A factorial-analysis-based Bayesian neural network method for quantifying China's CO2 emissions under dual-carbon target

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 …