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京冀水源保护林建设农户后续参与意愿研究——基于河北省四县104户农户的调查数据 被引量:5

Researches on Households' Follow-up Willingness to Participate in the Construction of Ecological Water Conservation Forest in Beijing and Hebei Province——Based on the survey data of the 104 households in four countys of Hebei province
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摘要 基于京冀水源保护林建设工程周边104户农户实地调查数据,利用Logisti回归模型研究了各相关因素对农户参与造林建设意愿的具体影响。结果表明:造林工程区样本农户的年龄、居住地、收入构成、政府及造林政策以及效益偏好对后续造林参与意愿的综合影响认知具有显著性影响。其中年龄越大、所在地区越偏远、非农收入占比越大、重视经济效益的农户越愿意参与到后续工程建设中来;有政府补助收入、一期工程中接受过技术指导及参与过一期工程的农户对后续工程的参与意愿则较低。因此,建议加强造林工程的信息宣传工作;进一步提高造林标准,加大投资力度,造林资金及时足额到位并增加管理和管护经费;提供技术指导的同时要注意方式和方法等。 This paper studys the specific impact of all relevant factors to participate in afforestation based on the survey data of the 104 households in four countys of Hebei province. The result turns out that following 6 factors have significant influence on farmer households' follow-up willingness to participate in the afforestawion engineering: age,residence,constitution of income,policy,benefit preference. Among them,the higher age,more remote areas,higher proportion on non-agricultural income,more importance to economic benefit show higher willingness to participate. However,households who not have government subsidies and have received technical guidance in the first period of program and have taken part in the first period of program show lower willingness. According to the findings,the paper gives some suggestions for the follow-up program,such as strengthening the information publicity of program,increasing afforestation standard and investment,making reforestation funds in timely and full, increasing the fund of administration and protection, providing technical guidance at the same time to pay attention to ways and means and so on.
出处 《林业经济问题》 北大核心 2015年第5期418-423,429,共7页 Issues of Forestry Economics
基金 国家林业局2014年林业重大问题调研课题(ZDWT201415) 北京市园林绿化局专项基金资助项目(2013HXFWJGXY010) 北京林业大学经济管理学院研究生专项基金资助项目(201307)
关键词 京冀 生态水源保护林 农户 参与意愿 LOGISTIC回归模型 Beijing and Hebei province ecological water conservation forest households willingness of participation Logistic regression model
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