A factorial-analysis-based Bayesian neural network method for quantifying China's CO2 emissions under dual-carbon target
Bibliographic record
Abstract
Energy-structure transformation and CO 2 -emission reduction are becoming particularly urgent for China and many other countries. Development of effective methods that are capable of quantifying and predicting CO 2 emissions to achieve carbon neutrality is desired. This study advances a factorial-analysis-based Bayesian neural network (abbreviated as FABNN) method to reflect the complex relationship between inputs and outputs as well as reveal the individual and interactive effects of multiple factors affecting CO 2 emissions. FABNN is then applied to analyzing CO 2 emissions of China (abbreviated as CEC), where multiple factors involve in energy (e.g., the consumption of natural gas, CONG), economic (e.g., Gross domestic product, GDP) and social (e.g., the rate of urbanization, ROU) aspects are investigated and 512 scenarios are designed to achieve the national dual carbon targets (i.e., carbon peak before 2030 and carbon neutrality by 2060). Comparing to the conventional machine learning methods , FABNN performs better in calibration and validation results, indicating that FABNN is suitable for CEC simulation and prediction. Results disclose that the top three factors affecting CEC under the dual‑carbon target are GDP, CONG, and ROU; energy, economic and social contributions are 43.5 %, 34.6 % and 21.9 %, respectively. CEC reaches its carbon peak during 2027–2032 and achieve carbon neutrality during 2053–2057 under all scenarios. Under the optimal scenario (S 195 ), the CO 2 -emission reduction potential is about 772.2 million tonnes and the consumptions of coal, petroleum and natural gas can be respectively reduced by 3.1 %, 9.9 % and 23.0 % compared to the worst scenario (S 466 ). The results can provide solid support for national energy-structure transformation and CO 2 -emission reduction to achieve carbon-peak and carbon-neutrality targets.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".