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FOREST RESOURCES WANAGEMENT ›› 2019›› Issue (6): 49-54.doi: 10.13466/j.cnki.lyzygl.2019.06.010

• Scientific Research • Previous Articles     Next Articles

Study on the Dynamic Trend of Carbon Sinks in Arbor Forests in Shanxi Province

LIU Hao(), XU Dongmei()   

  1. School of Economics and Management,Shanxi Agricultural University,Jinzhong,Shanxi 030801
  • Received:2019-09-06 Revised:2019-10-24 Online:2019-12-28 Published:2020-05-09
  • Contact: XU Dongmei E-mail:liuhaoshanxi@126.com;xxuling66@126.com

Abstract:

Under the background of the forest resource management mode and tree growth cycle,based on the biomass measurement method of timber volume source and the endogenous relationship of forest age-accumulation,a model of biomass and carbon density was established to compare and explore the aboveground carbon reserves and carbon sink potential of different resource utilization schemes from the perspective of different tree species,age classes and stands.The results show that:(1) assuming no large-scale logging in the next 50 years,the comprehensive carbon sequestration will reach 8,739.47×104t in 2065(the same below),the carbon sequestration in the new afforestation accounted for as 71.74%;(2) on the basis of the existing business model,selective felling or rotational felling of dominant tree species carried out in the plantations with small increment of carbon density in the original forest arbor vegetation,the comprehensive carbon sequestration will reach 8799.95×104t,and the carbon sequestration in the new afforestation accounted for as 73.34%;(3) there is a small difference between the carbon sequestration increment of the living wood that adopts the manual cutting and renewal plan and that does not adopt this plan;(4) the carbon sequestration contribution of new afforestation of regularly increased forestland area is greater,and it can ensure the socio-economic function of forest resources and promote the development of regional forest carbon sink.

Key words: the arbor forest, carbon sink potential, dynamic prediction, forest age transformation, Shanxi province

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