中国县域乡村脆弱性空间特征与形成机制及对策

doi:10.11821/dlxb202106009

Abstract

Abstract:

Based on the essential connotation of rural vulnerability, this study constructs an indicator system for the comprehensive measurement of rural vulnerability in China at the county level. Through the selection of five typical representative transects, we will deepen the analysis of the regional differences in China's rural vulnerability and its formation mechanism and propose targeted coping strategies. The results show that: (1) The rural vulnerability of counties in China is generally within the threshold range of low and medium vulnerability characterized by obvious spatial differences. Along the "Bole-Taipei Line", there is a spatial pattern of north-south differentiation. Villages in southwestern China tends to have higher vulnerability than those in northeastern China. (2) External environmental factors are the leading factors that induce rural vulnerability. The rural ecological subsystem, composed of ecological exposure, ecological sensitivity, and ecological adaptation, is the fundamental influencing factor of rural vulnerability. The rural economic subsystem, which is composed of economic exposure, economic sensitivity, and economic adaptation, is the core influencing factor of rural vulnerability. The social subsystem, composed of social exposure, social sensitivity, and social adaptation, is also an important factor influencing rural vulnerability. (3) On the basis of "geographical location, the dominant driving factors of rural vulnerability and the degree of rural vulnerability", rural vulnerability in China at the county level can be identified into eight categories. According to the principle of adapting measures to local conditions, we should break down the regional embeddedness and path dependence. We should strengthen the prediction and monitoring of the sources of disturbance in the rural-area system and scientifically control the sensitivity of the system itself, then improve the adaptation of the rural system to ensure sustainable development of rural areas.

Key words: rural vulnerability, rural regional system, rural sustainable development, rural revitalization, rural geography, China

YANG Ren, PAN Yuxin. Spatial patterns, formation mechanism and coping strategies of rural vulnerability in China at the county level[J].Acta Geographica Sinica, 2021, 76(6): 1438-1454.

Figures/Tables 9

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Tab. 4

Rural vulnerability-type zoning and its coping strategies in China

乡村类型	暴露度特征	敏感性特征	适应能力特征	应对策略
大西北高敏感性驱动型脆弱性中水平区(Ⅰ)	① 农业生产条件受限;② 自然灾害频发;③ 气候水热匹配差;④ 生态环境脆弱	① 地形起伏大,类型多样,未利用地资源丰富;② 农牧民业主体;③ 土地利用效率偏低	① 交通教育医疗等基础设施滞后; ② 农业现代化水平低	① 以乡村农业绿色健康发展为导向,发挥畜牧业优势,大力推进标准化生产,培育当地特色农业品牌;② 国家扶持贫困地区产业发展,制定生计扶贫政策
黄河中游低暴露度驱动型脆弱性较低水平区(Ⅱ)	① 土壤侵蚀、水土流失严重;② 部分坡耕地耕作条件差	① 乡村产业单一,以农业种植为主;② 水资源短缺,乡村生态环境脆弱	① 资源丰富,但经济带动力不足;② 城镇对乡村的辐射带动作用不显著	① 提高农业机械化水平,引进适合坡地沟渠地形耕种技术和手段;② 加强农村土地整治,促进都市、休闲、观光农业等新型农业发展;③ 加强生态保育和生态补偿力度
南部沿海高暴露度驱动型脆弱性较高水平区(Ⅲ)	① 人口密度高,耕地资源人均占有率低,人地关系矛盾突出;② 水热条件优越,资源承载能力强;③ 环境污染较严重	① 临近港澳,开放程度高,是对外开放的窗口;② 受区域外向型经济的影响,出现大量废旧厂房,空间破碎,乡村土地集约化程度低	① 基础设施配套较为完善;② 珠三角对外围地区的辐射带动作用有限	① 有序引导工商资本下乡,完善乡村金融服务体系;② 对“旧城镇、旧厂房、旧村庄”进行更新改造,完善乡村基础设施建设
长江中游综合驱动型脆弱性中水平区(Ⅳ)	① 低山丘陵为主宜耕;② 乡村耕地非农化、乡村生产用地空废化等乡村病问题突出	① 地形平坦,起伏小;② 农业生产多样性程度高,农业主体老龄化问题明显	① 基础设施配套不足;② 乡村社会经济发展水平较高	① 以长江经济带发展和成渝城市群一体化发展为契机,镇村联动发展,以城促镇,以镇带乡,形成乡村群;② 加强土地整治,提高集约化程度
东部沿海高暴露度—低敏感性驱动型脆弱性中水平区(Ⅴ)	① 多平原和丘陵,水热匹配良好;② 土壤肥沃耕地资源丰富,产品附加值高;③ 多撂荒耕地非农化	① 乡村人口密度较高;② 农户生计多样性程度高,发展大量乡镇工业和家庭手工业	① 基础设施配套完善;② 工业化和城镇化进程迅速,社会经济发展水平高	① 创新农业经营方式,探索互联网+农业,发展农村电商和物流平台,延伸农村产业链条,构建农业全价值链,提升农业品牌效应;② 推进一二三产业融合发展
北部沿海低敏感性驱动型脆弱性较低水平区(Ⅵ)	① 水热匹配良好,地势平坦开阔,农业生产条件良好;② 劳动力流失情况严重,空心化现象突出	京津冀一体化战略促进了区域间要素流动,但由于大量乡村资本、劳动力要素涌入城市,虹吸效应显著	① 城镇化和工业化推动下乡村整体发展水平较高; ② 交通通达性程度较高	① 加快城乡要素间流通共享;② 提升农民兼业化水平,鼓励就近乡镇就业;③ 促进农业结构调整和产业结构优化升级
西南生态高敏感性驱动型脆弱性中水平区(Ⅶ)	① 云贵高原高山地形和喀斯特地貌广布;② 土地荒漠化、石漠化,农业生产条件差;③ 易受地震、干旱等自然灾害的扰动	① 地形复杂,起伏大;② 乡村与外界联系较少,农村外出就业困难;③ 农业生产为主,并且主体老弱化现象突出	① 交通闭塞,基础设施落后;② 城镇化工业化水平低;③ 农业机械化水平较低	① 统筹村庄安全和防灾减灾工程,对自然灾害进行提前的监测;② 加强村庄教育、医疗卫生、网络、交通等基础服务设施的建设和完善
东北综合驱动型脆弱性低水平区(Ⅷ)	① 地势平坦开阔,土壤肥沃,适宜耕种;② 自然资源禀赋良好,林业资源丰富;③ 资源退化,生态环境破坏严重	① 受传统生产模式影响,产业发展随资源枯竭而衰弱,依靠老工业和资源开采为生的村民陷入困境;② 青壮年劳动力流失	① 地区粮食产量高,农业生产水平较高;② 计划经济背景下体制机制僵化	① 整合资源优势发展特色林业,改良农业经营方式,引进专门技术人员,建立特色农业生产基地,激活区域发展内生动力,推进东北地区振兴发展;② 培养环境保护和生态修复意识

Tab. 4

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准则层	指标层	要素内容	权重	指标性质
暴露度(+) 0.3020	植被退化	NDVI下降斜率平均值/GDP增量	0.0517	+
	土壤侵蚀	土壤侵蚀强度/GDP增量	0.0517	+
	气候因素	年平均降水量×人口规模	0.0467	+
	气候因素	≥ 10 ˚C积温×人口规模	0.0472	+
	工业废气	单位GDP工业SO₂排放量	0.0321	+
	大气污染	单位GDP大气细颗粒物(PM_2.5)浓度	0.0370	+
	经济活动强度	夜间灯光强度/人口规模	0.0356	+
敏感性(+) 0.3882	地形起伏	平均坡度	0.0480	+
	地形起伏	平均高程	0.0446	+
	植被覆盖	平均NDVI	0.0474	-
	人口密度	人口密度	0.0468	+
	经济实力	人均地区生产总值	0.0516	-
	财政能力	财政贡献量	0.0516	-
	农业发展	第一产业增加值	0.0482	+
	产业结构	第二、三产业产值/地区生产总值	0.0499	-
适应能力(-) 0.3098	粮食安全	人均粮食占有量	0.0516	+
	教育水平	每万人中小学生在校数	0.0517	+
	医疗卫生	每万人医疗卫生机构床位数	0.0516	+
	社会福利	每万人社会福利院床位数	0.0517	+
	储蓄水平	居民储蓄存款余额	0.0516	+
	农业机械化水平	农业机械总动力	0.0515	+

分级		乡村脆弱性	暴露度	敏感性	适应能力
一级	低脆弱度/低值区	< 0.445	< 0.309	< 0.429	< 0.093
二级	较低脆弱度/较低值区	0.446~0.512	0.310~0.374	0.430~0.522	0.094~0.154
三级	中脆弱度/中值区	0.513~0.573	0.375~0.463	0.523~0.616	0.155~0.230
四级	较高脆弱度/较高值区	0.574~0.698	0.464~0.622	0.617~0.748	0.231~0.346
五级	高脆弱度/高值区	≥ 0.699	≥ 0.623	≥ 0.749	≥ 0.347

地域类型	脆弱性指数		暴露度指数		敏感性指数		适用能力指数
地域类型	等级	均值	等级	均值	等级	均值	等级	均值
大西北地区	中	0.543	低	0.285	较高	0.711	较低	0.105
黄河中游地区	较低	0.499	较低	0.316	较低	0.512	较低	0.141
南部沿海地区	较高	0.595	中	0.434	中	0.529	较低	0.099
长江中游地区	中	0.534	中	0.391	较低	0.499	较低	0.147
东部沿海地区	中	0.517	中	0.416	较低	0.441	中	0.204
北部沿海地区	较低	0.463	较低	0.335	较低	0.473	中	0.213
西南地区	中	0.560	较低	0.355	中	0.598	较低	0.106
东北地区	较低	0.450	低	0.302	较低	0.486	中	0.205

Spatial patterns, formation mechanism and coping strategies of rural vulnerability in China at the county level

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[1]	SUN Pingjun, WANG Kewen. Identification and stage division of urban shrinkage in the three provinces of Northeast China [J]. Acta Geographica Sinica, 2021, 76(6): 1366-1379.
[2]	WEI Shimei, PAN Jinghu. Network structure resilience of cities at the prefecture level and above in China [J]. Acta Geographica Sinica, 2021, 76(6): 1394-1407.
[3]	GE Dazhuan, LU Yuqi. Rural spatial governance for territorial spatial planning in China: Mechanisms and path [J]. Acta Geographica Sinica, 2021, 76(6): 1422-1437.
[4]	YIN Jiangbin, LI Shangqian, JIANG Lei, CHENG Zhe, HUANG Xiaoyan, LU Gaigai. The spatio-temporal variations and driving factors of non-farm employment growth in contiguous destitute areas of China [J]. Acta Geographica Sinica, 2021, 76(6): 1471-1488.
[5]	GUO Yuanzhi, LIU Yansui. The process of rural development and paths for rural revitalization in China [J]. Acta Geographica Sinica, 2021, 76(6): 1408-1421.
[6]	HU Pan, CHEN Bo, SHI Peijun. Spatiotemporal patterns and influencing factors of rainstorm-induced flood disasters in China [J]. Acta Geographica Sinica, 2021, 76(5): 1148-1162.
[7]	LIU Zhenhai, WANG Shaoqiang, CHEN Bin. Spatial and temporal variations of frozen ground and its vegetation response in the eastern segment of China-Mongolia-Russia economic corridor from 2000 to 2015 [J]. Acta Geographica Sinica, 2021, 76(5): 1231-1244.
[8]	HUANG Xiaodong, MA Haitao, MIAO Changhong. Connectivity characteristics for city networks in China based on innovative enterprises [J]. Acta Geographica Sinica, 2021, 76(4): 835-852.
[9]	WANG Lucang, LIU Haiyang, LIU Qing. China's city network based on Tencent's migration big data [J]. Acta Geographica Sinica, 2021, 76(4): 853-869.
[10]	XIA Xingsheng, PAN Yaozhong, ZHU Xiufang, ZHANG Jinshui. Monthly calibration and optimization of Ångström-Prescott equation coefficients for agricultural comprehensive area in China [J]. Acta Geographica Sinica, 2021, 76(4): 888-902.
[11]	ZHOU Yang, LI Xunhuan, TONG Chunyang, HUANG Han. The geographical pattern and differentiational mechanism of rural poverty in China [J]. Acta Geographica Sinica, 2021, 76(4): 903-920.
[12]	MA Zuopeng, LI Chenggu, ZHANG Pingyu. Characteristics, mechanism and response of urban shrinkage in the three provinces of Northeast China [J]. Acta Geographica Sinica, 2021, 76(4): 767-780.
[13]	FAN Zemeng. Spatial identification and scenario simulation of ecotone distribution in China [J]. Acta Geographica Sinica, 2021, 76(3): 626-644.
[14]	XU Yu, LI Xiubin, XIN Liangjie. Differentiation of scale-farmland transfer rent and its influencing factors in China [J]. Acta Geographica Sinica, 2021, 76(3): 753-763.
[15]	GU Hengyu, SHEN Tiyan. Spatial evolution characteristics and driving forces of Chinese highly educated talents [J]. Acta Geographica Sinica, 2021, 76(2): 326-340.