Direct and Indirect Economic Losses Using Typhoon-Flood Disaster Analysis: An Application to Guangdong Province, China

Abstract

Guangdong Province is one of China’s largest and most developed regions. It is home to more than 113 million people and features unique geographical and climatic characteristics. Typhoons that pass through often result in heavy rainfall, which causes flooding. The region’s risk of typhoon and flood disasters, and the resulting indirect economic impacts, have not been fully assessed. The purpose of this paper is to introduce a method for assessing the spatial and temporal cumulative risk of typhoon-induced flood disasters, and the resulting indirect economic impacts, in order to deal with the uncertainty of disasters. We combined an analytic hierarchy process (AHP) and spatial analysis using a geographic information system (GIS) to produce a comprehensive weighted-risk assessment from three different aspects of disaster, vulnerability, and resilience, with 11 indicators. A new method for computing risk based on spatial and temporal cumulative patterns of typhoon-induced flood disasters was introduced. We incorporated those direct impacts into a computable general equilibrium (CGE) model to simulate indirect economic losses in alternative scenarios according to different risk levels. We found that the risk in the coastal area is significantly higher than that in the northern mountainous area. The coastal areas of western Guangdong, Pearl River Delta, and Chaoshan Plain face the greatest risk. Our results indicate that typhoon and flood disasters have negative effects on the real GDP, residents’ income, consumption, and several other macroeconomic indicators. We found differing disaster impacts across industrial sectors, including changes in the output, prices, and flow of labor among industries. Our estimates provide scientific support for environmental planning, spatial planning, and disaster-risk management in this important region. They are also of reference value for the development of disaster management strategies in similar climatic regions around the world.

Marine environmental pollution and offshore aquaculture structure: Evidence from China

“…So, under the objective restriction that the marine fishing scale expansion is limited, and the output can hardly meet the demand growth effectively, the demand for high-quality aquatic products has been an essential driving force that prompts the marine fishery culture to adjust its production structure (Merino et al, 2012). ②External risk, which is measured by the typhon-struck area (Rds) and the diseasestruck area (Dds), there is no doubt that factors such as marine hazards (typhoons and storms) will have a negative impact on nearshore farming structures (Gao et al, 2020); ③Comparative earning level (Earn), which is denoted by the ratio of the unit value of products obtained through marine fishing to the unit value of products provided by marine fishery culture, which can portray the demand for the wild aquatic products and cultivated aquatic products (Davidson et al, 2012); ④Production technology, which is measured by two dimensions (Ren, 2021a), namely the number of marine technical personnel (Srp) and the number of marine technological research projects (Orp). The structural adjustment and upgrade of the marine fishery culture calls for the effective support of supporting techniques (He et al, 2022).…”

Evaluating Indirect Economic Losses from Flooding Using Input–Output Analysis: An Application to China’s Jiangxi Province

“…Regarding practical application, CGE models require many parameters to be calibrated and can easily deviate from reality. Traditional IO models are limited to undertaking static analysis, and can easily overstate the impacts of non-affected regions because they do not consider substitution possibilities between imports from different regions [3,33]. To overcome these shortcomings in traditional IO models, there have been some developments to improve the measurement, for example, reflecting the conditions of a poor economy caused by disaster shocks [34], considering the influence of the time factor [35], and constructing an adaptive regional IO model [18,36].…”

“…Among all natural disasters, floods are the most common in terms of incidence, affected population, area, and related economic losses [1,2]. As flood disasters have negative effects on real GDP, residents' income, consumption, and several other macroeconomic indicators [3], quantifying the spatial distribution and corresponding effects after extreme shocks is critical for both post-disaster reconstruction and sustainable development objectives [4]. Flood exposure will further increase with the intensification of the global hydrological cycle, posing a serious threat to future generations [5][6][7].…”

Analyzing the response distribution of DO concentration and its environmental factors under the influence of typhoon rain events with remote sensing

“…The role of hierarchical analysis (AHP) as well as ratio weighting (RW) in determining indicator weights was also compared, and the AHP model was found to perform better in calculating the importance of indicators. Gao et al (2020) combined the spatial analysis method of AHP and geographic information system (GIS) to conduct a comprehensive weighted risk assessment based on the spatial and temporal cumulative patterns of typhooninduced flooding disasters in Guangdong Province as the research object. Guangdong Province was classified into six levels of risk zones based on the integrated typhoon disaster risk, and the indirect economic impacts were further analyzed on this basis.…”