Assessing Climate Change Impacts on Wind Energy Resources over China Based on CMIP6 Multimodel Ensemble
Bibliographic record
Abstract
Assessing how wind energy potential will change in the context of global warming is fundamental to local energy development and planning. Twenty-two CMIP6 GCM outputs under three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are fed into the convolutional neural networks based on efficient channel attention (ECA-Net) to generate wind energy density projections. This study demonstrates that the ECA-Net model can accurately capture the regional features of wind speed over China. Results show that the wind energy resource potential of China exhibits a significant ( p < 0.01) decreasing trend of 0.74% decade –1, 0.99% decade –1, and 1.36% decade –1 during 2015–2100 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. Compared with the baseline period (1985–2014), China’s average annual wind energy resource potential will decrease by 3.55%, 0.06%, and 2.24% (5.73%, 5.02%, and 8.84%) during 2031–2060 (2071–2100) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The results also highlight increased inter- and intra-annual variability of wind energy resources in areas such as parts of the Tibetan plateau, which poses a challenge for regional energy deployment and management. These findings suggest that the sustainability of China’s wind energy development may be challenged by climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".