Use of McKinsey abatement cost curves for climate economics modeling

This research paper describes the integration of McKinsey & Company's marginal abatement cost (MAC) data into the Climate and Regional Economics of Development (CRED) model. To maintain model realism, the authors treated negative-cost abatement opportunities as near-zero positive costs. The resulting estimates suggest that emission reductions may be cheaper than many integrated assessment models (IAMs) project. Specifically, CRED's projections for 2030 are significantly lower than those of the RICE and EMF-22 models, though they align more closely with the MIT EPPA model for developed nations.

• The authors address the controversy of 'negative-cost' abatement opportunities—where energy savings outweigh investment costs—by omitting these from their model and instead assigning a near-zero but positive cost to such measures. This prevents the model from creating an unrealistic surge of additional capital that would occur if negative costs were treated as immediate net benefits.
• To make the McKinsey data manageable for the CRED model, the authors aggregated 21 geographic regions into nine and collapsed 11 economic sectors into two: 'land use' (agriculture and forestry) and 'industry' (including transport, household/commercial energy, and waste management).
• The authors used a two-parameter equation to approximate the MAC curves, where the 'B' parameter represents the maximum technically feasible abatement potential. For the industry sector, they assumed this potential grows steadily to allow for complete abatement of regional emissions by the year 2105.
• Comparison with the MIT EPPA model shows that the positive-cost portion of the McKinsey curves used in CRED is broadly similar to EPPA's estimates for developed countries, though CRED identifies significantly more abatement opportunities in developing countries like China and Africa than EPPA projects for 2050.
• When compared to other IAMs using top-down approaches, the CRED estimates based on McKinsey data are the lowest. For the 'Copenhagen Convergence' scenario in 2030, CRED's global abatement cost as a percent of GDP is 0.08%, compared to 0.22% for the RICE model and 1.33% for the EMF-22 estimates.
• The authors conclude that the significant gap between bottom-up estimates (like McKinsey/CRED) and top-down models suggests that either bottom-up studies systematically underestimate costs or top-down models systematically overestimate them.

APA

Ackerman, F., & Bueno, R. (2011). Use of McKinsey abatement cost curves for climate economics modeling. Stockholm Environment Institute. https://www.sei.org/mediamanager/documents/Publications/Climate/sei-workingpaperus-1102.pdf

Chicago

Ackerman, Frank, and Ramón Bueno. Use of McKinsey abatement cost curves for climate economics modeling. Stockholm Environment Institute, 2011. https://www.sei.org/mediamanager/documents/Publications/Climate/sei-workingpaperus-1102.pdf.

Wikipedia

{{cite report |last1=Ackerman |first1=Frank |last2=Bueno |first2=Ramón |title=Use of McKinsey abatement cost curves for climate economics modeling |publisher=Stockholm Environment Institute |date=25 January 2011 |url=https://www.sei.org/mediamanager/documents/Publications/Climate/sei-workingpaperus-1102.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{ackerman2011use, author = {Ackerman, Frank and Bueno, Ramón}, title = {{Use of McKinsey abatement cost curves for climate economics modeling}}, institution = {Stockholm Environment Institute}, year = {2011}, month = jan, url = {https://www.sei.org/mediamanager/documents/Publications/Climate/sei-workingpaperus-1102.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }