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Costing climate change policy is an uncertain business. This uncertainty often manifests itself in the choice of technologies to mitigate and adapt to risks from climate change. Firms and nations can attempt to reduce risk by using more of the low-carbon technologies presently on the shelf or they can invent new ones. How quickly people will switch within the set of existing technologies with or without a change in relative energy prices is open to debate; how creative people are at inventing new technologies given relative prices is also a matter of discussion.
The key to addressing uncertainty is to capture a range of reasonable behaviours that underpins the choice to adopt existing or develop new low-carbon technology. Two key questions that should be addressed are:
Which answers to these questions are accepted determines whether some weighted
average of the estimates or a lower or upper estimate is used to guide policy.
For any given target and set of policy provisions, costs decline when consumers
and firms have more plentiful low-cost substitutes for high-carbon technologies.
Engineering studies suggest 20%-25% of existing carbon emissions could be eliminated
(depending on how the electricity is generated) at low cost if people switched
to new technologies, such as compact fluorescent light bulbs, improved thermal
insulation, heating and cooling systems, and energy-efficient appliances. The
critical issue is how this adoption of efficient technologies occurs in practice
and which sort of regulation and economic instruments could eventually support
this adoption. Chapter 5 of this report assesses the literature
regarding technology adoption and regulation frameworks.
Many economists have emphasized that technological progress is driven by relative prices, and that people do not switch to new technologies unless prices induce them to switch. New efficient technologies, according to this argument, then are not taken up without a proper price signal. People are also perceived to behave as if their time horizons are short, perhaps reflecting their uncertainty about future energy prices and the reliability of the technology. Also, factors other than energy efficiency matter to consumers, such as a new technology’s quality and features, and the time and effort required to learn about it and how it works. This issue has already been flagged in relation to technology adoption and implementation costs, but it also has an uncertainty element to it.
The different viewpoints on the origin of technological change appear in the assumed rate at which the energy-consuming capital can turnover without a change in relative energy prices. Modellers account for the penetration of technological change over time through a technical coefficient called the “autonomous energy efficiency improvement” (AEEI). The AEEI reflects the rate of change in energy intensity (the energy-to-GDP ratio) holding energy prices constant (see IPCC, 1996a, Chapter 8). The presumed autonomous technological improvement in the energy intensity of an economy can lead to significant differences in the estimated costs of mitigation. As such, many observers view the choice of AEEI as crucial in setting the baseline scenario against which to judge the costs of mitigation. The costs of mitigation are inversely related the AEEI– the greater the AEEI the lower the costs to reach any given climate target. The costs decrease because people adopt low-carbon technology of their own accord, with no change in relative prices.
Modellers have traditionally based the AEEI on historical rates of change, but now some are using higher values based on data from bottoms-up models and arguments about “announcement effects”. For instance, some analysts have optimistically argued that the existence of the Kyoto Protocol will accelerate the implementation of energy efficient production methods to 2% per year or more. Policymakers and modellers continue to debate the validity of this assumption (see, e.g., Kram, 1998; Weyant, 1998). A range of AEEIs has been adopted in the modelling literature (see Chapter 8 for more details). The AEEI has ranged from 0.4% to 1.5% per year for all of the regions of the world, and has generated large differences in long-term project baselines (e.g., Manne and Richels, 1992). Edmonds and Barns’ (1990) sensitivity study confirms the importance of the AEEI in affecting cost estimates. However, as noted by Dean and Hoeller (1992): “unfortunately there is relatively little backing in the economic literature for specific values of the AEEI ... the inability to tie it down to a much narrower range ... is a severe handicap, an uncertainty which needs to be recognized.”
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