山地乡村社区灾害韧性评价——以凉山州安宁河流域典型代表性乡村为例
吴元飞(1998—),男,安徽铜陵人,硕士研究生,主要从事乡村防灾减灾研究,(E-mail)2209382595@qq.com; |
收稿日期: 2024-11-22
修回日期: 2025-02-16
网络出版日期: 2025-04-22
基金资助
国家自然科学基金委员会:国际(地区)合作与交流项目(可持续发展国际合作科学计划)重点项目子课题——跨喜马拉雅经济走廊滑坡型多灾种形成机制与韧性社区构建(42361144880)
Disaster Resilience Evaluation of Mountainous Rural Communities: A Case Study of Representative Villages in the Anning River Basin, Liangshan Prefecture
Received date: 2024-11-22
Revised date: 2025-02-16
Online published: 2025-04-22
为提升山地乡村社区灾害韧性评价的科学性与实践价值,文章基于PSR(压力―状态―响应)模型,融合熵值法与(AHP)层次分析法,构建了包含3个维度(压力韧性、状态韧性、响应韧性)、9个要素、32项指标的多层级评价体系,并以四川省凉山州安宁河流域的4个典型社区(桃源村、曹古村、牛郎村、群英村)为例,结合实地调研、GIS空间分析及多源数据进行实证研究。结果表明:1)社区综合韧性排序为桃源村>牛郎村>群英村>曹古村,桃源村因全国防灾示范社区建设,韧性最高,而曹古村因高海拔地形和基础设施滞后,韧性最低;2)状态韧性对总体韧性贡献度最高,其中C9村民住宅建筑质量为核心驱动因素,压力韧性中C2单侧距活动断层距离与C6地质灾害隐患点威胁人数显著影响风险等级,响应韧性依赖C26灾害监测设备与C27预警信息发布效率;3)基于“压力―状态―响应”协同优化路径,提出差异化防灾策略,包括风险区划与工程治理(压力层)、住房抗震改造与社会资本培育(状态层)、智慧预警与传统知识融合(响应层)。文章验证了PSR模型在山地乡村灾害韧性评价的适用性,揭示了“状态韧性主导、响应能力短板”的典型特征,为同类社区防灾规划提供方法论支持。
吴元飞 , 刘梦颖 , 田兵伟 , 田人杰 , 胡逸凡 . 山地乡村社区灾害韧性评价——以凉山州安宁河流域典型代表性乡村为例[J]. 热带地理, 2025 , 45(4) : 704 -718 . DOI: 10.13284/j.cnki.rddl.20240758
To enhance the scientific rigor and practical relevance of disaster resilience evaluation in mountainous rural communities, this study developed a multilevel assessment framework based on the Pressure-State-Response (PSR) model by integrating the entropy method and Analytic Hierarchy Process (AHP). The framework comprised three dimensions (pressure, state, and response), nine elements, and 32 indicators tailored to the unique environmental and socioeconomic contexts of mountainous regions. Focusing on four representative communities (Taoyuan, Caogu, Niulang, and Qunying) in the Anning River Basin of Liangshan Prefecture, Sichuan Province, China, a combination of field surveys, GIS spatial analysis, and multi-source datasets were used to empirically evaluate community resilience. The key findings revealed the following: (1) The comprehensive resilience scores ranked Taoyuan > Niulang > Qunying > Caogu. Taoyuan's top performance stemmed from its designation as a national disaster prevention demonstration community featuring robust infrastructure and frequent emergency drills, whereas Caogu's lowest resilience resulted from its high-altitude topography, aging population, and inadequate infrastructure. (2) State resilience contributed most significantly to overall resilience (51.43%), with the building quality (C9) being the pivotal driver. Pressure resilience was predominantly influenced by the proximity to active faults (C2) and population exposure to geological hazards (C6), whereas response resilience relied on disaster-monitoring equipment (C26) and early warning efficiency (C27). (3) A synergistic optimization strategy was proposed, emphasizing risk zoning and engineering controls (pressure layer), housing retrofitting and social capital cultivation (state layer), and intelligent early warning systems integrated with indigenous knowledge (response layer). The study validates the applicability of the PSR model in mountainous rural contexts, highlighting a "state resilience dominance with response capacity gaps" pattern. Notably, communities with higher state resilience demonstrate stronger recovery capabilities despite elevated hazard pressures, underscoring the importance of robust infrastructure and social cohesion. Conversely, insufficient investment in monitoring technologies and external rescue coordination hinders response effectiveness in remote villages such as Caogu. The framework provides methodological support for tailored disaster-prevention planning, particularly in ethnic regions where traditional ecological knowledge complements modern governance. However, limitations include a focus on earthquakes and geological hazards, excluding concurrent multi-hazard scenarios (e.g., wildfires and pandemics), and a static assessment that overlooks temporal resilience dynamics. Future research should incorporate longitudinal monitoring and cross-scale interactions to refine the generalizability of the model. This study advances the theoretical integration of socioecological systems into resilience assessments and offers actionable insights for sustainable rural development in hazard-prone mountainous areas.
表2 数据来源Table 2 Detailed data sources |
数据 | 来源 |
---|---|
地震动峰值 加速度 | 《中国地震动参数区划图》GB18306-2015(中华人民共和国国家质量监督检验检疫总局 等,2015) |
单侧距活动 断层的距离 | 中国地震局地质研究所:《中国活动构造图(1∶400万)》 |
坡度>20% 的村域面积 | NASA地球科学数据网站(https://nasadaacs.eos.nasa.gov/) |
月平均 降雨量 | 国家青藏高原科学数据中心(彭守璋,2020);中国 1 km分辨率逐月降水量数据集(1901—2021);时空 三极环境大数据平台. DOI:10.5281/zenodo.3185722 |
NDVI植被 指数 | 地理遥感生态网科学数据注册与出版系统 (www.gisrs.cn) |
村域平均 海拔 | NASA地球科学数据网站 (https://nasadaacs.eos.nasa.gov/) |
村域路网 密度 | Open Street Map开源数据库 (https://www.openstreetmap.org/) |
图2 PSR模型中“压力―状态―响应”三者关系Fig.2 The relationship of "pressure-state-response" in PSR model |
图3 基于PSR模型的乡村社区灾害韧性认知框架Fig.3 Disaster resilience cognitive framework of rural communities based on PSR mode |
表3 “压力―状态―响应”各维度对应的韧性特征Table 3 Toughness characteristics correspond to each dimension of "pressure-state-response" |
韧性过程维度 | 作用主体 | 主要特性 |
---|---|---|
压力 | 外界的各种冲击扰动 | ①不确定性(Uncertainty) |
状态 | 乡村人居环境各系统的 耦合作用 | ①稳健性(Robustness) ②冗余性(Redundancy) |
响应 | 外部:政府;社会组织 内部:家庭;个人;基础 组织 | ①灵活性(Redundancy) ②适应性(Adaptive) ③协作性(Collaborative) |
表4 山地乡村社区灾害韧性评价指标体系Table 4 Disaster resilience evaluation index system of mountainous rural communities |
目标层L | 准则层A | 组合权重 | 要素层B | 组合权重 | 指标层C | 组合权重 | 属性 |
---|---|---|---|---|---|---|---|
山地 乡村 社区 灾害 韧性 | A1 灾害 压力 韧性 | 0.253 1 | B1 地震 | 0.132 5 | C1 地震动峰值加速度 | 0.058 8 | 负向 |
C2 单侧距活动断层的距离 | 0.073 7 | 负向 | |||||
B2 地质灾害 | 0.120 6 | C3 坡度大于20%的村域面积 | 0.019 9 | 负向 | |||
C4 地形起伏度 | 0.018 9 | 负向 | |||||
C5 雨季(6—9)月平均降雨量 | 0.029 0 | 负向 | |||||
C6 地质灾害隐患点威胁人数 | 0.052 7 | 负向 | |||||
A2 灾害 状态 韧性 | 0.514 3 | B3 自然系统 | 0.039 6 | C7 NDVI植被覆盖指数 | 0.027 8 | 正向 | |
C8 村域平均海拔高度 | 0.011 8 | 负向 | |||||
B4 居住系统 | 0.153 9 | C9 村民住宅建筑质量 | 0.078 5 | 正向 | |||
C10 人均应急避难场所面积 | 0.028 3 | 正向 | |||||
C11 应急标识导向系统建设情况 | 0.024 8 | 正向 | |||||
C12 村卫生院数量 | 0.022 2 | 正向 | |||||
B5 社会系统 | 0.116 7 | C13 减灾宣传教育和疏散演练活动情况 | 0.032 6 | 正向 | |||
C14 个人日常来往的村民数量 | 0.016 7 | 正向 | |||||
C15 村规民约的编制及实施情况 | 0.011 5 | 正向 | |||||
C16 传统防灾知识挖掘与使用情况 | 0.028 3 | 正向 | |||||
C17 灾害管理制度编制情况 | 0.027 8 | 正向 | |||||
B6 人类系统 | 0.085 8 | C18 60岁以上、14岁以下人口占总人口的比重 | 0.016 8 | 负向 | |||
C19 村民家庭年收入 | 0.019 0 | 正向 | |||||
C20 村民对灾害风险的认知情况 | 0.023 8 | 正向 | |||||
C21 村民对社区防灾空间及设施了解情况 | 0.026 2 | 正向 | |||||
B7 支撑系统 | 0.118 2 | C22 村域路网密度 | 0.030 7 | 正向 | |||
C23 供水设施建设情况 | 0.019 8 | 正向 | |||||
C24 供电设施建设情况 | 0.017 0 | 正向 | |||||
C25 通信基础设施情况 | 0.050 7 | 正向 | |||||
A3 灾害 响应 韧性 | 0.232 6 | B8 预警能力 | 0.115 5 | C26 灾害监测设备和技术配置情况 | 0.060 0 | 正向 | |
C27 灾害预警信息发布渠道 | 0.055 5 | 正向 | |||||
C28 所属县域应急救援队伍数量 | 0.024 6 | 正向 | |||||
C29 村内应急物资储备情况 | 0.033 8 | 正向 | |||||
B9 救援、 恢复能力 | 0.117 1 | C30 道路对外连通性 | 0.013 5 | 正向 | |||
C31 自备防灾救援组织数量 | 0.024 0 | 正向 | |||||
C32 村民帮助他人的意愿 | 0.021 2 | 正向 |
表5 典型乡村社区各要素层韧性水平得分Table 5 Resilience level scores of each element layer in typical rural communities |
要素层 | 曹古村 | 桃源村 | 牛郎村 | 群英村 |
---|---|---|---|---|
B1地震灾害 | 0.014 7 | 0.041 2 | 0.002 2 | 0.074 4 |
B2地质灾害 | 0.014 2 | 0.034 7 | 0.022 3 | 0.028 4 |
B3自然系统 | 0.013 6 | 0.002 7 | 0.006 0 | 0.017 4 |
B4居住系统 | 0.012 4 | 0.050 6 | 0.072 5 | 0.008 5 |
B5社会系统 | 0.027 5 | 0.058 2 | 0.023 3 | 0.007 8 |
B6人类系统 | 0.026 5 | 0.017 7 | 0.020 2 | 0.021 4 |
B7支撑系统 | 0.005 6 | 0.015 3 | 0.067 4 | 0.030 0 |
B8预警能力 | 0.010 0 | 0.047 8 | 0.033 9 | 0.033 9 |
B9救援、恢复能力 | 0.010 8 | 0.048 2 | 0.043 4 | 0.014 8 |
吴元飞:提出本文的研究思路,负责总体框架构建和研究方法设计,完成初稿撰写及全面修改;
刘梦颖:完成文献资料的收集与理论分析,参与数据处理与结果分析,协助论文撰写与修改完善;
田兵伟:指导研究设计与实施,组织并主持实地调研和数据采集,审阅并修改全文,对研究成果进行整体把关;
田人杰:参与数据整理,协助完成论文后期修改和校对工作;
胡逸凡:参与图表绘制,协助论文后期修改校对。
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