A key question is how a limited set of SSPs can be chosen to most effectively serve the goals of the scenario matrix architecture. In principle, two approaches can be taken. One is a forward approach, in which a small number of key socioeconomic drivers are combined into a set of plausible pathways highlighting the different directions in which the world may evolve. This approach has been frequently adopted in the past, for example in the SRES scenarios (Nakicenovic et al. 2000) and in the Millennium Ecosystem Assessment (MA; Bennett et al. 2005). Another is an inverse approach, in which one begins with the outcomes of interest for climate change research and then identifies combinations of key socioeconomic drivers that are likely to produce those outcomes. This approach has also been used previously, for example in scenarios for achieving global sustainability (Raskin et al. 1998), particular types of climate goals (Toth 2003), or sustainable energy transitions (Riahi et al. 2012).
Both approaches are complementary and indeed both contribute to the formulation of the SSPs. However, we have chosen to start the process with the inverse approach in order to ensure that the choice of SSPs produces a set of development pathways that is as relevant as possible to the goal of the scenario framework, which as discussed in section 1 is to explore uncertainty in mitigation, adaptation, and impacts associated with alternative climate and socioeconomic futures. A discussion of key socioeconomic elements and drivers that, in a second step, can be combined to create SSPs (i.e., the forward approach) is provided in the next section.
To help ensure that the set of SSPs developed actually spans a range of outcomes that will allow the characterization of uncertainty in mitigation, adaptation, and impacts, we define an outcome space in which socioeconomic and environmental challenges are represented on two axes: one axis depicts challenges pertaining to adaptation; the other axis challenges to mitigation (Fig. 1). The logic here is that for characterizing uncertainties in the implications of mitigating climate change to a given level, or of adapting to that level (key goals of the scenario framework), we need to describe future socioeconomic conditions that would make mitigation and adaptation relatively hard or relatively easy. In the figure axes, and in the text, “socioeconomic” is intended to be shorthand for a wide range of aspects of society or, more broadly, socioecological systems. These include demographic, political, social, cultural, institutional, life-style, economic, and technological aspects, and the conditions of ecosystems and ecosystem services that have been affected by human activity such as air and water quality, biodiversity, and ecosystem form and function. The intention of this “socioeconomic” label is primarily to communicate that we exclude conditions related to future climate change itself. This applies to ecological variables (such as biodiversity) just as much as it does to economic or other variables relating more directly to society. Although climate change and biodiversity in reality interact, the SSP describes a hypothetical future in which biodiversity is not affected by further climate change, so that scenarios can then be developed to estimate the effect of future climate change on biodiversity (among other things).
This “challenges space” is conceptually quite different from typical two-axes approaches to defining the space to be explored in forward scenarios, as for example in the SRES and Millennium Assessment scenarios. In those cases, the axes were defined by two key socio-economic driving forces that were assumed to be principal uncertainties determining outcomes of interest. Here, we use the outcomes of interest themselves to define the axes. We introduce this outcome space as part of the inverse approach to pathway construction in order to explicitly guide the process of developing SSPs toward producing a set that spans the space of interest and to provide a way to check whether this goal has been achieved once SSP development is complete. In this sense, the guidance of SSP construction by locating them in the challenge spaces, and their actual construction by combining assumptions about key socioeconomic drivers, are complementary.
Challenges to mitigation
Challenges to mitigation for the purpose of defining SSPs do not include the stringency of the mitigation target itself or the choice of mitigation action, which are accounted for by two other aspects of the scenario matrix: the forcing level of the representative concentration pathway (RCP) and the shared policy assumption (SPA), respectively. Rather, these challenges are defined by socioeconomic factors that would make the mitigation task easier or harder for any given target and mitigation policy.
Socioeconomic challenges to mitigation are defined as consisting of: (1) factors that tend to lead to high reference emissions in the absence of climate policy because, all else equal, higher reference emissions makes that mitigation task larger; and (2) factors that would tend to reduce the inherent mitigative capacity of a society. High reference emissions could be generated in a large number of ways, with possible contributions from high population growth rates, rapid economic growth, extensive land use, energy intensive economic systems, and carbon intensive energy supplies. More fundamental processes could drive each of these factors, such as technological and social changes that include (autonomous) energy efficiency improvements, fossil fuel availability, and dietary choices. An SSP would include assumptions about particular combinations of emissions drivers.
As discussed above, model outcomes such as emissions should be part of scenarios based on SSPs, rather than part of SSPs themselves, which emphasize development pathways and drivers. Yet the definition of challenges to mitigation includes outcomes, in particular high or low reference emissions. Thus a balance must be struck in designing SSPs by having outcomes in mind when designing assumptions about the determinants of emissions while avoiding the outright specification of outcomes. It is likely that some iteration between the design of SSPs and the development of scenarios based on them will be necessary before the set of SSPs and reference scenarios is developed that most effectively spans the space of future outcomes.
Factors that tend to influence the mitigative capacity of a society include the range of viable technological options, national and international institutions for policy making, the availability of financial resources necessary to support mitigation activities, stocks of human and social capital, and political will for addressing energy and environmental issues (Yohe 2001; Winkler et al. 2007; Klein et al. 2007). High (or low) mitigative capacity can result from the combination of a limited set of these factors, and need not involve all factors influencing capacity in the same direction. It also may be the case that key determinants of mitigative capacity, including the capacity for technological change in energy systems, overlap significantly with determinants of reference emissions, making these two components of challenges to mitigation closely related. A key task for developing SSPs will be to choose assumptions about the factors contributing to mitigative capacity that are (1) likely to produce the desired degree of challenge to mitigation, (2) consistent with assumptions about factors leading to the desired degree of challenge to adaptation, and (3) consistent with the overall logic of the particular development pathway being described.
Challenges to adaptation
Socioeconomic challenges to adaptation are defined as societal or environmental conditions that, by making adaptation more difficult, increase the risks associated with any given projection of climate change. Climate change risks arise from the combination of climate hazards (or physical impacts of climate change) such as sea level rise, changes in temperature and precipitation, and extreme events; who or what is exposed to those hazards; and their propensity to adverse impacts, whether it is geographic, socioeconomic, cultural, etc. (see Rothman et al. 2013, for a discussion of how challenges to adaptation relate to concepts in the impacts, adaptation, and vulnerability literature). Within the scenario matrix architecture, the component of climate change risk due to physical impacts of climate change is reflected in climate model projections based on the RCPs and therefore should not be contained in the SSPs. The remaining components of risk are inherent to human-environment systems potentially exposed to those hazards, and therefore are appropriately included in the SSPs. Challenges to adaptation are a function of the socioeconomic determinants of exposure to climate change hazards, sensitivity to these hazards, and the adaptive capacity to deploy coping measures. They include the limits of autonomous adaptation (i.e., the range of adaptive measures that are readily accessible to individuals and organizations) and the obstacles and constraints to adaptation policies, such as ineffective institutions and governance that impede policy implementation.
Exposure is the presence of people; livelihoods; infrastructure; ecosystem services and resources; and economic, social, and cultural assets in places that could be adversely affected by a climate hazard. For example, a population that is concentrated near a coastline has potentially high exposure to the impacts of sea level rise, while one that is heavily concentrated in urban areas has potentially high exposure to urban heat waves. Sensitivity, which is sometimes intertwined with exposure, indicates the responsiveness of socioeconomic systems to a given amount of climate change; it can be described by an exposure-response relationship. If coastal populations live in poorly constructed housing, for example, they would be more sensitive to the increased storm surges associated with sea level rise compared to a population living in better-constructed buildings. Likewise, an urban population that has higher proportions of elderly residents, who are physiologically more susceptible to extreme conditions than most of the remaining population, would be more sensitive to urban heat waves.
Adaptive capacity indicates the ability of a society to adjust to climate change in order to ameliorate its consequences or to take advantage of opportunities. Factors that influence this capacity include the availability of viable technological options for adaptation, the effectiveness of relevant institutions (such as agricultural research and development, markets for goods affected by climate change, forest management organizations, etc.), and the availability of human and financial resources, including their distribution across the population (Klein et al. 2007; Yohe and Tol 2002; Hallegatte et al. 2011). For example, a well functioning public health system would increase the capacity of a society to ameliorate health impacts of heat waves, while well functioning food markets and institutions for agricultural research and development would increase the capacity to ameliorate consequences of climate change for agriculture, including the possibility of taking advantage of outcomes such as lengthening growing seasons and higher CO2 concentrations that could be beneficial to some crops.
Domains within the challenges space
Figure 1 shows the challenges space divided into five domains with different combinations of socioeconomic challenges to mitigation and adaptation. Domain 1 in the lower left corner, for example, indicates a future in which challenges to both mitigation and adaptation are low. By contrast, Domain 3 indicates a future in which challenges to both are high. Different socioeconomic pathways could produce outcomes that fall within any of these domains, and many different pathways could fall within a given domain. The Shared Socio-economic Pathways (SSPs) are indicated by stars that represent a single socioeconomic pathway within each domain developed for common use across a wide range of studies within the overall scenario framework. The number and location of these domains are for illustrative purposes; the most appropriate number and characterization of the SSPs remain to be decided by the scientific community.
The time dimension is not explicitly indicated in Fig. 1, but the domains are intended to contain pathways that evolve over time. The two axes therefore represent challenges that can change over time and are defined in relative terms. That is, challenges to mitigation, or adaptation, mean challenges relative to a middle-of-the-road outlook for how such challenges may evolve over time. Thus, Domain 2 captures the location of socioeconomic pathways situated towards the center of the distribution of plausible outcomes for the challenges to mitigation and adaptation as they develop into the future. This does not imply that the challenges are static in Domain 2. They would be expected to change in an absolute sense over time consistent with middle-of-the-road development. Current expectations would likely be that future societies might have larger challenges to mitigation, but smaller challenges to adaptation, than current societies, given anticipated increases in emissions (in the absence of mitigation policy) as well as in incomes and human capital development, although we consider this a hypothesis rather than an established fact.
An important question is whether some of the domains in this challenges space are a higher priority to explore than others, and if so, for which purpose. For example, domains 1–3, lying along the diagonal from the lower left to upper right, represent futures in which socioeconomic challenges to mitigation co-vary with challenges to adaptation. In contrast, domains 4 and 5 indicate futures in which challenges are high to either mitigation, or to adaptation, but not both. It is possible that the drivers of these challenges are more likely to co-vary, which would favor focusing on the SSPs along the diagonal, but this question remains to be explored. In many cases, the determinants of mitigative and adaptive capacity are similar and can be conceptualized as a more general “response capacity” (Klein et al. 2007; Tompkins and Adger 2005). For example, human and social capital are important determinants for both, and the broader concept of resilience may be quite relevant here as well (Miller et al. 2010). On the other hand, these capacities need not share the same determinants (Hallegatte et al. 2011), because (for example) institutions important to adaptation challenges, such as disaster relief organizations and agricultural extension services, are not necessarily the same ones that are important to mitigation challenges. Furthermore the challenges to mitigation and adaptation as conceptualized here include not just response capacity, but also other elements of development pathways such as those that would lead to high reference emissions or to high levels of sensitivity to climate change.
Domains, socioeconomic pathways, and SSPs
In principle, all possible socioeconomic development pathways could be mapped to the challenges space, and any given domain of that space could contain a very large number of such pathways. The set of SSPs is intended to consist of a single pathway from each domain that is developed in order to be widely shared across research groups and studies in order to improve consistency and comparability of results and facilitate assessment of the literature produced. This approach keeps the number of socioeconomic pathways manageable, at the risk of appearing to simplify the complexity of drivers of mitigative and adaptive capacity (Rozenberg et al. 2013).
A further consideration is that the SSP should set the boundary conditions within which regional and sectoral variation could occur. For example, some pathways might envision response capacities that are low in some parts of the world and high in others, or that transition from one state to another over time. An additional consideration is that some futures may not be the most plausible outcomes, but nonetheless may be equally (or even more) important to explore given their potential consequences.