Feasibility and Challenges of Low-Carbon Transition of China’s Power System
Bibliographic record
Abstract
Low-carbon transition of China’s power system is pivotal for global climate management. National aggregate analysis in prior work ( 10.1038/s41560-021-00863-0, 10.1016/j.oneear.2021.09.012, 10.1038/s41558-019-0509-6, 10.1038/s41558-022-01570-8, 10.1093/ooenergy/oiad009 ) masks the conflicts between China’s power system mechanism, carbon mitigation, and economic development goals and conceals provincial heterogeneities in socioeconomic capabilities, costs, and risks. We address those issues by comparing decline in coal use and acceleration in renewable adoption rates (R CHI ) in China’s provinces along with China’s 2030–2060 carbon mitigation and economic development goals to that in 52 other countries at their historical fastest transformation (R MAX ) decade, based on their socioeconomic, power system structure, and mechanism conditions, and quantifying the unit-associated unemployment and stranded assets due to decline in coal use. We observed that R CHI distributes unevenly in time and space. In time scale, the transition follows a “fast-then-slow” trajectory in terms of stranded assets, leading to higher socioeconomic and political efforts at the beginning of the process. Spatially, certain provinces face heightened risks related to stranded assets, unemployment, and energy security, underscoring the urgent need for power system reforms and equitable carbon quota allocations for a just transition. To achieve its 2030–2060 carbon targets, China must attain higher R CHI than R MAX, overcoming stringent socioeconomic and political challenges and entrenched system inertia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".