The Curious Case of Selecting and Ranking GHG Mitigation Measures in Transport

The Curious Case of Selecting and Ranking GHG Mitigation Measures in Transport


R Kok, ECORYS Nederland BV and Delft University of Technology, NL; J Annema, Delft University of Technology, NL


This paper explores the current practices and limitations of using marginal abatement cost (MAC) and MAC curves to estimate the cost-effectiveness, and to prioritize and rank GHG mitigation measures in the transport sector.


The marginal abatement cost (MAC) and MAC curves are increasingly being used to estimate the cost-effectiveness of greenhouse gas (GHG) mitigation measures and to prioritize and rank the measures for achieving GHG reduction targets in the transport sector. The practices, problems and limitation of using MAC curves and the implications for policy making shows that MAC curves should be used very carefully and could potentially become much more comprehensive taking into account more soft measures and co-benefits changing it from a GHG reduction tool to a sustainability assessment tool.

Abatement costs are commonly defined by the net total costs divided by the GHG abatement potential resulting in a cost-effectiveness estimate (? per tonne CO2 equivalent). Consequently, a selected set of measures for which the abatement cost has been calculated, is plotted into a graph which is horizontally arranged from the left to the right in function of increasing abatement cost (and thus declining cost-effectiveness). The resulting stepped curve from the bottom left to the upper right is then considered to be the MAC-curve.

Practices, problems and limitations
MAC curves generally comprise predominantly technological ?hard? measures like vehicle and fuel technologies as compared to non-technological ?soft? measures like promoting modal shift to less carbon intensive modes, eco-driving, land use planning or road pricing, because it is considered to be more difficult to calculate the abatement cost of soft measures. This leads to reduced attention or unawareness of potential promising soft measures targeted at reducing transport activity, increasing system efficiency and promoting modal shift. Furthermore, MAC curves are used to select a number of the most cost-effective GHG reduction options that would cumulatively achieve the required GHG abatement target. Another purpose of MAC curves is the identification of the ?no regret? measures which have negative abatement cost (the benefits outweigh the costs) or to identify the measures that have abatement costs below the price level of the emission allowance of a ton of CO2 eq. within a cap & trade system (e.g. EU ETS).

Methodological practices often differ between studies and hamper the comparability of studies. Abatement cost calculations depend a.o. on the cost perspective (end-user, societal or government) and the scope of analysis (narrow vehicle/fuel level or broader market penetration/adoption). Abatement cost calculations are also highly sensitive to parameter variation regarding discount rates, time horizons, learning rates, frozen or dynamic baselines. Peculiarities of MAC curves are the increased sensitivity of outcomes when cost and benefits are in the same order of magnitude, and the mechanism that at constant net cost, an increase of the abatement potential would increase the cost-effectiveness of measures with positive abatement cost and decrease the cost-effectiveness of measures with negative abatement cost. Regarding prioritization it may therefore be interesting to order the measures in MAC curves as a function of abatement potential for measures with negative abatement cost and as a function of abatement cost for measures with positive abatement cost.

Implications of using MAC curves
There are several important implications of which transport policy makers should be aware of when using MAC curves. Firstly, hard measures are currently overrepresented in MAC curves as compared to soft measure that could also be very promising. Secondly, a broader approach for abatement cost calculation may reduce the cost-effectiveness of hard measures and increase the cost-effectiveness of soft measures. Thirdly, adding up the abatement potential of measures in a MAC curves does not necessarily mean that they are complementary; they could also be counterproductive. Policy makers should still carefully choose consistent policy packages. Fourthly, the measures do not take into account the (political or social) ease of implementation. Fifthly, policy makers should not forget that there are other arguments for selecting less cost-effective measures. For instance land use and infrastructure planning are long term policies that could be needed in order to ?lock? or ?trap? users into a low carbon path to the future. Sixthly, the scope of the benefits taken into account in MAC curves is currently limited to energy/fuel savings or alternative fuels with a lower carbon content resulting in GHG emission reductions. Important co-benefits such as energy security, accessibility, local air quality are often inherently linked to GHG mitigation measures but not taken into account in MAC curves, while these could substantially lower the abatement cost of different measures changing the MAC curve and prioritization completely. Co-benefits could also be larger for soft measures than for hard measures. Lastly, MAC curves could be based on very sensitive calculations making them unreliable.


Association for European Transport