2.4.6.4 Socio-economic scenarios
Socio-economic changes are key drivers of projected changes in future emissions and climate, and are also key determinants of most climate change impacts, potential adaptations and vulnerability (Malone and La Rovere, 2005). Furthermore, they also influence the policy options available for responding to climate change. CCIAV studies increasingly include scenarios of changing socio-economic conditions, which can substantially alter assessments of the effects of future climate change (Parry, 2004; Goklany, 2005; Hamilton et al., 2005; Schröter et al., 2005b; Alcamo et al., 2006a). Typically these assessments need information at the sub-national level, whereas many scenarios are developed at a broader scale, requiring downscaling of aggregate socio-economic scenario information.
Guidelines for the analysis of current and projected socio-economic conditions are part of the UNDP Adaptation Policy Framework (Malone and La Rovere, 2005). They advocate the use of indicators to characterise socio-economic conditions and prospects. Five categories of indicators are suggested: demographic, economic, natural resource use, governance and policy, and cultural. Most recent studies have focused on the first two of these.
The sensitivity of climate change effects to socio-economic conditions was highlighted by a series of multi-sector impact assessments (Parry et al., 1999, 2001; Parry, 2004; see Table 2.3). Two of these assessments relied on only a single representation of future socio-economic conditions (IS92a), comparing effects of mitigated versus unmitigated climate change (Arnell et al., 2002; Nicholls and Lowe, 2004). The third set considered four alternative SRES-based development pathways (see Box 2.6), finding that these assumptions are often a stronger determinant of impacts than climate change itself (Arnell, 2004; Arnell et al., 2004; Levy et al., 2004; Nicholls, 2004; Parry et al., 2004; van Lieshout et al., 2004). Furthermore, climate impacts can themselves depend on the development pathway, emphasising the limited value of impact assessments of human systems that overlook possible socio-economic changes.
Table 2.3. Key features of scenarios underlying three global-scale, multi-sector assessments: [a] Parry et al. (1999); [b] Arnell et al. (2002); [c] Parry (2004).
| | Impacts of unmitigated emissions [a] | Impacts of stabilisation of CO2 concentrations [b] | Impacts of SRES emissions scenarios [c] |
|---|
| Emissions scenarios | IS92a (1% per increase in CO2-equivalent concentrations per year from 1990) | Stabilisation at 750 and 550 ppm | Four SRES emissions scenarios: A1FI, A2, B1, and B2 |
| Climate scenarios (AOGCM-based) | Derived from four ensemble HadCM2 simulations and one HadCM3 simulation forced with IS92a emissions scenarios | Derived from HadCM2 experiments assuming stabilisation at 550 and 750 ppm; comparison with IS92a | Derived from HadCM3 ensemble experiments (number of runs in brackets): A1FI (1), A2 (3), B1 (1), and B2 (2) |
| Socio-economic scenarios | IS92a-consistent GDPa and population projections | IS92a-consistent GDPa and population projections | SRES-based socio-economic projections |
Box 2.6. SRES-based socio-economic characterisations
SRES provides socio-economic information in the form of storylines and quantitative assumptions on population, gross domestic product (GDP), and rates of technological progress for four large world regions (OECD-1990, Reforming Economies, Africa + Latin America + Middle East, and Asia). Since the TAR, new information on several of the SRES driving forces has been published (see also the discussion in Naki´cenovi´c et al., 2007). For example, the range of global population size projections made by major demographic institutions has reduced by about 1-2 billion since the preparation of SRES (van Vuuren and O’Neill, 2006). Nevertheless, most of the population assumptions used in SRES still lie within the range of current projections, with the exception of some regions of the A2 scenario which now lie somewhat above it (van Vuuren and O’Neill, 2006). Researchers are now producing alternative interpretations of SRES population assumptions or new projections for use in climate change studies (Hilderink, 2004; O’Neill, 2004; Fisher et al., 2006; Grübler et al., 2006).
SRES GDP growth assumptions for the ALM region (Africa, Latin America and Middle East) are generally higher than those of more recent projections, particularly for the A1 and B1 scenarios (van Vuuren and O’Neill, 2006). The SRES GDP assumptions are generally consistent with recent projections for other regions, including fast-growing regions in Asia and, given the small share of the ALM region in global GDP, for the world as a whole.
For international comparison, economic data must be converted into a common unit; the most common choice is US$ based on market exchange rates (MER). Purchasing-power-parity (PPP) estimates, in which a correction is made for differences in price levels among countries, are considered a better alternative for comparing income levels across regions and countries. Most models and economic projections, however, use MER-based estimates, partly due to a lack of consistent PPP-based data sets. It has been suggested that the use of MER-based data results in inflated economic growth projections (Castles and Henderson, 2003). In an ongoing debate, some researchers argue that PPP is indeed a better measure and that its use will, in the context of scenarios of economic convergence, lead to lower economic growth and emissions paths for developing countries. Others argue that consistent use of either PPP- or MER-based data and projections will lead to, at most, only small changes in emissions. This debate is summarised by Naki´cenovi´c et al. (2007), who conclude that the impact on emissions of the use of alternative GDP metrics is likely to be small, but indicating alternative positions as well (van Vuuren and Alfsen, 2006). The use of these alternative measures is also likely to affect CCIAV assessments (Tol, 2006), especially where vulnerability and adaptive capacity are related to access to locally traded goods and services.
The advantages of being able to link regional socio-economic futures directly to global scenarios and storylines are now being recognised. For example, the SRES scenarios have been used as a basis for developing storylines and quantitative scenarios at national (Carter et al., 2004, 2005; van Vuuren et al., 2007) and sub-national (Berkhout et al., 2002; Shackley and Deanwood, 2003; Solecki and Oliveri, 2004; Heslop-Thomas et al., 2006) scales. In contrast, most regional studies in the AIACC (Assessments of Impacts and Adaptations to Climate Change in Multiple Regions and Sectors) research programme adopted a participatory, sometimes ad hoc, approach to socio-economic scenario development, utilising current trends in key socio-economic indicators and stakeholder consultation (e.g., Heslop-Thomas et al., 2006; Pulhin et al., 2006).
Methods for downscaling quantitative socio-economic information have focused on population and gross domestic product (GDP). The downscaling of population growth has evolved beyond simple initial exercises that made the sometimes unrealistic assumption that rates of population change are uniform over an entire world region (Gaffin et al., 2004). New techniques account for differing demographic conditions and outlooks at the national level (Grübler et al., 2006; van Vuuren et al., 2007). New methods of downscaling to the sub-national level include simple rules for preferential growth in coastal areas (Nicholls, 2004), extrapolation of recent trends at the local area level (Hachadoorian et al., 2007), and algorithms leading to preferential growth in urban areas (Grübler et al., 2006; Reginster and Rounsevell, 2006).
Downscaling methods for GDP are also evolving. The first downscaled SRES GDP assumptions applied regional growth rates uniformly to all countries within the region (Gaffin et al., 2004) without accounting for country-specific differences in initial conditions and growth expectations. New methods assume various degrees of convergence across countries, depending on the scenario; a technique that avoids implausibly high growth for rich countries in developing regions (Grübler et al., 2006; van Vuuren et al., 2007). GDP scenarios have also been downscaled to the sub-national level, either by assuming constant shares of GDP in each grid cell (Gaffin et al., 2004; van Vuuren et al., 2007) or through algorithms that differentiate income across urban and rural areas (Grübler et al., 2006).