Emissions Scenarios - IPCC

202 An Overview of Scenarios
Box 4-7: The Role of Prices in SRES Scenarios
The price of energy comprises many components:
• Costs to establish and maintain the production, conversion, transport, and distribution infrastructure of energy supply.
• Profit margins.
• A whole host of levies such as royalties and taxes raised at the points of energy production or use.
• Consumers' willingness to pay for quality and convenience of energy services.
Fiuthermore, given the importance of energy and the vast volumes traded, prices are influenced by a whole range of additional
factors, from inevitable elements of speculation to geopolitical considerations, all of which can decouple energy price trends
from any underlying physical balance between supply and demand. Taxes are especially significant. In a number of OECD
countries, up to 80% of the consumer price of gasoline is taxes (OECD, 1998), and the differences between countries are
enormous. In 1997, 27% of the price of gasofine ui the USA was taxes, compared with 78% in France. Taxes vary substantially
even between large oil producers (and exporters). In Mexico taxes are 13% of gasoline prices, but in Norway they are 75%
(OECD, 1998).
Currently, no methodologies exist to project future energy prices taking all of above mentioned facti)rs into account, nor were
the SRES scenarios intended to make explicit assumptions on such factors such as future energy taxation. Price information
enters long-term emission models either in the form of exogenous scenario assumptions, or it is derived internally m models
based on simplified representations of price formation mechanisms usually based on (marginal) cost information.
The six models used for SRES range from detailed "bottom-up" models (e.g., AIM, IMAGE), through macro-economic (partial
equiübrium) models (e.g., MARIA, MiniCAM), to hybrid approaches (successive iteration between the engineering model
MESSAGE with a macro-economic model, or using the Worldscan model with IMAGE). Each has different representations of
price formation mechanisms and their relationship to macro-economic or sectoral energy demand. These are summarized in
Appendix IV. As a rule, "bottom-up" (optimization) models calculate only (average and marginal) costs endogenously. As a
result of their sectoral perspective (energy, agriculture, etc.), these models cannot determine macro-economic feedbacks on other
sectors or the entire economy and thus are unable to represent a consistent picture of price formation. Conversely, price
formation is endogenized in "top-down" models; however, these rely on the stringent assumption that demand and supply must
be in equilibrium and in addition provide little sectoral detail. Over recent years this simplified modeling dichotomy has
progressively weakened because of further advances in methodology and the development of "hybrid" modeling approaches. To
illusti-ate the methodologies deployed in the six SRES models, two (MARIA and MESSAGE) are discussed here, but (for space
limitations) only in terms of one scenario (B2). (Table 4-9 gives additional details of an mter-scenario comparison of energy
prices for the MiitiCAM and ASF models. Owing to méthodologie differences, a comparison of prices across scenarios is only
possible within a consistent approach (i.e. be comparing scenarios quantified with the same model).)
The energy prices represented in MARIA (see also Mori, 2000) consist of energy production and energy utiUzation costs. Market
prices are determined endogenously by model-calculated shadow prices (for further model details see Appendix IV and Mori
and Takahashi, 1999). Among various parameters, the extraction costs of fossil fuel resources and the coefficients of utilization
costs and their evolution over time are the most important determinants. For the MARIA runs, the resource estimates of Rogner
(1997) were used as input. For the sake of simplicity, all fossil resource categories of Rogner (1997) were aggregated mto two
classes and a quadratic production function was used to interpolate the extiaction costs of reserves and all other occurrences.
For coal, long-term extraction costs range up to US$6.3 per GJ in I990US$ prices, for gas up to US$25 per GJ, and for oil up
to US$28 per GJ (see Appendix IV for further details). The energy cost coefficients (representing 16 different energy conversion
technologies) are based on Manne and Richels (1992). For the B2 scenario quantification, the Manne and Richels (1992)
estimates were largely retained. For instance, electricity generation costs range between 14 mills^^/kWh for gas to 51 mills/kWh
for coal. (For the other scenario quantifications these cost values were modified to conform lo the different interpretations of a
particular scenario storyline.) Together these assumptions determined long-ran costs and shadow prices that were set equal to
energy prices in the macro-economic production function of MARIA. The energy prices were combined with assumed (low)
AEEI values and potential GDP growth rates (the latter from the B2 marker) to calculate the resultant aggregate energy demand
in the model. The resultant primary energy demand was (with exception of the REF region) within 15% of the respective B2
marker quantification at the regional level and within 5% of global energy demand. As a result of different model structures,
comparable price data for the MESSAGE model are only available for internationally traded primary energy forms (these are
given in Table 4-8).
26 1 mill is 0.1 USCents (US$0.001).