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自然资源学报 >

2025 , Vol. 40 >Issue 6: 1620 - 1643

DOI: https://doi.org/10.31497/zrzyxb.20250612

自然保护地体系建设

自然保护区域农地生态补偿的空间综合决策模型及其运用研究

  • 郑宇梅 , 1, 2, 3
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  • 1.中南林业科技大学经济管理学院，长沙 410004
  • 2.湖南省生态文明建设研究基地，长沙 410001
  • 3.湖南绿色发展研究院，长沙 410004

郑宇梅（1979- ），女，湖南辰溪人，博士，副教授，研究方向为自然保护地可持续发展、生态保护补偿、耕地绿色利用。E-mail:

收稿日期: 2024-10-21

  修回日期: 2025-01-26

  网络出版日期: 2025-05-26

基金资助

国家自然科学基金重点项目(U20A2089)

湖南省哲学社会科学基金项目(23YBA118)

湖南省教育厅科学研究重点项目(23A0226)

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A comprehensive spatial decision model and its application of ecological compensation for agricultural land in reserve areas

  • ZHENG Yu-mei , 1, 2, 3
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  • 1. School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China
  • 2. Research Base for Ecological Civilization Construction of Hunan Province, Changsha 410001, China
  • 3. Institute of Green Development of Hunan Province, Changsha 410004, China

Received date: 2024-10-21

  Revised date: 2025-01-26

  Online published: 2025-05-26

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摘要

自然保护区域是中国当前生态保护补偿重点覆盖区域。剖析了自然保护区域生态补偿的一般性和特殊性，综合考虑生态补偿决策的系统性和效应的多维性，并重视对决策中不确定性的表达，构建以“GIS（地理信息系统）—BN（贝叶斯网络）”为核心的决策模型。选择湖南毛里湖国家湿地公园所在的毛里湖流域为案例区，设计减施化肥等四种以改善毛里湖水环境为目标的农地利用调整措施，并形成多个待选生态补偿方案；通过自编程序开发决策工具集，实现案例区农地生态补偿优先方案与优先区域的精细决策。研究表明：（1）自然保护区域农地生态补偿具有多维效应，其决策应在保护地保护需求和区域经济社会稳定与发展间寻求协同。（2）农地生态补偿决策由农地利用调整措施、目标地类、补偿标准、实施区域等多个决策组件构成，其不同组合会形成效应各异的综合决策方案，综合决策相较单一组件决策更为全面和系统。（3）案例区农地生态补偿优先方案为面向水田和旱地实施“轮作休耕”，补偿标准为二级；在村级行政区进行区域优选可获得更高的实施效率。（4）基于BN的决策模型可自微观地块尺度向中观流域尺度完整地传递不确定信息，评价结果可同时提示决策的潜在收益与可能风险。

关键词： 保护区生态补偿; 精细空间尺度; 多目标综合效应; 资金利用效率; 贝叶斯网络; 决策工具开发

本文引用格式

郑宇梅 . 自然保护区域农地生态补偿的空间综合决策模型及其运用研究[J]. 自然资源学报, 2025 , 40(6) : 1620 -1643 . DOI: 10.31497/zrzyxb.20250612

Abstract

Reserve areas are the key coverage areas of ecological protection compensation in China. In this paper, the generality and particularity of eco-compensation decision in reserve areas were analyzed, and a decision framework for eco-compensation was built considering the systematicness of decision and multi-dimensional effects. Based on the emphasis on expressing uncertainty in decision information, a decision model with "GIS (Geographic Information System)-BN (Bayesian Network)" as the core was constructed. The Maoli Lake Watershed, where the Hunan Maoli Lake National Wetland Park is located, was selected as a case area to design four agricultural land-use adjustment measures aimed at improving the water environment of the Maoli Lake, such as the reduction of chemical fertiliser application, and to form a number of eco-compensatory schemes to be selected. The decision tools were developed by self-programming to realize the fine decision of priority scheme and implementation area of agricultural land eco-compensation in the case area. The results showed that: (1) Ecological compensation for agricultural land in nature reserve areas had a multidimensional effect, and its decision-making should seek synergies between the conservation needs of the reserve and regional economic and social stability and development. (2) Ecological compensation for agricultural land comprised multiple key components, such as land use adjustment measures, target land types, compensation standards, and implementation areas. These elements could be combined in various ways to form comprehensive decision-making plans. This multi-component approach offerd a more systematic and holistic framework compared to single-factor decision-making models. (3) The priority programme for ecological compensation of agricultural land in the case area was the implementation of crop rotation and fallow for paddy fields and drylands, with compensation rates at the second level; regional preferences at the village level of the administrative districts could be implemented more efficiently. (4) The decision model in this paper could entirely transfer uncertainty information from the microscale of plot to the mesoscale of watershed, and the assessment results could simultaneously suggest the potential benefits and possible risks of the decision.

Key words： reserves eco-compensation; fine spatial scale; multi-objective comprehensive effect; fund utilization efficiency; Bayesian Network; decision tool development

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