Development of a Multi‐Region Power System Risk Management Model for Supporting China's Carbon Neutrality Ambition in 2060s
Bibliographic record
Abstract
Abstract In order to peak emissions before 2030 and to achieve the net‐zero ambition around 2060, China urgently needs to accelerate low‐carbon transition, especially in the power system. Previous studies were mainly focused on deterministic optimization, with some of them being followed by sensitivity analyses. To tackle the gaps and to support the net‐zero ambition, this study develops a multi‐region power system risk management (MPRM) model to analyze composite effects of renewable energy development and inter‐regional electricity transmission under uncertainties, and their combinations to achieve carbon neutrality by 2060. In detail, MPRM can (a) reveal the downward trend in costs of renewable energy and the increasing in inter‐regional electricity transmission; (b) tackle the uncertainties expressed as intervals; (c) support the low‐carbon transition of the power system. Under the renewable‐dominated power structure, 90% of China's electricity demands can be derived from non‐fossil sources by 2060. Inter‐regional electricity transmission will continue to expand due to the dramatic decreases in the costs of renewables and fast‐growing demands for electricity. Northwest and east regions will be the main exporter and importer of renewable electricity. Carbon emissions from power system will peak in 2030 (about 6.21% above the 2020 level) and be eliminated by 96% (of 2030 levels) by 2060. These results can provide support for expansion of renewable capacities, acceleration of low‐carbon transition in power structure, elimination of barriers in electricity trading across regions, and exploration of the trade‐off between system costs and risk.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".