Figure 2.2. Ladder of stakeholder participation (based on Pretty et al., 1995; Conde and Lonsdale, 2005).
who often have an intuitive understanding of which risks can be coped with and which cannot - that can subsequently be developed into a quantitative model (Jones and Boer, 2005). It can be depicted as one or more climatic or climate-related variables upon which socio-economic responses are mapped (Figure 2.3). The core of the coping range contains beneficial outcomes. Towards one or both edges of the coping range, outcomes become negative but tolerable. Beyond the coping range, the damages or losses are no longer tolerable and denote a vulnerable state, the limits of tolerance describing a critical threshold (left side of Figure 2.3). A coping range is usually specific to an activity, group, and/or sector, although society-wide coping ranges have been proposed (Yohe and Tol, 2002).
Risk is assessed by calculating how often the coping range is exceeded under given conditions. Climate change may increase the risk of threshold exceedance but adaptation can ameliorate the adverse effects by widening the coping range (right side of Figure 2.3). For example, Jones (2001) constructed critical thresholds for the Macquarie River catchment in Australia for irrigation allocation and environmental flows. The probability of exceeding these thresholds was a function of both natural climate variability and climate change. Yohe and Tol (2002) explored hypothetical upper and lower critical thresholds for the River Nile using current and historical streamflow data. The upper threshold denoted serious flooding, and the lower threshold the minimum flow required to supply water demand. Historical frequency of exceedance served as a baseline from which to measure changing risks using a range of climate scenarios.
Communicating risk and uncertainty is a vital part of helping people respond to climate change. However, people often rely on intuitive decision-making processes, or heuristics, in solving complicated problems of judgement and decision-making (Tversky and Kahneman, 1974). In many cases, these heuristics are surprisingly successful in leading to successful decisions under information and time constraints (Gigerenzer, 2000; Muramatsu and Hanich, 2005). In other cases, heuristics can lead to predictable inconsistencies or errors of judgement (Slovic et al., 2004). For example, people consistently overestimate the likelihood of low-probability events (Kahneman and Tversky, 1979; Kammen et al., 1994), resulting in choices that may increase their exposure to harm (Thaler and Johnson, 1990). These deficiencies in human judgement in the face of uncertainty are discussed at length in the TAR (Ahmad et al., 2001).
Participatory approaches establish a dialogue between stakeholders and experts, where the experts can explain the uncertainties and the ways they are likely to be misinterpreted, the stakeholders can explain their decision-making criteria, and the two parties can work together to design a risk-management strategy (Fischoff, 1996; Jacobs, 2002; NRC, 2002). Because stakeholders are often the decision-makers themselves (Kelly and Adger, 2000), the communication of impact, adaptation, and vulnerability assessment has become more important (Jacobs, 2002; Dempsey and Fisher, 2005; Füssel and Klein, 2006). Adaptation decisions also depend on changes occurring outside the climate change arena (Turner et al., 2003b).
If the factors that give rise to the uncertainties are described (Willows and Connell, 2003), stakeholders may view that information as more credible because they can make their own judgements about its quality and accuracy (Funtowicz and Ravetz, 1990). People will remember and use uncertainty assessments when they can mentally link the uncertainty and events in the world with which they are familiar; assessments of climate change uncertainty are more memorable, and hence more influential, when they fit into people's pre-existing mental maps of experience of climate variability, or when sufficient detail is provided to help people to form new mental models (Hansen, 2004). This can be aided by the development of visual tools that can communicate impacts, adaptation, and vulnerability to stakeholders while representing uncertainty in an appropriate manner (e.g., Discovery Software, 2003; Aggarwal et al., 2006).
2.3.5 Data needs for assessment
Although considerable advances have been made in the development of methods and tools for CCIAV assessment (see previous sections), their application has been constrained by limited availability and access to good-quality data (e.g., Briassoulis, 2001; UNFCCC, 2005; see also Chapter 3, Section 3.8; Chapter 6, Section 6.6; Chapter 7, Section 7.8; Chapter 8, Section, 8.8; Chapter 9, Section 9.5; Chapter 10, Section 10.8; Chapter 12, Section 12.8; Chapter 13, Section 13.5; Chapter 15, Section 15.4; Chapter 16, Section 16.7).
In their initial national communications to the UNFCCC, a large number of non-Annex I countries reported on the lack of appropriate institutions and infrastructure to conduct systematic data collection, and poor co-ordination within and/or between different government departments and agencies (UNFCCC, 2005). Significant gaps exist in the geographical coverage and management of existing global and regional Earth-observing systems and in the efforts to retrieve the available historical data. These are especially acute in developing-country regions such as Africa, where lack of funds for modern equipment and infrastructure, inadequate training of staff, high maintenance costs, and issues related to political instability and conflict are major constraints (IRI, 2006). As a result, in some regions, observation systems have been in decline (e.g., GCOS, 2003; see also Chapter 16, Section 16.7).
Major deficiencies in data provision for socio-economic and human systems indicators have been reported as a key barrier to a better understanding of nature-society dynamics in both developed and developing countries (Wilbanks et al., 2003; but see Nordhaus, 2006). Recognising the importance of data and information for policy decisions and risk management under a changing climate, new programmes and initiatives have been put in place to improve the provision of data across disciplines and scales. Prominent among these, the Global Earth Observation System of Systems (GEOSS) plan (Group on Earth Observations, 2005) was launched in 2006, with a mission to help all 61 involved countries produce and manage Earth observational data. The Centre for International Earth Science Information Network (CIESIN) provides a wide range of environmental and socioeconomic data products.4 In addition, the IPCC Data Distribution Centre (DDC), overseen by the IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA), hosts various sets of outputs from coupled Atmosphere-Ocean General Circulation Models (AOGCMs), along with environmental and socio-economic data for CCIAV assessments (Parry, 2002). New sources of data from remote sensing are also becoming available (e.g., Justice et al., 2002), which could fill the gaps where no ground-based data are available but which require resourcing to obtain access. New and updated observational data sets and their deficiencies are also detailed in the WG I report for climate (Trenberth et al., 2007) and sea level (Bindoff et al., 2007).
Efforts are also being made to record human-environment interactions in moderated online databases. For instance, the
Deslnventar database5 records climatic disasters of the recent past in Latin America, documenting not only the adverse climatic events themselves, but also the consequences of these events and the parties affected. Information on local coping strategies applied by different communities and sectors is being recorded by the UNFCCC.6
Many assessments are now obtaining data through stakeholder elicitation and survey methods. For example, in many traditional societies a large number of social interactions may not be recorded by bureaucratic processes, but knowledge of how societies adapt to climate change, perceive risk, and measure their vulnerability is held by community members (e.g., Cohen, 1997; ACIA, 2005; see also Section 2.3.2). Even in data-rich situations, it is likely that some additional data from stakeholders will be required. However, this also requires adequate resourcing.
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