Editing IPCC:AR6/WGII/Chapter-18 (section)

== 18.2 Linking Development and Climate Action ==

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The AR5 examined the relationship between climate and sustainable development in [[IPCC:Wg2:Chapter:Chapter-13|Chapter 13]] ( [[#Olsson--2014|Olsson et al., 2014]] ) and Chapter 20 ( [[#Denton--2014|Denton et al., 2014]] ) in Working Group II and [[IPCC:Wg2:Chapter:Chapter-4|Chapter 4]] (Fleurbaey et al., 2014) in Working Group III. It concluded that dangerous levels of climate change would limit efforts to reduce poverty ( [[#Denton--2014|Denton et al., 2014]] ; Fleurbaey et al., 2014). Since the AR5, the adoption of the Paris Agreement and Agenda 2030 have demonstrated increased international consensus regarding the need to pursue climate change as a component of sustainable development. For example, climate change impacts ‘ ''undermine the ability of all countries to achieve sustainable development'' ’ ( [[#United%20Nations--2015|United Nations, 2015]] ) and can reverse or erase improvements in living conditions and decades of development ( [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ). However, recent analysis shows that actions to meet the goals of the Paris Agreement can undermine progress towards some SDGs ( ''high agreement'' , ''medium evidence'' ) ( [[#Pearce--2018b|Pearce et al., 2018b]] ; [[#Liu--2019|Liu et al., 2019]] ; [[#Hegre--2020|Hegre et al., 2020]] ) ( [[#18.2.5.3|Section 18.2.5.3]] ). Meanwhile efforts to achieve the SDGs can contribute to worsening climate change ( ''high agreement'' , ''medium evidence'' ) ( [[#Fuso%20Nerini--2018|Fuso Nerini et al., 2018]] ). These findings in the literature highlight the importance of identifying clear goals and priorities for both climate action and sustainable development as well as mechanisms for capitalising on potential synergies between them and for managing trade-offs. In assessing literature relevant to the intersection between climate action and development, we first explore the implications of different patterns of development and development trajectories followed by more focused assessment of the links between development and climate risk.

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=== 18.2.1 Implications of Current Development Trends ===

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Understanding the interactions between climate change, climate action and sustainable development necessitates consideration for the current development context in which different communities, nations and regions find themselves. For example, wealthy economies of the Global North will encounter different opportunities and challenges vis-à-vis climate change and sustainable development than developing economies of the Global South. Moreover, all economies are already following an existing development trajectory that has implications for the type and scale of interventions associated with pursuing CRD and managing climate risk. Some nations may experience particular challenges with reducing greenhouse gas emissions owing to the carbon-intensive nature of their energy systems ( ''very high confidence'' ) ( [[#18.3.1.1|Section 18.3.1.1]] ). Others may experience acute challenges with adaptation due to existing vulnerability associated with poverty and social inequality ( ''very high confidence'' ) ( [[#18.2.5.1|Section 18.2.5.1]] ). Overcoming such challenges is fundamental to the pursuit of CRD.

While demonstrable progress has been made towards the SDGs and improving human well-being, globally and in specific nations, some observed patterns of development are inconsistent with sustainable development and the principles of CRD ( ''very high confidence'' ) ( [[#van%20Dooren--2018|van Dooren et al., 2018]] ; [[#Eisenmenger--2020|Eisenmenger et al., 2020]] ; [[#Leal%20Filho--2020|Leal Filho et al., 2020]] ). A significant literature, for example, links development to the loss of biodiversity and the extinction crisis ( [[#Ceballos--2017|Ceballos et al., 2017]] ; [[#Gonçalves-Souza--2020|Gonçalves-Souza et al., 2020]] ; [[#Oke--2021|Oke et al., 2021]] ). Meanwhile, in human systems, indicators such as the limited convergence in income, life expectancy and other measures of well-being between poor and wealthy countries (with notable outliers such as China) ( [[#Bangura--2019|Bangura, 2019]] ), and the increase in income inequality and the decline in life expectancy and well-being in rich countries ( [[#Rougoor--2015|Rougoor and van Marrewijk, 2015]] ; [[#Alvaredo--2017|Alvaredo et al., 2017]] ; [[#Goda--2017|Goda et al., 2017]] ; [[#Harper--2017|Harper et al., 2017]] ; [[#Goldman--2018|Goldman et al., 2018]] ), suggest limitations of the current development paradigm to successfully deliver universal human and ecological well-being by the 2030s or even mid-century ( [[#TWI--2019|TWI, 2019]] ).

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=== 18.2.2 Understanding Development in CRD ===

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Development in this report is defined as efforts, both formal and informal, to improve standards of human well-being, particularly in places historically disadvantaged by colonialism and other features of early global integration. Development is not limited to the SDGs, however these represent an internationally agreed sub-set of goals. Prior IPCC reports employed development as a typological framing of the current state of a given country or population ( [[#IPCC--2014a|IPCC, 2014a]] ) ( [[IPCC:Wg2:Chapter:Chapter-1#1.1.4|Section 1.1.4]] ). Such framings frequently rest upon measures of economic activity, using them as proxies for the wider well-being of the population whose activity is measured. For example, the level of GDP is often equated with levels of social welfare, even though as a measure of market output, it can be an inadequate metric for gauging well-being over time, particularly in its environmental and social dimensions ( [[#Van%20den%20Bergh--2007|Van den Bergh, 2007]] ; Stiglitz et al., 2009).

The result of this broad framing linking economic growth to human well-being has been decades of policies, programmes and projects aimed at growing economies at scales from the household to regional and global. However, linking development to past and current modes of economic growth creates significant challenges for CRD, as it implies that the very processes that have contributed to current climate challenges, including economic growth and the resource use and energy regimes it relies upon, are also the pathways to improvements in human well-being. This places climate resilience and development in opposition to one another.

While there are many possible successful pathways to future development in the context of climate change, history shows that pathways positive for the vast majority of people typically induce significant impacts and costs, especially on marginal and vulnerable people ( ''high confidence'' ) ( [[#Hickel--2017|Hickel, 2017]] ). Frequently, considerations for social difference and equity are side-lined in these processes, for example through the assumption that a growing economy lifts opportunity for all, further marginalising those who are the most vulnerable to climate change ( [[#Matin--2018|Matin et al., 2018]] ; [[#Diffenbaugh--2019|Diffenbaugh and Burke, 2019]] ).

The Agenda 2030 and its 17 SDGs and 169 targets seeks to ‘leave no one behind’ through five pillars (5Ps): People, Planet, Prosperity, Peace and Partnership ( [[#United%20Nations--2015|United Nations, 2015]] ). The five pillars align with the dimensions of development that influence motion towards or away from CRD. The focus on '''people''' refers to inclusion rather than exclusion, and the extent to which people are empowered or disempowered to make decisions about their well-being, determine their futures and be in a position to assert their rights. This means being able to make decisions that determine whether people are on a pathway towards or away from CRD (Figure 18.1–18.3). The focus on '''planet''' refers to protecting the planet, ensuring a balance of ecosystems, biodiversity and human activities, and giving equal space and respect for its integrity. The focus on '''prosperity''' refers to equity in well-being grounded in unanimity over shared goals and resources, rather than individualism, and economic, social and technological progress grounded in stewardship and care, rather than exploitation. The focus on '''partnership''' refers to mutual respect embedded in solidarity that recognises multiple worldviews and their respective knowledges, rather than singular or hierarchy of knowledge, and acknowledges inherent nature-society connections, rather than posing nature as opposites or competitors. The focus on '''peace''' emphasises the need for just and equitable societies. These five pillars are inter-related but local and national contexts situate current status differently around the world. Successful achievement of Agenda 2030 is aligned with a safe climate with adequate mitigation and adaptation, and effective and inclusive systems transitions. With these conditions, a high CRD world can be attained, noting that when approached individually, the transformative potential of the SDGs is limited ( [[#Veland--2021|Veland et al., 2021]] ).

The need for transformational changes across sectors and scales to address the urgency and scope of action needed to enable a climate-resilient future in which goals such as the SDGs might be realised requires attention to the specific ways in which development action is defined and enacted (Box 18.1).

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==== 18.2.2.1 Development Perspectives ====

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Development is about ‘improvement’. However there have been different and often conflicting viewpoints on the improvement of ‘what’ and ‘how’ to improve. The diversity of positions has resulted in a multitude of metrics to track development, some more influential than others on policy. Alternative measures of development, while numerous, generally seek to nuance the connection between economic growth and human well-being. Because they maintain core notions of progress and, in some cases, economic growth seen in more mainstream models of development, they are less vehicles for transformation than continuations of thinking and action fundamentally at odds with the needs of CRD. These include the Measure of Economic Welfare ( [[#Nordhaus--1973|Nordhaus and Tobin, 1973]] ), the Index of Sustainable Economic Welfare ( [[#Cobb--1989|Cobb and Daly, 1989]] ), the Genuine Progress Indicator ( [[#Escobar--1995|Escobar, 1995]] ), the Adjusted Net Saving Index or the Genuine Savings Index (GSI), The Human Development Index (HDI), the Inequality-Adjusted Human Development Index (UNDP, 2016a), the Gender Development Index, the Gender Inequality Index, the Multidimensional Poverty Index, the Index of Sustainable Economic Welfare (ISEW) ( [[#Daly--1989|Daly and Cobb, 1989]] ), the Genuine Progress Indicator (GPI) ( [[#Kubiszewski--2013|Kubiszewski et al., 2013]] ), Gross National Happiness (GNH) ( [[#Ura--2004|Ura and Galay, 2004]] ), Measures of Australia’s Progress (MAP) ( [[#Trewin--2004|Trewin and Hall, 2004]] ), the OECD Better Life Index ( [[#OECD--2019a|OECD, 2019a]] ) and the Happy Planet Index ( [[#NEF--2016|NEF, 2016]] ).

In terms of their historical trajectory, different perspectives on development can be broadly divided into five categories.

# ''Development as economic growth (1950s onwards)'' : Equating development with economic growth was a natural outcome of the dominance of economics as the major discipline to study problems of newly independent countries in the 1950s ( [[#Escobar--1995|Escobar, 1995]] ), measured through GDP. Environment was not a policy concern in the immediate period after decolonisation. The GDP measure has withstood the test of time, in spite of being an inexact measure of human well-being, and is the widely used metric globally to track development. Recent improvements to GDP have tried to account for environmental factors ( [[#Gundimeda--2007|Gundimeda et al., 2007]] ; [[#United%20Nations--2021|]] [[#United%20Nations--2021|United Nations, 2021]] ).
# ''Development as distributional improvements (1970s onwards)'' : That economic growth does not automatically result in decline in poverty and improved distribution of income became apparent in the 1970s. Welfare measures were thus promoted that involved ‘redistribution with growth’ ( [[#Chenery--1974|Chenery, 1974]] ). These distributional concerns have re-emerged in the last two decades with the widening gap between the richer and poorer groups of the population ( [[#Chancel--2019|Chancel and Piketty, 2019]] ) and also the increased attention to ‘ecological distribution conflicts’ ( [[#Martinez-Alier--2021|Martinez-Alier, 2021]] ). The political economy perspective, highlighting continued dependencies of countries in the Global South on the Global North, now evolved into political ecology highlighting environmental concerns between and within countries. Environment was not yet a policy priority, despite the links between development and environment becoming clearer.
# ''Development as participation (1980s onwards)'' : Bottom-up responses emphasising sustainable livelihoods and local-level development emerged in the 1980s. The movement, which involved independent and uncoordinated efforts by grassroots activists, social movements and non-governmental organisations (NGOs), became ‘mainstreamed’ into development in the 1990s ( [[#Chambers--2012|Chambers, 2012]] ). The multi-dimensional nature of poverty was acknowledged at the global policy level ( [[#World%20Bank--2000|World Bank, 2000]] ) and there was wider acceptance of the role of non-economics social sciences as well as critical approaches in research on development and poverty ( [[#Thomas--2008|Thomas, 2008]] ). Participatory development involved decentralisation and local planning, emphasising protection of local natural resources in addition to improving living standards.
# ''Development as expansion of human capabilities (1980s onwards)'' : The human development and capabilities approach was the first formidable response to the GDP-centric view of development ( [[#Sen--2000|Sen, 2000]] ; [[#Deneulin--2009|Deneulin and Shahani, 2009]] ). Studies showed that improvements in income did not necessarily improve human well-being in other dimensions such as health and education, or more broadly put, ‘freedoms’ (Ruggeri Laderchi et al., 2003). The capabilities idea was influential in global policy making through Human Development Reports and metrics such as Human Development Index (HDI) and Multidimensional Poverty Index (MPI). However, environmental sustainability was not a major component in this approach until much later ( [[#Alkire--2018|Alkire and Jahan, 2018]] ). Recent improvements to HDI such as the planetary pressures-adjusted HDI ( [[#United%20Nations--2020|United Nations, 2020]] ) is a step in this direction.
# ''Development as post-growth (2010 onwards)'' : The late 1980s saw a big push towards taking the environment to the centre of the global policy agenda ( [[#World%20Commission%20on%20Environment%20and%20Development--1987|World Commission on Environment and Development, 1987]] ). However, progress in addressing environmental questions has been slow. As compared with Millennium Development Goals (MDGs), SDGs aim to tackle environmental concerns by explicitly tracking progress on multiple indicators. Nevertheless, the approach in these policy propositions sits largely within the economic growth framework itself. The climate change challenge and the financial crisis of 2008 led many scholars, ecological economists and environmental social scientists in particular, to argue for a post-growth world. Post-growth ( [[#Jackson--2021|Jackson, 2021]] ), degrowth ( [[#Kallis--2018|Kallis, 2018]] ; [[#Hickel--2021|Hickel et al., 2021]] ) and other environmentalist scholarship takes inspiration from critiques of development such as post-development ( [[#Escobar--1995|Escobar, 1995]] ). The argument here is not for better metrics but for imagining and working towards systemic change in the wake of the climate crisis. The challenge however is how to account for historical differences in economic growth and living standards between the Global North and the Global South and to protect the interests of Global South in the spirit of ‘common but differentiated responsibilities’ to climate change adaptation and mitigation. As empirical studies in the Global South have demonstrated ( [[#Lele--2018|Lele et al., 2018]] ), developing countries face multiple stressors, climate change being just one among them, and there are multiple normative concerns in developing country contexts, such as equity and justice, and not merely resilience ( ''very high confidence'' ).

Achieving CRD requires framings of development that move away from linear paradigms of development as material progress by focusing on diversity and heterogeneity, well-being and equality, not only in contemporary practices, but also pathways of change over time ( [[#Gibson-Graham--2005|Gibson-Graham, 2005]] ; [[#Gibson-Graham--2006|Gibson-Graham, 2006]] ). Such approaches, which are fundamentally aligned with ecological and ecosystem-based environmental assessments that identified heterogeneity of approaches and actions as the most effective path to a sustainable world ( [[#Millennium%20Ecosystem%20Assessment--2005|Millennium Ecosystem Assessment, 2005]] ), emphasise the importance of cultural, linguistic and religious diversity, not merely as alternative sources of information about the world, but as different paradigms of well-being ( [[#Kallis--2018|Kallis, 2018]] ). These include Indigenous and local knowledge that provide alternatives to these framings of the world (Cross-Chapter Box INDIG). This broad reframing of development includes a focus on visions such as ‘buen vivir’ ( [[#Cubillo-Guevara--2014|Cubillo-Guevara et al., 2014]] ; [[#Walsh--2018|Walsh, 2018]] ; [[#Acosta--2019|Acosta et al., 2019]] ), ecological Swaraj ( [[#Kothari--2014|Kothari et al., 2014]] ; [[#Demaria--2017|Demaria and Kothari, 2017]] ; [[#Shiva--2017|Shiva, 2017]] ) and Ubuntu ( [[#Dreyer--2015|Dreyer, 2015]] ; [[#Ewuoso--2019|Ewuoso and Hall, 2019]] ), among others. All are linked by relationships with nature radically different from the Western mechanistic vision, presenting not only framings of development and the environment that yield locally appropriate CRDPs, but serve as examples of alternative ways of living in balance with nature that might inform similar thinking in other places.

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==== 18.2.2.2. Complexity of Development and Climate Action ====

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Differing perspectives on development are in part determined by the multiple diverse priorities held by different actors and nations. Another reason is that development is not a linear process with a single goal, and active development planning requires simultaneously taking multiple processes and factors into account. This is well illustrated by growing attention to climate security. The AR5 delivered conflicting messages regarding climate change and security ( [[#Gleditsch--2014|Gleditsch and Nordås, 2014]] ), yet the understanding of climate-related security risks has made substantial progress in recent years ( [[#von%20Uexkull--2021|von Uexkull and Buhaug, 2021]] ). Although there remains considerable research gaps in certain regions ( [[#Adams--2018|Adams et al., 2018]] ), a large body of qualitative and quantitative studies from different disciplines provides new insight into the relationship of climate change and security ( [[#Buhaug--2015|Buhaug, 2015]] ; [[#De%20Juan--2015|De Juan, 2015]] ; [[#Brzoska--2016|Brzoska and Fröhlich, 2016]] ; [[#Abrahams--2017|Abrahams and Carr, 2017]] ; [[#Sakaguchi--2017|Sakaguchi et al., 2017]] ; Moran et al, 2018; [[#Scheffran--2020|Scheffran, 2020]] ). Though not the only cause ( [[#Sakaguchi--2017|Sakaguchi et al., 2017]] ; [[#Mach--2019|Mach et al., 2019]] ), climate change undermines human livelihoods and security, because it increases the populations vulnerabilities, grievances and political tensions through an array of indirect—at times nonlinear—pathways, thereby increasing human insecurity and the risk of violent conflict ( [[#van%20Baalen--2018|van Baalen and Mobjörk, 2018]] ; [[#Koubi--2019|Koubi, 2019]] ; [[#von%20Uexkull--2021|von Uexkull and Buhaug, 2021]] ). Indeed, context, as well as timing and spatial distribution, matter and need to be accounted for ( [[#Abrahams--2020|Abrahams, 2020]] ).

In line with this better understanding, climate change and security have been reframed in the political space, to focus more on human security. The solutions to climate-related security risks cannot be military, but are linked to development and people’s vulnerabilities in complex social and politically fragile settings ( [[#Abrahams--2020|Abrahams, 2020]] ). This has resulted in integration of climate-related security risk into institutional and national frameworks ( [[#Dellmuth--2018|Dellmuth et al., 2018]] ; [[#Scott--2018|Scott and Ku, 2018]] ; [[#Aminga--2020|Aminga and Krampe, 2020]] ), including several Nationally Determined Contributions (NDCs) ( [[#Jernnäs--2019|Jernnäs and Linnér, 2019]] ; [[#Remling--2021|Remling, 2021]] ). One example is the UN Climate Security Mechanism—set up in 2018 between UNDP, UNEP and UN DPPA to help the UN more systematically address climate-related security risks and devise prevention and management strategies. Yet work remains in bridging these concerns with practical responses on the ground ( [[#Busby--2021|Busby, 2021]] ). Especially since emerging research building on the maladaptation literature shows that this practice cannot just mean adding adaptation and mitigation to the mix of development strategies in a given location, as this may have unintended and unanticipated effects and might even backfire completely ( [[#Dabelko--2013|Dabelko et al., 2013]] ; [[#Magnan--2020|Magnan et al., 2020]] ; [[#Mirumachi--2020|Mirumachi et al., 2020]] ; [[#Schipper--2020|Schipper, 2020]] ; [[#Swatuk--2021|Swatuk et al., 2021]] ). In extremely underdeveloped, fragile contexts such as Afghanistan, the local-level side effects of climate adaptation and mitigation projects might result in different development outcomes and question the potential for sustainable peace ( [[#Krampe--2021|Krampe et al., 2021]] ). Given the clearer understanding of the intertwined nature of climate change, security and development—especially in fragile and conflict affected regions—a rethinking of how to transfer this knowledge into policy solutions is necessary for the formulation of CRD.

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=== 18.2.3 Scenarios as a Method for Representing Future Development Trajectories ===

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Sustainable development represents specific development processes and priorities that can affect climate risk. As a result, sustainable development both shapes the context in which different actors experience climate change and represents a potential opportunity, particularly by reducing climate risk by addressing vulnerability, inequity and shifting development towards more sustainable trajectories ( [[#IPCC--2012|IPCC, 2012]] ; [[#Denton--2014|Denton et al., 2014]] ; [[#IPCC--2014b|IPCC, 2014b]] ; [[#IPCC--2014a|IPCC, 2014a]] ; [[#IPCC--2018a|IPCC, 2018a]] ; [[#IPCC--2019b|IPCC, 2019b]] ). As assessed in past IPCC special reports and assessment reports, this same literature has also illustrated how different socioeconomic conditions affect mitigation options and costs. For example, variations in future economic growth, population size and composition, technology availability and cost, energy efficiency, resource availability, demand for goods and services, and non-climate-related policies (e.g., air quality, trade), individually and collectively have all been shown to result in different climates and contexts for mitigation and adaptation.

One common approach for exploring the implications of different development trajectories is the use of scenarios of future socioeconomic conditions, such as the SSPs ( [[#O’Neill--2017|]] [[#O’Neill--2017|O’Neill et al., 2017]] ). The SSPs represent sets of future global societal assumptions based on different societal, technological and economic assumptions that result in different development trajectories. Such scenarios often correspond to a small set of scenario archetypes ( [[#Harrison--2019|Harrison et al., 2019]] ; [[#Sitas--2019|Sitas et al., 2019]] ; [[#Fergnani--2020|Fergnani and Song, 2020]] ) in that they reflect core themes regarding the future of development such as sustainability versus rapid growth. Scenarios with assumptions more closely aligned with sustainability agendas (e.g., SSP1-Sustainability) commonly imply lower greenhouse gas emissions and projected climate change (Riahi et al., 2022), lower mitigation costs for ambitious climate goals (Riahi et al., 2022), lower climate exposure due in large part to the size of society (see Chapter 16) and greater adaptive capacity ( [[#Roy--2018|Roy et al., 2018]] ) (see also Chapter 16). In contrast, scenarios with rapid global economic and fossil energy growth (e.g., SSP5 Fossil-Fueled Development) imply higher emissions and project climate change and higher mitigation costs, as well as greater social and economic capacity to adapt to climate change impacts ( [[#Hunt--2012|Hunt et al., 2012]] ) (Table 18.1).

The SSPs incorporate various assumptions regarding population, GDP and greenhouse gas emissions, for example, that are relevant to development and climate resilience. In addition, the SSPs have been used to explore a broad range of development outcomes for human and ecological systems (Table 18.1), including multiple studies exploring futures for food systems, water resources, human health and income inequality. Limited, top-down modelling studies have used the SSPs to explore issues such as societal resilience ( [[#Schleussner--2021|Schleussner et al., 2021]] ) or gender equity ( [[#Andrijevic--2020a|Andrijevic et al., 2020a]] ). Such studies indicate that different development trajectories have different implications for future development outcomes, but results vary significantly among different climate (e.g., representative concentration pathways [RCPs]) and development contexts, resulting in ''limited agreement'' among different SSPs (Table 18.1). Nevertheless, for some outcomes, SSPs are associated with generally similar outcomes. Over the near-term (e.g., 2030), those outcomes are strongly influenced by development inertia and path dependence, reducing differences among SSPs. Outcomes diverge later in the century, but fewer studies explore futures beyond 2050. Collectively, the scenarios reflect trade-offs associated with different development trajectories ( [[#Roy--2018|Roy et al., 2018]] ), with some SSPs foreshadowing outcomes that are positive in some contexts, but negative in others (Table 18.1). For example, pathways that lead to poverty reduction can have synergies with food security, water, gender, terrestrial and ocean ecosystems that support climate risk management, but also poverty alleviation projects with unintended negative consequences that increase vulnerability (e.g., [[#Ley--2017|Ley, 2017]] ; [[#Ley--2020|Ley et al., 2020]] ).

While the scenarios literature is useful for characterising the potential climate risk implications of different global societal futures, important limitations impact their use in climate risk management planning ( ''very high confidence'' ). The first is the often highly geographically aggregated nature of the SSPs and other scenarios, which, in the absence of application of nesting or downscaling methods, often lack regional, national, or sub-national context, particularly regarding social and cultural determinants of vulnerability ( [[#van%20Ruijven--2014|van Ruijven et al., 2014]] ). Furthermore, there is limited understanding of the cost and process associated with transforming from today into each assumed socioeconomic future, or the opportunity to shift from one pathway to another ( [[#18.3|Section 18.3]] ). Furthermore, the characteristics of the pathways suggest that they are not equally likely , there are relationships implied in assumptions that are uncertainties to consider (e.g., land productivity improvements are land saving), it is difficult to identify the role of different development characteristics, and policy implementation is stylised. In general, global assessments are not designed to inform local planning, given that there are many local circumstances consistent with a global future and unique local development context and uncertainties to manage—demographic, economic, technological, cultural and policy.

Overall, pursuing sustainable development in the future is shown to have synergies and trade-offs in its relationships with every element of climate risk: the emissions and mitigation determining hazard; the size, location and composition of development determining exposure; and the adaptive capacity determining vulnerability. Importantly, the scenarios literature overall has found trade-offs such that none of the global societal projections achieve all the SDGs ( ''very high confidence'' ) ( [[#Roy--2018|Roy et al., 2018]] ) ( [[#18.2.5.3|Section 18.2.5.3]] ). Historical evidence supports this as well, for example, finding low-cost energy and food access is historically associated with higher emissions but greater adaptive capacity, and energy efficiency innovation contributing to lower emissions and greater adaptive capacity (e.g., [[#Blanford--2012|Blanford et al., 2012]] ; Blanco et al., 2014; [[#Mbow--2019|Mbow et al., 2019]] ; [[#USEPA--2019|USEPA, 2019]] ). The literature suggests that trade-offs in the pursuit of sustainable development are inevitable. Managing those trade-offs, as well as capitalising on the synergies, will be important for CRD, particularly given trade-offs have distributional implications that could contribute to inequities ( [[#18.2.5.3|Section 18.2.5.3]] ).

'''Table 18.1 |''' Implications of different socioeconomic development pathways for CRD indicators. Studies presented in the above table include qualitative storylines and quantitative scenarios for two or more SSPs. Arrows and colour coding reflect the positive or negative impacts on sustainability based on aggregation of results for the 2030–2050 time horizon across the identified studies. Confidence language reflects the number of studies upon which results are based (evidence) and the agreement among studies regarding the direction of change (agreement).

{| class="wikitable"
|-
! rowspan="2"| '''Development indicator'''

! rowspan="2"| '''Relevant SDG'''

! colspan="5"| '''Shared Socioeconomic Pathway'''

! rowspan="2"| '''Confidence'''

'''Evidence/'''

'''Agreement'''

! rowspan="2"| '''References'''

|-
! '''Sustainability'''

'''(SSP1)'''

! '''Middle of the road'''

'''(SSP2)'''

! '''Regional rivalry'''

'''(SSP3)'''

! '''Inequality'''

'''(SSP4)'''

! '''Fossil-fuelled development'''

'''(SSP5)'''

|-
| Agriculture, food and forestry

* ''Agriculture production''
* ''Forestry production''
* ''Food security''
* ''Hunger''

| SDG 2

| ↗

| ↔

| ↘

| ↘

| ↘

| ''Low agreement/''

''robust evidence''

| ( [[#Hasegawa--2015|Hasegawa et al., 2015]] ; [[#Palazzo--2017|Palazzo et al., 2017]] ; [[#Riahi--2017|Riahi et al., 2017]] ; [[#Duku--2018|Duku et al., 2018]] ; [[#Chen--2019|Chen et al., 2019]] ; [[#Daigneault--2019|Daigneault et al., 2019]] ; [[#Mitter--2020|Mitter et al., 2020]] ; [[#Mora--2020|Mora et al., 2020]] )

|-
| Health and well-being

* ''Excess mortality''
* ''Air quality''
* ''Vector-borne disease''
* ''Life Satisfaction''

| SDG 3

| ↔

| ↔

| ↔

| ↘

| ↘

| ''Medium agreement/robust evidence''

| ( [[#Chen--2017|Chen et al., 2017]] ; [[#Mora--2017|Mora et al., 2017]] ; [[#Aleluia%20Reis--2018|Aleluia Reis et al., 2018]] ; [[#Asefi-Najafabady--2018|Asefi-Najafabady et al., 2018]] ; [[#Chen--2018|Chen et al., 2018]] ; [[#Harrington--2018|Harrington and Otto, 2018]] ; [[#Marsha--2018|Marsha et al., 2018]] ; [[#Sellers--2018|Sellers and Ebi, 2018]] ; [[#Ikeda--2019|Ikeda and Managi, 2019]] ; [[#Rohat--2019|Rohat et al., 2019]] ; [[#Wang--2019|Wang et al., 2019]] ; [[#Chae--2020|Chae et al., 2020]] )

|-
| Water and sanitation

* ''Water use''
* ''Sanitation access''
* ''Sewage discharge''

| SDG 6

| ↗

| ↘

| ↘

| ↔

| ↔

| ''High agreement/medium evidence''

| ( [[#Wada--2016|Wada et al., 2016]] ); ( [[#van%20Puijenbroek--2014|van Puijenbroek et al., 2014]] ; [[#Yao--2017|Yao et al., 2017]] ); ( [[#Mouratiadou--2016|Mouratiadou et al., 2016]] ; [[#Graham--2018|Graham et al., 2018]] )

|-
| Inequality

* ''Gini coefficient''

| SDG 10

| ↗

| ↗

| ↗

| ↔

| ↗

| ''Medium agreement/limited evidence''

| ( [[#Rao--2019b|Rao et al., 2019b]] ; [[#Emmerling--2021|Emmerling and Tavoni, 2021]] ; [[#Gazzotti--2021|Gazzotti et al., 2021]] )

|-
| Ecosystems and ecosystem services

* ''Aquatic resources''
* ''Urban expansion''
* ''Habitat provision''
* ''Carbon sequestration''
* ''Biodiversity''

| SDG 14 SDG 15

| ↘

| ↘

| ↘

| ↘

| ↘

| ''High agreement/medium evidence''

| ( [[#Li--2017|Li et al., 2017]] ; [[#Chen--2019|Chen et al., 2019]] ; [[#Li--2019b|Li et al., 2019b]] ; [[#Chen--2020b|Chen et al., 2020b]] ; [[#Song--2020b|Song et al., 2020b]] ; [[#McManamay--2021|McManamay et al., 2021]] ; [[#Pinnegar--2021|Pinnegar et al., 2021]] )

|}

'''Legend'''

↑ Balance of studies suggest large increasing threat to sustainable development

↗ Balance of studies suggest moderate increasing threat to sustainable development

↔ Studies suggest both threats and benefits to sustainable development

↘ Balance of studies suggest moderate increasing benefit to sustainable development

↓ Balance of studies suggest large increasing benefit to sustainable development

Studies presented in the above table include qualitative storylines and quantitative scenarios for two or more SSPs. Arrows and colour coding reflect the positive or negative impacts on sustainability based on aggregation of results for the 2030–2050 time horizon across the identified studies. Confidence language reflects the number of studies upon which results are based (evidence) and the agreement among studies regarding the direction of change (agreement).

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<span id="climate-change-risks-to-development"></span>
=== 18.2.4 Climate Change Risks to Development ===

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In the near-term, additional climate change is expected regardless of the scale of greenhouse gas mitigation efforts ( [[#IPCC--2021a|IPCC, 2021a]] ). Across the global scenarios analysed in the AR6, global average temperature changes relative to the reference period 1850–1900 range from 1.2°C to 1.9°C for the period 2021–2040 and 1.2°C to 3.0°C for the period 2041–2060 (WGI AR6 SPM [ [[#IPCC--2021b|IPCC, 2021b]] ], ''very likely'' range). However, the feasibility of emissions pathways (particularly RCP8.5) affect the plausibility of the associated climate projections, potentially lowering the upper end of these ranges because the likelihood of the higher warming levels is a function of the likelihood of the higher emissions scenarios (Riahi et al., 2022) . There is significant overlap between climate scenario ensemble ranges from different emissions scenarios through 2050, more so than through 2100 ( [[#Lee--2021|Lee et al., 2021]] ). There is also overlap between emissions scenario ensembles consistent with different temperature outcomes (Riahi et al., 2022) . Emissions pathway ranges represent uncertainties for policymakers and organisations to consider and manage ( [[#Rose--2018|Rose and Scott, 2018]] , 2020) regarding, among other things, economic growth and structure, available technologies, markets, behavioural dynamics, policies and non-CO 2 climate forcings (Riahi et al., 2022), while climate pathway ranges represent bio-physical climate systems and carbon cycle uncertainties ( [[#Lee--2021|Lee et al., 2021]] ). For all climate projections and variables, there is significant regional heterogeneity and uncertainty in projected climate change ( ''very high confidence'' ) ( [[#IPCC--2021a|IPCC, 2021a]] ). Figure 18.4 apresents examples for average and extreme temperature precipitation change (see also [[#18.5|Section 18.5]] and Tables 18.4–18.5 for more regional detail and ranges of climate outcomes). Higher global warming levels also can affect geographic patterns of change and probability distributions of regional climate outcomes ( [[#Ahmad--2019|Ahmad, 2019]] ). Similarly, for all emissions projections, there is significant regional, sectoral and local heterogeneity and uncertainty regarding potential pathways for climate action (Lecocq et al., 2022; Riahi et al., 2022). Not all uncertainties are represented in projected emissions pathway ensembles, such as policy timing and design (e.g., [[#Rose--2018|Rose and Scott, 2018]] ) or climate projection ensembles.

<div id="_idContainer016" class="Figure"></div>

[[File:d272ea8cb03c80d9db17efe749d730a4 IPCC_AR6_WGII_Figure_18_004a.png]]

'''Figure 18.4 |''' '''Regional projected select climate change and sustainable-development-related climate impact indicators by global warming level.''' Sources: WGI AR6 Interactive Atlas ( https://interactive-atlas.ipcc.ch/ ) and WGII Figures 3.21, 4.17, 5.19, and 6.3. The GWLs shown are multi-model means derived from Hauser et al. (2019) for the respective RCP and SSP and time periods associated with each figure.

<div id="_idContainer016" class="Figure"></div>

[[File:f2f14d91b49358e810bc743095b0eb0b IPCC_AR6_WGII_Figure_18_004b.png]]

'''Figure 18.4 |''' '''Regional projected select climate change and sustainable-development-related climate impact indicators by global warming level.''' Sources: WGI AR6 Interactive Atlas ( https://interactive-atlas.ipcc.ch/ ) and WGII Figures 3.21, 4.17, 5.19, and 6.3. The GWLs shown are multi-model means derived from Hauser et al. (2019) for the respective RCP and SSP and time periods associated with each figure.

The projected ranges for near- and mid-term global average warming levels are estimated to result in increasing key risks and reasons for concern (Chapter 16). [[IPCC:Wg2:Chapter:Chapter-16|Chapter 16]] developed aggregate ‘Representative Key Risks’ (RKRs) as indicators for subsets of approximately 100 sectoral and regional key risks indicators. The RKRs include risks to coastal socio-ecological systems, terrestrial and ocean ecosystems, critical physical infrastructure, networks and services, living standards and equity, human health, food security, water security, and peace and migration. The majority of these risks are directly linked to sustainable development priorities and the SDGs (Chapters 2 to 16; ( [[#Roy--2018|Roy et al., 2018]] ; [[#IPCC--2019d|IPCC, 2019d]] ; [[#IPCC--2019b|IPCC, 2019b]] ). Therefore, climate risks represent a potential additional challenge to pursuing sustainable development priorities, but also potential opportunities due to geographic variation in climate impacts. In addition, positive synergies have been found between sustainable development and adaptation, but trade-offs are also possible (e.g., [[#Roy--2018|Roy et al., 2018]] ).

For all RKRs, additional global average warming is expected to increase risk. However, the increases vary significantly by RKR, and across the underlying key risks represented within each RKR. Geographic variation in key risk implications is only partially assessed in Chapter 16, but evidence can be drawn from the WGII individual regional chapters. Regionally, key risks are found to be potentially greatest in developing and transition economies ( [[IPCC:Wg2:Chapter:Chapter-16|Chapter 16]] and sectoral chapters), which is also where the least-cost emissions reductions globally are projected to be (Riahi et al., 2022).See Figure 18.4 for an example of key risk geographic heterogeneity (see also [[#18.5|Section 18.5]] for regional detail). [[IPCC:Wg2:Chapter:Chapter-16|Chapter 16]] also maps the RKRs to an updated aggregate ‘Reasons for Concern’ (RFC) framing. Thus, increasing RKR implies increasing RFC associated with unique and threatened systems, extreme weather events, distribution of impacts, global aggregate impacts and large-scale singular events.

Climate risks are found to vary with future warming levels, the development context and trajectory, as well as by the level of investment in adaptation. Together, these three dimensions define risk—with projected climate changes defining the hazard, development defining the exposure, and development and adaptation defining vulnerability. However, how these different dimensions interact and the level of scientific understanding vary significantly among different types of risk. For human systems, in general, the poor and marginalised are found to have greater vulnerability for a given hazard and exposure level. With some level of global average warming expected regardless of mitigation efforts, human and natural systems will be exposed to new conditions, but some level of adaptation should also be expected.

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<span id="options-for-managing-future-climate-risks-to-climate-resilient-development"></span>
=== 18.2.5 Options for Managing Future Climate Risks to Climate Resilient Development ===

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The pursuit of CRD requires not only the implementation of individual adaptation, mitigation and sustainable development initiatives, but also their careful coordination and integration. This section assesses the literature on CRD in the context of key climate change risks (Chapter 16); gaps in adaptation that contribute to risk; potential synergies and trade-offs among mitigation, adaptation and sustainable development; and the mechanisms for managing those trade-offs.

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<span id="adaptation"></span>
==== 18.2.5.1 Adaptation ====

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<span id="adaptation-and-climate-resilient-development"></span>
===== 18.2.5.1.1 Adaptation and Climate Resilient Development =====

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Given that adaptation is recognised as a key element of addressing climate risk and CRD, the capacity for adaptation implementation is an important consideration for CRD. The AR5 noted a significant overlap between indicators of sustainable development and the determinants of adaptive capacity, and suggested that adaptation presents an opportunity to reduce stresses on development processes and the socio-ecological foundations upon which they depend ( [[#Denton--2014|Denton et al., 2014]] ). At the same time, it also noted that building adaptive capacity for sustainable development might require transformational changes that shift impacted systems to new patterns, dynamics or places ( [[#Denton--2014|Denton et al., 2014]] ). Thus, adaptation interventions and pathways can further the achievement of development goals such as food security ( [[#Campbell--2016|Campbell et al., 2016]] ; [[#Douxchamps--2016|Douxchamps et al., 2016]] ; [[#Richardson--2018|Richardson et al., 2018]] ; [[#Bezner%20Kerr--2019|Bezner Kerr et al., 2019]] ) and improvements in human health ( [[#Watts--2019|Watts et al., 2019]] ) including in systems where animals and humans live in close proximity ( ''very high confidence'' ) ( [[#Zinsstag--2018|Zinsstag et al., 2018]] ). However, to do so requires not only the avoidance of incremental adaptation actions that extend current unsustainable practices, but also the ability to manage and overcome the barriers which arise when the limits of incremental adaptation are reached ( ''high agreement'' , ''medium evidence'' ) ( [[#Few--2017|Few et al., 2017]] ; [[#Vermeulen--2018|Vermeulen et al., 2018]] ; [[#Fedele--2019|Fedele et al., 2019]] ).

Since AR5, the scientific community has deepened its understanding of the relationship between adaptation and sustainable development ( ''very high confidence'' ), particularly with regard to the place of resilience at the intersection of these two arenas. The literature has moved forward in its identification of specific overlaps in sustainable development indicators and determinants of adaptive capacity, how adaptation might reduce stress on development processes and their socio-ecological foundation, and how building adaptive capacity might facilitate needed transformative changes. Broadly speaking, work on these topics comes from one of two perspectives. One perspective speaks to adaptation practices that might further sustainable development outcomes, while another perspective draws on deeper understandings of the socio-ecological dynamics of the systems in which we live, and which we may have to transform in the face of climate change impacts. These two literatures are not yet well integrated, leaving gaps in our knowledge of how best to implement adaptation in a manner that achieves sustainable development.

The literature considering adaptation and development in practice since AR5 suggests that efforts to connect adaptation to sustainable development should address proximate and systemic drivers of vulnerability ( [[#Wise--2016|Wise et al., 2016]] ), while remaining flexible and reversable to avoid the lock-in of undesirable or maladaptive trajectories ( [[#Cannon--2010|Cannon and Müller-Mahn, 2010]] ; [[#Wise--2016|Wise et al., 2016]] ). Such goals require critical reflection on processes for decision making and learning. In the AR5, more inclusive, participatory adaptation processes were presumed to benefit development planning by including a wider set of actors in discussions of future goals ( [[#Denton--2014|Denton et al., 2014]] ). The post-AR5 literature expands on these critical perspectives to provide context regarding when participation is most effective. For example, ( [[#Eriksen--2015|Eriksen et al., 2015]] ) emphasise the need to build participatory adaptation processes to avoid subsuming adaptation goals to development-as-usual, while ( [[#Kim--2017b|Kim et al., 2017b]] ) argues that this practice is most effective when it is focused on development efforts and considers how climate change will challenge the goals of those efforts. Adaptation, while presenting an opportunity to foster transformations needed to address the impacts of climate change on human well-being, is also a contested process that is inherently political ( ''medium agreement'' , ''medium evidence'' ) ( [[#Eriksen--2015|Eriksen et al., 2015]] ; [[#Mikulewicz--2019|Mikulewicz, 2019]] ; Nightingale Böhler, 2019; [[#Eriksen--2021b|Eriksen et al., 2021b]] ). How adaptation can challenge development and create a situation where CRD effectively becomes transformative adaptation, adaptation that generates transformation of broader aspects of development, remains unclear ( ''medium agreement'' , ''limited evidence'' ) ( [[#Few--2017|Few et al., 2017]] ; [[#Schipper--2020c|Schipper et al., 2020c]] ).

The critical literature on socio-ecological resilience, which has grown substantially since the last AR ( ''very high confidence'' ), speaks to some of these questions. Since AR5, the IPCC and the wider literature on socio-ecological resilience have shifted their use of the term to reflect not only the capacity to cope with a hazardous event or trend or disturbance, but also the ability to adapt, learn and transform in ways that maintains socio-ecology’s essential function, identity and structure (Chapter 1; Glossary, Annex II). This change in usage is significant in that it shifts resilience from an emergent property of complex socio-ecological systems to a deeply human product of efforts to manage ecology, economy and society to specific ends. This definition of resilience recognises the need to define what is an essential identity, function and structure for a given system, questions rooted not in ecological dynamics, but in politics, agency, difference and power that emerge around the management of ecological dynamics ( [[#Cote--2011|Cote and Nightingale, 2011]] ; [[#Brown--2013|Brown, 2013]] ; [[#Cretney--2014|Cretney, 2014]] ; [[#Forsyth--2018|Forsyth, 2018]] ; [[#Matin--2018|Matin et al., 2018]] ; [[#Carr--2019|Carr, 2019]] ).

By connecting this framing of socio-ecological dynamics to the literature on the principles for adaptation efforts that meet development goals, new work has begun to identify 1) how adaptation can reduce stress on development processes, 2) how it might facilitate transformative change and 3) where adaptation interventions might either drive system rigidity and precarity, or otherwise challenge development goals ( [[#Castells-Quintana--2018|Castells-Quintana et al., 2018]] ; [[#Carr--2020|Carr, 2020]] ). For example, [[#Jordan--2019|Jordan (2019)]] draws upon these contemporary framings of resilience to highlight the ways in which coping strategies perpetuate the gendered norms and practices at the heart of women’s vulnerability in Bangladesh. [[#Forsyth--2018|Forsyth (2018)]] draws upon this work to highlight the ways in which the theory of change processes used by development organisations tend to exclude local experiences and sources of risk, and thus foreclose the need for transformative pathways to achieve development goals. Carr ( [[#Carr--2019|Carr, 2019]] ; 2020) draws upon evidence from sub-Saharan Africa to develop more nuanced understandings of the ways in which different stressors and interventions either facilitate or foreclose transformative pathways, while pointing to the existence of yet poorly understood thresholds for transformation in systems that can be identified and targeted by interventions.

<div id="18.2.5.1.2" class="h4-container"></div>

<span id="adaptation-gaps"></span>
===== 18.2.5.1.2 Adaptation gaps =====

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Adaptation gaps are defined as ‘the difference between actually implemented adaptation and a societally set goal, determined largely by preferences related to tolerated climate change impacts and reflecting resource limitations and competing priorities’ ( [[#UNEP--2014|UNEP, 2014]] ; [[#UNEP--2018a|UNEP, 2018a]] ). Adaptation deficit is a similar concept, described as an inadequate or insufficient adaptation to current conditions (Chapter 1). Adaptation gaps or deficits arise from a lack of adequate technological, financial, social, and institutional capacities to adapt effectively to climate change and extreme weather events, which are in turn linked to development ( ''very high confidence'' ) ( [[#Fankhauser--2014|Fankhauser and McDermott, 2014]] ; [[#Milman--2014|Milman and Arsano, 2014]] ; [[#Chen--2016|Chen et al., 2016]] ; [[#Asfaw--2018|Asfaw et al., 2018]] ) ( [[#18.2.2|Section 18.2.2]] ).

Currently, there is no consensus around approaches to assess the effectiveness of adaptation actions across contexts and therefore measure adaptation gaps at a global scale ( [[#Singh--2021a|Singh et al., 2021a]] ). [[#UNEP--2021|UNEP (2021)]] suggests that comprehensiveness, inclusiveness, implementability, integration and monitoring, and evaluation can be used to assess them (see also Cross-Chapter Box FEASIB). However, limited information is available about future trends in national-level adaptation and the development of monitoring and evaluation mechanisms. Despite the challenges of measurement associated with adaptation gaps, available evidence from smaller scales across several regions, communities and businesses suggest that significant adaptation gaps have existed in historical contexts of climate change, while expectations of extreme heat, increasing storm intensity and rising sea levels will create the context for the emergence of new gaps ( ''very high confidence'' ) ( [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#UNEP--2018a|UNEP, 2018a]] ; [[#Dellink--2019|Dellink et al., 2019]] ; [[#UNEP--2021|UNEP, 2021]] ). These adaptation gaps create risks to well-being, economic growth, equity, the health of natural systems and other societal goals. The negative impacts of these gaps can be compounded by adaptation efforts that are considered maladaptive or by development actions that are labelled as adaptation (see Chapter 16).

A higher level of adaptation finance is critical to enhance adaptation planning and implementation and reduce adaptation gaps, particularly in developing countries ( ''very high confidence'' ) ( [[#UNEP--2021|UNEP, 2021]] ) (Cross-Chapter Box FINANCE in Chapter 17, [[#18.4.2.2|Section 18.4.2.2]] ). However, adaptation finance is not keeping pace with the rising adaptation costs in the context of increasing and accelerating climate change, as ‘annual adaptation costs in developing countries alone are currently estimated to be in the range of US$70 billion, with the expectation of reaching US$140–300 billion in 2030 and US$280–500 billion in 2050’ ( [[#UNEP--2021|UNEP, 2021]] ). Investment in attaining SDGs helps bridge adaptation gaps ( [[#Birkmann--2021|Birkmann et al., 2021]] ), but care needs to be taken to avoid maladaptation through mislabelling. Integration of the Indigenous and local knowledge systems is anticipated to reduce existing adaptation gaps and secure livelihood transitions.

Analysis of investments by four major climate and development funds (the Global Environment Facility, the Green Climate Fund, the Adaptation Fund and the International Climate Initiative) by [[#UNEP--2021|UNEP (2021)]] suggests that support for green and hybrid adaptation solutions has been increasing over the past two decades. These could be effective at reducing climate risks and bridging adaptation gaps while simultaneously bringing important additional benefits for the economy, environment and livelihoods ( [[#UNEP--2021|UNEP, 2021]] ) (see also Cross-Chapter Box NATURAL in Chapter 2).

Lately, the evidence of adaptation activity in the health sector has been increasing ( [[#Watts--2019|Watts et al., 2019]] ), yet substantial adaptation gaps persist ( [[#UNEP--2018a|UNEP, 2018a]] ; [[#UNEP--2021|UNEP, 2021]] ), including gaps in humanitarian response to climate-related disasters ( [[#Watts--2019|Watts et al., 2019]] ). It is the under-investment in climate and health research in general and health adaptation in particular that has led to adaptation gaps in the health sector ( [[#Ebi--2017|Ebi et al., 2017]] ).

Costs of implementing efficient adaptation measures and water-related infrastructure in water-deficient regions have received attention at the global and regional level to bridge the ‘adaptation gap’ ( [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#UNEP--2018a|UNEP, 2018a]] ; [[#Dellink--2019|Dellink et al., 2019]] ; [[#UNEP--2021|UNEP, 2021]] ). Livelihood sustainability in the drylands, which cover more than 40% of the land surface area, are home to roughly 2.5 billion people, and support approximately 50% of the livestock and 45% of the food production, is threatened by a complex and inter-related range of social, economic and environmental changes that present significant challenges to rural communities, especially women ( [[#Abu-Rabia-Queder--2018|Abu-Rabia-Queder and Morris, 2018]] ; [[#Gaur--2018|Gaur and Squires, 2018]] ). Adaptation deficits in arid and semi-arid regions are of high order (see CROSS-CHAPTER BOX 3). To reduce adaptation deficit in arid and semi-arid regions, comprehensive and efficient adaptation interventions integrating better water management, use of non-traditional water sources, changes in reservoir operations, soil ecosystem rejuvenation and enhanced institutional effectiveness are needed ( [[#18.5|Section 18.5]] ) ( [[#Makuvaro--2017|Makuvaro et al., 2017]] ; [[#Mohammed--2017|Mohammed and Scholz, 2017]] ; [[#Morote--2019|Morote et al., 2019]] ). Communities facing the lack of adequate technological, financial, human and institutional capacities to adapt effectively to current and future climate change often encounter adaptation deficits. To address current adaptation barriers and adaptation deficits, there is a need to promote efficient adaptation measures, coupled with inclusive and adaptive governance involving marginalised groups such as Indigenous communities and women.

Although unevenly distributed urban adaptation gaps exist in all world regions (see Chapter 6). Such gaps are higher in the urban centres of the poorer nations. [[IPCC:Wg2:Chapter:Chapter-6|Chapter 6]] identified that the critical capacity gaps at city and community levels responsible for adaptation gaps are the ‘ability to identify social vulnerability and community strengths, and to plan in integrated ways to protect communities, alongside the ability to access innovative funding arrangements and manage finance and commercial insurance; and locally accountable decision making with sufficient access to science, technology and local knowledge to support the application of adaptation solutions at scale’.

Insufficient financial resources are the main reasons for the coastal adaptation gap, particularly in the Global South (see CROSS-CHAPTER BOX 2). Engaging the private sector with a range of financial tools is crucial to address such gaps (see CROSS-CHAPTER BOX 2). An urgent and transformative action to institutionalise locally relevant integrative adaptation pathways is crucial for closing coastal adaptation gaps. Additional efforts are in place for assessing global adaptation progress (see Cross-Chapter Box PROGRESS in Chapter 17).

<div id="18.2.5.1.3" class="h4-container"></div>

<span id="adaptation-implementation"></span>
===== 18.2.5.1.3 Adaptation implementation =====

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As discussed in Chapter 16, adaptation is a key mechanism for managing climate risks, and therefore for pursuing CRD. The lower estimates in Table 18.2 are associated with higher levels of adaptation and more conducive development conditions. Furthermore, additional adaptation demand is associated with greater levels of climate change. Adaptation is a broad term referring to many different levels of response and options for natural and human systems, from individuals, specific locations and specific technologies, to nations, markets, global dynamics and strategies at the system level. Adaptation also includes endogenous reflexive and exogenous policy responses. Perspectives on limits to adaptation, synergies, trade-offs and feasibility therefore depend on where the boundaries are drawn and the objective. Overall, there are a broad range of adaptation options relevant to reducing risks posed by climate change to development. However, current understanding of how such options are implemented in practice, their effectiveness across a range of possible climate futures and their potential limits, is modest.

The IPCC’s SR1.5 report evaluated individual adaptation options in terms of economic, technological, institutional, socio-cultural, environmental/ecological and geophysical feasibility ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). This analysis has been updated for AR6 (Cross-Chapter Box FEASIB). These assessments identify types of barriers that could affect an option’s feasibility. Among other things, this work finds that every adaptation option evaluated had at least one feasibility dimension that represented a barrier or obstacle. The barriers also imply that there are trade-offs in these feasibility dimensions to consider. Overall, insights from this work are high-level and difficult to apply to a specific adaptation context. The feasibility and ranking of adaptation opportunities, as well as the list of opportunities themselves, for a given location will vary from location to location, with different criteria and weighting of criteria that reflect the priorities of society and decision-makers as well as differences in markets, technology options and policies for managing risks and trade-offs. Integrated evaluation of criteria and options is needed, that accounts for the relevant geographic context and interactions between options and systems ( [[#18.5|Section 18.5]] ).

Sustainable development is regarded as generally consistent with climate change adaptation, helping build adaptive capacity by addressing poverty and inequalities and improving inclusion and institutions ( [[#Roy--2018|Roy et al., 2018]] ). Some sustainable development strategies could facilitate adaptation effectiveness by addressing wider socioeconomic barriers, addressing social inequalities and promoting livelihood security ( [[#Roy--2018|Roy et al., 2018]] ). With a common goal of reducing risks, sustainable development and adaptation are relatively synergistic. For example, “low-regrets” adaptation strategies have been identified, such as improvements in health systems that reduce climate health impacts in cities (Barata, 2018). However, trade-offs also have been found and are important to consider and potentially address. Synergies have been found between adaptation and poverty reduction, hunger reduction, clean water access and health; while, trade-offs have also been found, particularly when adaptation strategies prioritise one development objective (e.g., food security or heat-stress risk reduction) or promote high-cost solutions with budget allocation and equity implications ( [[#Roy--2018|Roy et al., 2018]] ) (Sections 18.2.5.3, 18.5, Box 18.4). There are also opportunities for addressing the trade-offs, in particular distributional effects—by recognising that there are trade-offs and considering alternatives and complementary strategies to address those trade-offs ( [[#18.2.5.3|Section 18.2.5.3]] ).

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<span id="mitigation"></span>
==== 18.2.5.2 Mitigation ====

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Mitigation, including greenhouse gas emissions reductions, avoidance, and removal and sequestration, as well as management of other climate forcing factors (WGIII AR6), is a key element of addressing climate risk and pursuing CRD. There are numerous individual and system mitigation options throughout the economy and within human and natural systems ( ''very high confidence'' ) (Chapter 16; [[#18.5|Section 18.5]] ). Limiting global average warming has been found to reduce climate risks ( [[#IPCC--2018a|IPCC, 2018a]] ; [[#IPCC--2019b|IPCC, 2019b]] ), and limiting global average warming to any temperature level has also been found to be associated with broad ranges of potential global emissions pathways that represent future uncertainty in the evolution of socioeconomic, technological, market and physical systems ( ''very high confidence'' ) ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ). Pathways consistent with limiting warming to 2°C and below have been found to require significant deployment of mitigation options spanning energy, land use and societal transformation ((Lecocq et al., 2022; Riahi et al., 2022); [[#18.3|Section 18.3]] ). and substantial economic, energy, land use, policy and societal transformation (Lecocq et al., 2022; Riahi et al., 2022). Such emissions pathways would represent deviations from current trends that raise issues about their feasibility and therefore plausibility ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ).

The technical and economic challenge of limiting warming has been found to increase nonlinearly with greater ambition, fewer mitigation options, less than global cooperative policy designs and delayed mitigation action ((Riahi et al., 2022); Table 18.2). Table 18.2 provides a high-level summary of pathway characteristic ranges based on the WGIII AR6 assessment. Global pathways find large regional differences in mitigation potential, as well as the degree of regional nonlinearity with greater mitigation ambition. These represent opportunities for mitigation, but how this effort and cost would be facilitated and distributed respectively is a policy question.

Table 18.2 illustrates that greater climate ambition implies more aggressive emissions reductions in each region, and earlier regional peaking of emissions (if they have not peaked to date). Near-term regional emissions increases are possible, even for 1.5°C compatible pathways, but significantly lower emissions than today are shown in all regions by 2050. Increases in total regional energy consumption and fossil energy are observed for many pathways, even in the most ambitious where energy consumption growth is potentially slower compared with less ambitious pathways. By 2050, regional fossil energy declines, but is not eliminated in any region. Regional growth in electricity use is substantial in all pathways, even the most ambitious, with the growth continuing and accelerating with time and regional dependence on electricity (share of total energy consumption) also growing significantly. The broad ranges are an indication of uncertainty and risk for regional transitions, noting that full uncertainty is likely broader than what is captured by emissions scenario databases ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ). Among other things, pathways commonly assume idealised climate policies with immediate implementation, and model infeasibilities (i.e., models unable to solve) increase with climate ambition and pessimism about mitigation technologies (e.g., Clarke et al., 2014; [[#Bauer--2018|Bauer et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ; [[#Muratori--2020|Muratori et al., 2020]] ), highlighting the increasing challenge and potential for actual infeasibility with lower global warming targets. Together, Table 18.2 provides insights into the increasingly demanding system and development transitions associated with lower global warming levels, as well as some of the low-carbon transition uncertainties and risks (see also Figure 18.5).

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[[File:0c7b9b48c848d5e8ba015059a51a373a IPCC_AR6_WGII_Figure_18_005.png]]

'''Figure 18.5 |''' '''Regional implications of climate mitigation pathways in 2050 for different global mean peak temperature outcomes (during the century) for various development and sustainable development proxy variables.''' Each row reports results for a different variable for each of the five global regions (columns) used by WGIII, and SDG associated with each variable is noted. Blue dots represent individual emissions scenario results from each of the respective WGIII climate outcome scenario categories, with red bars the median results. All results are changes (percentage or fraction) relative to each WGIII scenario’s reference scenario. In some circumstances the reference case emissions are below those from the scenario consistent with a global warming level, which can produce results that appear counter-intuitive (e.g., increases in GDP or consumption). Data sample sizes vary substantially across temperature levels for a given variable and across variables due to model infeasibilities and model differences in reporting. Model infeasibilities, in particular, result in significantly fewer data points for 1.5°C compatible emissions pathways compared to 2°C pathways (i.e., models are more often unable to solve for a 1.5°C consistent pathway, than a 2°C pathway, with a given set of assumptions). Food/feed crop price results were not available for 1.5°C and 4°C warming levels. Sample sizes for each variable and warming level respectively—1.5°C, 2°C, 3°C, and 4°C—are as follows (and apply to all regions): GDP (n = 2, 93, 29, 12); Consumption (2, 93, 30, 13); Black Carbon (2, 100, 39, 16), NOx (2, 100, 39, 15), SO2 (2, 100, 39, 16), price food/feed crops (0, 44, 23, 0); price electricity (2, 94, 38, 15); price natural gas (10, 86, 44, 10). The sample sizes are very small for the 1.5°C and 4°C results; therefore, the medians for these warming levels are statistically unreliable, which should be considered in comparing across warming levels. Individual values in the samples exceed y-axis’ ranges in a few cases: black carbon 2°C Latin America minimum equals 0.08, food/feed price change 3°C minimums in Asia, Latin America, Middle East/Africa, OECD, and Reforming Economies equal respectively -33%, -28%, -28%, -29%, and -29%, natural gas price change 2°C maximums in Asia, Latin America, Middle East/Africa, OECD, and Reforming Economies equal respectively 962%, 1240%, 2768%, 917%, and 3588%. Figure developed from the WGIII AR6 scenarios database, with scenarios filtered according to WGIII exclusions and regional vetting.

Past assessment has evaluated representative mitigation strategies in terms of economic, technological, institutional, socio-cultural, environmental/ecological and geophysical viability, as well as relationships to SDGs ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). The strategies assessment analysis has been updated for AR6 (Cross-Chapter Box FEASIB). These assessments identify types of barriers that could affect an option’s feasibility. Among other things, this work finds that, other than public transport and non-motorised transport, every other mitigation option evaluated had at least one feasibility dimension that represented a barrier or obstacle. The barriers also imply that there are trade-offs in these feasibility dimensions to consider. The assessment of mitigation option-sustainable development relationships identifies related literature and derives aggregate characterisations. Concerns about the potential sustainable development implications of some mitigation technologies may be motivation for precluding the use of some mitigation options. For instance, the potential food security and environmental quality implications of bioenergy have received significant attention in the literature (e.g., [[#Smith--2013|Smith et al., 2013]] ). However, constraining or precluding the use of bioenergy without or with CCS could have significant implications for the cost of pursuing ambitious climate goals, and potentially the attainability of those goals (e.g., Clarke et al., 2014; [[#Bauer--2018|Bauer et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ; [[#Muratori--2020|Muratori et al., 2020]] ). Bioenergy is not unique in this regard. Social, environmental, and sustainability concerns have also been raised about the large-scale deployment of many low-carbon technologies, for example, REDD+, wind, solar, nuclear, fossil with CCS and batteries. See WGIII [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] (Riahi et al., 2022) for examples of the potential implications of limiting or precluding different low-carbon technologies.

Overall, as with adaptation options, insights from this aggregate feasibility and sustainable development mapping work are high level and difficult to apply to a specific mitigation context. The feasibility, ranking and sustainable development implications of mitigation options, as well as the list of options themselves, for a given location will vary from location to location, with different criteria and weighting of criteria that reflect the relevant social priorities and differences in markets, technology options and policies for managing risks and trade-offs. Integrated evaluation of criteria and options is needed here as well, that accounts for the relevant geographic context and interactions between options, systems and implications.

Analyses of the potential implications of mitigation on sustainable development has various strands of literature—studies exploring general greenhouse gas mitigation feedbacks to society, assessments of mitigation implications on specific societal objectives other than climate and literature evaluating mitigation implications specifically for sustainable development objectives (Denton et al., 2022; Lecocq et al., 2022; Riahi et al., 2022). In general, mitigation alters development opportunities by constraining the emissions future society can produce, which affects markets, resource allocation, economic structure, income distribution, consumers and the environment (besides climate) ( ''very high confidence'' ). Examples of general development feedbacks from mitigation include estimated price changes, macroeconomic costs, and low carbon energy and land system transformations ( [[#Fisher--2007|Fisher et al., 2007]] ; Clarke et al., 2014; [[#Popp--2014|Popp et al., 2014]] ; [[#Rose--2014|Rose et al., 2014]] ; [[#Weyant--2014|Weyant and Kriegler, 2014]] ; [[#Bauer--2018|Bauer et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ). Examples of mitigation implications for other specific variables of societal interest include evaluating potential effects on air pollutant emissions, crop prices, water and land use change (e.g., [[#McCollum--2018b|McCollum et al., 2018b]] ; [[#Roy--2018|Roy et al., 2018]] ), while the literature evaluating mitigation implications specifically for sustainable development objectives includes evaluations on energy access, food security and income equality (e.g., [[#Roy--2018|Roy et al., 2018]] ; [[#Arneth--2019|Arneth et al., 2019]] ; [[#Mbow--2019|Mbow et al., 2019]] ). Proxy indicators are frequently used to represent whether there might be implications for a sustainable development objective. For example, changes in energy prices are used as a proxy for effects on energy security (e.g., [[#Roy--2018|Roy et al., 2018]] ). This is common with aggregate modelling studies, such as those associated with global or regional emissions scenarios and energy systems.

Figure 18.5, derived from WGIII scenarios data, illustrates estimated relationships between mitigation and various sustainable development proxy variables for different global regions. Figure 18.5 illustrates synergies and trade-offs with mitigation, as well as regional heterogeneity, that can intensify with the level of climate ambition—synergies in air pollutants, such as black carbon, NOx and SO 2 ; and trade-offs in overall economic development, household consumption, food crop prices and energy prices for electricity and natural gas. For comparison, recent IPCC assessments also observed similar synergies and trade-offs but did not directly make comparisons regarding overall development nor evaluate potential climates above 2°C ( [[#Rogelj--2018|Rogelj et al., 2018]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Mbow--2019|Mbow et al., 2019]] ). Regional nonlinearity in the economic costs of mitigation with greater climate ambition (i.e., costs rising at an increasing rate with lower warming goals) can be significant within individual models ( [[#Rose--2018|Rose and Scott, 2018]] ; [[#Rose--2020|Rose and Scott, 2020]] ). Figure 18.5 also illustrates transition risks in the potential for significant synergistic and trade-off implications with, for instance, potentially large regional commodity price implications and household consumption losses, as well as more significant air pollution benefits. Note that the 1.5°C results in Figure 18.5 (and Table 18.2) are biased by model infeasibilities. Many models are unable to solve, especially with less optimistic assumptions, resulting in small sample sizes and a different representation of models compared to the 2°C and higher results.

Results such as those in Figure 18.5 illustrate that mitigation–development trade-offs are inevitable and need to be considered and addressed. For instance, Roy (2018) found that although limiting warming to 1.5°C would make it markedly easier to achieve most of the UN’s SDGs, none of the 1.5°C pathways assessed achieved all of the SDGs. A similar conclusion follows from the results in Figure 18.5 based on WGIII AR6 scenarios.. A newer literature is developing, evaluating the potential for managing SDG trade-offs. Results like those in Figure 18.5 provide insights regarding some of the types of strategy sets to consider. [[#Roy--2018|Roy et al. (2018)]] discuss the potential for policies that address distributional implications, such as payments, food support and revenue recycling, as well as education, retraining and technology outreach, subsidies or prioritisation. Recent studies have begun to estimate potential payments to offset trade-offs, such as related to food, water and energy access (e.g., [[#McCollum--2018a|McCollum et al., 2018a]] ). These analyses estimate investments to address specific trade-offs; however, with mitigation redirecting resources away from other productive activities, there is a need to also evaluate the aggregate economy-wide, distributional and welfare effects, including the redistribution effects of managing sustainable development trade-offs.

There are a wide range of mitigation options and systems to consider, with assessment suggesting that a diverse portfolio is practical for pursing climate policy ambitions. However, local context will impact mitigation choices, with unique sustainable development priorities, available mitigation options, sustainable development synergies and trade-offs, and policy design and implementation possibilities.

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<span id="combining-adaptation-mitigation-and-sustainable-development-options"></span>
==== 18.2.5.3 Combining Adaptation, Mitigation and Sustainable Development Options ====

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In practice, adaptation, mitigation and sustainable development interventions are likely to be implemented in portfolio packages rather than as individual discrete options in isolation ( ''high agreement'' , ''limited evidence'' ). However, there is a dearth of literature estimating optimal portfolios of global adaptation and mitigation strategies. This is not surprising given the geographic-specific nature of climate impacts and adaptation and the information and computational complexity of representing that detail, as well as mitigation options and interactions. There are, however, different literatures relevant to considering potential combinations of adaptation, mitigation and sustainable development.

At the most aggregate level, there is a long-standing literature exploring economically optimal global trade-offs between climate risks and mitigation (e.g., [[#Manne--1992|Manne and Richels, 1992]] ; Nordhaus, 2017; [[#Rose--2017|Rose, 2017]] ), as well as global stochastic analysis exploring global risk hedging for a small number of uncertainties (e.g., ( [[#Lemoine--2014|Lemoine and Traeger, 2014]] ). Recent work has found optimal global emissions and climate pathways to be highly sensitive to uncertainties and plausible alternative assumptions, with uncertainties throughout the causal chain from society to emissions to climate to climate damages shown to imply a wide range of different possible economically optimal pathways ( [[#Rose--2017|Rose, 2017]] ). Among other things, this work identifies assumptions consistent with limiting warming to different temperature levels. For example, the combination of potential annual climate damages of 15% of global GDP at 4°C of warming and a less sensitive climate system were consistent with an economically efficient global pathway limiting warming to 2°C. In addition, this work highlights the importance of characterising and managing uncertainties. These types of global aggregate analyses inform discussions regarding long-run global pathways and goals but are not designed to inform local planning.

As discussed in [[#18.2.5.3.1|Section 18.2.5.3.1]] , there are synergies and trade-offs in mitigation, adaptation and sustainable development. For instance, the literature on the global cost-effectiveness of mitigation pathways provides insights regarding aggregate synergies and trade-offs between mitigation and sustainable development (e.g., Figure 18.5). Furthermore, linkages between mitigation and adaptation options have been shown, such as expected changes in energy demand due to climate change interacting with energy system development and mitigation options, changes in future agricultural production practices to manage the risks of potential changes in weather patterns affecting land-based emissions and mitigation strategies, or mitigation strategies placing additional demands on resources and markets. This increases pressure on and costs for adaptation, or ecosystem restoration that provides carbon sequestration and natural and managed ecosystem resiliency benefits, but also could constrain mitigation and impact consumer welfare (WGIII AR6).

Nonlinearities are an important consideration in evaluating risk management combinations. Nonlinearities have been estimated in global and regional mitigation costs and potential economic damages from climate change ''(very high confidence'' ) ((Riahi et al., 2022); (Clarke et al., 2014; [[#Burke--2015|Burke et al., 2015]] ; [[#Rose--2017|Rose, 2017]] ). Nonlinear mitigation costs mean increasingly higher costs for each additional incremental reduction in emissions (or incremental reduction in global average temperature). Nonlinear increases in estimated economic climate damage means increasingly higher damages for each additional incremental increase in climate change (e.g., global average temperature). However, the evidence on whether damages increase at an increasing or decreasing rate is mixed ( [[IPCC:Wg2:Chapter:Chapter-16|Chapter 16]] CWGB: ECONOMIC). Nonlinearities are also suggested in estimated changes in key risks and adaptation costs (Chapters 2 to 16). However, to date, they have not been as explicitly characterised. These nonlinearities imply nonlinearities in climate risk management synergies and trade-offs with sustainable development. Not only do trade-offs vary by climate level, as do synergies, but they increase at an increasing rate and their relative importance can shift across climate levels ( ''very high confidence'' ). Some of this is evident in results such as those shown in Figure 18.5 for mitigation (keeping in mind differences in sample sizes across temperature levels). Uncertainty about the degree of nonlinearity in mitigation, climate damages, key risks and adaptation costs creates uncertainties in the strength of the trade-offs and synergies, but also represents opportunities. For instance, additional mitigation options and more economically efficient policy designs have been shown to reduce mitigation costs and the nonlinearities in mitigation costs ( ''very high confidence'' ) (Riahi et al., 2022). The same is true for adaptation options and adaptation costs.

Infeasibilities of mitigation and adaptation options (Sections 18.4.2.2.1, 18.4.2.2.2), as well as global pathways (Riahi et al., 2022) , are also relevant to consideration of combinations of risk management options. Infeasibility of options implies higher costs and greater cost nonlinearity due to fewer and/or more expensive options, while infeasibility of pathways bounds some of the uncertainty about the pathways relevant to decision making and planning.

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<span id="trade-offs-and-synergies-in-adaptation-mitigation-and-climate-resilient-development"></span>
===== 18.2.5.3.1 Trade-offs and synergies in adaptation, mitigation and climate resilient development =====

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Since AR5, a growing body of literature has emerged that frames adaptation processes as endogenous socioeconomic dynamics, exogenous driving forces and explicit decisions ( [[#Barnett--2014|Barnett et al., 2014]] ; [[#Maru--2014|Maru et al., 2014]] ; [[#Butler--2016|Butler et al., 2016]] ; [[#Kingsborough--2016|Kingsborough et al., 2016]] ; [[#Werners--2018|Werners et al., 2018]] ). Central to this framing is a shift away from viewing adaptation as discrete sets of options that are selected and implemented to manage risk, to thinking about adaptation as a social process that evolves over time, includes multiple decision points, and requires dynamic adjustments in response to new information about climate risk, socioeconomic conditions and the value of potential adaptation responses ( ''very high confidence'' ) ( [[#Haasnoot--2013|Haasnoot et al., 2013]] ; [[#Wise--2016|Wise et al., 2016]] ). This aligns adaptation with aspects of development thinking, including questions around the capacity and agency of different actors to effect change, the governance of adaptation, and the contingent nature of adaptation needs and effectiveness on the future evolution of society and climate change risk.

While ensuring development and adaptation produce synergies that allow for the achievement of sustainable development is challenging, modelling exercises suggest that there are pathways where synergies among the SDGs are realised ( ''very high confidence'' ) ( [[#Roy--2018|Roy et al., 2018]] ; [[#Van%20Vuuren--2019|Van Vuuren et al., 2019]] ) ( [[#18.5|Section 18.5]] ), particularly if longer time horizons are used. These pathways require progress on multiple social, economic, technological, institutional and governance aspects of development, including building human capacity, managing consumption behaviour, decarbonisation of the global economy, improving food and water security, modernising cities and infrastructure, and innovations in science and technology ( [[#Van%20Vuuren--2019|Van Vuuren et al., 2019]] ) ( [[#18.3|Section 18.3]] ). In addition, Olsson et al, ( [[#Olsson--2014|Olsson et al., 2014]] ) and [[#Roy--2018|Roy et al. (2018)]] emphasise the importance of integrating considerations for social justice and equity in the pursuit of sustainable development ( [[#Gupta--2017|Gupta and Pouw, 2017]] ).

The significant overlaps and linkages between development and adaptation practice and a lack of conceptual clarity about adaptation pose a conundrum for scholars (e.g., [[#Bassett--2013|Bassett and Fogelman, 2013]] ; [[#Webber--2016|Webber, 2016]] ), who raise concerns that this potentially leads to trade-offs or mislabelling ( [[#Few--2017|Few et al., 2017]] ). This framing of adaptation and development can result in competition between attainment of sustainable development and policies to reduce the impacts of climate change ( [[#Ribot--2011|Ribot, 2011]] ). Such trade-offs are illustrated by ( [[#Moyer--2019|Moyer and Bohl, 2019]] ) who use a baseline development trajectory based on current trends to project progress on SDGs by 2030. This work concluded that only marginal gains are likely to be achieved under that pathway over the next decade ( [[#Barnes--2019|Barnes et al., 2019]] ).

Emerging evidence also suggests that many adaptation-labelled strategies may exacerbate existing poverty and vulnerability or introduce new inequalities, for example by affecting certain disadvantaged groups more than others, even to the point of protecting the wealthy elite at the expense of the most vulnerable ( [[#Eriksen--2019|Eriksen et al., 2019]] ). Pelling et al. (2016) find that adaptation has been conceived and implemented in such a manner that most projects preserve rather than challenge the status quo. Specifically, the potential for knowledge and the goals of adaptation to be contested by different actors and stakeholders and the need to sustain progress over extended periods of time can constrain the ability to effectively implement actions that lead to sustainable development outcomes that are protected from the impacts of climate change while also delivering climate mitigation outcomes, that is, for CRD ( [[#Bosomworth--2017|Bosomworth et al., 2017]] ; [[#Bloemen--2019|Bloemen et al., 2019]] ). This creates the possibility for specific adaptation actions to result in outcomes that undermine greenhouse gas mitigation and/or broader development goals ( [[#Fazey--2016|Fazey et al., 2016]] ; [[#Wise--2016|Wise et al., 2016]] ; [[#Magnan--2020|Magnan et al., 2020]] ). For example, a study in Bangladesh revealed how local elites and donors used adaptation projects as a lever to push vulnerable populations away from their agrarian livelihoods and into uncertain urban wage labour ( [[#Paprocki--2018|Paprocki, 2018]] ). These types of outcomes are categorised as maladaptation, interventions that increase rather than decrease vulnerability, and/or undermine or eradicate future opportunities for adaptation and development ( [[#Barnett--2010|Barnett and O’Neill, 2010]] ; [[#Juhola--2015|Juhola et al., 2015]] ; [[#Magnan--2016|Magnan et al., 2016]] ; [[#Antwi-Agyei--2017|Antwi-Agyei et al., 2017]] ; [[#Schipper--2020|Schipper, 2020]] ). This inadvertent impact on equity appears to fundamentally contradict a benevolent understanding of transformative adaptation that also champions social justice ( [[#Patterson--2018|Patterson et al., 2018]] ), thus posing long-term maladaptation in opposition to transformative adaptation ( [[#Magnan--2020|Magnan et al., 2020]] ).

Similarly, mitigation efforts, while reducing emissions, can also increase climate impacts vulnerability and undermine adaptation efforts. The same can be said for some poverty alleviation and sustainable development efforts that increase vulnerability for specific segments of the population. For example, in Central America, an evaluation of 12 rural renewable energy projects (either forthe clean development mechanism, early warning systems or rural electrification goals) found that some mitigation and poverty alleviation projects increased vulnerability to families—by excluding them, not adhering to local safety and quality codes and standards, or significantly altering community power dynamics and contributing to conflict ( [[#Ley--2017|Ley, 2017]] ; [[#Ley--2020|Ley et al., 2020]] ).

Synergies between adaptation, mitigation and sustainable development might be promoted by prioritising those CRD strategies most likely to generate synergies ( ''very high confidence'' ) ( [[#Roy--2018|Roy et al., 2018]] ; [[#Karlsson--2020|Karlsson et al., 2020]] ). This could include focusing on poverty alleviation that improves adaptive capacity (e.g., [[#Kaya--2016|Kaya and Chinsamy, 2016]] ; [[#Kuper--2017|Kuper et al., 2017]] ; [[#Ley--2017|Ley, 2017]] ; [[#Sánchez--2017|Sánchez and Izzo, 2017]] ; [[#Stańczuk-Gałwiaczek--2018|Stańczuk-Gałwiaczek et al., 2018]] ; [[#Ley--2020|Ley et al., 2020]] ); renewable energy systems that improve water management and preservation of river ecological integrity (e.g., [[#Berga--2016|Berga, 2016]] ; [[#Rasul--2016|Rasul and Sharma, 2016]] ); or internalising positive externalities, such as subsidies for mitigation options thought to also improve water use efficiency (e.g., [[#Roy--2018|Roy et al., 2018]] ). Similarly, trade-offs might be managed by prioritising strategies such as disqualifying mitigation options thought to have negative social implications ( [[#18.2.5.3.1|Section 18.2.5.3.1]] ), internalising externalities, such as placing a fee or constraint on a negative externality or related activity (Dubash et al., 2022) ( [[#Bistline--2018|Bistline and Rose, 2018]] ), or using complementary policies, such as transfer payments to offset negative mitigation, adaptation or sustainable development strategy implications ( ''very high confidence'' ) (e.g., [[#McCollum--2018b|McCollum et al., 2018b]] ). [[#Roy--2018|Roy et al. (2018)]] discusses the latter, noting, for instance, the possibility of complementary sustainable development payments to avoid global energy access, food security and clean water trade-offs (Box 4.7).

SR1.5 and AR6 assessments of system transitions also find opportunities for synergies and managing trade-offs ( [[#18.3|Section 18.3]] ; Cross-Chapter Box FEASIB). Within each system, mitigation and adaptation options are assessed for their specific benefits and the impacts they can have on one another, as well as with sustainable development. For example, within energy system transitions, the three adaptation options (power infrastructure resilience, reliability of power systems, efficient water use management) have strong synergies with mitigation. While not all mitigation options have strong synergies, the trade-offs can be managed when adaptation and SDGs are also considered. Under land and other ecosystems system transitions, the main trade-off is the competition for land use between potential alternative uses, for example, sustainable agriculture, afforestation/reforestation, purpose-grown biomass for energy. On the other hand, assessment of urban and infrastructure system transitions finds mainly synergies between mitigation and adaptation options with trade-offs that are considered manageable, and there is growing evidence of rural landscape infrastructure benefits to adaptation.

Overall, this literature is relatively new and still developing. It highlights the importance of societal priorities and policy design for realizing synergies. However, the literature is not well developed in terms of how to optimize mitigation, adaptation and sustainable development interventions to achieve multiple priorities.

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<span id="risk-management-combinations-with-lower-to-higher-climate-change"></span>
===== 18.2.5.3.2 Risk management combinations with lower to higher climate change =====

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Given the global climate system is committed to additional future warming, different portfolios of adaptation, mitigation, and sustainable development interventions are relevant for climate risk management. The different strands of literature discussed above can be integrated to help inform thinking about combinations of approaches to climate risk management. Globally, low climate change projections, versus higher climate change projections, imply greater mitigation, lower climate risks and less adaptation. This implies greater mitigation trade-offs in terms of overall economic development, food crop prices, energy prices and overall household consumption, but lower climate risk, with sustainable development synergies such as human health and lower adaptation trade-offs, and an uneven distribution of effects ( ''very high confidence'' ) ( [[#Roy--2018|Roy et al., 2018]] ).

Sustainable development considerations could be used to prioritise mitigation options, but as noted earlier, there are trade-offs, with a potentially significant impact on the economic cost of mitigation, as well as a potential trade-off in terms of the climate outcomes that are still viable (Riahi et al., 2022). For instance, all of the 1.5°C scenarios used in [[#IPCC--2018a|IPCC (2018a)]] deploy carbon dioxide removal technologies ( [[#Rogelj--2018|Rogelj et al., 2018]] ). Without these technologies, most models cannot generate pathways that limit warming to 1.5°C, and those that are able to adopt strong assumptions about global policy development and socioeconomic changes. Sustainable development might also affect the design of policies by prioritising specific sustainable development objectives. However, there are trade-offs here as well, with costs and the distribution of costs varying with alternative policy designs. For instance, prioritising air quality has climate co-benefits but does not ensure the lowest cost climate strategy ( [[#Arneth--2009|Arneth et al., 2009]] ; [[#Kandlikar--2009|Kandlikar et al., 2009]] ). Similarly, prioritising land protection has a variety of co-benefits but could increase food prices significantly, as well as the overall cost of climate mitigation ( [[#IPCC--2019b|IPCC, 2019b]] ). In this context, with lower climate risk and adaptation levels and larger mitigation effort, managing mitigation trade-offs could be a sustainable development priority. Furthermore, sustainable development could also be tailored to facilitate adaptation and manage mitigation costs.

Globally, high climate change projections imply lower mitigation effort, higher climate risks and greater adaptation. This implies lower mitigation trade-offs, but greater climate risk with greater demand of adaptation and potential for trade-offs in terms of competing sustainable development priorities. Sustainable development considerations could affect adaptation options. For instance, constraining options such as relocation or facilitating adaptation capacity and community resilience. Sustainable development might also be tailored to affect the climate outcome by shaping the development of emissions. In this context, with greater climate risk and adaptation levels and less mitigation effort, facilitating adaptation addressing adaptation costs and trade-offs could be a sustainable development priority.

Locally, there are many qualitative similarities to the global perspective in thinking about risk management combinations across lower versus higher levels of warming. However, there is one very important difference. Local decision makers are confronted with uncertainty about what others will do beyond their local jurisdiction. With future climate a function of the sum of global decisions, sustainable development planning needs to consider the possibility of more and less emissions reduction action globally and the potential associated climates. This implies the need for sustainable development to manage for the possibility of higher levels of warming by further facilitating adaptation and managing adaptation trade-offs. Prioritising sustainable development locally is also supported by the insight that the impacts on poverty depend at least as much or more on development than on the level of climate change ( ''very high confidence'' ) ( [[#Wiebe--2015|Wiebe et al., 2015]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ).

With surpassing 1.5°C a distinct possibility, considering higher levels of warming is a necessity. CRD could be pursued with additional adaptation, recognizing increasing challenges for adaptation and sustainable development with higher warming, just as there are increasing challenges for mitigation and sustainable development with limiting warming to lower levels. There are many possible pathways for pursuing climate resilient development, though our understanding of the possibilities with different levels of warming is currently limited (e.g., David [[#Tàbara--2018|Tàbara et al., 2018]] ; [[#O’Brien--2018|O’Brien, 2018]] ). The current literature suggests that different mixes of adaptation and mitigation strategies, and sustainable development and trade-off management priorities, measures and reallocations ( [[#18.5.3|Section 18.5.3.1]] ), will be appropriate for different expected climates and locations ( [[#18.1.2|Section 18.1.2]] ); while trade-offs between climates will be dictated by relative nonlinearities, feasibilities, shifts in priorities, and trade-off and reallocation options across future climates.

Finally, it is important to note that there is currently limited information available regarding the following: (1) local implications of 1.5°C versus warmer futures with respect to local climate outcomes, avoided impacts and sustainable development implications and interactions, given that applying global conclusions to local, national and regional settings can be misleading; (2) local context-specific synergies and trade-offs with respect to adaptation, mitigation and sustainable development for 1.5°C futures; and (3) standard indicators for monitoring factors related to CRD ( [[#Roy--2018|Roy et al., 2018]] ).

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'''Box 18.3 | Climate Resilient Development in Small Islands'''
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Small islands are particularly vulnerable to climate change and many are already pursuing climate resilient development pathways that enable integrated responses ( [[#Allen--2018a|Allen et al., 2018a]] ; [[#Mycoo--2018|Mycoo, 2018]] ; [[#Hay--2019|Hay et al., 2019]] ; [[#Robinson--2021|Robinson et al., 2021]] ). Countries such as Belize have opted for a systems approach and are working across the sustainable development goals (SDGs) to increase integration ( [[#Allen--2018a|Allen et al., 2018a]] ). This includes rethinking disaster reconstruction mechanisms in the Caribbean and introducing more diversified and sustainable tourism economies that can better withstand external shocks such as disruptions and loss of markets from COVID-19 ( [[#Sheller--2021|Sheller, 2021]] ). In the Seychelles, various government and tourism industry initiatives are focused on the promotion of sustainable tourism ventures that lower emissions, protect and promote biodiversity conservation (e.g., new marine protected areas with mitigation and adaptation benefits), and are climate resilient ( [[#Robinson--2021|Robinson et al., 2021]] ). In 2016, the Seychelles signed the world’s first nature-for-debt swap, wherein a non-governmental organisation (NGO; The Nature Conservancy) agreed to pay off Seychelles’ public debt to the Paris Club (foreign creditors) in return for the Seychelles government establishing marine conservation areas ( [[#Silver--2018|Silver and Campbell, 2018]] ).

One key area where enhanced climate risk integration is critical is infrastructure-related decisions, especially on coastal areas ( [[#World%20Bank--2017|World Bank, 2017]] ). However, despite increasing awareness of climate risks and experienced impacts, decisions on, for example, infrastructure locations still reflect cultural preferences. For example, Hay et al. (2019) report that, despite recommendations to relocate the redevelopment site of the Parliamentary Complex in Samoa away from the coast, multiple cultural and historical factors influenced the decisions to redevelop at the original site. In the Solomon Islands, however, emerging evidence suggests that adaptation efforts to enhance the resilience of infrastructure are also serving to help urban areas address problems associated with rapid urbanisation and provide new opportunities for sustainable development ( [[#Robinson--2021|Robinson et al., 2021]] ).

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Box 18.3

Energy system transitions in small islands can produce synergies with SDG implementation and can lead to transformational outcomes. The Pacific Island territory of Tokelau has demonstrated a nationwide energy transition, sourcing 100% of their energy needs from solar power ( [[#Michalena--2018|Michalena and Hills, 2018]] ), and many other countries such as Fiji, Niue, Tuvalu, Vanuatu, Solomon Islands and Cook Islands also have 100% renewable energy targets. Benefits of small island distributed energy systems (such as solar photovoltaic [PV] systems) include less need for large, centralised infrastructure; reduced reliance on volatile fossil fuel markets; enhanced international climate negotiations power; and enhanced local job markets/skills ( [[#Dornan--2015|Dornan, 2015]] ; [[#Cole--2017|Cole and Banks, 2017]] ; [[#Weir--2018|Weir, 2018]] ). Additionally, renewable systems can enhance resilience to hydro-meteorological disasters ( [[#Weir--2020|Weir and Kumar, 2020]] ). For example, well-secured ground-based PV systems withstood cyclones in the Pacific Island of Tonga during cyclone Gita and across the Caribbean during Hurricane Maria, with power restored in days rather than weeks associated with more centralised systems ( [[#Weir--2020|Weir and Kumar, 2020]] ). Yet a multitude of challenges remain. In the Pacific islands region, these include: the high up front capital investment of renewables; lack of private sector investment; limited renewable energy data for policymaking; land tenure/rent costs; ongoing infrastructure maintenance skills and requirements; political turnover; failed experimentation; difficulty in obtaining and transporting replacement parts; and a highly corrosive environment for equipment ( [[#Dornan--2015|Dornan, 2015]] ; [[#Cole--2017|Cole and Banks, 2017]] ; [[#Lucas--2017|Lucas et al., 2017]] ; [[#Weir--2018|Weir, 2018]] ; [[#Weir--2020|Weir and Kumar, 2020]] ). The example of Pacific energy transitions demonstrates that a nuanced and context specific analysis of synergies and trade-offs for energy transitions is required to lessen the impact on fragile economies and maximise benefits for remote populations.

Labour migration is increasingly recognised as a significant factor that can contribute to climate resilient development pathways for small islands. In the Pacific islands region, labour mobility schemes are already allowing for climate change adaptation and economic development to occur in labour migrants’ countries of origin ( [[#Smith--2015|Smith and McNamara, 2015]] ; [[#Klepp--2016|Klepp and Herbeck, 2016]] ; [[#Dun--2020|Dun et al., 2020]] ). [[#Dun--2020|Dun et al. (2020)]] demonstrates that temporary or circular migrants from the Solomon Islands, working in Australia under its Seasonal Worker Programme (similar programmes operate in other developed countries), are using the money they earn to invest in adaptation and development activities back home. Similarly, labour migrants from Vanuatu, Kiribati and Samoa contribute to development and ''in situ'' climate change adaptation (at a household, village and regional level) that enable discussions about more resilient futures for their countries ( [[#Barnett--2018|Barnett and McMichael, 2018]] ; [[#Parsons--2018|Parsons et al., 2018]] ).

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'''Box 18.4 | Adaptation and the Sustainable Development Goals'''
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The achievement of the Sustainable Development Goals (SDGs) represents near-term positive sustainability as well as indicating the quality of development processes and actions (inclusion and social justice, alternative development models, planetary health, well-being, equity, solidary, different forms of knowledge and human–nature connectivity) that enable climate resilient development (CRD) in the long term (Sections 18.2.2.2, 18.2.5.3). A key question is the extent to which adaptation actions (or non-action) may contribute to (or undermine) SDG achievement and, in particular, shift the quality of development processes and engagement within the political, economic, ecological, socio-ethical and knowledge-technology arenas, and hence contribute to climate resilient development pathways (CRDPs).

Table Box 18.4.1 (below) provides a set of examples of how adaptation actions can either contribute to or undermine SDG achievement for SDGs 2, 3, 6, 11 and 16. In general, formal adaptation policies as well as household and community-based adaptation strategies can generate positive outcomes, particularly if they are responsive to the local context and needs, with real participation and leadership by target populations ( [[#Remling--2016|Remling and Veitayaki, 2016]] ; [[#Buckwell--2020|Buckwell et al., 2020]] ; [[#McNamara--2020|McNamara et al., 2020]] ; [[#Owen--2020|Owen, 2020]] ). For example, integrated adaptation approaches to the water–energy–food (WEF) nexus aiming to build resilience in those sectors can lead to increased resource use efficiency and coherent strategies for managing the complex interactions and trade-offs among the water, energy and food SDGs ( [[#Mpandeli--2018|Mpandeli et al., 2018]] ; [[#Nhamo--2020|Nhamo et al., 2020]] ). One such approach could involve cultivating indigenous crops suited to harsh growing conditions, which would allow for agricultural expansion for food and energy without increased water withdrawals ( [[#Mpandeli--2018|Mpandeli et al., 2018]] ). Overall, adaptation commitments aiming to build resilience of vulnerable populations have typically shown to contribute to SDGs focused on ending extreme poverty (SDG 1), improving food security (SDG 2), improving access to water (SDG 6), ensuring clean energy (SDG 7), tackling climate change (SDG 13) and halting land degradation and deforestation (SDG 15) ( [[#Antwi-Agyei--2018|Antwi-Agyei et al., 2018]] ).

However, evidence also suggests limitations of adaptation actions, with the objectives and actions often being too narrow to address social justice and enable CRD. As such, adaptation actions can sometimes undermine SDG achievement through exacerbating social vulnerability, inequity and uneven power relations ( [[#Antwi-Agyei--2018|Antwi-Agyei et al., 2018]] ; [[#Atteridge--2018|Atteridge and Remling, 2018]] ; [[#Paprocki--2018|Paprocki, 2018]] ; [[#Mikulewicz--2019|Mikulewicz, 2019]] ; [[#Satyal--2020|Satyal et al., 2020]] ; [[#Scoville-Simonds--2020|Scoville-Simonds et al., 2020]] ). This is due to adaptation practices often not accounting for the differentiated ways in which minority groups are especially vulnerable. For example, designs of emergency shelters should consider the fear of social stigma or abuse faced by women and girls ( [[#Pelling--2019|Pelling and Garschagen, 2019]] ).

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Box 18.4

Such maladaptive adaptation practices can undermine SDG achievement through increasing vulnerability of marginalised groups by failing to address the underlying root causes of vulnerability and poverty that are related to political economy, power dynamics and vested interests more broadly, instead treating the symptoms as the cause ( [[#Magnan--2016|Magnan et al., 2016]] ; [[#Ajibade--2019|Ajibade and Egge, 2019]] ; [[#Schipper--2020|Schipper, 2020]] ). For example, evidence exists of flood defence measures through large-scale infrastructure development leading to the violent displacement of poor communities, forcibly resettling people in areas far from their employment or pushing up land and housing costs without providing compensation ( [[#Fuso%20Nerini--2018|Fuso Nerini et al., 2018]] ; [[#Reckien--2018|Reckien et al., 2018]] ). Moreover, sectoral approaches to adaptation that fail to acknowledge the linkages between SDGs can counter development efforts and generate further trade-offs ( [[#Terry--2009|Terry, 2009]] ; [[#Rasul--2016|Rasul and Sharma, 2016]] ; [[#von%20Stechow--2016|von Stechow et al., 2016]] ; [[#Klinsky--2017|Klinsky et al., 2017]] ; [[#Hallegatte--2019|Hallegatte et al., 2019]] ).

The literature recommends a set of strategies for ensuring that adaptation actions are aligned with SDG achievement and do not further perpetuate poverty and inequality. These include ensuring that marginalised voices are central to adaptation decision making, with participatory approaches that empower and compensate affected communities ( [[#Moser--2011|Moser and Ekstrom, 2011]] ; [[#Broto--2015|Broto et al., 2015]] ; [[#Pelling--2019|Pelling and Garschagen, 2019]] ; [[#Palermo--2020|Palermo and Hernandez, 2020]] ). Gender mainstreaming and gender transformative approaches within climate policies can also help ensure gender-sensitive design of adaptation projects, with appropriate equity analyses of policy ( [[#Klinsky--2017|Klinsky et al., 2017]] ) decisions to identify the actual implications of trade-offs for vulnerable groups ( [[#Beuchelt--2013|Beuchelt and Badstue, 2013]] ; [[#Alston--2014|Alston, 2014]] ; [[#Bowen--2017|Bowen et al., 2017]] ; [[#Fuso%20Nerini--2018|Fuso Nerini et al., 2018]] ).

In addition, a substantial literature also argues for policy coherence measures that adopt whole-of-government approaches and mainstream and nationalise SDG targets within national climate policies ( [[#Nilsson--2012|Nilsson et al., 2012]] ; [[#Le%20Blanc--2015|Le Blanc, 2015]] ; [[#Ari--2017|Ari, 2017]] ; [[#Collste--2017|Collste et al., 2017]] ; [[#Dzebo--2017|Dzebo et al., 2017]] ; [[#Nilsson--2019|Nilsson and Weitz, 2019]] ). Institutional coordination mechanisms that aim to break down silos between different agencies and actors at the national level are suggested as beneficial for avoiding trade-offs between adaptation actions and SDGs ( [[#Mirzabaev--2015|Mirzabaev et al., 2015]] ; [[#Howlett--2018|Howlett and Saguin, 2018]] ; [[#Scherer--2018|Scherer et al., 2018]] ). However, these need to be paired with an investigation of the deep-seated ideologies and vested interests that are creating goal conflicts and negatively impacting marginalised groups to begin with ( [[#Purdon--2014|Purdon, 2014]] ; [[#Bocquillon--2018|Bocquillon, 2018]] ). Ultimately, adaptation measures need to acknowledge and address the underlying drivers that make certain groups particularly vulnerable, such as social disenfranchisement, unequal power dynamics and historical legacies of colonialism and exploitation ( [[#Magnan--2016|Magnan et al., 2016]] ; [[#Schipper--2020|Schipper, 2020]] )

'''Table Box 18.4.1 |''' Examples of linkages between adaptation and the SDGs. For several key SDGs aligned with the concept of CRD, the table below identifies evidence from the literature where adaptation policies and practices contribute to achievement of the SDG, as well as where they undermine achievement of the SDG.

{| class="wikitable"
|-
! '''''SDG'''''

! '''''Evidence of adaptation contributing to SDG'''''

! '''''Evidence of adaptation undermining SDG'''''

|-
| SDG 2: Zero Hunger

| Adaptation measures implemented by smallholder farmers (e.g., adjustments in farm operations timing, on-farm diversification, soil–water management) exhibit higher levels of productivity and technical efficiency in food production ( [[#Bai--2019|Bai et al., 2019]] ; [[#Sloat--2020|Sloat et al., 2020]] ; [[#Khanal--2021|Khanal et al., 2021]] )

Some climate smart agriculture measures (e.g., intercropping) can significantly increase yields and contribute to zero hunger ( [[#Lipper--2014|Lipper et al., 2014]] ; [[#Arslan--2015|Arslan et al., 2015]] ; [[#Saj--2017|Saj et al., 2017]] )

| Some adaptation policies can increase land and food prices, negatively impacting smallholder farmers ( [[#Fuso%20Nerini--2018|Fuso Nerini et al., 2018]] ; [[#Zavaleta--2018|Zavaleta et al., 2018]] ; [[#Albizua--2019|Albizua et al., 2019]] )

Potential trade-offs for food production through adaptation actions within the water or energy sector, if integrated approaches not taken ( [[#Howells--2013|Howells et al., 2013]] ; [[#FAO--2014|FAO, 2014]] ; [[#Biswas--2016|Biswas and Tortajada, 2016]] )

|-
| SDG 3: Good Health and Wellbeing

| Increased resilience of societies and reduced vulnerability through investments in public health care and access ( [[#Marmot--2020|Marmot, 2020]] ; [[#Mullins--2020|Mullins and]] [[#White--2020|White, 2020]] )

Adaptation measures that leverage solidarity, equity and nature connectedness contribute to physical and psychological health and well-being ( [[#Gambrel--2009|Gambrel and Cafaro, 2009]] ; [[#Capaldi--2015|Capaldi et al., 2015]] ; [[#Soga--2016|Soga and Gaston, 2016]] ; [[#Woiwode--2020|Woiwode, 2020]] )

| Societal measures beyond adaptation required to address underlying causes of inequities that drive poor health and well-being, including cuts in public spending and neoliberalisation and commodification of healthcare ( [[#Hall--2020|Hall, 2020]] ; [[#Walsh--2020|Walsh and Dillard-Wright, 2020]] )

|-
| SDG 6: Clean Water and Sanitation

| Integrated water resources management as an adaptation strategy ( [[#Tan--2018|Tan and Foo, 2018]] ; [[#Sadoff--2020|Sadoff et al., 2020]] )

| Potential trade-offs for water security through adaptation actions within the food or energy sector, if integrated approaches not taken ( [[#Howells--2013|Howells et al., 2013]] ; [[#Rasul--2016|Rasul and Sharma, 2016]] ; [[#Mpandeli--2018|Mpandeli et al., 2018]] )

Local, regional or national ‘grabs’ for water from shared resources with poorly defined property rights ( [[#Olmstead--2014|Olmstead, 2014]] )

|-
| SDG 11: Sustainable Cities and Communities

| Vulnerability reducing adaptation measures that aim to upgrade informal settlements, create affordable housing and protect populations living in disaster prone areas ( [[#Major--2018|Major et al., 2018]] ; [[#Sanchez%20Rodriguez--2018|Sanchez Rodriguez et al., 2018]] ; [[#Ajibade--2019|Ajibade and Egge, 2019]] )

| Need to ensure that adaptation measures understand how power dynamics and cultural norms shape urban form and communities’ vulnerability and adaptive capacity ( [[#Sanchez%20Rodriguez--2018|Sanchez Rodriguez et al., 2018]] )

Risk of built infrastructure aiming to increase resilience ignoring local population needs and creating low-skilled jobs that concentrate land, capital and resources in the hands of the elite ( [[#Ajibade--2019|Ajibade and Egge, 2019]] )

|-
| SDG 16: Peace, Justice and Strong Institutions

| Potential for adaptation projects to support livelihood incomes and resource management, and thereby reduce tensions and the risk of conflicts ( [[#Matthew--2014|Matthew, 2014]] ; [[#Dresse--2018|Dresse et al., 2018]] ; [[#Barnett--2019|Barnett, 2019]] )

| Studies from Bangladesh, Cambodia and Nepal found that climate change adaptation-related policies and projects were an underlying cause of natural resource-based conflicts, as well as land dispossession and exclusion, entrenchment of dependency relations, elite capture and inequity ( [[#Sovacool--2018|Sovacool, 2018]] ; [[#Sultana--2019|Sultana et al., 2019]] )

Adaptation projects can reinforce top-down knowledge and decision-making processes, asymmetric power relations and elite capture of adaptation resources ( [[#Nightingale--2017|Nightingale, 2017]] ; [[#Eriksen--2021b|Eriksen et al., 2021b]] )

Need for conflict-sensitive adaptation approaches that aim to ‘do no harm’ ( [[#Babcicky--2013|Babcicky, 2013]] ; [[#Ide--2020|Ide, 2020]] )

|}

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