A Study of the Labor Parameter in the Model of Indirect Economic Loss in Typhoon Disaster
-
摘要: 为进一步研究自然灾害间接损失模型中受灾劳动力参数问题，调查了“山竹”台风严重影响的广州市和深圳市的劳动力受灾情况.基于实地调研的845份问卷调查数据，分析了受灾劳动力部门分布、影响时长以及恢复路径的实际情况，并与灾害间接损失前沿评估模型(AMIL模型)中的理论假设参数进行对比.研究结果表明：(1)从受灾劳动力所属部门来看，信息传输、软件和信息技术服务业、制造业、金融业、建筑业、批发零售业5个部门的实际受灾劳动力较多，而AMIL模型假设低估了信息传输、软件和信息技术服务业、金融业、建筑业等14个部门的劳动力受灾情况；(2)从恢复时间来看，所有劳动力实际恢复工作的时间是台风过后的第4天，远少于AMIL模型设置的时间；(3)从恢复路径来看，综合劳动力恢复路径呈先快后慢的趋势，幂函数对该路径的拟合效果较好，与AMIL模型假设的双曲正切函数曲线不符；(4)广州市劳动力受台风影响情况比深圳市的更加严重，且恢复速度较慢.研究表明：AMIL模型中对劳动力所属部门假设和劳动力灾后动态恢复路径的函数设置与选择方面存在进一步调整修正的空间.Abstract: A survey on labor force losses in Guangzhou and Shenzhen during the typhoon Mangkhut was conducted in order to study the parameter of the affected labor force in the indirect loss model of natural disasters. Based on the data of 845 questionnaires, the three parameters of sector of the affected labor, recovery time and recovery path were analyzed. They were also compared with the assumed parameters in the AMIL model, a model for frontier assessment of indirect disaster loss. The following results were obtained. First, in terms of the sector of labor force affected by the disaster, five sectors (information transmission, software and information technology services, manufacturing, finance, construction, and wholesale and retail) had the most labor force affected. The AMIL model's assumptions understated the impact on the workforce in 14 sectors, such as information transmission, software and information technology services, finance and construction. Second, on the 4th day after the typhoon, all labor had recovered, and the recovery time was far less than the assumed time of the AMIL model. Third, the labor recovery path showed a trend of being first fast and then slow. The power function had a good fitting effect on the recovery path, which was different from the hyperbolic tangent function curve assumed by the AMIL model. Fourth, the labor force in Guangzhou was more seriously affected by typhoon disaster than that in Shenzhen and the labor force reco-very rate was slower. The AMIL model still has room for improvement in the function setting and selection of the dynamic recovery path of labor force after a disaster.
-
Keywords:
- typhoon /
- labor parameter /
- AMIL model /
- questionnaire survey
-
-
![]()
图 2 不同误工时长的受灾劳动力占比
Figure 2. The proportion of the affected labor force under diffe-rent lengths of missed work
![]()
图 6 广州市和深圳市的不同经济部门受灾劳动力对比
Figure 6. The comparison of affected people in different sectors in Guangzhou and Shenzhen
![]()
图 7 广州市和深圳市的劳动力恢复路径对比
Figure 7. The comparison of labor recovery path between Guang-zhou and Shenzhen
表 1 被调查人员基本信息统计
Table 1 The statistics of basic information of respondents
调查项目 调查人数/人 比例/% 城市 广州市 426 50.4 深圳市 419 49.6 性别 男 582 68.9 女 263 31.1 年龄 ＜18岁 9 1.1 18~30岁 455 53.8 31~40岁 249 29.5 41~50岁 110 13.0 51~60岁 17 2.0 ＞60岁 5 0.6 
下载: 导出CSV
表 2 调查劳动力的部门分布
Table 2 The sector distribution of the labor force surveyed
部门编号 部门名称 产业类型 各部门就业人员占比/% 样本 实际 S1 农林牧渔业 1 1.18 3.87 S2 采矿业 2 0.36 0.02 S3 制造业 2 21.89 30.09 S4 电力、热力、燃气及水生产和供应业 2 1.07 0.41 S5 建筑业 2 5.80 4.00 S6 批发和零售业 2 10.89 12.32 S7 交通运输、仓储和邮政业 2 6.04 4.78 S8 住宿和餐饮业 2 6.04 3.96 S9 信息传输、软件和信息技术服务业 2 6.27 2.70 S10 金融业 2 4.85 1.35 S11 房地产业 2 3.55 3.54 S12 租赁和商务服务业 3 14.32 21.28 S13 科学研究、技术服务业 3 1.18 1.85 S14 水利、环境和公共设施管理业 3 3.31 0.50 S15 居民服务、修理和其他服务业 3 2.13 2.67 S16 教育 3 2.84 2.62 S17 卫生和社会工作 3 2.84 1.26 S18 文化、体育和娱乐业 3 1.89 0.64 S19 公共管理、社会保障和社会组织 3 3.55 2.13 注：产业类型1、2、3分别表示第一产业、第二产业、第三产业.
下载: 导出CSV
表 3 不同函数模型拟合结果
Table 3 The simulation results of different function models
函数模型 R2 Sig F 残差 对数函数 0.962 0.003 75.346 0.11 二次项函数 0.987 0.013 76.319 0.04 三次项函数 0.989 0.135 29.167 0.03 幂函数 0.964 0.003 80.312 0.02 指数函数 0.856 0.024 17.792 0.09
下载: 导出CSV
-
[1] 黄崇福.自然灾害基本定义的探讨[J].自然灾害学报, 2009, 18(5):41-50. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zrzhxb200905007 HUANG C F. A discussion on basic definition of natural disaster[J]. Journal of Natueal Disasters, 2009, 18(5):41-50. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zrzhxb200905007
[2] 李宁, 张正涛, 陈曦, 等.论自然灾害经济损失评估研究的重要性[J].地理科学进展, 2017, 36(2):256-263. http://d.wanfangdata.com.cn/Periodical/dlkxjz201702011 LI N, ZHANG Z T, CHEN X, et al. Importance of economic loss evaluation in natural hazard and disaster research[J].Progress in Geography, 2017, 36(2):256-263. http://d.wanfangdata.com.cn/Periodical/dlkxjz201702011
[3] 吴吉东, 何鑫, 王菜林, 等.自然灾害损失分类及评估研究评述[J].灾害学, 2018, 33(4):160-166. http://d.wanfangdata.com.cn/periodical/zhx201804026 WU J D, HE X, WANG C L, et al. A review on classification and loss assessment of natural disasters[J]. Joumal of Catastrophology, 2018, 33(4):160-166. http://d.wanfangdata.com.cn/periodical/zhx201804026
[4] 张正涛, 李宁, 冯介玲, 等.从重建资金与效率角度定量评估灾后经济恢复力的变化——以武汉市"2016.07.06 "暴雨洪涝灾害为例[J].灾害学, 2018, 33(4):214-219. http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&filename=ZHXU201804036 ZHANG Z T, LI N, FENG J L, et al. Quantitative assessment of changes in post-disaster resilience from the perspective of rescue funds and rescue efficiency——a case study of a flood disaster in Wuhan city on July 6, 2017[J]. Joumal of Catastrophology, 2018, 33(4):214-219. http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&filename=ZHXU201804036
[5] HASS J E, KATES R W, BOWDEN M J. Reconstruction following disaster[M]. Massachusetts:The MIT Press, 1977.
[6] OOSTERHAVEN J. The supply-driven input-output model: a new interpretation but still implausible[J]. Journal of Regional Science, 1989, 29:459-465
[7] COCHRANE H C. Economic impacts of a midwestern earthquake[J]. The Quarterly Publication of NCEER (National Center for Earthquake Engineering Research), 1997, 11(1):1-55. http://pesquisa.bvsalud.org/brasil/resource/en/des-11345
[8] OKUYAMA Y, HEWINGS G J D, SONIS M. Economic impacts of an unscheduled, disruptive event: a miyazawa multiplier analysis[M]//HEWINGS G J D, SONIS M, MADDEN M, et al ed. Understanding and interpreting economic structure. Berlin: Springer, 1999: 113-144.
[9] BROOKSHIRE D S, CHANG S E, COCHRANE H, et al. Direct and indirect economic losses for earthquake damage[J]. Earthquake Spectra, 1997, 13:683-701. http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=EASPEF000013000004000683000001&idtype=cvips&gifs=Yes
[10] Federal Emergency Management Agency(FEMA). Hazus-MH-MR5 technical manual[M]. Washington:Department of Homeland Security, 2011.
[11] OKUYAMA Y, HEWINGS G J D, SONIS M. Measuring economic impacts of disasters: interregional input-output analysis using sequential interindustry model[M/OL]//OKUYAMA Y, CHANG S E, ed. Modeling Spatial and Economic Impacts of Disasters. Berlin: Springer, 2004: 77-101. https://doi.org/10.1007/978-3-540-24787-6_5.
[12] HALLEGATTE S. An adaptive regional input-output model and its application to the assessment of the economic cost of katrina[J]. Risk Analysis, 2008, 28(3):779-799. http://www.mapfre.com/documentacion/publico/i18n/consulta/registro.cmd?id=103286
[13] LI J, CRAWFORD-BROWN D, SYDDALL M, et al. Mode-ling imbalanced economic recovery following a natural di-saster using input-output analysis[J]. Risk Analysis, 2013, 33(10):1908-1923. http://www.istic.ac.cn/suoguan/detailed.htm?dbname=xw_qk&wid=0220140300299808
[14] KOKS E E, BOCKARJOVA M, DE MOEL H, et al. Integrated direct and indirect flood risk modeling:develop-ment and sensitivity analysis[J]. Risk Analysis, 2015, 35(5):882-900. http://europepmc.org/abstract/MED/25515065
[15] BÄUMEN H S I D, TÖBBEN J, LENZEN M. Labour forced impacts and production losses due to the 2013 flood in Germany[J]. Journal of Hydrology, 2015, 527:142-150. http://www.sciencedirect.com/science/article/pii/S0022169415002899
[16] XIA Y, GUAN D, JIANG X, et al. Assessment of socioeconomic costs to China's air pollution[J]. Atmospheric Environment, 2016, 139:147-156. http://www.sciencedirect.com/science/article/pii/S1352231016303776
[17] MENDOZA-TINOCO D, GUAN D, ZENG Z, et al. Flood footprint of the 2007 floods in the UK:the case of the Yorkshire and the Humber region[J]. Journal of Cleaner Production, 2017, 168:655-667. http://www.sciencedirect.com/science/article/pii/S0959652617320048
[18] 吴先华, 徐中兵, 袁迎蕾, 等.台风灾害的关联经济损失评估——以江苏省为例[J].灾害学, 2014, 29(2):77-83. http://www.cnki.com.cn/Article/CJFDTotal-ZHXU201402017.htm WU X H, XU Z B, YUAN Y L, et al. Relational economic loss assessment of typhoon disaster——a case study of Jiangsu Province[J]. Journal of Catastrophology, 2014, 29(2):77-83. http://www.cnki.com.cn/Article/CJFDTotal-ZHXU201402017.htm
[19] ZHANG Z, LI N, CUI P, et al. How to integrate labor disruption into an economic impact evaluation model for postdisaster recovery periods[J]. Risk Analysis, 2019(4):1-21. doi: 10.1111/risa.13365
[20] 刘诗白.论马克思关于生产劳动和非生产劳动的理论[J].社会科学战线, 1982(3):55-65. http://www.cnki.com.cn/Article/CJFDTotal-SHZX198203011.htm [21] BRUNEAU M, CHANG S E, EGUCHI R T, et al. A framework to quantitatively assess and enhance the seismic resi-lience of communities[J]. Earthquake Spectra, 2003, 19(4):733-752.
[22] 广东省统计局.广东省统计年鉴[M].北京:中国统计出版社, 2019. [23] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准委员会.国民经济行业分类: GB/T 4754—2017[S].北京: 中国标准出版社, 2017: 4-83. [24] 张小丽.气象灾害预警信号发布的历程与深化[C]//中国气象学会2007年年会气象软科学论坛分会场论文集.中国气象学会: 中国气象学会, 2007: 159-165. [25] 陈申鹏.深圳市气象灾害预警机制研究[D].哈尔滨: 哈尔滨工业大学, 2019. CHEN S P. A study on Shenzhen's meteorological disaster warning mechanism[D]. Harbin: Harbin Institute of Technology, 2019.
-
期刊类型引用(2)
1. 郭贺媛熙，李利军，冯军，林鑫，李睿. 基于DNA杂交指示剂和银纳米棒阵列芯片构建氯霉素SERS适配体传感器的研究. 光谱学与光谱分析. 2023(11): 3445-3451 . 
百度学术
2. 赵倩雯，李南希，陈琳琳，李红. 亚甲基蓝介导抗坏血酸氧化动力学的研究. 华南师范大学学报(自然科学版). 2018(06): 25-30 . 百度学术
其他类型引用(0)
计量
- 文章访问数: 507
- HTML全文浏览量: 276
- PDF下载量: 49
- 被引次数: 2
