, as the technology and policy conditions mature. Against this background, this international collaboration project identified cost-effective integrated CCUS opportunities in Shaanxi and developed recommendations to advance the implementation of these opportunities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".