Editing IPCC:AR6/WGII/Chapter-10 (section)

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<div id="footnote-012" class="_idFootnote"></div>

[[#footnote-012-backlink|1]] In this Report, the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium or high. A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high, and is typeset in italics (e.g., ''medium confidence'' ) ''.'' For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.

<div id="footnote-011" class="_idFootnote"></div>

[[#footnote-011-backlink|2]] In this Report, the following terms are used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not &gt;50–100% and extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics (e.g., ''very likely'' ). This Report also uses the term ‘ ''likely'' range’ to indicate that the assessed likelihood of an outcome lies within the 17–83% probability range.

<div id="footnote-010" class="_idFootnote"></div>

[[#footnote-010-backlink|3]] Tropical Coastal Ecosystems Portal. Available at [http://www.nies.go.jp/TroCEP/index.html www.nies.go.jp/TroCEP/index.html] , accessed 8 October 2020.

<div id="footnote-009" class="_idFootnote"></div>

[[#footnote-009-backlink|4]] Average annual loss is the average amount that a country could expect to lose each year over the long term due to hazard incidence. It corresponds to the expected average loss per year considering all the events that could occur over a long time frame, including very intensive events. It is a probabilistic indication of the direct economic losses expected due to total or partial damage of physical assets existing in the affected area ( [[#UNISDR--2017|UNISDR, 2017]] ).

<div id="footnote-008" class="_idFootnote"></div>

[[#footnote-008-backlink|5]] Guangzhou, Mumbai, Kolkata, Shenzhen, Tianjin, Ho Chi Minh, Jakarta, Chennai, Surat, Zhanjiang, Bangkok, Xiamen and Nagoya

<div id="footnote-007" class="_idFootnote"></div>

[[#footnote-007-backlink|6]] Cumulative migration in high SLR scenarios is always higher, but since much of the migration has already occurred in earlier decades, the additional migration is lower in the high-SLR scenarios than the A1B scenario.

<div id="footnote-006" class="_idFootnote"></div>

[[#footnote-006-backlink|7]] While Hallegatte et al. (2019) estimate that in low- and middle-income countries, the cost of infrastructure disruptions ranges from 391 to 647 billion USD, they emphasise that ‘while these estimates are incomplete, they highlight the substantial costs that unreliable infrastructure impose on people in low- and middle-income countries’.

<div id="footnote-005" class="_idFootnote"></div>

[[#footnote-005-backlink|8]] Estimates are based on the DIVA model, which uses SSP2, 3 and 5, and RCP2.6, 4.5 and 8.5 in [[#Nicholls--2019|Nicholls et al. (2019)]] , and investments from [[#Ward--2017|Ward et al. (2017)]] . According to this study, uncertainty regarding socioeconomic changes and climate change is small compared with the uncertainty around construction costs and tolerance to risk.

<div id="footnote-004" class="_idFootnote"></div>

[[#footnote-004-backlink|9]] Ecosystem-based adaptation (EbA) is defined by IPBES as the conservation, sustainable management and restoration of natural ecosystems to help people adapt to climate change (Glossary, 2019). In urban areas, EbA includes improving ecological structures (e.g., maintaining watersheds, forests, green roofs), ecological functions and processes (e.g., wetland functioning for flood protection), valuation measures (including monetary or non-monetary values to ecosystem service benefits) and investing in ecosystem management practices (i.e., enabling adaption co-benefits through the maintenance, preservation and restoration or creation of ecological structures) ( [[#Liu--2014a|Liu et al., 2014a]] ; [[#Brink--2016|Brink et al., 2016]] ). Thus, EbA adaptation actions include protecting urban green spaces, improving permeability, fostering urban agriculture, mangrove restoration in coastal cities, improved wetland management and so forth ( [[#Doswald--2014|Doswald et al., 2014]] ; [[#Brink--2016|Brink et al., 2016]] ; [[#de%20Coninck--2018|de Coninck et al., 2018]] ).

<div id="footnote-003" class="_idFootnote"></div>

[[#footnote-003-backlink|10]] 11 The set of technical, financial and institutional capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organisations threatened by a hazard to prepare to act promptly and appropriately to reduce the possibility of harm or loss. Depending on context, EWS may draw upon scientific and/or Indigenous knowledge. These EWS are also considered for ecological applications (e.g., conservation, where the organisation itself is not threatened by hazard but the ecosystem under conservation is; an example is coral bleaching alerts), in agriculture (e.g., warnings of ground frost and hailstorms) and in fisheries (e.g., storm and tsunami warnings) ( [[#IPCC--2018a|IPCC, 2018a]] ).

<div id="footnote-002" class="_idFootnote"></div>

[[#footnote-002-backlink|11]] 12 Two exceptions to this were found. One is a survey in Saudi Arabia which tested public perceptions of disaster risk and found that direct experience with such disasters does not directly influence risk perception ( [[#Alshehri--2013|Alshehri et al., 2013]] ). Second, a study on flood experience and ensuing adaptation behaviour in Pakistan found that those with prior flood experience do not make significantly different choices than those who have no experience of flooding. What is more significant is ''repeated'' exposure to flooding events ( [[#Said--2015|Said et al., 2015]] ).

<div id="footnote-001" class="_idFootnote"></div>

[[#footnote-001-backlink|12]] 13 Some studies compare nationally representative surveys on climate perceptions and their impacts on climate action to demonstrate that higher risk perception leads to higher motivations to undertake climate action (Corner et al., 2014; Smith and Mayer 2018). Others, however, highlight that higher risk perception can lead to a normalisation of risks, leading to lower climate action (Luís et al., 2018). In all of these papers, there is a recognition that the literature on perceptual drivers of climate action is USA-centric and is negligible in Asia (Capstick et al. 2015).

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