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引用本文:陈柏旭,周颖,王立刚,王丽英,张彦东.玉米秸秆还田的补偿意愿影响因素及补偿标准研究——基于徐水区319位农户的实证分析[J].中国农业资源与区划,2022,43(9):106~115
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玉米秸秆还田的补偿意愿影响因素及补偿标准研究——基于徐水区319位农户的实证分析
陈柏旭1,周颖1,王立刚1,王丽英2,张彦东3
1.中国农业科学院农业资源与农业区划研究所,北京 100081;2.河北省农林科学院农业资源环境研究所,石家庄 050057;3.河北省保定市徐水区农业农村局,保定 072550
摘要:
目的 玉米秸秆还田技术是我国实施耕地质量保护与提升的重要技术模式,也是实现农业废弃物资源化再利用的重要途径,探究农户对秸秆还田技术的采纳意愿并对技术采纳的补偿标准进行精准测度,有助于建立秸秆还田技术持续推广的长效机制,促进农业生产的绿色发展。方法 文章以河北省徐水区319份农户调查为基础,采用条件价值评估法定量分析农户对秸秆还田技术的支付意愿及其影响因素,并进一步构建补偿意愿价值评估模拟模型来估算支付意愿价值和秸秆还田外部环境成本。结果 (1)农户对秸秆还田技术的支付意愿较高,有90%的农户表达肯定支付意愿;(2)基于农户支付意愿和秸秆还田的温室气体减排环境效应测度,徐水地区玉米秸秆还田生态补偿标准的上限和下限分别为661.8元/hm2和171.47元/hm2;(3)支付意愿的影响因子按从大到小的强度排序为耕地平整化程度>化肥成本>农户性别>化肥用量认知>家庭农业纯收入>受教育程度>政策认知>社会网络>地力提升认知。结论 (1)细化补贴类型,制定科学补偿标准,建议玉米秸秆还田的补偿标准为416.7元/hm2(27.78元/0.067hm2);(2)通过秸秆还田技术培训,提升农户生态认知水平;(3)聚焦不同主体的愿望与诉求,实施针对女性农户群体的补偿政策,为补偿精准施策找准靶向。
关键词:  秸秆还田  行为意愿  影响因素  补偿标准  二元Logistic模型
DOI:10.7621/cjarrp.1005-9121.20220911
分类号:F323.2
基金项目:国家重点研发计划部省联动项目“黄淮海多因子障碍粮田产能提升定向培育技术模式与应用”(2021YFD1901002);中央级公益性科研院所基本科研业务费专项“外部效应视角下农户资源化利用秸秆补偿政策优化研究”(1610132020035)
STUDY ON THE INFLUENCING FACTORS AND COMPENSATION STANDARDS OF FARMERS' WILLINGNESS TO ADOPT MAIZE STRAW RETURNING TECHNOLOGY——BASED ON THE SURVEY DATA OF 319 HOUSEHOLDS IN XUSHUI DISTRICT
Chen Baixu1, Zhou Ying1, Wang Ligang1, Wang Liying2, Zhang Yandong3
1.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;2.Institute of Agricultural Resources and Environment, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050057, Hebei, China;3.Agriculture and Rural Bureau of Xushui District, Baoding City, Hebei Province, Baoding 072550, Hebei, China
Abstract:
As an important technical approach to protection and improvement of cultivated land's quality in China, maize straw returning technology also plays an important role in recycling the agricultural waste. To establish a long-term mechanism for the continuous promotion of straw returning technology and facilitate the green development of agricultural production, it is helpful to identify farmers' willingness to adopt this technology and accurately measure the compensation standards that should be adopted. Based on a survey on 319 farmers in Xushui district, Hebei province, this study quantitatively analyzed farmers' willingness to pay for adoption of this technology as well as the influencing factors by using conditional value assessment method, while further constructed a compensation willingness value assessment simulation model to estimate such willingness and the external environmental cost incurred by this technology. The results of this study showed that: (1) Farmers' willingness to pay for straw returning technology was very high, and 90% of farmers expressed a positive willingness. (2) Based on the measurement of farmers' willingness for such payment and the environmental effect of greenhouse gas emission reduction by straw returning technology, it was found that the upper and lower limits of ecological compensation standards for maize straw returning in Xushui were RMB 661.8 yuan /hm2, and 171.47 yuan /hm2, respectively. (3) The influencing factors for such willingness were, in a descending order, leveling of cultivated land > fertilizer costs > gender > cognition of fertilizer dosage > household agricultural income > education level > policy cognition > social networking > cognitive improvement of soil productivity. According to the analysis results of the survey data, the following suggestions are put forward: Firstly, the subsidy types shall be refined, and scientific compensation standards shall be formulated. It is suggested that the compensation standard for returning maize straw to farmland be RMB 414.64 yuan/hm2 (27.78 yuan/Mu). Secondly, training on the straw returning technology can enhance farmers' ecological cognition. Finally, it is necessary to focus on the aspirations and demands of different subjects, implement compensation policies for women farmer groups, and find out accurate targets for such compensation.
Key words:  straw returned  behavior intention  influencing factor  compensation standard  binary Logistic model
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