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Demand-side correlated economic losses in different sectors in different regions (RMB 100 million). Note: Agriculture, forestry, animal husbandry and fishery (AGR); mining (MIN); manufacturing (MAN); electricity, heat, gas, and water supply (EHG); construction (CON); wholesale and retail trade (WHR); transportation, storage and postal services (TSP); accommodation and catering (ACC); information transmission, software, and information technology services (ITS); finance (FIN) Real Estate (RES); Rental and Business Services (LBS); Scientific Research and Technical Services (STS); Water, Environment, and Public Facilities Management (WEP); Residential Services, Repairs, and Other Services (OTS); Education (EDU); Health and Social Work (HES); Culture, Sports, and Entertainment (CSE); Public Administration, Social Security, and Social Organizations (PAS).
Source publication
China is an extremely sensitive nation severely impacted by global climate change, with frequent floods in the Yangtze River Economic Zone causing severe socioeconomic losses and ecological and environmental issues. To investigate the potential industry-related economic losses and comprehensive hazards of flooding in the Yangtze River Economic Zone...
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Citations
... This method has a strong mathematical theoretical foundation, making it more objective and scientifically grounded. Many scholars have employed objective weighting methods to assess risk or vulnerability [23][24][25] . In recent years, machine learning models have also been applied to risk assessments in addition to the entropy weight method 26 . ...
In vulnerability assessments, accurately determining the indicator weights is essential to ensure the results’ precision and reliability. This paper proposes an optimized comprehensive symmetric Kullback–Leibler (K–L) distance weighting method, in which the comprehensive symmetric K–L distance for each indicator is calculated using a grid-based approach, and the normalized distance serves as the weight for each indicator. ArcGIS software was employed to assess the Ili River Basin flood vulnerability as a case study. The results reveal the following: (1) The grid-based optimized symmetric K–L distance method facilitated variable weight processing in the disaster assessments, where it offered a scientific and adaptable approach for indexing and weighting the Ili River Basin flood vulnerability, thus improving both the evaluation accuracy and practicality. (2) The spatial distribution of the Ili River Basin flood vulnerability levels was uneven, with higher vulnerability observed in the northwestern, southwestern, and southeastern regions, and lower vulnerability in the eastern and northeastern areas. Yining County, Yining City, and certain southern regions of Cocodala City were particularly vulnerable due to multiple influencing factors, including the population, economy, and society. These areas require focused attention and preventive measures.
