You have entered the Pathfinder's decision tree for capacity analytical tasks.

Adaptive capacity is a broad concept that refers to the availability of all kinds of resources, such as natural, financial, cognitive, social and institutional ones, which may be mobilised for adapting to climate change. See, for example, the discussion of these resources in the sustainable livelihood framework (Carwell et al., 1997). As a consequence, a wide variety of methods for assessing capacity can be found in the literature. The applicability of these methods depends on the type of AS encountered (see the currently displayed  decision tree).

For public adaptation challenges the public actors wishes to understand adaptive capacity of private actors in order to influence their actions at later stages in the adaptation process. Towards this end, capacity indicators or indices are used. These approaches attempt to ‘indicate’ possible future impacts based on data collected on the current state of the exposed individuals, groups of people, communities or countries. In the literature, these approaches are also called social vulnerability indices. Different types of variables are used for this.

The main group of variables used in adaptive capacity and social vulnerability indication approaches relate to the generic and potential capacity of social groups to adapt and includes variables at a micro-analytical level and at a macro-analytical level. The former focus on individuals or households, and analyse the resources available to individuals. The latter, the macro-analytical level approaches, generally focus on aggregate characteristics of social systems, such as, for example, GDP, education levels, age structure, information management (McGray et al., 2007) or polycentric decision making contexts (Pahl-Wostl, 2007). Adaptive capacity indicators may also include variables that refer to the current climate as well as experienced disaster damage/losses. See the Toolbox section on Participatory Vulnerability and Capacity Assessments for a more comprehensive treatment of
these appraoches.

Generally, adaptive capacity and social vulnerability indication methods face the challenge that the aggregation of indicating variables into a vulnerability index can hardly be supported by theory nor can the results be validated empirically (Hinkel 2011a). Due to the lack of theory, some approaches seek to validate through data generated in interviews and focus groups against the “narratives” of vulnerability present in the literature (e.g. Mustafa et al. 2008). Other approaches use expert judgement, but different experts usually rank dimensions differently (Brooks and Adger 2005). See Table 2.3 for a summary and examples.

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Table 2.3: Impact-analytical methods (1).

Method Type	Trend detection	Impact attribution	Vulnerability indication
Task	Trend detection in time series data.	Explaining observed changes in study unit through (combination of) variables.	Indicating how climate change may impact study unit based on (combination of) variables.
Characteristics of AS	Time-series data is available on the study unit.	Data on explanatory variables is available. Data on observed impacts on the study unit is available.	Data on indicating variables is available. Data on observed impacts is NOT available. Future impacts cannot be reliably simulated using computational models.
Theoretical assumptions		Explanation of observed impacts through climate or socio-economic variables.
Steps taken	Selection of variables of interest. Application of statistical methods.	Selection of potential explanatory variables based on literature and theory. Application of statistical methods.	Selection of potential indicating variables based on the literature. Aggregation of indicating variables based on normative or theoretical arguments (Hinkel 2011).
Results	Statistical significant trend in data.	Statistical model explaining observed impacts.	A function that maps the current state of the entity to a measure of possible future impacts. The measure is often called adaptive capacity.
Example cases	Emanual (2005) develops an index of accumulated annual power-dissipation from tropical storms in 5 ocean basins. The index is based on measures of wind-speed and precipitation in the storms. Using statistical methods an upward trend in the index is observed over the period since the 1970s. Pielke et al. (2008) find no trend in the annual hurricane damage in the US normalised for inflation, population and wealth.	Checkley et al., (2000), for example, explain changes in daily hospital admissions in Lima through the stimuli variables temperature, humidity and rainfall. Singh et al., (2001) explain observed incidences of diarrhoea in Fiji based on variations in temperature and rainfall. Tol and Yohe (2007) address the question whether national level socio-economic variables can explain observed impact data found in the EM-DAT database. An initial list of 34 variables was selected based on the IPCC's eight determinants of adaptive capacity. Six alternative indicators such as number of people affected by natural disasters, infant mortality and life expectancy were selected for which data was available in the EMDAT database. 24 of the 34 indicating variables were found to be statistically not significant. Amongst the statistical significant ones, different ones were found significant for different hazards. They conclude that there are no universal explanations; mechanisms that cause impacts vary from case to case and hazard to hazard.	Hahn et al. (2009) develop a Livelihood Vulnerability Index based on surveying 220 household in Mozambique. The indicating variables describing aspects such as demographics, social networks, resource availability and past exposure to climate variability were selected based on the literature and then aggregated using equal weights.
Issues involved		A general issue for the complex social-ecological systems considered in CCVIA is that the amount of possible explanatory variables is thus very large and not conducive to building statistical models. Second, most impact data has only begun to be collected with respect to slow-onset changes, most impact data is on extreme events

It is thus important to note that adaptive capacity indicators only provide a rough, high-level and rapid assessment of the potential and generic capacity of actors threatened by climate change. Whether this potential capacity is realized in the context of a specific climate threat depends on many contextual institutional and cognitive factors. As public actors are concerned with influencing action of private actors, they may generally be interested in further exploring these factors through applying methods of behavioural and institutional analysis in order to understand and predict how the actors they aim to influence will act given a particular public adaptation option. These methods are, however, applicable once an adaptation problem or decision has been identified as only with respect to a specific adaptation decision can the relevant institutions and cognitive factors be identified. These methods will thus be treated in the Pathfinder's section on identifying measures. Methods that aim at building adaptive capacity refer to implementation and are therefore treated in the Pathfinder's section on implementing adaptation actions.