Afforestation Subsidy Determination for Haloxylon ammodendron (C.A.Mey.) Bunge in China Based on Cost–Benefit Analysis

Forests

Xiaoyu Zhang¹  Shuyu Song²  Duanyang Xu² 

[1] Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

关键词: afforestation;    subsidy;    cost–benefit analysis;    ecosystem services;    Alxa;   

DOI  :  10.3390/f13040497

来源: DOAJ

【 摘 要 】

Subsidies are an effective instrument to encourage farmers to engage in afforestation which provides many ecosystem services. A scientific and reasonable subsidy level should reflect the demands of various stakeholders so that the subsidy scheme can be accepted by both farmers and the government. This study aimed to establish a framework for designing an afforestation subsidy to attract voluntary participation in a way that seeks to raise both farmers’ and government’s returns while reducing financial expenditure. The framework determines the optimal subsidy level by integrating direct costs, opportunity costs, and ecological benefits to conduct cost–benefit analysis over the period of afforestation. As a case study, we focus on the planting of Haloxylon ammodendron (C.A.Mey.) Bunge in Alxa, Inner Mongolia. The results show that the subsidy level required to motivate farmers is lower than the subsidy level according to the government’s willingness to pay. Under the optimal subsidy level, the government and farmers can reach a balance point that provides effective incentives for afforestation without requiring unreasonable spending. Additionally, the current subsidy is substantially lower than the recommended subsidy; there is still room for improvement in the subsidy policy. The research framework developed herein can help policy and decision makers to design afforestation subsidy schemes and contribute to ecological restoration in the future.

【 授权许可】

Unknown