地理科学进展 ›› 2019, Vol. 38 ›› Issue (4): 546-555.doi: 10.18306/dlkxjz.2019.04.007

• 研究论文 • 上一篇    下一篇

中国省域尺度17部门资本存量的时空特征分析

刘丽1,2(), 李宁1,2,*(), 张正涛1,2, 冯介玲1,2, 陈曦1,2, 白扣1,2, 黄承芳1,2   

  1. 1. 北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
    2. 北京师范大学地理科学学部民政部/教育部减灾与应急管理研究院,北京 100875
  • 收稿日期:2018-06-07 修回日期:2019-01-20 出版日期:2019-04-28 发布日期:2019-05-07
  • 通讯作者: 李宁 E-mail:liul0210@163.com;ningli@bnu.edu.cn
  • 作者简介:

    第一作者简介:刘丽(1994— ),女,安徽滁州人,硕士生,研究方向为自然灾害损失评估。E-mail: liul0210@163.com

  • 基金资助:
    国家重点研发计划项目(2016YFA0602403);国家自然科学基金项目(41505134);北京市自然科学基金项目(9172010)

Spatiotemporal distribution of capital stock exposure of 17 sectors for individual provinces in China

Li LIU1,2(), Ning LI1,2,*(), Zhengtao ZHANG1,2, Jieling FENG1,2, Xi CHEN1,2, Kou BAI1,2, Chengfang HUANG1,2   

  1. 1. Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
    2. Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2018-06-07 Revised:2019-01-20 Online:2019-04-28 Published:2019-05-07
  • Contact: Ning LI E-mail:liul0210@163.com;ningli@bnu.edu.cn
  • Supported by:
    National Key Research and Development Program of China, No. 2016YFA0602403;National Natural Science Foundation of China, No. 41505134;Beijing Municipal Natural Science Foundation, No. 9172010.

摘要:

随着灾害强度、频率以及承灾体暴露的增加,自然灾害造成的损失日益严重。资本存量作为承灾体的经济暴露指标之一,是灾害损失评估的前提和基础。针对目前中国缺乏省域尺度长时间序列的经济部门分类的资本存量数据基础,论文通过永续盘存法,建立了2003—2015年中国大陆31省17部门的资本存量数据库,并分析其时空特征。结果显示:① 全国总资本存量与灾害直接损失的年际变化均呈增加趋势。省域尺度上,通过相关性分析显示,在99%置信度水平上,两者呈显著正相关(r=0.3)。② 时间上,各省17部门资本存量基本也呈增加趋势,但增速不同。在各部门增速最快的省份中,黑龙江省的居民服务、修理和其他服务业增速最快,增长约454.3倍;其次是青海省的租赁和商务服务业(398.3倍)、江苏省的金融业(295.1倍)、安徽省的科学研究和技术服务业(125.1倍)等。③ 空间上,2015年各省17部门资本存量最多的前4个部门分别是房地产业,工业,交通运输、仓储和邮政业,水利、环境和公共设施管理业,占比均在60%以上;且这4个部门资本存量暴露最多的省份是江苏省和广东省。该结果有助于从时空角度了解各省各部门资本存量暴露情况,为各省灾害风险管理者的防灾减灾工作提供重要的参考价值。

关键词: 资本存量, 17部门, 省域尺度, 经济暴露, 灾害损失, 中国

Abstract:

As the intensity and frequency of natural hazard-induced disasters and human and asset exposure increase, the losses caused by these disasters have become increasingly serious. It is particularly important to estimate the capital stock, which is an indicator of economic exposure, in the assessment of disaster losses. At present, time series data of capital stock by economic sectors at the provincial scale in China are largely unavailable. Thus, this study used the perpetual inventory method to establish a capital stock database of 17 sectors for individual provinces in China from 2003 to 2015. The conclusions are as follows: Interannual changes of the total capital stock and direct losses of disasters nationwide both showed an increasing trend. At the provincial scale, the correlation analysis shows that there was a significant positive correlation between them (r= 0.3) at the 99% confidence level. The capital stock of 17 sectors for individual provinces also showed an increasing trend, but the growth rate was different. Among the provinces with the fastest growth rates in the 17 sectors, the growth rate of resident services, repair and other services in Heilongjiang Province was the fastest, which increased 454.3 times. This is followed by leasing and business services in Qinghai Province (398.3 times), finance in Jiangsu Province (295.1 times), and scientific research, technical services and geological exploration in Anhui Province (125.1 times). The top four sectors with the highest capital stock in the 17 sectors in 2015 were real estate; industry; transport, storage and post; and water conservancy, environment and public facilities management, and together their proportion was above 60%. The provinces with the most capital stock in these four sectors were Jiangsu and Guangdong. The study results are helpful for further understanding exposure of capital stock in individual provinces and various sectors from a temporal and spatial perspective, and providing important references for disaster prevention and mitigation work of provincial disaster risk managers.

Key words: provincial scale, 17 sectors, capital stock, economic exposure, disaster loss, China