Evaluation of Economic Loss of 2018/2019 African Swine Fever outbreaks in China

This study evaluates the total economic loss due to the outbreaks of African Swine Fever (ASF) in China between August 2018 and July 2019. We propose a systematic framework to assess both the direct economic loss on the Chinses breeding industry and indirect economic loss on other sectors. Our evaluation system integrates various statistical analysis methods by applying the cost-loss model and substitution indicator method to calculate direct economic losses and using input-output (IO) model to estimate indirect economic losses. The proposed method provides an innovative and commonly applicable way to deal with the missing data that needs to be adjusted and repaired to be used for economic shock evaluation. We find that total economic loss caused by ASF is 224.768 billion yuan consisting of 99.042 billion yuan direct economic loss of the breeding industry and 125.726 billion yuan indirect economic loss of other industries. Our findings suggest that the overall impact of ASF on China's national economy is relatively small, mainly local and short-term impacts on the swine industry.

Seismic Indirect Economic Loss Assessment and Recovery Evaluation Using Night-time Light Images – Application for Wenchuan Earthquake

Seismic indirect economic loss not only has a major impact on regional economic recovery policies, but also related to the economic assistance at the national level. Due to the Cross-regional economic activities and the difficulty of obtaining data, it's difficult that the indirect economic loss survey covers all economic activities. However, night-time light in an area can reflect the economic activity of the region. This paper focuses on the indirect economic losses caused by the Wenchuan earthquake in 2008 and evaluated the progress of restoration and reconstruction based on night-time light Images. First, the functional relationship between GDP and night-time light parameters was established based on the pre-earthquake data. Next, the indirect loss of the earthquake was evaluated by the night-time light attenuation in the disaster area after the earthquake. Then, the capacity recovery, which is characterized by the brightness recovery process of the light area, was evaluated. Lastly, the process of light expansion in the disaster area was analyzed to evaluate the economic expansion speed and efficiency.

Assessment of the ripple effects and spatial heterogeneity of total losses in the capital of China after a great catastrophic shock

The total losses caused by natural disasters have spatial heterogeneity due to the different economic development levels inside the disaster-hit areas. This paper uses scenarios of direct economic loss to introduce the sectors' losses caused by the 2008 Wenchuan earthquake (2008 WCE) in Beijing, utilizing the Adaptive Regional Input–Output (ARIO) model and the Inter-regional ripple effect (IRRE) model. The purpose is to assess the ripple effects of indirect economic loss and spatial heterogeneity of both direct and indirect economic loss at the scale of the smallest administrative divisions of China (streets, villages, and towns). The results indicate that the district of Beijing with the most severe indirect economic loss is the Chaoyang District; the finance and insurance industry (15, see Table 1) of Chaowai Street suffers the most in the Chaoyang District, which is 1.46 times that of its direct economic loss. During 2008–2014, the average annual GDP (gross domestic product) growth rate of Beijing was decreased 3.63 % by the catastrophe. Compared with the 8 % of GDP growth rate target, the decreasing GDP growth rate is a significant and noticeable economic impact, and it can be efficiently mitigated by increasing rescue effort and by supporting the industries which are located in the seriously damaged regions.

Assessment of ripple effect and spatial heterogeneity of total losses in the capital of China after a great catastrophe shocks

The total losses caused by natural disaster have spatial heterogeneity due to different economic development level inside the disaster-hit areas. This paper set the scenarios of direct economic loss to introduce the sectors' loss caused by 2008 Wenchuan earthquake into Beijing, utilized Adaptive Regional Input–Output (ARIO) model and Inter-regional ripple effect (IRRE) model. The purpose is to assess the ripple effects of indirect economic loss and spatial heterogeneity of both direct and indirect economic loss at the scale of smallest administrative divisions of China: streets/(villages and towns). The results indicate that the district of Beijing with the most severe indirect economic loss is Chaoyang district; Finance & Insurance industry (#15) of Chaowai Street suffers the most in Chaoyang district, which is 1.46 times of its direct economic loss. During 2008–2014, the average annual GDP growth rate of Beijing could be decreased 3.63 % affected by the catastrophe. Compared with the 8 % of GDP growth rate target, the decreasing GDP growth rate is a significant and noticeable economic impact, and it can be efficiently reduced by increasing rescue effort and priority supporting the industries which are located in the seriously damaged regions.

Indirect Economic Loss Estimation due to Seismic Highway Transportation System Disruption in “5.12” Wenchuan Earthquake

This paper aims to model the indirect economic loss due to damaged highway transportation systems in earthquake-affected areas that tends to be overlooked by decision-makers compared to direct economic loss that can be measured by repair or replacement cost. A regional Computable General Equilibrium (CGE) model is applied for estimating indirect economic loss in the whole economic system of the affected area. Shifang is a county-level city located in northwest Chengdu city, which is the capital of Sichuan Province, China. In the “5.12” Wenchuan earthquake, Shifang was one of the ten hardest-hit counties or cities and its highway transportation system was seriously damaged. It was selected as a case study to evaluate the indirect economic loss by seismic highway transportation system disruption. Results show that indirect economic loss accounted for nearly 36.4% of direct economic loss, which is as significant as direct economic loss resulting from earthquakedamaged highway transportation systems, and thus should be paid enough attention by decision-makers in pre-earthquake prevention and postearthquake recovery and reconstruction.