China's carbon budget inventory from 1997 to 2017 and its challenges to achieving carbon neutral strategies

The global climate change situation partly depends on the climate change policies of countries around the world, including China. Therefore, it is necessary to reasonably reduce carbon emissions (CEs) and increase carbon sink in order to progressively achieve the carbon neutral (CN) goal. However, the capacity and the potential to achieve carbon neutral with the support of its ecosystem, that is to say the carbon neutral capacity (CNC) and potential, are still unclear. To this end, based on China's energy emissions data, meteorological and hydrological data, lithology data, and vegetation data, we used the GEM-CO2 model, soil respiration model, spatial autocorrelation analysis method and other methods to establish the spatial information map of China city-scale CNC from 1997 to 2017. Furthermore, based on the future climate, vegetation data, CEs data and their influencing factors in 2025–2060, the Back Propagation neural network model was used to predict the CN potential of China's provinces. This study found during the study period, annual CEs of 5.63 Pg CO2 were not absorbed, which is about 90% of the average annual CEs. And the carbon surplus regions were mainly concentrated in the less developed northeastern and southwestern border regions. Moreover, the change in the CNC in China from 1997 to 2017 was −13.37 Tg/yr, indicating that the CNC of China's terrestrial ecosystems overall reduced. It should be noted that most provinces are not highly polarized, that is, there is no significant differences in CNC between cities in the provinces. Moreover, the scenario simulation's method of IPCC provides a reference for this manuscript, so we set up two future scenarios (A2 and B1 scenarios). In the future, China is expected to achieve a carbon emission peak before 2030 under the B1 scenario while it will continue to grow under the A2 scenario. From 2017 to 2060, the CNC under the two scenarios (A2/B1) will decrease by 44.58% and increase by 15.54%. It follows then that the road to CN in China will be difficult without corresponding policy intervention. In short, this study has clarified China's CNC from the past to the future, as well as CNC's spatial distribution and changing trends. This provided theoretical and data support for China to introduce corresponding zero-carbon solutions based on its understanding of CNC.