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4.4.7. Technological Change
Chapter 3 highlights the importance of technological change in long-run productivity
growth, but also for the historical transformations of energy end-use and supply
systems. The importance of technological change in explaining wide-ranging outcomes
in future emissions has been highlighted by Alcamo et al., (1995) and Grübler
and Nakicenovic (1996), among others. The latter reference also provides a
critical assessment of the previous IS92 scenario series and its comparison
to the literature. Prominent scenario studies of possible technological change
in future energy systems in the absence of climate policies include Ausubel
et al. (1988), Edmonds et al. (1994, 1996a), IIASA-WEC (1995), and Nakicenovic
et al. (1998). Future technology characteristics must therefore be treated as
dynamic, with future improvement rates subject to considerable uncertainty.
This is reflected in the SRES scenarios that adopt a wide range of improvement
rates for energy extraction, conversion, and end-use technologies (Table 4-11).
The actual representation of technological change in the six SRES models ranges
from exogenously prescribed availability, through cost and performance profiles
(which in some cases also include consumer or end-use costs for technology use),
to stylized representation of learning processes31.
Yet, as summarized in Chapter 3, model representations
of technological change are poorly developed, although evolving rapidly.
| Table 4-11: Summary of technology
improvements for extraction, distribution, and conversion technologies assumed
for the SRES scenarios. The classification reviews technology dynamics across
the four marker scenarios and the four A1 scenario groups relative to each
other. Illustrative, scenario-specific technology assumptions are discussed
in the text. A1C and A1G have been combined into one fossil-intensive group
A1FI in the SPM (see also footnote
1). |
|
|
Technology Improvement Rates
|
| Scenario |
Coal |
Oil |
Gas |
Non-fossil |
|
| A1B |
High |
High |
High |
High |
| A2a |
Medium |
Low |
Low |
Low |
| B1b |
Medium |
Medium |
Medium |
Moderate-high |
| B2c |
Low |
Low-medium |
Moderate-high |
Medium |
| A1G |
Low |
Very high |
Very high |
Medium |
| A1C |
High |
Low |
Low |
Low |
| A1T |
Low |
High |
High |
Very high |
|
|
A. Technology improvement rates in the A2 scenario are heterogeneous
among the world regions.
B. B1: The assumed time-dependent learning coefficients range from 0.9
(i.e. a 10% reduction in the capital:output ratio on a doubling of cumulated
production) for oil, 0.9-0.95 for gas, and 0.9-0.95 for surface coal mining
to about 0.94-0.96 for non-fossil electric power generation options and
0.9-0.95 for commercial biofuels.
C. In the specific model implementations, "inconvenience costs" of energy-end
use, including social externalities costs, are expected to be particularly
important for traditional coal technologies (e.g., underground mining,
cooking with coal stoves).
|
4.4.7.1. A1 Scenarios
The A1B marker scenario represents the "balanced" technology development group
of A1 scenarios; it assumes significant innovations in energy technologies,
which improve energy efficiency and reduce the cost of energy supply. Consistent
with the A1 scenario storyline, such improvements occur across the board and
neither favor nor penalize particular groups of technologies. A1 assumes, in
particular, drastic reductions in power-generation costs, through the use of
solar, wind, and other modern renewable energies, and significant progress in
gas exploration, production, and traansport. For a different view, alternative
scenario groups embedded within the overall A1 scenario family explore pathways
of cumulative technological change; that is, path-dependent scenarios in which
technologies evolve on mutually largely exclusive development paths. In general
this has been the historical experience, in which the success of particular
energy technologies (the steam engine in the 19th century, or internal combustion
in the 20th) have "locked out" other technological alternatives. These scenario
groups explore alternative spectra of technology dynamics in the domains of
unconventional oil and gas, coal, as well as post-fossil technologies. Salient
technology assumptions are described below.
Keeping in mind the very different degrees of technological detail and the
mechanisms for technology improvements represented in the different models,
a consistent inter-scenario comparison of technology assumptions is best achieved
within the framework of one particular model. An overview of different technology
developments for the scenario groups of the A1 scenario is given in Box 4-8
for the AIM model, which was also used to develop the A1B marker scenario. (A
comparison with the MARIA model indicated that technology cost assumptions and
their dynamics are quite congruent.) To illustrate differences in technology
characteristics that drive the four different SRES scenario families, corresponding
scenario-specific data based on MESSAGE data are presented at the end of this
Section.
4.4.7.2. A1 Scenario Groups
As outlined above, besides the marker, three different groups of A1 scenarios
were developed by the different modeling groups (combined into two in the SPM,
see also footnote
1 ). In total, nine alternative runs are clustered in three scenario groups
based on the AIM, MARIA, MESSAGE, and MiniCAM models.
In the A1G scenario group, technological change enables a larger fraction of
the large occurrences of unconventional oil and gas, including oil shales, tar
sands, and especially methane hydrates (clathrates) to be tapped. High technological
learning and cost reduction effects could lower unconventional oil and gas extraction
costs by approximately 1% per year and conversion technology costs by about
factor of two (A1G-MESSAGE, see Roehrl and Riahi, 2000). As mentioned in Section
4.4.6, although these assumptions yield higher extractions of unconventional
oil and gas resources, they are not sufficient to tap significant fractions
of unconventional resources such as gas clathrates. Future scenario studies
might reassess the current state of knowledge on possible technology development
of these "exotic" fossil-fuel occurrences and the conditions under which they
could become a major future source of unconventional hydrocarbon supply (and
a massive source of carbon emissions). For the A1G scenario group, substantial
improvements and extensions of the present pipeline grids and entirely new natural
gas pipelines systems from Siberia and the Caspian to South East Asia, China,
Korea, and Japan after 2010/2020 would be needed. Since unconventional oil and
gas resources are distributed unevenly geographically, the scenario implies
both capital-intensive infrastructure investments and unprecedented large-scale
gas and oil trade flows. There is also little pressure to develop non-fossil
alternatives in such scenarios, so costs of non-fossil alternatives remain comparatively
high, even after significant technological improvements. For instance, solar
electricity costs could drop to US$0.05 per kWh (A1G-AIM).
|
Box 4-8: Technological Change in the AIM-based Quantifications for
the A1 Scenario Family
The A1 storyline describes a world with rapid economic development.
High economic growth results in pressures on resource availability, counterbalanced
by technological progress, which is assumed to be highest among the four
scenario families. In the AIM quantifications of the A1 storyline, rates
of technological change are high both with respect to "supply push" factors
(most notably RD&D) as well as with respect to "demand pull" factors (most
notably high capital turnover rates). Since large resource availability
and high incomes stimulate demand growth, technological change in energy
supply receives a higher emphasis compared to changes in energy end-use
technologies. Common technology assumptions in the A1 scenarios can be
summarized as follows.
The supply of oil, gas, and biomass in the A1 scenario family is assumed
to be very high and results from high rates of technological progress
for fossil fuel and biomass exploitation technologies. Unconventional
oil and gas, such as deep-sea methane hydrates, oil shale, etc., become
available at relatively low cost. Also, large amounts of biomass are utilized
through well-developed biomass farm plantations and harvest technologies,
and biomass utilization technologies, such as biomass power generation
and biofuel conversion technologies, become available at low costs through
RD&D and other mechanisms of technology improvements (learning by doing
and learning by using). High levels in the use of other renewable energy
are reached when technologies for solar photovoltaics and thermal utilization,
wind farms, geothermal energy utilization, and ocean energy are introduced
at low cost. Energy end-use technologies are assumed to progress at medium
rates compared with the fast rates of technological change in energy supply
technologies.
The A1B marker and A1T scenarios assume drastic reductions in cost for
solar, wind, and other renewable energies. A1C assumes lower coal costs
and emphasizes coal exploitation technology progress and the introduction
of advanced coal-fired power generation technology, such as integrated
gasification combined cycle (IGCC). A1G assumes lower oil and gas costs
than other A1 scenarios. The cost of nuclear power is assumed to be the
lowest in A1G and A1T, and highest in A1C. The different cost assumptions
that drive and result from technological change in the A1 scenario family
are summarized in Table 4-12.
| Table 4-12: Technology costs (1990US$/GJ and
1990USCents/kWh) in AIM-based A1 scenarios. |
|
| |
Scenarios |
2020 |
2050 |
2100 |
|
| Coal (1990US$/GJ) |
A1B
A1C
A1G
A1T |
2.6
1.5
3.5
2.6 |
3.2
1.5
3.8
2.8 |
3.1
1.1
3.7
2.8 |
|
| Natural Gas, ConventionalA1B & Unconventional (1990US$/GJ) |
2.0
A1C
A1G
A1T |
1.9
3.5
1.8
1.5 |
1.4
5.0
1.6
1.5 |
4.6
1.6
1.4 |
|
| Crude Oil, Conventional & Unconventional (1990US$/GJ) |
A1B
A1C
A1G
A1T |
7.3
9.4
7.3
7.9 |
10.1
13.1
8.2
8.4 |
14.9
14.0
8.4
15.7 |
|
| Nuclear (1990UScent/kWh) |
A1
A1C
A1G
A1T |
5.4
5.7
5.9
5.6 |
3.9
4.4
4.7
4.1 |
2.3
2.8
3.1
2.5 |
|
| Solar, wind, geothermal electricity (1990UScent/kWh) |
A1B
A1C
A1G
A1T |
12.2
13.1
15.2
12.4 |
5.9
6.9
9.3
6.2 |
2.0
3.3
5.2
2.7 |
|
As mentioned above, improvements in energy efficiency on the demand side
are assumed to be comparatively lower in the A1 scenario family, except
for the A1T scenario, because the low energy prices give very little incentive
to improve end-use energy efficiencies. Efficient technologies are not
fully introduced into the end-use side, dematerialization processes in
the industrial sector are not promoted, lifestyles become energy intensive,
and private motor vehicles are used more in developing countries as per
capita incomes increase. As a result, energy efficiency improvement in
the industrialized countries (IND) is around 0.8% per year, and in developing
countries (DEV) it is 1.0% per year over the next 100 years to 2100. Only
A1T assumes greater efficiency improvements (1.1% per year for IND and
1.5% per year for DEV), as a result of the diffusion of new highly efficient
energy end-use devices such as fuel cell vehicles.
Technology progress is also assumed for land-use changes and sulfur
emissions. Higher productivity increases in biomass and crop land (1.5%
per year) in comparison to 0.5-1.0%) are assumed for the A1 world in the
AIM quantification compared to those in the A2 and B2 scenario families.
Desulfurization technologies could be introduced because of concerns of
economic damage caused by acid rain and there would be strong financial
support to install these technologies with the rapid income growth associated
with the A1 world.
|
The high-growth coal-intensive scenario group A1C assumes relatively large
cost improvements in new and clean coal technologies, such as coal high-temperature
fuel cells, IGCC power plants, and coal liquefaction. More modest assumptions
are made for all the other technologies, except for nuclear technologies in
A1C-MESSAGE, as this requires zero-carbon options to ease resource and environmental
constraints. The relative costs between coal and oil- or gas-related technologies
also shift in A1C-AIM. Progress in renewables is also assumed to be substantial.
For instance, solar photovoltaic costs would decline to USCents3/kWh (A1C-AIM).
In the dynamic technology scenario group A1T, technological change, driven
by market mechanisms and policies to promote innovation, favors non-fossil technologies
and synfuels, especially hydrogen from non-fossil sources. Liquid fuels from
coal, unconventional oil and gas sources, and renewables become available at
less than US$30 per barrel, with costs that fall further, by about 1% per year,
through exploitation of learning-curve effects (A1T-MESSAGE). A1T-MARIA also
projects declining costs for biofuels, from about US$30 to US$20, after the
2020 period (and in comparison to the A1- MARIA scenario biofuels substitute
coal-derived synfuels). Non-fossil electricity (e.g., photovoltaics) begin massive
market penetration at costs of about USCents1 to 3 per kWh (A1T-MARIA, A1T-MESSAGE,
A1T-AIM), and could continue to improve further (perhaps as low as USCents0.1/kWh
in A1T-MESSAGE) as a result of learning-curve effects. An important difference
between the marker scenario A1B and the A1T group is that in A1T additional
end-use efficiency improvements are assumed to take place with the diffusion
of new end-use devices for decentralized production of electricity (fuel cells,
microturbines). As a result, final energy demand in the A1T scenario group is
between 30% (A1T-AIM, A1T-MESSAGE) and 40% (A1T-MARIA) lower compared to the
A1B marker scenario.
4.4.7.3. A2 Scenarios
The A2 scenario family includes slow improvements in the energy supply efficiency
and a relatively slow convergence of end-use energy efficiency in the industrial,
commercial, residential, and transportation sectors between regions. A combination
of slow technological progress, more limited environmental concerns, and low
land availability because of high population growth means that the energy needs
of the A2 world are satisfied primarily by fossil (mostly coal) and nuclear
energy. However, in some cases regional energy shortages force investments into
renewable alternatives, such as solar and biomass. For instance, intermittent
renewable electricity supply options, such as solar and wind, are assumed to
decline in costs to about USCents4/kWh and (because of storage requirements)
to about twice that value when these intermittent sources are used for medium
load applications (50% of electricity supply).
4.4.7.4. B1 Scenarios
Consistent with the general environmentally conscious and resource-conservation
thrust of the B1 scenario storyline, technological change is largely directed
at improving conversion efficiency rather than costs for fossil technologies.
Within the SRES Terms of Reference, no additional climate initiatives are assumed
that could bar the application of certain technologies or yield forced diffusion
of others. The thermal efficiency of centrally generated electricity is assumed
to rise to 45% (conventional coal) or to 65% (gas combined cycles) by 2100,
while specific investment costs decline slightly from 1990 levels. It is assumed
that subsidies on coal for electricity generation are removed entirely. A specific
feature of the IMAGE model used to generate the B1 marker scenario is that it
treats non-fossil electricity generation technologies as highly generic; for
instance, it does not distinguish between nuclear, solar, or wind-power generation
technologies. The specific investment costs of generation options for non-fossil
electricity and of the production and conversion of commercial biofuels are
assumed to fall by 5-10% for every doubling of cumulated production. Cost decreases
down to USCents2.5/kWh are anticipated once non-fossil options penetrate on
a large scale. The costs of gaseous biofuels in the major producing regions
(Latin America, Africa, NIS) are assumed to be in the order of US$3 to 5 per
GJ from 2020 to 2030 onward. Liquid biofuels are produced in small amounts in
almost all regions at costs in the order of US$3 to 6 per GJ. In all regions
a gradual transition occurs from fossil fuels to non-fossil options in electric-power
generation, because of rising fuel prices and declining specific investment
costs for fossil alternatives. Learning rates were assumed, conservatively,
to yield 2 to 6% cost reductions for every doubling of cumulative production.
The shift would start in resource-poor industrialized regions such as Japan
and Western Europe, but is somewhat tempered by rising conversion efficiencies
of fossil-fueled power plants. One of the factors that constrains the use of
natural gas in the scenario is the assumption that only a limited part of the
transport market is open to competition from non-liquid fuels (between 50% around
2050 to 80% around 2100). Also, the market share of coal in industry is fixed
exogenously at 10 to 15% in some regions, to reflect the decreasing environmental
and social attractiveness of the more "dirty" coal.
4.4.7.5. B2 Scenarios
The approach that underlies the B2 scenario storyline translates into important
future improvements of technologies, albeit at more conservative rates than
in scenarios A1 or B1, but with higher rates than in scenario A2. Compared to
A1 and B1, cost improvements are more modest, because of the regionally fragmented
technology policies assumed to characterize a B2 world. Hence, technology-spillover
effects and benefits from shared development expenditures are more limited in
the scenario. The high emphasis of environmental protection at the local and
regional levels is reflected in faster development and diffusion of energy technologies
with lower emissions, including advanced coal technologies, nuclear, and renewables.
For instance, solar and wind electricity-generating costs are assumed to decline
to USCents3/kWh, that is, a similar level as assumed for the long-term costs
of advanced, clean coal technologies (such as IGCCs). As conventional oil supplies
dwindle, initially high-cost synfuels from coal and also biofuels are introduced
as substitutes. With increasing production volume, costs are assumed to decline
from initial levels of some US$7/GJ to US$2.6/GJ. Conventional coal technologies
undergo the lowest aggregate rates of improvement in the scenario and are also
subject to increasing controls of social and environmental externalities (mining
safety, particulates, and sulfur emissions). Increasingly, therefore, only advanced
coal technologies are deployed. Nonetheless, extraction and conversion costs
increase, especially in regions with a large share of deep-mined coal and in
high population density agglomerations. In regions with abundant surface minable
coal reserves (e.g., North America and Australia), coal extraction costs remain
relatively low.
4.4.7.6. Harmonized and Other Scenarios
As a consequence of the "multi-model approach" used in SRES, detailed improvement
assumptions and scenario implementations for individual technologies vary greatly
from one model to another, although the same storyline characteristics were
used as guiding principles and many scenarios share similar assumptions on improvement
potentials for different technologies. Detailed quantitative comparisons are
difficult because of different time profiles of technology improvements assumed
in the different models, different representations of regional technology, and
the modeling of the international diffusion of technology. For instance, many
models assume aggregate regional rates of technological change (e.g., MARIA,
MiniCAM, ASF), whereas others attempt to represent spatial and temporal diffusion
patterns more explicitly (e.g., MESSAGE, AIM).
It is difficult to quantify the influence of varying technology-specific scenario
assumptions on scenario outcomes, because in most model simulations the technology
assumptions were varied in conjunction with other salient scenario characteristics,
such as economic growth and resource availability (e.g. in the MiniCAM simulations).
Therefore, the impact of alternative assumptions with respect to technological
change can be best quantified within a particular scenario family and with "fully
harmonized" scenario quantifications (i.e. with comparable energy demand), as
discussed for the A1 scenario groups above. In some scenarios within other scenario
families, technology-specific sensitivity analyses were performed, such as in
the B2C-MARIA scenario variant of the B2-MARIA quantification. The main differences
between the two scenarios are the respective costs of coal and nuclear power.
In B2C-MARIA, the price of coal was assumed to be US$1.4/GJ, while that in B2-MARIA
is US$1.8/GJ. In contrast, the capital costs of nuclear power stations are US$1400/kW
in B2-MARIA, while those in B2C-MARIA are assumed to remain at US$1800/kW. Thus,
even comparatively small variations in relative technology characteristics such
as costs and efficiencies can lead to wide differences in scenario outcomes.
As discussed in Chapter 5, for instance, changing the relative economics between
coal and nuclear in the two MARIA scenarios results in a difference of more
than 200 GtC cumulative emissions32
over the 21st century.
An illustration of inter-scenario variability in technology costs and diffusion
is given in Box 4-9 for the MESSAGE model simulations for one representative
scenario of each scenario family and scenario group. As stated above, differences
in technology diffusion across scenarios are influenced by many more factors
than just alternative technology characteristics and cost assumptions. Growth
of energy demand, resource availability and costs, and local circumstances (local
air-quality regulations that require desulfurization of fuels or stack 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.
|