IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v107y2021i3d10.1007_s11069-021-04507-0.html
   My bibliography  Save this article

Construction and verification of a rainstorm death risk index based on grid data fusion: a case study of the Beijing rainstorm on July 21, 2012

Author

Listed:
  • Xianhua Wu

    (Shanghai Maritime University
    Nanjing University of Information Science & Technology)

  • Jiqiang Zhao

    (Shanghai Maritime University)

  • Yun Kuai

    (China united network communication Co., LTD.)

  • Ji Guo

    (Shanghai Maritime University
    Nanjing University of Information Science & Technology)

  • Ge Gao

    (Nanjing University of Information Science & Technology
    National Climate Center)

Abstract

Rainstorm disaster brings serious threat to people's life and property safety. Constructing reasonable rainstorm disaster risk index and drawing rainstorm disaster risk map can help decision-makers to deal with rainstorm disaster effectively and reduce disaster loss. It has important practical significance. This paper, for the first time, proposes a comprehensive risk index for death caused by rainstorm disasters. According to this index, the regional hazard map of Beijing is drawn, so as to directly reflect the damage degree of rainstorm disaster in Beijing. In the process of index construction, the weight is determined by regression coefficient innovatively, and the disadvantage of subjective setting weight is avoided, and the spatial distribution of risk can be more accurately reflected by constructing disaster risk index by using fused grid data. Research shows that the rainstorm death risk index proposed in this paper can well reflect the risk of death caused by rainstorms in various areas of Beijing. Combined with the ArcGIS software, a risk map of death due to rainstorm disasters is drawn. It is found that Fangshan District of Beijing is a major disaster area with the highest risk of death. Finally, the managerial implication included that government administrators should evaluate the risk of rainstorm disasters in a certain area according to the established rainstorm death risk indexes and draw a risk map accordingly.

Suggested Citation

  • Xianhua Wu & Jiqiang Zhao & Yun Kuai & Ji Guo & Ge Gao, 2021. "Construction and verification of a rainstorm death risk index based on grid data fusion: a case study of the Beijing rainstorm on July 21, 2012," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2293-2318, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04507-0
    DOI: 10.1007/s11069-021-04507-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04507-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-04507-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Das, Priyam & Ghosal, Subhashis, 2018. "Bayesian non-parametric simultaneous quantile regression for complete and grid data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 172-186.
    2. Song, Malin & Peng, Jun & Wang, Jianlin & Zhao, Jiajia, 2018. "Environmental efficiency and economic growth of China: A Ray slack-based model analysis," European Journal of Operational Research, Elsevier, vol. 269(1), pages 51-63.
    3. HaiBo Hu, 2016. "Rainstorm flash flood risk assessment using genetic programming: a case study of risk zoning in Beijing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 485-500, August.
    4. Cai, Bofeng & Guo, Huanxiu & Ma, Zipeng & Wang, Zhixuan & Dhakal, Shobhakar & Cao, Libin, 2019. "Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data," Applied Energy, Elsevier, vol. 242(C), pages 994-1009.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiale Zhao & Fuqiang Yang & Yong Guo & Xin Ren, 2022. "A CAST-Based Analysis of the Metro Accident That Was Triggered by the Zhengzhou Heavy Rainstorm Disaster," IJERPH, MDPI, vol. 19(17), pages 1-20, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Can & Wang, Zhen & Cai, Bofeng & Peng, Sha & Wang, Yang & Xu, Chengdong, 2021. "Evolution-based CO2 emission baseline scenarios of Chinese cities in 2025," Applied Energy, Elsevier, vol. 281(C).
    2. Wang, Xiuli, 2023. "Exploring the role of resource industry dependence and green finance in green development efficiency in the context of post-Covid-19 period," Resources Policy, Elsevier, vol. 85(PB).
    3. Salman, Muhammad & Long, Xingle & Wang, Guimei & Zha, Donglan, 2022. "Paris climate agreement and global environmental efficiency: New evidence from fuzzy regression discontinuity design," Energy Policy, Elsevier, vol. 168(C).
    4. Ziheng Niu & Jianliang Xiong & Xuesong Ding & Yao Wu, 2022. "Analysis of China’s Carbon Peak Achievement in 2025," Energies, MDPI, vol. 15(14), pages 1-18, July.
    5. Xiangqian Wang & Shudong Wang & Yongqiu Xia, 2022. "Evaluation and Dynamic Evolution of the Total Factor Environmental Efficiency in China’s Mining Industry," Energies, MDPI, vol. 15(3), pages 1-19, February.
    6. Wang, Ke-Liang & Sun, Ting-Ting & Xu, Ru-Yu & Miao, Zhuang & Cheng, Yun-He, 2022. "How does internet development promote urban green innovation efficiency? Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. Guanchun Liu & Chien-Chiang Lee & Yuanyuan Liu, 2020. "Growth path heterogeneity across provincial economies in China: the role of geography versus institutions," Empirical Economics, Springer, vol. 59(2), pages 503-546, August.
    8. Khoshroo, Alireza & Izadikhah, Mohammad & Emrouznejad, Ali, 2022. "Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index," Energy, Elsevier, vol. 258(C).
    9. Feng, Chao & Huang, Jian-Bai & Wang, Miao, 2019. "The sustainability of China’s metal industries: features, challenges and future focuses," Resources Policy, Elsevier, vol. 60(C), pages 215-224.
    10. Mingming Zhu & Jigan Wang & Jie Zhang & Zhencheng Xing, 2022. "Urban Low-Carbon Consumption Performance Assessment: A Case Study of Yangtze River Delta Cities, China," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    11. Meng, Ming & Qu, Danlei, 2022. "Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis," Energy, Elsevier, vol. 239(PA).
    12. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    13. Jiasen Sun & Guo Li, 2022. "Optimizing emission reduction task sharing: technology and performance perspectives," Annals of Operations Research, Springer, vol. 316(1), pages 581-602, September.
    14. Feng, Chao & Zhong, Siqi & Wang, Miao, 2024. "How can green finance promote the transformation of China's economic growth momentum? A perspective from internal structures of green total-factor productivity," Research in International Business and Finance, Elsevier, vol. 70(PA).
    15. Liu, Qilu & Cheng, Kaiming & Zhuang, Yanjie, 2022. "Estimation of city energy consumption in China based on downscaling energy balance tables," Energy, Elsevier, vol. 256(C).
    16. Wei Fan & Chunxia Zhu & Lijun Fu & Charbel Jose Chiappetta Jabbour & Zhiyang Shen & Malin Song, 2024. "Role of land use in China’s urban energy consumption: based on a deep clustering network and decomposition analysis," Annals of Operations Research, Springer, vol. 339(1), pages 835-859, August.
    17. Jiao Hou & Xinhai Lu & Shiman Wu & Shangan Ke & Jia Li, 2022. "Analysis of the Dynamic Relationship between Green Economy Efficiency and Urban Land Development Intensity in China," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    18. Amjad Ali & Marc Audi & Yannick Roussel, 2021. "Natural Resources Depletion, Renewable Energy Consumption and Environmental Degradation: A Comparative Analysis of Developed and Developing World," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 251-260.
    19. Nelson Amowine & Zhiqiang Ma & Mingxing Li & Zhixiang Zhou & Benjamin Azembila Asunka & James Amowine, 2019. "Energy Efficiency Improvement Assessment in Africa: An Integrated Dynamic DEA Approach," Energies, MDPI, vol. 12(20), pages 1-17, October.
    20. Hongxin Yu & Yaohui Jiang & Zhaowen Zhang & Wen-Long Shang & Chunjia Han & Yuanjun Zhao, 2022. "The impact of carbon emission trading policy on firms’ green innovation in China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04507-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.