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Forests and the dynamic changes play decisive roles in soil erosion prevention, biodiversity conservation and climate warming mitigation, and are crucial for China to achieve carbon neutrality because of the ability for carbon uptake. China's forests experienced a substantial change over the past century, primarily since the forest conservation and restoration programs were implemented. In this paper, the progress of China's forest change and the similarities and differences of the spatio-temporal patterns from multi-dimension coupling with ground surveys and satellite observations were reviewed comprehensively, and the drivers of forest change were discussed. Our review reported that: 1) China's forests experienced an extensive forest gain/restoration in vegetation greenness, forest cover, and aboveground biomass since the 1970s. 2) Changes in various forest parameters suggested distinctive spatiotemporal patterns in China. Consistent hotspots of forest gain clustered in the Loess Plateau and the southwestern China from most studies, while controversial conclusions about forest loss/degradation were found in the spatial distribution. 3) The geographic discrepancy of forest changes may be related to climate change, human activities, and forest disturbances. Variations in forest dynamics are prerequisites to exploring the effects of natural conditions and forestry policies on forest cover, and strengthening understanding of the carbon cycle. These findings can provide valuable insights for various ecological services in further studies and a scientific basis for forest management and policy-making in China. [ABSTRACT FROM AUTHOR]

To explore the potential effect of industrial transfer on carbon lock-in in China, this paper constructed the measurement formula of industrial transfer based on output and demand, and calculated carbon lock-in according to carbon sink and carbon emissions. By means of a spatial autoregressive model and a panel threshold model, we discussed the spatial spillover of carbon lock-in and the effect of industrial transfer. The findings are as follows: (1) The level of carbon lock-in does have a positive spatial spillover effect between economic neighboring provinces. (2) The effect of industrial transfer on carbon lock-in depends on industry characteristics. (3) Technological progress has direct and indirect carbon unlocking effects, energy structure, trade openness and increased fiscal expenditure exacerbate the carbon lock-in dilemma, while economic growth and carbon lock-in show an inverted "U" relationship. (4) There is a non-linear relationship between industrial transfer and carbon lock-in. Finally, we propose some recommendations to reduce dependence on high-carbon fossil energy and speed up the process of carbon unlocking in China. [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]