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马志波, 黄清麟, MAHwan-OK. 天然商品林生态效益补偿的Logistic模型分析[J]. 北京林业大学学报(社会科学版), 2012, 11(1): 63-68.
引用本文: 马志波, 黄清麟, MAHwan-OK. 天然商品林生态效益补偿的Logistic模型分析[J]. 北京林业大学学报(社会科学版), 2012, 11(1): 63-68.
MA Zhi-bo, HUANG Qing-lin, MA Hwan OK. Logistic Modeling Analysis on Compensation for Ecosystem Servicesof Natural Commercial Forest[J]. Journal of Beijing Forestry University (Social Science), 2012, 11(1): 63-68.
Citation: MA Zhi-bo, HUANG Qing-lin, MA Hwan OK. Logistic Modeling Analysis on Compensation for Ecosystem Servicesof Natural Commercial Forest[J]. Journal of Beijing Forestry University (Social Science), 2012, 11(1): 63-68.

天然商品林生态效益补偿的Logistic模型分析

Logistic Modeling Analysis on Compensation for Ecosystem Servicesof Natural Commercial Forest

  • 摘要: 分析了造成持有天然商品林林权证的农民自主经营权、受益权等权益落实困难的原因,提出通过生态效益补偿解决公共生态效益诉求与个人经济利益追求之间的矛盾。采用Logistic模型分析了基于条件价值法取得的示范点数据资料,结果表明农户接受当地公益林生态效益补偿标准的概率与种植业收入占家庭收入的比例呈负相关关系,预测正确率达到67.9%。降低当地农民对种植业的依赖,有助于天然商品林生态效益补偿政策的实施,有效保护天然林资源,促进当地环境与经济的可持续发展。

     

    Abstract: This paper analyzes the reasons that causing difficulty of farmers’ operating right and benefit right could not be ensured although they gained the using rights of Natural Commercial Forest from the Reform of Collective Forest Using Rights System (RCFURS) in south China recently. A policy suggestion is raised that compensation for ecological benefit can resolve the contradictions between public ecology benefit and farmers’ economic benefit. With a logistic regression model, the paper analyzes investigation data of demonstration area, Dagan village of Hainan Province, based on contingent valuation method. Results show that there exists the negative correlation between the probability that farmers accept the payments for ecological services (PES) according to the local standard of public welfare forest compensation and proportion of planting income to family income. The model prediction accuracy is 67.9%. Reducing farmers’ dependence of planting is helpful to implement the PES policy of natural commercial forest, and can protect natural forest resources and achieve sustainable development of local environment and economy.

     

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