Achieving China's ‘double carbon goals’, an analysis of the potential and cost of carbon capture in the resource-based area: Northwestern China
Bibliographic record
Abstract
China has committed to achieving carbon peak by 2030 and carbon neutrality by 2060. Carbon Capture Utilization and Storage (CCUS) has great potential to help China achieve Intended Nationally Determined Contributions (INDCs) commitments. In this regard, understanding the role of CCUS in Northwestern regions in China characterized by resource-based industries and high greenhouse gas emissions , is key to support China's low carbon transition. Therefore, taking Northwestern China as an example, we applied the Stochastic Effects of Population, Affluence and Technology Regression (STRIPAT) model, Bias-corrected Least Squares Dummy Variable (LSDVC) method, and scenario analyses to reveal the potential and costs of carbon mitigation(PCCM) for the CCUS . The results show that the current detected geologic carbon sequestration capacity is estimated to be about 762.48 GT, which meets the carbon mitigation needs of CCUS under different scenarios in the Northwestern China. And the potential and cost of carbon mitigation (PCCM) of CCUS vary greatly in different scenarios in 2030. The PCCM of CCUS in Xinjiang Uygur Autonomous Region is much higher than those other provinces in Northwestern China. This study provides a convincing evidence for the Northwestern local governments to prioritize CCUS technology under ‘double carbon goals’ in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".