Editing IPCC:AR6/WGII/Chapter-1 (section)

==== 1.4.1.2 Equitable and Effective Adaptation Informed by Concepts and Measures of Well-being ====

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Planning and assessment of effective and just adaptation require appropriate measures of both criteria. This report uses both single and multi-criteria measures. Local and regional decision makers employ benefit–cost analysis to efficiently allocate scarce resources among alternative adaptation efforts and among adaptation and other societal needs. Decision makers at national and global levels can employ measures of social welfare to consider trade-offs and synergies among adaptation, mitigation, and development. Such measures can avoid wasteful allocation of resources and help avoid maladaptation. Such measures also prove useful because well-established approaches exist to evaluate such quantities, and because income is highly correlated with a wide range of indicators of social progress and climate change adaptation capacity ( [[#Dasgupta--2018|Dasgupta et al., 2018]] ).

Aggregate, monetised economic measures are, however, insufficient to address issues of climate justice fully or to reflect that wide range of worldviews and values that different people bring to questions of climate action and development ( [[#Chambwera--2014|Chambwera et al., 2014]] ). While recent work has enriched the consideration of distributive justice in aggregate social welfare functions ( [[#Adler--2012|Adler, 2012]] ), multi-objective approaches that separately report several biophysical and socioeconomic attributes can prove valuable (Section 17.3.3). Many adaptation measures, in particular those that encompass transformational social changes (Section 1.5), involve complicated trade-offs among multi-dimensional benefits and costs ( [[#Adger--2016|Adger, 2016]] ). Different people commonly value such trade-offs differently, particularly in heterogeneous societies. Multi-objective measures can thus enhance transparency, fairness, legitimacy and participation by highlighting the different outcomes that different people and communities might find important, making the specific trade-offs more transparent and explicit, and avoiding privileging any particular view on the appropriate trade-offs ( [[#Lempert--2018|Lempert et al., 2018]] ; [[#Siders--2019b|Siders, 2019b]] ; [[#Siders--2020|Siders and Keenan, 2020]] ).

The SDGs and Key Representative Risks (Chapter 16) exemplify such multi-criteria measures. In addition, many communities increasingly measure policy outcomes using multi-objective measures, often organised around the concept of well-being and designed to allocate resources and implement policies to advance social progress ( [[#Lee--2015|Lee et al., 2015]] ; [[#City%20of%20Santa%20Monica--2018|City of Santa Monica, 2018]] ). Similarly, the Human Development Index (HDI), which derives from the capabilities approach, combines income (as gross national income, GNI, and parity purchasing power, PPP) with an education and a health indicator and integrates human and socioeconomic factors (Herrero et al., 2012; [[#USEPA--2016|USEPA, 2016]] ; [[#Leal%20Filho--2018|Leal Filho et al., 2018]] ; [[#Nagy--2018|Nagy et al., 2018]] ; [[#UNDP--2018|UNDP, 2018]] ). The inequality-adjusted HDI value, or IHDI, can be interpreted as the level of human development when inequality is accounted for ( [[#UNDP--2018|UNDP, 2018]] ).

The multi-criteria concept of well-being has been increasingly employed as a structured framework for measuring social progress in many areas of public policy ( [[#Lamb--2017|Lamb and Steinberger, 2017]] ) including climate and health (Chapter 7) and, to a lesser extent, in other areas of the climate change adaptation literature ( [[#Singh--2021|Singh et al., 2021]] ). Well-being reflects the ability of a person to pursue and realise the goals that they value ( [[#Sen--1985|Sen, 1985]] ). The disaster risk management community employs well-being to evaluate mental health impacts in terms of peoples’ abilities to cope with trauma and loss because of natural disasters ( [[#Berry--2010|Berry et al., 2010]] ; [[#MacDonald--2015|MacDonald et al., 2015]] ; [[#Willox--2015|Willox et al., 2015]] ). The term appears in the literature with concepts such as human security ( [[#Koren--2006|Koren and Butler, 2006]] ; [[#Adger--2010|Adger, 2010]] ; [[#Pasgaard--2017|Pasgaard et al., 2017]] ), subjective well-being or happiness ( [[#Sekulova--2013|Sekulova and van den Bergh, 2013]] ; [[#Rehdanz--2015|Rehdanz et al., 2015]] ; [[#Fanning--2019|Fanning and O’Neill, 2019]] ), welfare ( [[#Gough--2015|Gough, 2015]] ) and living standards or quality of life ( [[#Degorska--2018|Degorska and Degorski, 2018]] ; [[#Rao--2018|Rao and Min, 2018]] ).

Recent work has used quantified measures of well-being and multi-objective decision-support tools to balance among equity and efficiency objectives in disaster risk management (Section 1.5.2; Chapter 17; [[#Markhvida--2020|Markhvida et al., 2020]] ). Rather than focus on the economic value of lost assets, the well-being measure evaluates disaster impacts and recovery policies by considering the fraction of consumption lost at the household level for different income cohorts. Not surprisingly, poor households account for twice as much of the disaster losses when evaluated by effects on well-being rather than by asset losses. The most effective policy responses also differ when using well-being and asset loss-based measures. Ciullo et. al. (2020) compare flood control strategies using multi-objective decision criteria that include both benefit–cost and distributional components, show how the favoured strategy can depend on whether one seeks equitable risk or equitable risk reduction, and propose tools that can help embed both ethical and efficiency considerations in adaptation decisions. Widespread use of such approaches could strengthen consideration of climate justice along with efficiency in the evaluation of climate risks and adaptation (Section 1.5.2; [[#Dryzek--2013|Dryzek et al., 2013]] ).

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