Enhancing the relevance of Shared Socioeconomic Pathways for climate change impacts, adaptation and vulnerability research

A recent initiative to develop a new set of global socioeconomic scenarios to serve different areas of climate change research using a matrix architecture including Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) (Ebi et al. this issue; Kriegler et al. this issue; O’Neill et al. this issue; van Vuuren et al. 2011, this issue) may offer new opportunities to increase coherence between scenarios at different scales and expand their use in IAV studies. This broadening analytical frame should, in turn, provide fresh insights into how future socioeconomic conditions may determine the magnitude and extent of potential climate change risks as well as society’s ability to adapt. RCPs characterize varying levels of greenhouse gas and aerosol concentrations in the atmosphere as well as changes in land use that can affect the global climate during the 21st century and beyond (van Vuuren et al. 2011) and are being used as inputs to climate models in the Climate Model Intercomparison Project (CMIP5—Taylor et al. 2011).

A framework for constructing scenarios using SSPs and RCPs is described in this issue (Kriegler et al. this issue; O’Neill et al. this issue; van Vuuren et al. this issue) and narrative descriptions of five SSPs have been drafted (O’Neill et al. forthcoming). Quantification of several SSP elements is underway using Integrated Assessment Models (IAMs). Some of the key drivers of social, economic, and environmental change, such as population and gross domestic product (GDP) are globally specified on the country-level. The development of SSPs is proceeding in two stages, with a “basic” set of SSPs being developed that include broad-scale narratives and global quantifications. This high-level set of global SSPs establishes a platform for developing “extended” SSPs that include substantive elaborations for specific sectors and regions. The matrix architecture of combining SSPs with RCPs into new scenarios allows IAV researchers to ask questions such as: “what could be the impacts of a given amount of climate change in worlds characterized by different development pathways?” (i.e. combining a single RCP with multiple SSPs), or “what could be the impacts of different levels of climate change under one possible future world?” (i.e. combining a single SSP with multiple RCPs). The unpacking of the scenarios into climate, development pathway, and policy provides researchers with a tool kit for asking more policy relevant questions than were possible with earlier scenario sets.

The process of new scenario development offers new opportunities for advancing the use of scenarios in IAV studies (see e.g. Rothman et al. this issue; Wilbanks and Ebi this issue). The process could be enhanced by addressing the question of how to bridge the scales between the global and the regional, local, and sectoral interests of the IAV community. The literature provides numerous and diverse examples of approaches for bridging across scales, but such methods can introduce further uncertainties and raise questions of internal consistency. Also, earlier global scenario sets lack a number of elements of socioeconomic development relevant to IAV analyses including trajectories of key indicators to characterize vulnerability and adaptive capacity.

In this paper, we explore how the SSPs and new scenarios can be made more useful to IAV researchers. What methods exist to extend the SSPs across multiple scales, and how can the SSPs increase coherence among studies with a strong bottom-up component? What elements and variables have IAV researchers added to global scenarios in the past, in order to be able to use them in IAV analyses? We aim to find a pragmatic compromise between generating as many indicators and variables as possible, whilst also recognising that only a limited number of variables can be meaningfully projected.

Section 2 discusses the main uses of socioeconomic scenarios in IAV analyses and details the elements and indicators needed for such studies. Section 3 presents an overview of the use of global environmental scenarios during the past decade for IAV studies and identifies shortcomings of those scenarios for IAV applications. Section 4 discusses methods for working across scales, and presents a rationale and procedures for adding additional elements to the SSPs. Finally, Section 5 surveys some of the remaining challenges and offers concluding remarks.

Socioeconomic scenarios have varied uses in impacts, adaptation, or vulnerability studies, each application placing different demands on the types of scenario information required (Carter et al. 2001, 2007). Impact studies analyze the impacts and risks of climate changes for human and natural systems, with the aim to estimate the character, magnitude and rate of impacts across a number of contrasting scenarios and to investigate the effectiveness of various mitigation or adaptation measures in reducing risks or exploiting opportunities. In these studies, socioeconomic development pathways are generally used to characterise future exposure to climate changes. Studies focusing on adaptation require scenarios that characterise the conditions, incentives or barriers that affect adaptation options and adaptive capacity. Here, different socioeconomic scenarios can be combined with different climate scenarios to estimate the effectiveness of adaptation, residual damages after mitigation and adaptation, and the associated costs. In research on vulnerability, scenarios can be used to enhance understanding of the key factors predisposing systems and communities to potential adverse impacts of climate change, exploring how causal relations between those factors might develop or change in the future. Key questions for vulnerability and adaptation studies include “adaptation to what?”, “who or what adapts?”, “how does adaptation occur?”, “have objectives been achieved?” (Funfgeld and McEvoy 2011; Preston and Stafford-Smith 2009; Smith et al. 2000). Each of these questions is dependent upon socioeconomic futures. More discussion on the role of vulnerability assessment and an explicit discussion of the (lack of) use of socioeconomic scenarios in IAV research and the potential consequences of that can be found in Preston et al. (2011).

Over the past decade, several global scenario sets for global environmental assessments were developed (van Vuuren et al. 2012a) such as the IPCC SRES (Nakicenovic et al. 2000), the Global Environmental Outlook scenarios (GEO) (UNEP 2002, 2007) and the Millennium Ecosystem Assessment (Carpenter et al. 2005). The Special Report on Emissions Scenarios (SRES) presented scenarios of greenhouse gas emissions for mitigation research, consisting of simple storylines and model quantifications. The four global and seven regional GEO scenarios have a broader scope, characterising drivers of global environmental change and explicitly including indicators related to poverty, educational attainment, and malnutrition, as well as discussing progress on achieving the Millennium Development Goals (MDGs). The design of the Millennium Ecosystem Assessment (MA) scenarios was similar to that of SRES, but with a focus on ecosystems and human well-being rather than on climate change. An innovative aspect of the MA-scenarios was the multi-scale design. Besides global scenarios, much effort was directed to 35 sub-global assessments linking across scales. This resulted in a large body of empirical evidence and methodological findings of how to develop multi-scale scenarios (Zurek and Henrichs 2007). The results of the MA spurred thinking on the role of global scenarios in sub-global studies.

In practice, these global scenarios—at least in their original form—had limited application to IAV analyses (Arnell et al. 2004; Kriegler et al. 2012) because the storylines, assumptions, and model outputs were elaborated at different scales than those needed for most IAV studies. The GEO and MA scenario exercises were partly designed to overcome this limitation by nesting global and regional scenarios. However, as these exercises did not use climate model runs, there are only a few applications in IAV studies (e.g. Kok et al. 2011). For the SRES, climate runs based on emissions and land use are available, but offer only limited elaboration of socioeconomic variables and regional information. However, the storylines offer a guide for additional assumptions for climate impact and mitigation analyses (Nakicenovic et al. 2000); examples include institutions and governance (Berkhout et al. 2002), land use (Rounsevell et al. 2006), income distribution and poverty (Calzadilla 2010), and regional exercises (Carter et al. 2004; Holman et al. 2005). However, although many IAV studies use SRES-based climate projections, only a few studies use socioeconomic projections, mostly limited to downscaled projections of population and GDP (Carter et al. 2007). The first set of these downscaled SRES projections was published by Gaffin et al. (2004) using simple techniques. More refined methods of downscaling were published later. One of these accounts for partial convergence in per capita GDP within world regions based on the SRES storylines (van Vuuren et al. 2007). Another distinguishes urban and rural areas in population and GDP projections and adds a geographic gravity model framework (see Section 4.2.2) to represent urban spatial expansion more effectively (Grübler et al. 2007).

IAV studies that used these downscaled projections for population and GDP also sometimes included additional elements; examples include studies of water availability and flooding that used assumptions about development indicators and sectoral scenarios (Döll 2009; Hinkel et al. 2012; Nicholls 2004; Pérez Urrestarazu et al. 2010). Studies of agricultural impacts made additional assumptions about food demand, trade, yields, and other sectoral information (Parry et al. 2004; Verburg et al. 2008; Wu et al. 2011). Ecosystem impact studies made additional assumptions about technology and deforestation (Golding and Betts 2008; Metzger et al. 2008). Population and health studies have used downscaled population (McCarthy et al. 2010) combined with expert judgment on adaptive capacity instead of using the socioeconomic assumptions in the SRES (van Lieshout et al. 2004). Overall, studies that use the SRES tend to focus more on impacts than on vulnerability or adaptive capacity, possibly because of the paucity of relevant variables.

Table 1 describes variables and indicators from several global scenario sets, those added to the SRES scenarios in individual IAV studies for different sectors, and those included in two vulnerability indices (WRI and GAIN, see Section 2). There is considerable overlap in variables and indicators of vulnerability between the scenario sets and the indices. The dominant variables in the vulnerability indices are income/poverty, education, spatial population, population structure, health/life expectancy, infrastructure, and governance/resource management. Some of these variables are included traditionally in global scenario sets, such as GDP per capita, while others are extensions of traditional variables, such as education, dependency ratios, and life expectancy; the latter are included in the extended set of population projections for the SSPs (Lutz et al. 2013). Some elements, such as infrastructure, are available from IAM models (see e.g. Pachauri et al. 2013; Sassi et al. 2010). There are methods available to develop projections for other elements, such as poverty/income distribution, spatial population, health and governance, and a research agenda for pursuing this is presented in Section 4.2.

The SSPs, and the new scenarios based on SSPs and RCPs provide an opportunity to reduce several of the limitations identified above for the use of scenarios in IAV work, including benefiting from lessons learnt in the MA and GEO exercises to improve applicability and coherence across scales. The SSPs and new scenarios can also include a richer set of elements more useful to projections of vulnerability and adaptive capacity. Below, we elaborate on efforts to improve working across scales and to developing a richer set of elements.

The SSPs and new scenarios will be crucial for improved projections and assessments of climate change risks and options for their management. They can improve coherence in the growing body of regional scenario studies, provide global boundary conditions to frame regional studies and inform large-scale impact, adaptation and vulnerability assessments. Strategies to reduce vulnerability and increase adaptive capacities of societies facing climate change risks have to account for a variety of potential changes in societal conditions. The usefulness of the SSPs and new scenarios would be enhanced for IAV researchers by adding a richer set of elements to the projections—including income distribution, spatial population, human health, and governance—and by improving scenario application across spatial and temporal scales. This paper suggests a research agenda to start this process.

As part of the broader ‘parallel’ scenario process (Moss et al. 2010), the scenarios provide a range of new opportunities for integrating alternative socioeconomic and climatic futures for the purposes of local, regional, or sectoral impact analyses and for evaluating possible adaptation and mitigation policies (van Vuuren et al. 2012b). In contrast to the SRES storylines and scenarios, the matrix architecture of the new scenarios allows greater flexibility in exploring combinations of socioeconomic and climate futures from multiple alternatives. For regional/sectoral applications, climate projections based on the RCPs may need to be downscaled to generate climate information at the scales relevant for particular IAV applications. The IAV community is sensitive to uncertainty in model projections for a given RCP and thus often uses multi-model ensembles to explore the uncertainty space associated with different model realizations of the future climate. A similar approach might be desired in using multiple socioeconomic models (or IAMs) for each SSP.

One of the remaining challenges is how to balance the need for producing long-term averaged/smoothed scenarios of socioeconomic development with the interest to remain sensitive to crises and shocks at local/regional and decadal scales. The latter are of great importance for the vulnerability and impact context as they might trigger long-term—positive or negative—changes in terms of vulnerability and adaptive capacity (Birkmann et al. 2010). One possible way forward would be to integrate the possibility for medium scale fluctuations—e.g. economic crises or a major pandemic—into the scenarios and to assess whether and how such events might alter the speed and direction of the larger pathway. Such amendments can provide important and policy-relevant bridges and insights.