Floods and Exports: An Empirical Study on Natural Disaster Shocks in Southeast Asia

Tembata, Kaori; Takeuchi, Kei

doi:10.1007/s41885-018-0033-6

Cited by 20 publications

(9 citation statements)

References 21 publications

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“…SEA includes 11 countries: Brunei, Cambodia, East Timor, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam. Southeast Asia is one of the most disaster-prone areas in the world [54], where earthquakes, volcano eruptions, tsunami and seasonal typhoons occur. The vast majority of SEA falls within the warm, humid tropics with plentiful rainfall.…”

Section: Study Areamentioning

confidence: 99%

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Jiang

Wang

et al. 2021

Remote Sensing

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Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA.

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Section: Study Areamentioning

confidence: 99%

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Jiang

Wang

et al. 2021

Remote Sensing

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“…Disasters wreak havoc on life, property, and overall human well-being [57,58]. Years of economic development and infrastructural assets are washed off with every flooding event, particularly in the developing countries of Southeast Asia, such as India, Pakistan, and Bangladesh [59][60][61][62][63]. The difference between the developed and developing worlds in the case of managing floods lies in their complex differential socioeconomic setups [64][65][66][67][68].…”

Section: Resultsmentioning

confidence: 99%

GIS-Based Urban Flood Risk Assessment and Management—A Case Study of Delhi National Capital Territory (NCT), India

et al. 2021

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Urban floods are very destructive and have significant socioeconomic repercussions in regions with a common flooding prevalence. Various researchers have laid down numerous approaches for analyzing the evolution of floods and their consequences. One primary goal of such approaches is to identify the areas vulnerable to floods for risk reduction and management purposes. The present paper proposes an integrated remote sensing, geographic information system (GIS), and field survey-based approach for identifying and predicting urban flood-prone areas. The work is unique in theory since the methodology proposed finds application in urban areas wherein the cause of flooding, in addition to heavy rainfall, is also the inefficient urban drainage system. The work has been carried out in Delhi’s Yamuna River National Capital Territory (NCT) area, considered one of India’s most frequently flooded urban centers, to analyze the causes of its flooding and supplement the existing forecasting models. Research is based on an integrated strategy to evaluate and map the highest flood boundary and identify the area affected along the Yamuna River NCT of Delhi. In addition to understanding the causal factors behind frequent flooding in the area, using field-based information, we developed a GIS model to help authorities to manage the floods using catchment precipitation and gauge level relationship. The identification of areas susceptible to floods shall act as an early warning tool to safeguard life and property and help authorities plan in advance for the eventuality of such an event in the study area.

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“…Pelli and Tschopp (2017) investigated 46 countries' exports to the United States from 1980 to 2000 and concluded that hurricanes' effects on exports were monotonically increasing in comparative advantage, with industries on both ends of the comparative advantage distribution being affected most significantly. Tembata and Takeuchi (2019) estimated the annual export loss to be $305-$557 million due to flooding in Southeast Asia. Dallmann (2019) studied temperature and precipitation differences between trade partners and found chronic negative effects on labour productivity and therefore trade flows worldwide from 1992 to 2014.…”

Section: Literature Reviewmentioning

confidence: 99%

Natural disasters, climate change, and structural transformation: A new perspective from international trade

2022

World Economy

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Natural disasters are well known to profoundly affect global economies, and they have received more and more discussions lately. However, it remains inconclusive how natural disasters would affect an economy in the long run. In this study, we empirically investigate the economic impacts of natural disasters under different time horizons. We use a difference-in-difference estimator and innovatively calculate a structural transformation index from the perspective of trade flows of a country's low-and high-skill industries. We find that natural disasters, especially climate-related disasters, will significantly cause a structural downgrade in the short run but are likely to be followed up by an upgrade in the long run when structural growth also features a diminishing rate of return. The impacts of natural disasters are also subject to the aggregate development level and trade openness of a country. For example, countries that are more developed, with higher income, or more engaged in international trade suffer less from natural disasters. As a result, their structural transformations are less affected by natural disasters than in developing, low-income, or domestic-oriented countries.

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Floods and Exports: An Empirical Study on Natural Disaster Shocks in Southeast Asia

Cited by 20 publications

References 21 publications

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

GIS-Based Urban Flood Risk Assessment and Management—A Case Study of Delhi National Capital Territory (NCT), India

Natural disasters, climate change, and structural transformation: A new perspective from international trade

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