Feasibility and Challenges of Low-Carbon Transition of China’s Power System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 it