Working Group III: Mitigation

Other reports in this collection Common Messages from Bottom-up Results

Clearly, the impact of policy scenarios has a large influence on abatement costs. Certain studies propose a series of public measures (regulatory and economic) that tap deep into the technical potential of low carbon and/or energy-efficient technologies. In many cases, such policies show low or negative costs. A comparison with least-cost approaches is difficult because these evaluate systematically both the baseline and the policy scenario as optimized systems and do not incorporate market or institutional imperfections in the current world. It would be of great interest to conduct a more systematic comparison of the results obtained via the various B-U approaches, so as to establish the true cause of the discrepancies in reported costs. A timid step in this direction is illustrated in Loulou and Kanudia (1999a).

This leads to a general discussion about the extent to which all these results suffer from a lack of representation of transaction costs, which are usually incurred in the process of switching technologies or fuels. This category of transaction cost encompasses many implementation difficulties that are very hard to capture numerically. The general conclusion from SAR (that costs computed using the B-U approach are usually on the low side compared to costs computed via econometric models, which assume a history-based behaviour of the economic agents) is no longer generally applicable, since some B-U models take a more behavioural approach. Models such as ISTUM, NEMS, PRIMES, or AIM implicitly acknowledge at least some transaction costs via various mechanisms, with the result that market share is not determined by visible (market-based) least-cost alone. Least-cost modellers (using MARKAL, EFOM, MESSAGE, ETO) also attempt to impose penetration bounds, or industry-specific discount rates, which approximately represent the unknown transaction costs and other manifestations of resistance to change exhibited by economic agents. In both cases these improvements result in partially eschewing the “sin” of optimism and blur the division between B-U and T-D models. While the former, indeed, tend to be less optimistic when they account for real behaviours, it is symmetrically arguable that the latter underestimate the possibility of altering these behaviours through judicious policies or better information. All this area still remains underworked.

A common message is the attention that must to be paid to the marginal cost curve. Despite the limitations and differences in results discussed above, B-U analyses convey important information that lies beyond the scope of T-D models, by computing both the total cost of policies and their marginal cost. Very often, indeed, the marginal abatement cost of a given target is high, although the average abatement cost is reasonably low, or even negative. This is because the initial reductions of GHG emissions may have a very low (or negative) cost, whereas additional reductions have, in general, a much higher marginal cost. This fact is captured in the curve representing marginal abatement cost versus reduction quantity, which starts with negative marginal costs, as illustrated in Figure 8.1. The initial portion of the curve (section A–B) exhibits negative cost options, which may add up to a significant portion of the reductions targetted by a given GHG scenario. As the reduction target increases (section B–C–D of the curve), the marginal cost becomes positive, and also eventually the total mitigation cost if the reduction target is large enough. But there is systematically a wedge between the marginal and total costs of abatement, and this wedge is all the more important as the macroeconomic impacts of climate policies are driven in large part by the marginal costs (because the latter dictate the change in relative commodity prices). They are driven only modestly by the total amount of abatement expenditures.

A crucial, albeit indirect, message, is the importance of innovation: indeed, B-U models depend on a reasonable representation of emerging or future technologies. When this representation is deficient, the models present a pessimistic view of the costs of more drastic abatements in the long term. This issue is not one of the modelling paradigm, but rather of feeding the models with good estimates of technical progress. Some works are currently underway to make explicit the drivers of technical change, such as learning-by-doing (LBD) or uncertainty. These studies are discussed further in Section 8.4.

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