Marginal abatement cost curve for nitrogen oxides incorporating controls, renewable electricity, energy efficiency, and fuel switching.

Journal Article (Journal Article)

A marginal abatement cost curve (MACC) traces out the relationship between the quantity of pollution abated and the marginal cost of abating each additional unit. In the context of air quality management, MACCs are typically developed by sorting control technologies by their relative cost-effectiveness. Other potentially important abatement measures such as renewable electricity, energy efficiency, and fuel switching (RE/EE/FS) are often not incorporated into MACCs, as it is difficult to quantify their costs and abatement potential. In this paper, a U.S. energy system model is used to develop a MACC for nitrogen oxides (NOx ) that incorporates both traditional controls and these additional measures. The MACC is decomposed by sector, and the relative cost-effectiveness of RE/EE/FS and traditional controls are compared. RE/EE/FS are shown to have the potential to increase emission reductions beyond what is possible when applying traditional controls alone. Furthermore, a portion of RE/EE/FS appear to be cost-competitive with traditional controls.

Implications

Renewable electricity, energy efficiency, and fuel switching can be cost-competitive with traditional air pollutant controls for abating air pollutant emissions. The application of renewable electricity, energy efficiency, and fuel switching is also shown to have the potential to increase emission reductions beyond what is possible when applying traditional controls alone.

Full Text

Duke Authors

Cited Authors

  • Loughlin, DH; Macpherson, AJ; Kaufman, KR; Keaveny, BN

Published Date

  • October 2017

Published In

Volume / Issue

  • 67 / 10

Start / End Page

  • 1115 - 1125

PubMed ID

  • 28613998

Pubmed Central ID

  • PMC6095130

Electronic International Standard Serial Number (EISSN)

  • 2162-2906

International Standard Serial Number (ISSN)

  • 1096-2247

Digital Object Identifier (DOI)

  • 10.1080/10962247.2017.1342715

Language

  • eng