Decomposition and prediction of direct residential carbon emission indicators in Guangdong Province of China

[Display omitted] •Established DRCE calculation model to improve the accuracy.•Re-calculated power carbon emission factors of Guangdong Province.•The ratio of per capita rural and urban DRCE in Guangdong reached 1.35 in 2017.•Adapted LMDI method to understand the influence of various factors.•Applied system dynamics to establish DRCE prediction model. A direct residential carbon emission (DRCE) indicators calculation model and influence factor decomposition model were established in the Guangdong Province of China to study the dynamic changes and influence factors of DRCE from 1995 to 2017 respectively. A system dynamics prediction model was also established to predict future development trends of DRCE. The results showed that the DRCE in Guangdong Province had grown linearly over the years and presented an obvious urban–rural dual structure. The growth rates of urban and rural DRCE were relatively close, resulting in little change in the ratio between the two. However, in 2010, the per capita rural DRCE began to exceed the per capita urban DRCE. By 2017, the per capita rural DRCE was 1.35 times that of urban DRCE, which is obviously different from other provinces in China. The analysis of the influence factors of DRCE in Guangdong Province shows that the per capita consumption expenditure has a significant positive effect on DRCE, followed by the size of the population. Energy price is the main negative influence factor, followed by the energy carbon emission factor. The energy consumption structure and urban and rural population structure have a little effect on DRCE. Based on the system dynamics method, the results obtained by the DRCE prediction model show that the per capita urban and rural DRCE in Guangdong Province will be 1.06 tCO2e and 2.22 tCO2e, respectively by 2030. With the continuous growth of population in Guangdong Province, the DRCE in Guangdong will increase, but the increase in the aging population will have a certain inhibitory effect on long-term carbon emissions.