188.8.131.52 Technological change in climate policy scenarios
In addition to the technology assumptions that enter typical ‘no-climate policy’ baselines, technology availability and the response of technology development and adoption rates to a variety of climate policies also play a critical role. The assessment of which alternative technologies are deployed in meeting given GHG emission limitations or as a function of ex ante assumed climate policy variables, such as carbon taxes, again entails calculations that span many decades into the future and typically rely on (no-climate policy) baseline scenarios (discussed above).
Previous IPCC assessments have discussed in detail the differences that have arisen with respect to feasibility and costs of emission reductions between two broad categories of modelling approaches: ‘bottom-up’ engineering-type models versus ‘top-down’ macro-economic models. Bottom-up models usually tend to suggest that mitigation can yield financial and economic benefits, depending on the adoption of best-available technologies and the development of new technologies. Conversely, top-down studies have tended to suggest that mitigation policies have economic costs because markets are assumed to have adopted all efficient options already. The TAR offered an extensive analysis of the relationship between technological, socio-economic, economic and market potential of emission reductions, with some discussion of the various barriers that help to explain the differences between the different modeling approaches. A new finding in the underlying literature (see, for example, the review in Weyant, 2004a) is that the traditional distinction between ‘bottom-up’ (engineering) and ‘top down’ (macro-economic) models is becoming increasingly blurred as ‘top down’ models incorporate increasing technology detail, while ‘bottom up’ models increasingly incorporate price effects and macro-economic feedbacks, as well as adoption barrier analysis, into their model structures. The knowledge gained through successive rounds of model inter-comparisons, such as implemented within the Energy Modeling Forum (EMF) and similar exercises, has shown that the traditional dichotomy between ‘optimistic’ (i.e. bottom-up) and ‘pessimistic’ (i.e. top-down) views on feasibility and costs of meeting alternative stabilization targets is therefore less an issue of methodology, but rather the consequence of alternative assumptions on availability and costs of low- and zero-GHG-emitting technologies. However, in their meta-analysis of post-SRES model results, Barker et al. (2002) have also shown that model structure continues to be of importance.
Given the infancy of empirical studies and resulting models that capture in detail the various inter-related inducement mechanisms of technological change in policy models, salient uncertainties continue to be best described through explorative model exercises under a range of (exogenous) technology development scenarios. Which mitigative technologies are deployed, how much, when and where depend on three sets of model and scenario assumptions. First, assumptions on which technologies are used in the reference (‘no policy’) case, in itself a complex result of scenario assumptions concerning future demand growth, resource availability, and exogenous technology-specific scenario assumptions. Second, technology deployment portfolios depend on the magnitude of the emission constraint, increasing with lower stabilization targets. Finally, results depend critically on assumptions concerning future availability and relative costs of mitigative technologies that determine the optimal technology mix for any given combination of baseline scenarios with alternative stabilization levels or climate policy variables considered.