资源科学 ›› 2018, Vol. 40 ›› Issue (7): 1387-1396.doi: 10.18402/resci.2018.07.07

• • 上一篇    下一篇

新一轮退耕还林工程农户风险感知的影响因素分析——基于新疆阿克苏地区的调研数据

张朝辉()   

  1. 石河子大学经济与管理学院,石河子 832000
  • 收稿日期:2018-03-22 修回日期:2018-05-20 出版日期:2018-07-20 发布日期:2018-07-20
  • 作者简介:

    作者简介:张朝辉,男,河南商丘人,博士,副教授,从事农林经济理论与政策、林业可持续发展等方面的研究。E-mail:zzh545675656@163.com

  • 基金资助:
    国家自然科学基金资助项目(71663043);石河子大学青年创新人才计划项目(CXRC201708);石河子大学农业现代化研究中心培育项目(ZZZC201723A)

Factors affecting risk perception of farmers in the new round Returning Farmland to Forest Project in the Aksu Region

Zhaohui ZHANG()   

  1. School of Economic & Management, Shihezi University, Shihezi 832000, China
  • Received:2018-03-22 Revised:2018-05-20 Online:2018-07-20 Published:2018-07-20

摘要:

退耕风险感知是影响农户退耕参与意愿和决策行为的关键要素。基于新疆阿克苏地区1451户农户的调查,应用广义有序Logit模型,从预置性因素、政策性因素、过程性因素与外部性因素出发,探索新一轮退耕还林工程农户风险感知的影响因素,为降低农户退耕参与风险感知、激发农户退耕参与意愿、优化退耕还林政策设计提供信息支持。研究结果表明:① 农业经营收入比重等预置性因素、退耕还林配套政策等政策性因素、退耕还林直接成本等过程性因素、非农就业能力等外部性因素具有统计显著性;② 家庭收入水平、社会保障情况、退耕还林政策认知水平、退耕配套政策、非农就业能力等变量对显著降低农户参与退耕的高风险感知或提升低风险感知的概率相对较大,农业经营收入比重、退耕还林直接成本、林业自然生产弱质性、林产品市场销售损失等变量对显著提高农户参与退耕高风险感知的概率相对较大;③ 退耕政策认知水平、退耕还林直接成本、林产品市场销售损失等是影响农户参与退耕风险感知水平的关键变量,也是降低农户退耕风险感知水平的关注重点。

关键词: 新一轮退耕还林, 风险感知, 广义有序Logit模型, 影响因素, 风险管理

Abstract:

Risk perception of the Returning Farmland to Forest Project (RFFP) is a key factor influencing farmer willingness and decision behavior to participate in the RFFP. Based on a survey of 1451 farmers in the Aksu region of Xinjiang, China, a General Ordered Logit Model was used to analyzes factors affecting farmer risk perception of the new round RFFP according to preset factors, policy factors, process factors and externality factors. The aim was to provide information support to reduce farmer risk perception, stimulate willingness to participate in the RFFP and optimize RFFP policy. Preset factors such as the proportion of agricultural income, policy factors such as supporting policy of RFFP, process factors such as the direct cost of RFFP, and external factors such as off-farm employment ability were found to be significant. Variables such as family income, social security, awareness of RFFP, supporting policy of RFFP and off-farm employment ability were found to have a high probability of reducing higher risk reception or enhancing lower risk reception of famers. Variables such as the proportion of agricultural income, direct costs of the RFFP, weakness of forestry natural production and marketing loss of forest products were found to have a high probability of enhancing higher risk reception. Variables such as awareness of RFFP, direct costs of the RFFP and marketing loss of forest products are key variables to influencing farmer risk reception in the new round RFFP. These factors will reduce farmers risk reception and enhance farmers participation enthusiasm in the RFFP.

Key words: new round RFFP, risk perception, Gologit model, influencing factor, risk management