China’s carbon emission forecasts in different development models
Bibliographic record
Abstract
China is the world's largest carbon emitter, its carbon peaking and carbon neutrality goals significantly reduce climate change and global warming problems. Although the Chinese government proposed carbon emissions will peak by 2030, how do different development models affect carbon emissions in the future? Here, we analyzed the spatial and temporal trends of carbon emissions over the past two decades. Then we constructed the STIRPAT carbon emissions model based on ridge regression analysis. Finally, one national STIRPAT model and eight STIRPAT models at the sub-regional scales were generated because the effects of population, economy, and energy consumption vary significantly across regions and periods. Meanwhile, we established nine different future development models based on various population, economy, and energy consumption levels, and forecast their carbon emissions from 2020 to 2060 by eight sub-region STIRPAT models. Our results documented that (1) China's carbon emissions significantly increased by 445.79 million-ton/yr between 2000 and 2019. Meanwhile, population, total energy consumption, and GDP were growing at a rate of 7.60 million people/yr, 185.37 million-ton/yr, and 4791.74 billion-yuan/yr, respectively. (2) Ridge regression results indicated that carbon emissions are positively influenced by population, economic growth, and energy consumption in all regions, but the degree of influence varies across sub-regions. (3) In 2060, carbon emissions will be lowest across all variables at low levels of development and highest at high levels of development. Meanwhile, low energy consumption and population levels are possibly the main directions for controlling carbon emissions in the future. The findings indicate that the carbon peak target could be achieved by 2030 by controlling population and energy consumption alone. However, relying solely on these strategies may pose significant challenges in meeting the dual carbon targets, which emphasizes the need for a scientific foundation to inform low-carbon development policies. The findings provide a scientific basis and reference for the low-carbon sustainable development in China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".