Emissions Scenarios - IPCC

216 An Overview of Scenarios
gases, or land availability and prices that influence biomass
costs) are also important determinants of speed and potentials
for the diffusion of new energy technologies.
4.4.8. Prospects for Future Energy Systems
In the energy systems models used to generate the scenarios
reported here, the entire energy systems structure is
represented from primary energy extraction, through
conversion, transport, and distribution, all the way to the
provision of energy services. Primary energy harnessed from
nature (e.g., coal from a mine, hydropower, biomass, solar
radiation, produced crude oil, or natural gas) is converted in
refineries, power plants, and other conversion facilities to give
secondary energy in the form of fuels and electricity. This
secondary energy is transported and distributed (including
trade between regions) to the point of final energy use. Final
energy is transformed into useful energy (i.e., work or heat) in
appliances, machines, and vehicles. Finally, application of
useful energy results in delivered energy services (e.g., the
light from a light bulb, mobility).
Important differences exist in accounting conventions on how
to calculate the primary energy equivalent of particularly
renewable and nuclear energy (see Watson et al., 1996). To
assure comparability of model results, the SRES writing team
agreed to adopt as a common accounting methodology the
Box 4-9: Dynamics of Teclinological Change in the MESSAGE-Based Quantifications for the Four SRES Marker
Scenarios.
Technological change in energy supply and end-use technologies has historically been a main driver of structural changes in
energy systems, efficiency improvements, and improved environmental compatibility. Yet, despite its crucial role, the
mechanisms that underlie technological innovation and diffusion of new technologies remain poorly understood, so modeling
technological change as an endogenous process to the economy and society is still in its infancy. Historically, the track record
of technology forecasts has at best been mixed, with a number of notable failures particularly in the energy sector. In the 1960s,
for mstance, R&D in the US attempted to develop nuclear-propelled aircraft, and nuclear electricity was anticipated to become
"too cheap to meter." Conversely, the dynamic technological changes in microprocessors, information technologies, and
aeroderivative turbines (and their combination with the steam cycle in the form of combined cycle gas turbines) were largely
underestimated. This is similar to the pessimistic market outlook for gasoline-powered cars at the end of the 19"' and start of the
20* centuries.
In recognition of the considerable uncertainty in describing future technological tiends, a scenario approach was adopted to vary
technology-.specific assumptions in the MESSAGE model runs of the SRES scenarios. Depending on the specific interpretation
of the four SRES scenario storylines, altemative technologies and altemative ranges of their future characteristics were assumed
as model inputs.
Two guiding principles determined the choice of particular technology assumptions in MESSAGE.
First, technologies not yet demonstrated to function on a prototype scale were excluded. Therefore, for instance, nuclear fusion
is excluded from the technology portfolio of all SRES scenarios calculated with the MESSAGE model. However, production of
hydrogen- or biomass-based synfuels (e.g. ethanol) or advanced nuclear and solar electricity generation technologies are
included, as they have demonstrated their physical feasibility at least on a laboratory or prototype scale, or m some specific niche
markets (even if they are uneconomic at currently prevailing energy prices). Second, the range of technology-specific
assumptions is empnically derived. Statistical distributions of technology characteristics based on a large technology mventory
(consisting of 1600 technologies) and developed at IIASA (Messner and Strubegger, 1991; Strubegger and Reitgmber, 1995)
were used. Means, maxima, and nunima from these distributions (e.g. of estimated future technology costs) guided which
particular values to adopt across scenarios on the basis of the scenario taxonomy suggested by the scenario storylines (ranging
from conservative to optimistic).
Tables 4-13a to 4-13e summarize the technology characteristics and resultant dilfusion rates across the four SRES scenario
families and their scenario groups. Table 4-13a presents a brief overview of a selection of major energy technologies represented
in the MESSAGE model. (Being a detailed "bottom-up" model, MESSAGE Hterally contams hundreds of individual
technologies, too many to summarize here; mstead, only the most hnportant technology groups, aggregated across many
individual technologies, are presented.) Table 4-13b summarizes salient technology characteristics hi terms of levelized costs
(investment and operating costs levelized per unit energy output, excluding fuel costs) and Table 4-13c summarizes the resultant
marker deployment (diffusion) of these technologies by 2050 and 2100 for the B2-MESSAGE marker scenario. This scenario
is characterized by intermediate levels of growth in energy demand and conservative assumptions as to future technological
change. The latter were adopted based on a literature survey (Strubegger and Reitgruber, 1995) as well as an expert opinion poll.