Optimizing carbon emission forecast for modelling China's 2030 provincial carbon emission quota allocation

Abstract

Rational allocation of carbon quotas is the fundamental premise for the orderly operation of carbon markets. To achieve the set target of carbon peak by 2030, there is an urgent need to establish China's 2030 provincial carbon quota allocation scheme. Although some proposed schemes have been formulated, there are problems with the methods used for carbon emission forecasting and evaluating the rationality of a proposed allocation scheme. This study aimed to optimize carbon emission forecast by incorporating terrestrial carbon sinks into the mechanism for building China's 2030 provincial carbon emission quota allocation schemes. Aquila Optimizer's Double Support Vector Regression (AO-based TWSVR) that has the advantages in solving problems associated with small sample size, nonlinear and high-dimensional pattern recognition with fast training speed and insensitivity to noise was adopted to predict the net carbon emission. The results show that the application of AO-based TWSVR model allows satisfactory forecast of the net carbon emission in China for the period from 2021 to 2035. This allowed terrestrial carbon sequestration being incorporated into the mechanism to formulate China's 2030 provincial carbon quota allocation schemes. Comparison of the three provincial carbon quota allocation schemes using social network analysis suggests that the equity-based carbon quota allocation scheme is more suitable for China's national conditions compared to the efficiency-based scheme and the combined principle-based scheme. The findings obtained from this study have implications for optimizing the scheme of China's 2030 provincial carbon quota allocation.