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4.4.5. Energy Intensities, Energy Demand, and Structure of Energy Use

Population and GDP assumptions, along with structural change and technological change that affect energy efficiency and energy costs (and prices), drive the demand for energy services. Given the different model representations of energy service demands, in this section final energy use is discussed as a common measurement point across all SRES models and scenarios. Final energy use per unit economic activity, that is, energy intensity, is a frequently used measure of comparative efficiency of resource use, and reflects a whole range of structural, technological, and lifestyle factors (Schipper and Meyers, 1992).

Figure 4-6: Relationship between final energy intensity and per capita income in the four marker scenarios. The data points represent values in 1990, 2020, 2050, and 2100. The 1990 value is at the top of each curve and the 2100 value at the bottom.

Figure 4-6 illustrates the evolution of final energy intensities for the four SRES marker scenarios. Instead of time, per capita income is shown on the horizontal axis, to illustrate a conditional convergence of regional final energy intensities. Invariably, intensities are projected to decline with increasing income levels. As discussed in Chapter 3, the main reason for this trend stems from the common source of economic growth and energy intensity improvements - technological change. All else being equal, the faster intensity improvements are, the faster aggregate productivity (per capita income) grows. An important methodologic improvement over previous studies is the explicit inclusion of non-commercial energy forms in some SRES models, drawing on estimates as reported in IPCC WGII SAR (Watson et al., 1996) and in Nakicenovic et al. (1998).

In the A1 and B1 scenarios, per capita income differences are substantially narrowed and convergent because of increased economic integration and rapid technological change. Therefore, differences in energy intensities are also narrowed significantly and are convergent, as shown in Figure 4.6. The B1 storyline describes a development path to a less material-intensive economy. Hence, the final energy intensities in the B1 marker are lowest among the four SRES marker scenarios for a given per capita income level. The A2 storyline reflects a world with less rapid technological change, as shown by the smallest rate of energy intensity improvement among the four marker scenarios25. Different interpretations of the four scenario storylines, as well as alternative rates of energy intensity improvement to the four marker scenarios, are discussed below.

Owing to methodologic differences across the six models (see Box 4-7) it is not possible to disaggregate energy intensity improvements into various components, such as structural change, price effects, technological change, etc., in a consistent way. In some models (macro-economic) price effects are differentiated from "everything else" (frequently labeled AEEI, or autonomous energy intensity improvements). As a rule, the importance of non-price factors is an inverse function of the time horizon considered. Over the short-term, the impacts of economic structural change and technology diffusion are necessarily low. Hence, prices assume a paramount importance in driving alternative energy demand patterns in short-term (to 2010-2020) scenario studies (e.g., IEA, 1998; EIA, 1997, 1999). Over the longer term (i.e., the time horizon considered by the SRES scenarios), economic structural and technological changes become more pronounced, as does their influence on energy intensity improvements and energy demand. This does not imply that prices do not matter over the long term, but simply that "everything else" (e.g., AEEI) is likely to outweigh the impacts of prices, as indeed suggested by quantitative scenario analyses performed within the Energy Modeling Forum EMF-14 (Weyant, 1995).

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.

Furthermore, 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 gasoline in 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 factors 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 in 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 equilibrium) 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 illustrate 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 inter-scenario comparison of energy prices for the MiniCAM and ASF models. Owing to methodologic 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 utilization 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 into two classes and a quadratic production function was used to interpolate the extraction costs of reserves and all other occurrences. For coal, long-term extraction costs range up to US$6.3 per GJ in 1990US$ 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 mills26 /kWh for gas to 51 mills/kWh for coal. (For the other scenario quantifications these cost values were modified to conform to the different interpretations of a particular scenario storyline.) Together these assumptions determined long-run 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).

The bottom-up, systems engineering (optimization) model MESSAGE does not compute energy prices. Instead, the model is entirely based on cost information, but such costs are treated as dynamic. Their overall treatment follows the lines outlined above for the MARIA model, except that greater technology-specific detail is contained in the model. Altogether 19 different fossil resource grades are differentiated, based on the estimates of Rogner (1997). The resultant (levelized) extraction costs for the B2 marker are in the range US$1.1 to US$5.4 per GJ for coal, US$1.2 to US$5.3 per GJ for oil, and US$1.2 to US$5.7 per GJ for gas (range indicates costs variations between lowest and highest costs of the four SRES regions for 2020, 2050, and 2100 respectively, see Appendix IV). Technology-specific cost assumptions cannot be summarized here as MESSAGE contains literally hundreds of energy supply and end-use technologies. Examples of cost assumptions are given in Section 4.4.7 and more detail is reported in Riahi and Roehrl (2000). However, as in MARIA, MESSAGE also calculates shadow prices for internationally traded primary energy forms and therefore these two indicators can be compared (Table 4-8).

Important feedback mechanisms between technological change and costs (and thus also prices) exist over the long term. These are as a rule treated endogenously in the models, for instance when modeling long-run resource extraction costs or structural changes in energy supply options (see Sections 4.4.6 and 4.4.7). Energy prices are also strongly affected by policies (e.g., taxation), but to project these far into the future is both outside the capability of currently available methodologies and outside the general "policy neutral" stance of the SRES scenarios. Therefore, most models treat dynamic changes in (average and marginal) costs as the driving force for energy intensity improvements and for technology choice (see Sections 4.4.6 and 4.4.7).

4.4.5.1. A1 Scenarios

Improvements in energy efficiency on the demand side are assumed to be relatively low in the A1B marker scenario, because of low energy prices caused by rapid technological progress in resource availability and energy supply technologies (see Sections 4.4.6 and 4.4.7). These low energy prices provide little incentive to improve end-use-energy efficiencies and high income levels encourage comfortable and convenient(and often energy intensive) lifestyles (especially in the household, service, and transport sectors). Efficient technologies are not fully introduced into the end-use side, dematerialization processes in the industrial sector are not well promoted, lifestyles become energy intensive, and private motor vehicles are used more in developing countries as per capita GDP increases. Conversely, fast rates of economic growth and capital turnover and rising incomes also enable the diffusion of more efficient technologies and economic structural changes, with consequent improvements in energy intensity. As a result, the rate of energy intensity improvement in Annex I countries is around 1.16% per year, and in non-Annex I countries 1.44% over the 100 years to 2100. Thus, final energy use for A1 is much higher than those in the A2, B1, and B2 scenarios, with a substantial long-term convergence in final energy use per capita between Annex 1 countries and non-Annex 1 countries.


Table 4-8: International price (MARIA) and calculated shadow price (MESSAGE) of internationally traded energy (1990US$/GJ) by 2020, 2050, 2100 for the SRES B2 scenario.

Coal
Oil
Gas
Biofuels
Synfuels

MARIA MESSAGEa MARIA MESSAGEb MARIA MESSAGEc MARIA
MESSAGEd

2020
0.5
3.4
3.5
3.9-4.4
2.9
2.8-4.4
4.8
n.a.
2050
0.8
2.5
4.9
7.5-8.2
4.3
5.1-6.4
6.5
10.4-16.2
2100
1.4
8.1
6.3
17.3-18.2
5.4
5.2-11.4
6.3
17.1-20.7

A. Costs include export and/or import infrastructure and transport.
B. Range between crude oil and light and heavy oil products.
C. Range between liquid natural gas and direct pipeline imports to North America, Europe, Japan, and North Africa.
D. Range between methanol, ethanol, and liquid hydrogen.

To achieve consistency between model-calculated energy cost dynamics and energy demand assumptions an iterative modeling procedure between MESSAGE and MACRO (a macro-economic production function model based on Manne and Richels, 1992) was used, on the basis of model-calculated shadow prices as indicators of future price dynamics. The methodology is described in more detail in Wene (1996). This approach requires time-intensive model iterations, but has the advantage that the impact of price increases can be separated from efficiency improvements through fuel substitution (e.g., a gas-fired cook stove energy end-use efficiency is up to 10 times higher than a traditional cook stove fired with fuelwood) as well as from "everything else," i.e., the AEEI in the traditional sense). Aggregated, the impact of (shadow) price increases in MESSAGE's B2 scenario accounts for 8% of global primary energy demand by 2020, 23% by 2050, and 30% by 2100. This impact is calculated as a reduction in energy demand compared to a hypothetical scenario with constant 1990 prices (and correspondingly higher energy demand). The impact of price increases on future energy demand in the B2 scenario is thus relatively small compared to that of other factors, although far from negligible. This also explains why the two B2 scenario quantifications by MARIA and MESSAGE have quite similar energy demand figures, even if international trade prices may differ. First, trade prices are only one component of the cost-price mechanism treated in the models (which also includes domestic energy production, conversion, and end-use costs). Second, models differ in their parametrizations of the "everything else" (AEEI) model parameters, for which a wide range of views on applicable ranges exists. Therefore it is one of the model calibration parameters frequently used to replicate existing scenarios or to standardize inter-model comparison projects such as EMF-14 (Weyant, 1995).



Table 4-9: Energy prices (1990US$/GJ) across SRES scenarios as calculated in the ASF (top) and MiniCAM models for their respective A1, A2, B1, and B2 (cf. Table 4-8) scenarios. Note in particular significant base-year differences in fuel prices because of different cost accounting definitions used in models (c.i.f. versus f.o.b.27 ), in particular with respect to transportation costs (included in the price figures given for ASF, but excluded in the numbers given for MiniCAM).

    A1 A2 B1 B2

ASFa          
Coal

2000
2020
2050
2100

1.5
1.6
1.9
2.0
1.5
1.5
1.7
1.8
1.5
1.6
1.7
1.6
1.5
1.5
1.6
1.7
Oil 2000
2020
2050
2100
4.4
5.3
7.1
7.7
4.4
4.7
6.2
7.5
4.4
5.1
6.3
6.1
4.4
4.7
6.1
7.1
Gas 2000
2020
2050
2100
5.0
5.0
5.3
7.9
5.0
5.0
5.0
6.1
5.0
4.9
4.8
4.9
5.0
5.0
4.9
5.8
MiniCAMb          
Coal 1990
2020
2050
2100
1.0
1.6
1.9
2.5
1.0
1.7
2.0
2.5
1.0
1.6
1.7
1.9
1.0
1.6
1.7
2.0
Oil 1990
2020
2050
2100
3.9
8.6
10.4
9.6
3.9
10.2
13.3
15.2
3.9
6.4
9.9
8.5
3.9
7.3
10.4
10.2
Gas 1990
2020
2050
2100
1.6
2.8
3.8
6.8
1.6
3.2
5.7
8.7
1.6
2.0
2.5
1.9
1.6
2.4
3.0
2.3
Biofuels 1990
2020
2050
2100
n.a.
2.1
2.4
2.3
n.a.
2.2
2.6
3.2
n.a.
2.0
2.0
1.5
n.a.
2
2.1
2.0

A. ASF: global average supply price, including transportation.
B. MiniCAM: as determined by solution to a partial equilibrium supply and demand model.



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