A MAC curve is a graph that shows the cost-effectiveness index deﬁned above of all the available measures (each measure represented as a bar) that are ordered in a function of increasing abatement costs, see Fig. 2.8. The height of each bar represents the average marginal cost of avoiding a ton of CO2-eq given that all measures on its left are already applied and the width represents the potential of that

Fig. 2.8 Schematic example of a marginal abatement cost curve

measure to reduce emissions. Note that the word 'marginal' denotes that all available reduction measures are mutually exclusive.

The MAC curves have been popularized by the consulting ﬁrm McKinsey & Company, which used them on a global and country-wide scale to target areas for carbon abatement.

Figure 2.9 presents an example of a marginal abatement cost (MAC) curve published in a global study by McKinsey in 2007 (Enkvist, Naucle´r, & Rosander, 2007). It shows the annual abatement needed to achieve stable atmospheric greenhouse gas concentrations of 500 ppm (parts per million), 450 and 400 ppm of CO2-equivalents. For example, a global emissions reduction of 26 Gtons of CO2-eq per year would stabilize greenhouse gas concentrations at 450 ppm of CO2-eq, and that reduction would need all the abatement measures up to a cost of €40 per ton of CO2eq.

It is interesting to note that according to Fig. 2.9 the most cost-effective

abatement measure is building insulation. There are indeed many measures that have a negative abatement cost. This means they carry no net life cycle cost, and they come free of charge. However, although the low cost measures are efﬁcient and cost-effective, in general they may not be sufﬁcient to deliver the required emissions reductions by themselves. If so, we will have to move up the MAC curve progressively, adopting more and more expensive measures, until the desired emissions reduction is achieved. Thus, the role of policy makers is to enforce or provide incentives to adopt these measures.

2.4.3 Caveats of MAC Curves

For all their usefulness, MAC estimates have some weak points. First of all, MAC prices depend heavily on emission reduction targets and stabilization targets

Fig. 2.9 Cost Curve for GHG abatement measures. Source: Enkvist et al. (2007)

and, therefore, differ among sectors and countries. Furthermore, they do change massively over time as innovation kicks in with more cost-effective measures (FOEI, 2008).

Ekins, Kesicki, and Smith (2011) discuss the various caveats and methodological problems associated with the MAC curves. Obviously, the easily-digestible form of the graphic MACC led to these problems being overlooked, placing a strong conﬁdence in the results. The general shortcomings presented in their work include the focus on emission reduction without considering ancillary beneﬁts such as health improvement which can not be easily monetized, and a static representation of costs which fails to consider path dependency. Another problem has to do with hidden costs, including transaction and monitoring costs. In addition, these authors note the lack of full disclosure of the assumptions and the non-consideration of interdependencies and “intersectoral, intertemporal, behavioral, macroeconomic, and international interactions” which can lead to problems deﬁning the emissions baseline especially in a time horizon of 20 years or even more.

Finally, another problem is related to the fact that MAC curves cannot capture well the related uncertainties. Thus, we should look beyond the estimated marginal abatement cost and obviously pay attention to the assumptions behind.

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