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

=== 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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