A factorial-analysis-based Bayesian neural network method for quantifying China's CO2 emissions under dual-carbon target
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abstract
Energy-structure transformation and CO2-emission reduction are becoming particularly urgent for China and many other countries. Development of effective methods that are capable of quantifying and predicting CO2 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 CO2 emissions. FABNN is then applied to analyzing CO2 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 (S195), the CO2-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 (S466). The results can provide solid support for national energy-structure transformation and CO2-emission reduction to achieve carbon-peak and carbon-neutrality targets.
