Indicator Systems – Previous Studies and Indicator Selection

This study bases indicators on the determinants of adaptive capacity, which were adapted from IPCC’s report “Adaptation to Climate Change in the Context of Sustainable Development and Equity” (Smit et al 2001). Below are the indicators

chosen for the Urbanizing Adaptive Capacity Index (UACI) and some reasoning for their selection, as well as a list of previous studies that used variants of these determinants in their indicator systems. Data availability, previous literature, and relationship to urbanization were the major priorities in choosing indicators.

2.3.3.1 Biophysical Indicators

Storm water and Runoff: percent impervious surface – This is a very important indicator associated with urbanization and its effect on adaptive capacity. LULC change can influence flood intensity and frequency (IPCC 2012). As discussed in Lin et al (2002, 2009), built up land area is closely related to the increase in runoff and stress on storm water systems, as water is not able to be absorbed into the soil and trickle down into aquifers and instead runs along the top of impervious surfaces in larger volumes. It

increases the number of flood events or intensity of flood events (Remondi et al 2016).

With the increase of impervious surfaces like concrete or asphalt, biophysical systems are less able to adapt to changing precipitation patterns, as the land’s ability to retain

water or ability to remove it from the surface decreases. Cutter (2008), Jubeh and Mimi (2012), and Monterroso (2014) also used similar measures in their indicator systems.

Temperature Variance: degrees difference – Surface temperature is another aspect affected by urbanization through transformation of material with varying capacity for heat-retention (Fu and Weng 2016, Zhou et al 2016, Maimaitiyiming et al 2014). The urban heat island effect is a phenomenon associated with cities, and it can exacerbate many health-related issues (Li et al 2016). With more urban surfaces, areas are less able to adapt to changes as temperature differences are exacerbated, affecting both people and ecosystems. Monterroso (2014) and Binita et al (2015) both included temperature related indicators in their assessments.

Surface Water Stability: percent natural land cover – This indicator is related to the issues discussed with impervious surfaces. Hydrological changes are often linked to amount of natural vegetated land cover (Lin et al 2002, 2007). The more natural land cover, such as wetland or forest, rather than agriculture, the better the land is able to

provide ecosystem services and remain productive. Development into higher elevations or steeper sloped areas can be decrease the stability of land, potentially resulting in landslides during rains. Brooks et al (2005) also used a similar indicator.

Each of these adaptive capacity indicators has been used in previous studies and are correlated with urbanization in one way or another. Iframed differently, they may also be seen as indicators of exposure to climate impacts or indicators of urbanization as a driving force of climate change. But in this study, they will be used as indicators of ecosystem adaptive capacity

. The UACI as a whole will be used to measure adaptive capacity as a function of urbanization, which will be covered in the Methods section.

2.3.3.2 Socioeconomic Indicators

Networks – occupancy rate and trust: Networks, social capital, and community ties are important support systems for individuals and families during times of crisis, but are among some of the most difficult things to measure. The social ties that people have to others that can help them are very important for adaptive capacity and other benchmarks of wellbeing. However, urban populations are often found to have fewer community

ties as compared to rural areas (Beggs et al 2010, Ziersch et al 2009). Because of this determinant’s difficulty to measure and lack of comparable measures across

international borders, two indicators, occupancy rate and trust, were chosen to represent them. Cutter et al 2008, Monterroso 2014, Salik et al 2015, Xenarios et al 2016, and Nhuan et al 2016 all used some form of network measure in their indicator systems.

Economic Resources – GDP and median household income: GDP and income were the most frequent and straightforward measure used in many of the previous studies on adaptive capacity and vulnerability. More economic resources allows entities to prepare better for or recover more quickly from disasters or stressors. GDP is an indicator of the health of the entire economy, whereas median household income measures household or individual level adaptive capacity, and both are included in the UACI.

GDP and household income are both positively correlated with urbanization (Hope and Edge 1996). Acosta et al 2013, Cutter et al 2008, Jubeh and Mimi 2012, Posey 2009, Daramola et al 2016, Brooks et al 2005, Cutter et al 2008, Kelly 2000, Metzger et al 2006, Sietchipeng 2006, Vincent 2007, Binita et al 2015, Ahsan and Warner 2014, Xenarios et al 2016, Nhuan et al 2016, Panda et al 2013 all used economic resources in their indicator systems or listed it as a contributing factor for adaptive capacity and vulnerability.

Information and Skills – education level: Education level or literacy are almost ubiquitously positive characteristics and are the most commonly used general measure of information and skills. Education levels are also shown to be positively related to urbanization (USDA 2015, Pradhan et al 2000, Hope and Edge 1996). Among those that used education level in their studies of adaptive capacity and vulnerability, some are listed here: Acosta et al 2013, Brooks et al 2005, Jubeh and Mimi 2012, Metzger et al 2006, Monterroso 2014m, Sietchipeng 2006, Binita et al 2015, Ahsan and Warner 2014, Salik et al 2015, Xenarios et al 2016, Nhuan et al 2016, Panda et al 2013, Posey 2009.

Equity – Gini Index Score: The Gini index has its shortcomings, but has been measured at various levels for an extended period of time and remains the most widely used and comparable measure of economic equality. Urban areas, particularly in the developing world, have higher levels of inequality than rural areas (UN 2014, 2011). Equity is one of the 6 main determinants of adaptive capacity, and thus this indicator uses the Gini to account for this influence. Acosta et al 2013, Brooks et al 2005, Kelly 2000, Metzger et al 2006, Pandey and Bardsley 2015, McManus et al 2014 are several previous studies

which used equity in their indicators, on the assumption that greater levels of equality were positively correlated with adaptive capacity.

Management and Institutions – existence of government plans: Governmental management is a huge factor in adaptive capacity and general well-being. Effective government is also measured in a number of ways, and has been used for many different indicator systems, but for the purpose of this study existence of emergency plans were chosen to show government attention to adaptive capacity. Trends show that rural areas do not receive the same level of service from government entities as urban areas (UN 2014). Brooks et al 2005, Cutter et al 2008, Jubeh and Mimi 2012, Kelly 2000, Sietchipeng 2006, and Posey 2009 all utilized a measure of government or institutions in their studies.

Technology and Infrastructure – internet access: Internet access was chosen to represent technology and infrastructure. Internet access is and will continue to be a major conduit of information access, which is crucial for adaptation. Internet access tends to be higher

in urban areas, and is an important technology. There are a number of factors that influence internet access and its diffusion, but “countries with a larger urban population

and stronger participation within a global network of urban civilization would develop

the internet faster than others” (Li and Shiu 2012). Acosta et al 2013, Cutter et al 2008,

Metzger et al 2006, Sietchipeng 2006, Vincent 2007, Ahsan and Warner 2014, Salik et al 2015, Xenarios et al 2016, Nhuan et al 2016, and Panda et al 2013 all highlighted the importance of technology and infrastructure in their indicator systems.