Climatic Change

, Volume 117, Issue 4, pp 677–690 | Cite as

Implications of alternative metrics for global mitigation costs and greenhouse gas emissions from agriculture

  • A. ReisingerEmail author
  • P. Havlik
  • K. Riahi
  • O. van Vliet
  • M. Obersteiner
  • M. Herrero


100-year Global Warming Potentials (GWPs) are used almost universally to compare emissions of greenhouse gases in national inventories and reduction targets. GWPs have been criticised on several grounds, but little work has been done to determine global mitigation costs under alternative physics-based metrics . We used the integrated assessment model MESSAGE to compare emission pathways and abatement costs for fixed and time-dependent variants of the Global Temperature Change Potential (GTP) with those based on GWPs, for a policy goal of limiting the radiative forcing to a specified level in the year 2100. We find that fixed 100-year GTPs would increase global abatement costs (discounted and aggregated over the 21st century) under this policy goal by 5–20 % relative to 100-year GWPs, whereas time-varying GTPs would reduce costs by about 5 %. These cost differences are smaller than differences arising from alternative assumptions regarding agricultural mitigation potential and much smaller than those arising from alternative radiative forcing targets. Using the land-use model GLOBIOM, we show that alternative metrics affect food production differently in different world regions depending on regional characteristics of future land-use change to meet growing food demand. We conclude that under scenarios of complete participation, the choice of metric has a limited impact on global abatement costs but could be important for the political economy of regional and sectoral participation in collective mitigation efforts, in particular changing costs and gains over time for agriculture and energy-intensive sectors.


Abatement Cost Mitigation Cost Enteric Fermentation Alternative Metrics Abatement Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



A.R. was funded by the New Zealand Ministry for Primary Industries under contract C10X1010. The work and models presented in this article have also been supported by the EU-funded FP7 projects ANIMALCHANGE (grant no. 266018), CC-TAME (grant no. 212535), GHG-Europe (grant no. 244122), LIMITS (grant no. 282846), and PASHMINA (grant no. 244766). Very constructive comments from three anonymous reviewers greatly helped improve the manuscript and clarify its key assumptions.

Supplementary material

10584_2012_593_MOESM1_ESM.pdf (459 kb)
ESM 1 (PDF 458 KB)


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • A. Reisinger
    • 1
    Email author
  • P. Havlik
    • 2
    • 3
  • K. Riahi
    • 2
  • O. van Vliet
    • 2
  • M. Obersteiner
    • 2
  • M. Herrero
    • 3
  1. 1.New Zealand Agricultural Greenhouse Gas Research CentreWellingtonNew Zealand
  2. 2.International Institute for Applied Systems AnalysisLaxenburgAustria
  3. 3.International Livestock Research InstituteNairobiKenya

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