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Carbon dioxide has a significant impact on global climate change due to its natural greenhouse effect. The objective and credible forecast of carbon emissions is very important for the government to formulate and implement the corresponding emission reduction targets. For controlling the growth of carbon emissions, Chinese government has put forward the low-carbon pilot policy and carbon trading policy. However, the existing grey models cannot measure the impact of policies and their interactions. In order to remedy the defect, a novel grey multivariable model based on dummy variables and their interactions is established. Two kinds of grey multivariable models and back propagation neural network model are chosen as comparison models to highlight that the introduction of dummy variables and their interactions plays an important part in improving the model performance. To verify the effectiveness, these four models are selected to simulate and predict the carbon emissions generated from primary energy consumption in Guangdong Province of China. The empirical results indicate that the mean absolute percentage errors of the novel model are 2.87% and 0.86%, respectively, which is significantly better than these three competing models. Finally, based on the outstanding performance of the novel model, it is chosen to forecast the fluctuating tendency of carbon emissions in the next 5 years. [ABSTRACT FROM AUTHOR]

The escalation of risks associated with environmental and climatic aspects has accelerated the introduction of voluntary personal carbon trading (PCT), a policy instrument for promotion and facilitation of low-carbon behavior in the domestic sector. This paper examines participants' motivation in the Guangdong pilot project, which aims to develop the first voluntary PCT scheme (Carbon Generalized System of Preferences, CGSP) in China to reduce the domestic sector's emissions and increase the liquidity of the Guangdong pilot emission trading scheme. The CGSP acceptance model is based on the technology acceptance model and explores the drivers of the public's participation willingness (PW) in the CGSP scheme. Further, the structural equation model has been adopted to explore the influence paths of each driver by using survey data (N = 1190) collected from Guangzhou, Guangdong Province, China. The empirical results demonstrated that the strong and direct drivers are associated with the institutional technical environment, perceived usefulness and participation risk. However, other drivers affect PW through the mediation effect of perceived usefulness. Notably, implementation cost had no significant effect on PW. • An innovative voluntary personal carbon trading scheme in China was introduced. • A conceptual model was proposed to study the determinants of public acceptance. • Structural equation model was used to study the influence path of each determinant. • Both the direct and indirect paths of each determinant have been analyzed in depth. [ABSTRACT FROM AUTHOR]