Elsevier

Journal of Environmental Management

Volume 231, 1 February 2019, Pages 321-328
Journal of Environmental Management

How earthquake-induced direct economic losses change with earthquake magnitude, asset value, residential building structural type and physical environment: An elasticity perspective

https://doi.org/10.1016/j.jenvman.2018.10.050Get rights and content

Highlights

  • Earthquake loss attribution is performed considering three components of risk.

  • Elasticity value is used to compare loss relationships at earthquake event level.

  • Physical environment is an important factor amplifying direct economic losses.

  • A 13% increase in earthquake magnitude would double direct economic losses.

  • A 133% increase in asset value exposure would double direct economic losses.

Abstract

Diagnosing all components of risk is essential in earthquake loss attribution science, but quantitative estimates on how sensitive the earthquake-induced direct economic losses (DELs) are to changes in hazard, exposure and vulnerability is rarely known. Here the relationship between earthquake DELs and earthquake magnitude (Ms), asset value exposure (K), proportion of non-steel-concrete residential buildings (H) and physical environment instability (E) is quantified using the concept of economic elasticity. Earthquake disaster event-based DEL records over the period from 1990 to 2016 for the mainland of China are fitted to a regression model. Elasticity values for Ms, K, H and E are 7.63, 0.75, 4.92 and 0.91, respectively, indicating that on average, DEL changes are more sensitive to changes in Ms and H—a 13% increase in Ms or a 20% increase in H would double earthquake DELs, while it may take a 133% increase in K or a 110% increase in E to cause the same economic losses. In turn, this suggests that human factors—decreasing H and K—could be efficient ways to reduce earthquake risk, while these two factors will become increasingly relevant for risk assessment in the future with continued economic growth. The elasticity estimate results could be used for studying future change in earthquake risks and for supporting disaster risk reduction strategies.

Introduction

Unlike weather-related hazards, earthquakes have a short duration, a strong destructive force and are usually unpredictable (Convers and Newman, 2011). In the 20th century, earthquake disasters have killed approximately 1.87 million people globally (Doocy et al., 2013). Millions of seismic events occur each year and some of them cause economic damage. Extreme earthquake disasters such as the 2008 Wenchuan Earthquake in China, the 2010 Haiti earthquake, the 2011 East Japan Earthquake and Tsunami and the 2015 Nepal earthquake resulted in severe damages and fatalities (Motamedi et al., 2012). With the increasing earthquake losses, disaster risk management has become an even greater challenge. Earthquake disaster risk assessment increasingly focuses on comprehensive considerations of multiple factors (Strunz et al., 2011, Huggel et al., 2013, Barnett et al., 2016). Disaster impacts depend on both the physical and socioeconomic environments. Addressing the issue of DEL change requires a careful evaluation of the drivers of damage from earthquake disasters, as well as separating damage attributable to changes in seismic hazard and changes in exposure and vulnerability.

How earthquake disaster damage changes with its three drivers, namely hazard, exposure and vulnerability within a risk framework (Huggel et al., 2013, Zischg et al., 2018), is a pivotal issue worth exploring. Earthquake magnitude is known as an effective proxy for representing seismic hazard intensity and has been used in earthquake disaster damage models (Chen et al., 2001). Except for hazard, an earthquake's potential of causing damage is tied to other two drivers (Huggel et al., 2013, Shi et al., 2014) including: 1) Exposure, that is, the amount of properties located in earthquake-stricken areas and the number of people who potentially can be affected by the earthquake. Rapid growth of wealth in earthquake-prone areas corresponds to increased potential losses (Cavallo et al., 2010, Zhou et al., 2014, Wu et al., 2017). 2) Vulnerability, that is, the susceptibility of assets to the impacts of earthquakes determined by socioeconomic (Cutter and Finch, 2008) and physical environment factors (UNISDR, 2017). For example, steel structures are generally less susceptible to structural failure than poorly designed brick structures that are more vulnerable to damage (Chen et al., 2001), and structural types of buildings are closely related to socioeconomic conditions. With regard to the physical environment dimension, for example, secondary disasters such as landslides are more likely to be induced by an earthquake in mountainous areas than in plain areas (Qi et al., 2010, Wang et al., 2016, Sæmundsson et al., 2018) and the cascading disasters will amplify disaster losses. The 2008 Wenchuan Earthquake directly caused more than 15,000 geohazards in the form of landslides, rockfalls and debris flows that resulted in about 20,000 deaths and enormous property damages, reflecting the susceptibility of high and steep slopes in mountainous areas affected by the earthquake (Yin et al., 2009, Cui et al., 2011). Research needs to consider the linkage of environmental and social factors of risk, and each element should be assessed (Huggel et al., 2013, Liu et al., 2015, Aitsi-Selmi et al., 2016). Such research is likely to provide opportunities to develop more effective disaster management strategies that place greater emphasis on preparedness pre-disaster by identifying the risk factors (Kreibich et al., 2017). Although it is well known that earthquake disasters cause great damage, advances in methods to quantify the relationship between direct economic losses (DELs) and hazards and social development are needed to address this important relationship (Fricker et al., 2017).

The goal of this study is two-fold: 1) to understand the relationship between earthquake-induced DELs and earthquake magnitude, asset value exposure, and the two vulnerability factors including non-steel-concrete residential building proportion and physical environment instability; and 2) to establish a quantitative model using the economic concept of “elasticity” based on an event-based earthquake disaster dataset for the mainland of China, and to see how sensitive DELs are to changes in earthquake magnitude, asset value exposure, non-steel-concrete residential building proportion and physical environment instability. The negative binomial model, which has shown great potential in loss attribution research considering nonlinear relationships (Fricker et al., 2017), was used in this study. Overall, this study not only deepens the understanding on how earthquake-induced DELs quantitatively change with its multiple driving factors based on a risk framework, but also enables the improvement of earthquake loss estimation models and loss prediction for supporting risk-based decisions in mitigation and prevention measures. R project for statistical computing (R Core Team, 2016) was used for the statistical analysis.

Section snippets

Materials and methods

Property losses in the context of earthquake disasters are determined by a variety of factors. A summary of these factors is shown in a simple conceptual framework (Fig. 1). It is worth noting that physical environment conditions during a disaster and pre/post-disaster have crucial impacts on earthquake-induced DELs when considering secondary disasters (Yin et al., 2009, Qi et al., 2010, Wang et al., 2016). Physical environment conditions generally relate to slope, lithology, topographic

Bivariate relationships between earthquake-induced DELs and causative variables

As can be seen in Fig. 4a and b, the denary logarithmic earthquake-induced DELs increase with both Ms and the denary logarithmic of asset value exposure—for higher Ms or higher asset value exposure, higher DELs tend to occur. For example, with both a high earthquake magnitude of Ms 8.0 and the largest asset value exposure, the 2008 Wenchuan Earthquake resulted in the highest DELs (the point in the upper right corner of Fig. 4a and b). Earthquake-induced DELs are limited by earthquake magnitude

Discussion

How earthquake-induced DEL changes with hazard, exposure and vulnerability within a risk framework remains an open and challenging question. This study aimed to better understand the relationship between earthquake magnitude, asset value exposure, proportion of non-steel-concrete residential buildings and physical environment instability level and earthquake-induced DELs by establishing statistical estimates on how sensitive DEL changes are to changes in each of the four factors.

Conclusions

Understanding the linkage of earthquake direct economic losses (DELs) to hazard, exposure and vulnerability within a risk framework is critical for risk assessment and taking adaptation measures. This study quantified the relationships between earthquake-induced DELs and the four drivers, including earthquake magnitude, asset value exposure, proportion of non-steel-concrete residential buildings and physical environment instability level. A negative binomial regression model was employed to

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant number 41571492); the National Key Research and Development Program (grant number 2016YFA0602403); and the 111 Project (grant number B08008).

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