# Marginal Abatement Cost Curves (MACC): Unsolved Issues, Anomalies, and Alternative Proposals

**Jose Luis Ponz-Tienda, Andrea Victoria Prada-Hernandez, Alejandro Salcedo-Bernal and Daniel Balsalobre-Lorente**

**Abstract **Policy makers proposed the MACC as an instrument to rank possible mitigation measures available in a market. This tool orders measures according to their cost-efficiency, taking into account only two variables: costs and emissions reductions. Although this tool has been used in relevant settings like the first treaty of the United Nations Framework Convention on Climate Change (UNFCCC), it has shown mathematical failures that might produce unreliable rankings. This chapter presents existing alternatives to the use of traditional MACC for ranking GHG abatement measures: (1) Taylor’s method by the application of the dominance concept. (2) Ward’s method directly related to the net benefit of each measure. (3) The GM method, which supports an environmentalist attitude and performs a direct comparison of measures with negative and positive costs. (4) An extension of traditional MACC (EMAC method), that considers the economically driven point of view of the decision maker, weighting the negative cost options according to its economic savings over its reduction potential. (5) And the BOM method, consisting of a linear-weighted combination of two discretional seed methods, allowing decision makers to take into account the goodness of multiple methods in order to create new rankings adjustable to a specific GHG policy, whether it is fully or

J.L. Ponz-Tienda (H)

Civil and Environmental Department, Universidad de Los Andes,

Bogota, Colombia

e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

A.V. Prada-Hernandez

School of Public Policy, University of Maryland, College Park, MD, USA e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

A. Salcedo-Bernal

Department of Systems and Computing Engineering, Universidad de Los Andes, Bogota, Colombia

e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it D. Balsalobre-Lorente

Department of Political Economy and Public Finance Economic and Business Statistics and Economic Policy, University of Castilla-La Mancha,

Ciudad Real, Spain

e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it © Springer International Publishing AG 2017

R. Alvarez Fernandez et al. (eds.), *Carbon Footprint and the Industrial Life Cycle, *Green Energy and Technology, DOI 10.1007/978-3-319-54984-2_12

partially driven by economical or environmental positions. Finally, several case studies and discussions are presented showing the advantages of the exposed methods.

**Notations**

**The following concepts, symbols and acronyms are used in this article**

*Acronyms* Meaning

A*B** _{m}* Economic benefit generated by the energy savings for a

measure *m*

AC_{m} Associated net present value associated to a measure m

A*E** _{m}* GHG abatement potential associated to a measure m

*BOM*^{a}(*p _{J},* p

_{2})(m) Balanced ordering method for methods

*p*and p

_{J}_{2}, and a balance a

*Cost** _{m}* Total cost of the measure m

*(*

*Cost*

*= AC*

_{m}_{m}—AB

_{m})

*ENV* Environmentalist benchmark

*GHG* Greenhouse gases

*GM(e)* Gain maximizing method being e a very small value

*GM(1)* Gain maximizing method being e = J

*GM** _{m}* Gain value for a measure

*m*

*GRE* Greedy benchmark

*EMAC* Extended MACC method

*EMAC** _{m}* Extended MACM value for a measure

*m*

*MACC* Marginal abatement costs curve

*MAC** _{m}* Marginal cost of abating a tonne of CO

_{2}for a measure

*m*

*m* Measure

*NPV* Net present value

*sign(x)* Sign of x

*s** _{MAC}* Set of ordered measures applying method MACC

*s** _{Ward}* Set of ordered measures applying method Ward

*s** _{Tay}i_{or}* Set of ordered measures applying method Taylor

*T**G _{M}(_{e})* Set of ordered measures applying method GM(e) being e a very

small value

sg_{M}(_{J}) Set of ordered measures applying method GM(e) being e = J

*s** _{EMAC}* Set of ordered measures applying method. EMAC

*K* (_{Spi}, x_{l2}) Kendall tau distance between methods p_{J} and p_{2}