Prediction of urban carbon peak by considering water-energy-carbon nexus of land use: The case of Zhengzhou, China
Bibliographic record
Abstract
Predicting urban carbon peak by considering water-energy-carbon nexus of land use has great significance for improving resources utilization efficiency and realizing carbon peak target. Previous studies were focused on multi-factors nexus evaluation from the perspective of industries or sectors, and less attention was paid to carbon emission prediction by considering multi-factors nexus from the perspective of land use. The paper employed the coupling coordination degree model to measure the water-energy-carbon nexus in Zhengzhou City and used the method of system dynamics to predict water-energy consumption and carbon emissions during 2021–2035. The results showed that there had significant differences in water-energy consumption and carbon emissions of different land use types. The coupling coordination degree changed from the near imbalance state to the high-quality coordination level. The comprehensive scenario had the greatest potential for resource conservation and carbon emission reduction, and the peaks of water, energy and carbon emissions would appear in 2034, 2031 and 2029, respectively. In the future, implementing collaborative utilization planning of resources, promoting utilization efficiency of water and energy, and building a precise carbon emission assessment system should be adopted. This study improved carbon peak prediction by considering multi-elements, which helped providing practical references for promoting water-energy utilization efficiency and carbon emission reduction.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".