Editing IPCC:AR6/SROCC/Chapter-4 (section)

==== 4.3.2.4 Other Human Dimensions ====

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The development of local scale case studies from a social science perspective, for example, in the Arctic (Ford et al., 2012 <sup>[[#fn:r1075|1075]]</sup> ; Ford et al., 2014 <sup>[[#fn:r1076|1076]]</sup> ) , small islands (Petzold, 2016 <sup>[[#fn:r1077|1077]]</sup> ; Duvat et al., 2017 <sup>[[#fn:r1078|1078]]</sup> ) and within cities (Rosenzweig and Solecki, 2014 <sup>[[#fn:r1079|1079]]</sup> ; Paterson et al., 2017 <sup>[[#fn:r1080|1080]]</sup> ; Texier-Teixeira and Edelblutte, 2017 <sup>[[#fn:r1081|1081]]</sup> ) or at the household level (Koerth et al., 2014 <sup>[[#fn:r1082|1082]]</sup> ) support a better understanding of the anthropogenic drivers of exposure and vulnerability. Four examples of drivers that were only emerging at the time of the AR5 are discussed below. Very importantly, another major emerging dimension that is not discussed here but rather in Section 4.4.4, relates to power asymmetries, politics, and the prevailing political economy, which are important drivers of exposure and vulnerability to SLR-related coastal hazards, and consequently adaptation prospects (Eriksen et al., 2015 <sup>[[#fn:r1083|1083]]</sup> ; Dolšak and Prakash, 2018 <sup>[[#fn:r1084|1084]]</sup> ) . Recent literature provides examples in coastal megacities like Jakarta, Indonesia (Shatkin, 2019 <sup>[[#fn:r1085|1085]]</sup> ) as well as in smaller cities, like Maputo, Mozambique (Broto et al., 2015 <sup>[[#fn:r1086|1086]]</sup> ) and Surat, India (Chu, 2016a; Chu, 2016b) , and many other coastal cities and settlements around the world ( ''high confidence'' ; Jones et al., 2015 <sup>[[#fn:r1087|1087]]</sup> ; Allen et al., 2018 <sup>[[#fn:r1088|1088]]</sup> ; Hughes et al., 2018 <sup>[[#fn:r1089|1089]]</sup> ; Sovacool, 2018 <sup>[[#fn:r1090|1090]]</sup> ) .

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<span id="gender-inequality"></span>
===== 4.3.2.4.1 Gender inequality =====

Gender inequality came to prominence only recently in climate change studies (~15 years ago; see Pearse, 2017 <sup>[[#fn:r1091|1091]]</sup> ) . In light of sea-related hazards and SLR specifically, the issue is still mainly investigated in the context of developing countries, although growing attention is paid to the issue in developed countries (e.g., Lee et al., 2015; Pearse, 2017) . Recent studies in southern coastal Bangladesh, for example, show that women get less access than men to climate- and disaster-related information (both emergency information and training programmes), decision making processes at the household and community levels, economic resources including financial means such as micro-credit, land ownership, and mobility within and outside the villages (Rahman, 2013 <sup>[[#fn:r1092|1092]]</sup> ; Alam and Rahman, 2014 <sup>[[#fn:r1093|1093]]</sup> ; Garai, 2016 <sup>[[#fn:r1094|1094]]</sup> ) . Gender inequity may be inherent in unfavourable background conditions (higher illiteracy rates, deficiencies in food and calories intake and poorer health conditions) as a result of, among other things, traditions, social norms and patriarchy. Together, these barriers disadvantage women more than men in developing effective responses to anticipate gradual environmental changes such as persistent coastal erosion, flooding and soil salinisation ( ''medium evidence, high agreement'' ). Such conclusions are in line with the literature on gender inequality and climate change at large (Alston, 2013 <sup>[[#fn:r1095|1095]]</sup> ; Pearse, 2017 <sup>[[#fn:r1096|1096]]</sup> ) , thus suggesting no major SLR-inherent specificities.

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<span id="loss-of-indigenous-knowledge-and-local-knowledge"></span>
===== 4.3.2.4.2 Loss of indigenous knowledge and local knowledge =====

Despite the identification of this issue in AR4, its treatment in AR5 remained limited. Recent literature partly focussing on SLR reaffirms that indigenous knowledge and local knowledge (IK and LK; Cross-Chapter Box 4 in Chapter 1 and Glossary) are key to determining how people recognise and respond to environmental risk (Bridges and McClatchey, 2009 <sup>[[#fn:r1097|1097]]</sup> ; Lefale, 2010 <sup>[[#fn:r1098|1098]]</sup> ; Leonard et al., 2013 <sup>[[#fn:r1099|1099]]</sup> ; Lazrus, 2015 <sup>[[#fn:r1100|1100]]</sup> ), and therefore to increasing adaptive capacity and reducing long-term vulnerability (Ignatowski and Rosales, 2013 <sup>[[#fn:r1101|1101]]</sup> ; McMillen et al., 2014 <sup>[[#fn:r1102|1102]]</sup> ; Hesed and Paolisso, 2015 <sup>[[#fn:r1103|1103]]</sup> ; Janif et al., 2016 <sup>[[#fn:r1104|1104]]</sup> ; Morrison, 2017 <sup>[[#fn:r1105|1105]]</sup> ).

IK and LK contribute both as a foundation for and an outcome of customary resource management systems aimed at regulating resource use and securing critical ecosystem protection (examples in Indonesia; Hiwasaki et al., 2015 <sup>[[#fn:r1106|1106]]</sup> ), structuring the relationship between people and authorities, and framing and maintaining a strong sense of place in the community (examples in Timor Leste; Hiwasaki et al., 2015 <sup>[[#fn:r1107|1107]]</sup> ). In turn, this allows local communities to predict and prepare for both sudden shock events that have historical precedent and, when IK and LK are embedded in day-to-day rituals and decision making processes, to also anticipate the consequences of gradual changes, as in sea level (examples in Indonesia; Hiwasaki et al., 2015 <sup>[[#fn:r1108|1108]]</sup> ). Customary resource management systems based on IK and elders’ leadership—for instance, Rahui in French Polynesia (Gharasian, 2016 <sup>[[#fn:r1109|1109]]</sup> ), or Mo in the Marshall Islands (Bridges and McClatchey, 2009)—also allow communities to diversify access to marine and terrestrial resources using seasonal calendars, to ensure collective food and water security, and to maintain ecological integrity (McMillen et al., 2014 <sup>[[#fn:r1111|1111]]</sup> ). In rural Pacific atolls, traditional food preservation and storage (e.g., storing germinated coconuts or drying fish) still play a role in anticipating disruptions in natural resource availability (Campbell, 2015 <sup>[[#fn:r1112|1112]]</sup> ; Lazrus, 2015 <sup>[[#fn:r1113|1113]]</sup> ). Such practices have enabled the survival of isolated communities from the Arctic to tropical islands in constraining sea environments for centuries to millennia (McMillen et al., 2014 <sup>[[#fn:r1114|1114]]</sup> ; Nunn et al., 2017a <sup>[[#fn:r1115|1115]]</sup> ). Morrison (2017) argues that IK and LK can also play a role in supporting internal migration in response to SLR, by avoiding social and cultural uprooting (Cross-Chapter Box 4 in Chapter 1).

In some specific contexts, climate change will also imply no-analogue changes, such as rapid ice-melt and changing conditions in the Arctic that have no precedent in the modern era, and could thus limit the relevance of IK and LK in efforts to address significantly different circumstances. Except in these specific situations, the literature suggests that the loss of IK and LK, and related social norms and mechanisms, will increase populations’ exposure and vulnerability to SLR impacts (Nakashima et al., 2012 <sup>[[#fn:r1117|1117]]</sup> ). The literature notably points out that modern, externally-driven socioeconomic dynamics, such as the introduction of imported food (noodles, rice, canned meat and fish, etc.), diminish the cultural importance of IK-based practices and diets locally, together with introducing dependency on monetisation and external markets (Hay, 2013; Campbell, 2015 <sup>[[#fn:r1118|1118]]</sup> ).

As a result, the loss of IK and LK may increase long-term vulnerability to SLR ( ''medium evidence, high agreement'' ). Given that IK and LK are largely based on observing and ‘making sense’ of the surrounding environment (moon, waves, winds, animal behaviours, topography, etc.), such a loss reflects a more general concern about the weakening of environmental connectedness in contemporary societies, which is not limited to remote, rural and developing communities ( ''medium confidence'' ). In developed contexts too, the loss of LK has played a critical role in recent coastal disasters (e.g., Katrina in 2005 in the USA, Kates et al., 2006) and increasing vulnerability to SLR (e.g., Newton and Weichselgartner, 2014; Wong et al., 2014 <sup>[[#fn:r1119|1119]]</sup> ).

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<span id="social-capital"></span>
===== 4.3.2.4.3 Social capital =====

Coastal communities draw on social structures and capabilities that can reduce risk and increase adaptive capacity in the face of coastal hazards (Aldrich, 2017 <sup>[[#fn:r1120|1120]]</sup> ; Petzold, 2018 <sup>[[#fn:r1121|1121]]</sup> ) . Although the term is subject to debate (Meyer, 2018 <sup>[[#fn:r1122|1122]]</sup> ) , social capital—that is, the level of cohesion between individuals, between groups of individuals, and between people and institutions, within and between communities—is considered to be a key enabler for collective action to reduce risk and build adaptive capacity (Adger, 2010 <sup>[[#fn:r1123|1123]]</sup> ; Aldrich and Meyer, 2015 <sup>[[#fn:r1124|1124]]</sup> ; Petzold and Ratter, 2015 <sup>[[#fn:r1125|1125]]</sup> ) . Levels of social capital can be influenced by underlying social processes, such as socioeconomic (in)equalities, gender issues, health, social networks and social media. It applies to both developing and developed countries, for example in densely populated deltas (Jordan, 2015 <sup>[[#fn:r1126|1126]]</sup> ) , European coasts (Jones and Clark, 2014 <sup>[[#fn:r1127|1127]]</sup> ; Petzold, 2016 <sup>[[#fn:r1128|1128]]</sup> ) , Asian urban or semi-urban coastal areas (Lo et al., 2015 <sup>[[#fn:r1129|1129]]</sup> ; Triyanti et al., 2017 <sup>[[#fn:r1130|1130]]</sup> ) and Pacific islands (Neef et al., 2018 <sup>[[#fn:r1131|1131]]</sup> ) . Social capital framed as an enabler for reducing vulnerability has been studied in the context of extreme events (risk prevention mechanisms, emergency responses and post-crisis actions) and collective environmental management (e.g., replanting mangroves, beach cleaning, etc.). Social capital also enables adaptation prospects. For example, its role has been explored in public acceptability of long-term coastal adaptation policies in the UK (Jones and Clark, 2014 <sup>[[#fn:r1132|1132]]</sup> ; Jones et al., 2015 <sup>[[#fn:r1133|1133]]</sup> ) . The role of social capital in building resilience to climate stress in coastal Bangladesh was explored by Jordan (2015) , who found complex and even contradictory interactions between social capital and resilience to climate stress. Among others, Jordan (2015) also advises caution about uncritical importation of such Westernised concepts in seeking to understand and address coastal vulnerability in developing countries.

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<span id="risk-perception"></span>
===== 4.3.2.4.4 Risk perception =====

Risk perception, which is context-specific and varies from one individual to another, may influence communities’ exposure and vulnerability as it shapes authorities’ and people’s attitudes towards sudden and slow onset hazards, as shown by Terpstra (2011) <sup>[[#fn:r1134|1134]]</sup> , Lazrus (2015), Elrick-Barr et al. (2017) <sup>[[#fn:r1135|1135]]</sup> and O’Neill et al. (2016) in the Netherlands, Tuvalu, Australia and Ireland, respectively. The progressive discounting of coastal hazard risks and subsequent loss of risk memory also played a role in coastal disasters such as Hurricane Katrina in 2005 in the USA (Burby, 2006 <sup>[[#fn:r1136|1136]]</sup> ; Kates et al., 2006 <sup>[[#fn:r1137|1137]]</sup> ) and Storm Xynthia in 2010 in France (Vinet et al., 2012 <sup>[[#fn:r1138|1138]]</sup> ; Genovese and Przyluski, 2013 <sup>[[#fn:r1139|1139]]</sup> ; Chadenas et al., 2014 <sup>[[#fn:r1140|1140]]</sup> ).

Risk perceptions stem from intertwined predictors such as ‘gender, political party identification, cause-knowledge, impact-knowledge, response-knowledge, holistic affect, personal experience with extreme weather events, [social norms] and biospheric value orientations’ (Kellens et al., 2011 <sup>[[#fn:r1141|1141]]</sup> ; Carlton and Jacobson, 2013 <sup>[[#fn:r1142|1142]]</sup> ; Lujala et al., 2015 <sup>[[#fn:r1143|1143]]</sup> ; van der Linden, 2015, p. 112; Weber, 2016 <sup>[[#fn:r1144|1144]]</sup> ; Elrick-Barr et al., 2017 <sup>[[#fn:r1145|1145]]</sup> ; Goeldner-Gianella et al., 2019 <sup>[[#fn:r1146|1146]]</sup> ). In general, there is a lack of education, training and thus knowledge and literacy on recent and projected trends in sea level, which compromises ownership of science facts and projections at all levels, from individuals and institutions to society at large.

While some studies have begun to highlight the influence of the distance from the sea on risk perceptions (Milfont et al., 2014 <sup>[[#fn:r1147|1147]]</sup> ; Lujala et al., 2015 <sup>[[#fn:r1148|1148]]</sup> ; O’Neill et al., 2016), there is still little knowledge about how risk perceptions vary across different geographical and social contexts, and how this influences exposure and vulnerability to coastal hazards (e.g., Terpstra, 2011; van der Linden, 2015 <sup>[[#fn:r1149|1149]]</sup> ). There is a critical lack of studies specifically addressing SLR. Some recent works conducted in coastal Australia suggest that while people are confident about their ability to cope with an already experienced event, when it comes to SLR, the dominant narrative is articulated around the barriers related to the ‘uncertainty in the nature and scale of the impacts as well as the response options available’ (Elrick-Barr et al., 2017, p. 1147). Similar conclusions have been highlighted in the Caribbean islands of St. Vincent (Smith, 2018 <sup>[[#fn:r1150|1150]]</sup> ) and the Bahamas (Thomas and Benjamin, 2018 <sup>[[#fn:r1151|1151]]</sup> ). SLR is rarely addressed separately from sea-related extreme events, which masks a crucial difference between already-observed and delayed impacts. Climate change is considered a “distant psychological risk” (Spence et al., 2012 <sup>[[#fn:r1152|1152]]</sup> ), making it and SLR per se ‘markedly different from the way that our ancestors have traditionally perceived threats in their local environment’ (Milfont et al., 2014 <sup>[[#fn:r1153|1153]]</sup> ; Lujala et al., 2015 <sup>[[#fn:r1154|1154]]</sup> ; van der Linden, 2015, p. 112; O’Neill et al., 2016).

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