Abstract：[Background] The selection of greening tree species is the key work to promote land greening and ecosystem restoration. In addition, the diversity of tree species is one of the most important ways to improve regional biodiversity. Due to the potential risks for the selection of greening tree species, how to search the optimum balance among varied greening tree species becomes the urgent problem in practices. It was well acknowledged that the classification and control of risk grading for greening tree species selection is an important measures to promote the diversity of tree species selection and improve the quality of afforestation.[Methods] Based on the theory of risk management, this study constructed the standards for the classification of risk grading for greening tree species from the perspectives of theory and practice using the combined results of vegetation division and survey monitoring in China.[Results] According to the classification principle, the risk level of tree species selection was divided into five levels, and correspondingly mitigation measures were supplied. 1) The first level owned the small risk. It should be used as the preferred tree species, but not the conservative selection to reduce the diversity of tree species selection. 2) The second risk level was also the small risk and tree species in this grade were worth to recommend. 3) The third level is general risk and tree species in this grade were applied appropriately. 4) The risk in the fourth level was higher. These tree species in this grade were not recommended to apply in large-scale. If exceeding a certain scale, it was necessary to demonstrate feasibility argumentation for preventing biological invasion. 5) The fifth level was the highest risk, this grade of tree species should be strictly prohibited except for necessary scientific research.[Conculsions] The establishment of risk classification of greening tree species and corresponding resolving measures is helpful to enrich tree species selection, and has great meaning for afforestation in large-scale and ecosystem restoration.
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2021, Vol. 19 
