3.1.1 The definition and purpose of scenarios
Scenarios describe possible future developments. They can be used in an exploratory manner or for a scientific assessment in order to understand the functioning of an investigated system (Carpenter et al., 2005).
In the context of the IPCC assessments, scenarios are directed at exploring possible future emissions pathways, their main underlying driving forces and how these might be affected by policy interventions. The IPCC evaluation of emissions scenarios in 1994 identified four main purposes of emissions scenarios (Alcamo et al., 1995):
- To provide input for evaluating climatic and environmental consequences of alternative future GHG emissions in the absence of specific measures to reduce such emissions or enhance GHG sinks.
- To provide similar input for cases with specific alternative policy interventions to reduce GHG emissions and enhance sinks.
- To provide input for assessing mitigation and adaptation possibilities, and their costs, in different regions and economic sectors.
- To provide input to negotiations of possible agreements to reduce GHG emissions.
Scenario definitions in the literature differ depending on the purpose of the scenarios and how they were developed. The SRES report (Nakicenovic et al., 2000) defines a scenario as a plausible description of how the future might develop, based on a coherent and internally consistent set of assumptions (‘scenario logic’) about the key relationships and driving forces (e.g. rate of technology change or prices). Some studies in the literature apply the term ‘scenario’ to ‘best-guess’ or forecast types of projections. Such studies do not aim primarily at exploring alternative futures, but rather at identifying ‘most likely’ outcomes. Probabilistic studies represent a different approach, in which the range of outcomes is based on a consistent estimate of the probability density function (PDF) for crucial input parameters. In these cases, outcomes are associated with an explicit estimate of likelihood, albeit one with a substantial subjective component. Examples include probabilistic projections for population (Lutz and Sanderson, 2001) and CO2 emissions (Webster et al., 2002, 2003; O’Neill, 2004).