Forest carbon sink in China: Linked drivers and long short-term memory network-based prediction

The study contributes to existing literature by proposing a decomposition framework combining productiontheoretical decomposition analysis with Gini coefficient, and demonstrating superiority of long short-term memory neural network (LSTM) in predicting forest carbon sink (FCS) in the case of small dataset. We linked FCS with socioeconomic factors ignored by most previous studies (e.g., urbanization, income inequality, technical efficiency and technological change). We then investigated China's forestry sector as a case during 2005-2017. The results showed that FCS and its technical efficiency presented an upward trend during the study period. In terms of drivers, GDP and urbanization were the positive drivers driving the increase in FCS. The dominant driver has changed from urbanization to the urban-rural income inequality in explaining the distribution pattern of residential income for determining FCS. Furthermore, LSTM showed the excellent performance for projecting city-level FCS. We finally provided some policy proposals according to the results.

Automated summary

This study contributes to the existing literature by proposing a unique decomposition framework for predicting forest carbon sink and analyzing the impact of socioeconomic factors on it. The study finds that FCS and its technical efficiency show an upward trend during the study period, with urbanization and income inequality being the main driving factors. LSTM performs exceptionally well in predicting city-level FCS.