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With the implementation of carbon neutrality strategies in China, both carbon emissions reduction and carbon sequestration should be considered. In order to exert the carbon neutrality potential of forest carbon sinks, it is necessary to calculate the cost of them in China. Based on the basic principles of land use opportunity cost and net present value from previous research, this paper constructs a forest carbon sink cost estimation model based on the afforestation cost method. Empirical analysis of the carbon sequestration cost is explored at the city level in China. According to the results, the regional distribution of carbon sink costs varies greatly. On the whole, Southwest China is suitable for afforestation to create carbon sinks since its cost is relatively low. It can obtain carbon benefits from forest carbon sinks projects, while there are higher carbon sink costs in the eastern coastal areas. Furthermore, it dynamically analyzes the impact of the rotation period, timber price, discount rate and afforestation cost on the cost of carbon sinks. The rotation period and afforestation cost have more sensitivity for the forest carbon sink cost. Therefore, it should implement different low-carbon policies in different regions, and encourage forest management to create carbon sinks in areas rich in forest resources. In addition, an inter-regional carbon sink trading mechanism can play the economic value of forest carbon sinks effectively and achieve win-win cooperation between regions. • Analysis of the carbon sink cost is explored at the city level in China. • The regional distribution of carbon costs varies greatly. • Southwest China is suitable for afforestation to make carbon sequestration. • Rotation period, discount rate and afforestation cost have higher sensitivity. [ABSTRACT FROM AUTHOR]

• We propose a new forest prediction model and use actual deforestation data for optimization and verification. • Use the XGBoost method to reconstruct deforestation intensity data. • Deforestation hotspots in China are mainly concentrated in the southern and southeastern regions. • Our model provides certain reference information for forest disaster prevention and forest management. Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China. [ABSTRACT FROM AUTHOR]