Int J Disaster Risk Sci (2016) 7:393–412 www.ijdrs.com DOI 10.1007/s13753-016-0109-2 www.springer.com/13753 ARTICLE Measuring County Resilience After the 2008 Wenchuan Earthquake 1,2 2 3 2 Xiaolu Li • Nina Lam • Yi Qiang • Kenan Li • 4 1 1,2 Lirong Yin • Shan Liu • Wenfeng Zheng Published online: 21 December 2016 Ó The Author(s) 2016. This article is published with open access at Springerlink.com Abstract The catastrophic earthquake that struck Sichuan Keywords China Á Resilience Á Resilience inference Province, China, in 2008 caused serious damage to measurement (RIM) model Á Vulnerability Á Wenchuan Wenchuan County and surrounding areas in southwestern earthquake China. In recent years, great attention has been paid to the resilience of the affected area. This study applied the resilience inference measurement (RIM) model to quantify 1 Introduction and validate the community resilience of 105 counties in the impacted area. The RIM model uses cluster analysis to Wenchuan County in Sichuan Province, China, and its classify counties into four resilience levels according to the surrounding counties are part of a region prone to frequent exposure, damage, and recovery conditions. The model and destructive earthquakes and their accompanying sec- then applies discriminant analysis to quantify the influence ondary disasters (Chen et al. 2007; Li et al. 2016). The of socioeconomic characteristics on the county’s resilience. Wenchuan Earthquake, which occurred on 12 May 2008 is Analysis results show that counties located at the epicenter known for its huge destruction and high mortality. The had the lowest resilience, but counties immediately adja- magnitude 7.9 earthquake caused more than 69,227 deaths cent to the epicenter had the highest resilience capacities. and property damages of over RMB 845.1 billion Yuan Counties that were farther away from the epicenter (Guo 2012). Due to the mountainous landscape, low eco- returned to normal resiliency quickly. Socioeconomic nomic development, and poor infrastructure, Wenchuan variables—including sex ratio, per capita GDP, percent of County and its surrounding regions are extremely vulner- ethnic minority, and medical facilities—were identified as able to earthquakes and secondary disasters such as land- the most influential characteristics influencing resilience. slides and barrier lake floods. Although these counties have This study provides useful information to improve county similar characteristics in many aspects, observers noted resilience to earthquakes and support decision making for that some counties experienced less damage during earth- sustainable development. quakes and recovered more quickly afterwards (Guo 2012). Based on these observations, two questions are put for- ward: (1) are some counties more resilient to earthquakes & Wenfeng Zheng than others; and (2) what socioeconomic characteristics [email protected] make a county more resilient? The answers to these two 1 School of Automation, University of Electronic Science and questions could help improve the resilience of counties by Technology of China, Chengdu 610054, Sichuan, China promoting or controlling certain socioeconomic charac- 2 Department of Environmental Sciences, Louisiana State teristics of an area. University, Baton Rouge, LA 70803, USA Using the definition followed by a US National 3 The Earth Lab, University of Colorado Boulder, Boulder, Research Council report (NRC 2012, p. 1), this study CO 80303, USA defines resilience of a community as ‘‘the ability to prepare 4 Geographical & Sustainability Sciences Department, and plan for, absorb, recover from, and more successfully University of Iowa, Iowa City, IA 52242, USA 123 394 Li et al. Measuring County Resilience After the 2008 Wenchuan Earthquake adapt to adverse events over time.’’ There is an extensive essential to study community resilience to seismic disaster literature on definitions (Holling 1996), frameworks (Bru- in this region. The Wenchuan Earthquake received great neau et al. 2003; Cutter et al. 2003), and case studies attention because of the massive damage and heavy casu- (Cutter et al. 2003, 2010; Boruff et al. 2005; Reams et al. alties it caused. Yet studies on community resilience to 2012) of resilience. But with a few exceptions (for exam- seismic disasters in this region or in China are scarce. This ple, Fekete 2009; Tate 2012), studies that quantitatively study on community resilience to earthquakes in China measured resilience and were accompanied with validation intends to contribute to filling a gap in this research field. are rare. The challenges of measuring community resi- Due to the limitation on data availability, a total of 105 lience to disaster are many. First, due to the diverse char- counties around the epicenter that had the most serious acteristics of disasters, complex natural and social economic losses caused by the Wenchuan Earthquake were processes, and varying definitions of the terms (Cutter et al. selected for this study. 2014), there is significant controversy on how to identify the main factors leading to resilience. Second, the many subjective factors and inaccurate weights assigned to 2 Related Work variables make the measurement model difficult to gener- alize and apply to other contexts (Bruneau et al. 2003; The term resilience is encountered in multiple disciplines NRC 2012). Third, studies that explored community resi- ranging from engineering, psychology, environment, and lience to seismic disasters have seldom been validated with sociology to geography and the humanities. The original empirical data (Bruneau et al. 2003; Chang and Shinozuka definition of resilience from the Merriam-Webster online 2004). dictionary1 is ‘‘the capability of a strained body to recover To address some of these issues, Lam and her fellow its size and shape after deformation caused especially by researchers developed the resilience inference measure- compressive stress; an ability to recover from or adjust ment (RIM) model to assess community resilience and to easily to misfortune or change’’. Holling (1996) defined identify the key variables affecting resilience (Lam et al. resilience in two forms: engineering resilience, which 2015a). The RIM model has been applied in the Gulf of refers to how fast the system returns to its original state Mexico region to measure community resilience to coastal after disturbance, and ecological resilience, which indicates hazards (Lam et al. 2015a, b), and at different geographical how far the system can be perturbed without shifting to a scales in Louisiana (Li 2013). The RIM model is theoret- different state. Adger et al. (2010) elaborated that resilience ically sound; it incorporates empirical validation and can includes two elements: the ability to self-organize and the be easily extended to study various disasters in different capacity to learn and adapt. Bruneau et al. (2003) suggested places (Lam et al. 2015a, b). The RIM framework is not that a broad conceptualization of resilience should include confined to a specific type of disaster or indicators. Instead, the ability of a unit to reduce failure probabilities, conse- it defines a general framework and methodology for mea- quences from failures, and time to recovery. They further suring community resilience to different hazards. The RIM defined resilience for both physical and social systems to framework consists of three dimensions—exposure, dam- consist of four properties: robustness, redundancy, age, and recovery—and the two (dis)abilities—vulnera- resourcefulness, and rapidity (Bruneau and Reinhorn bility and adaptability—that link the three dimensions. By 2006). Recently, the concept of resilience is often mixed selecting appropriate indicators to represent these three with other closely related concepts such as vulnerability, dimensions, the RIM framework can be transformed to adaptability, and sustainability, making the measurement measure community resilience in confronting various dis- of resilience more complicated (Cutter et al. 2008; Lam asters in different places and to identify key variables that et al. 2015a). affect that resiliency. As discussed in detail in Sect. 3, the The different understandings of the resilience concept RIM model overcomes two major difficulties in assessing lead to various resilience measurements in many studies. resilience: validation of the derived resilience index and The concept also varies when disaster occurs in different statistical inference. natural and socioeconomic environments, which makes it This study applies the RIM model to analyze quantita- very difficult to define indicators that are universally tively community resilience after the 2008 Wenchuan applicable for resilience measurement. Cutter et al. (2003) Earthquake. We focus on the quake-prone region in developed the Social Vulnerability Index (SoVI) to assess southwestern China, specifically the hardest-hit counties of social vulnerability to environmental hazards using county- Sichuan, Gansu, and Shaanxi Provinces affected by the level socioeconomic and demographic data in the United 2008 Wenchuan Earthquake. In this region, earthquakes States. The method produced 11 factors from 42 variables are an active hazard and can be tremendously destructive at fairly regular and frequent intervals. Therefore, it becomes 1 https://www.merriam-webster.com/dictionary/resilience. 123 Int J Disaster Risk Sci 395 to explain 76.4% of variance based on factor analysis, and Gansu, and Shaanxi Provinces by the 2008 Wenchuan created SoVI scores using