Climatic Change

, Volume 120, Issue 1–2, pp 55–70 | Cite as

The AVOID programme’s new simulations of the global benefits of stringent climate change mitigation

  • R. WarrenEmail author
  • J. A. Lowe
  • N. W. Arnell
  • C. Hope
  • P. Berry
  • S. Brown
  • A. Gambhir
  • S. N. Gosling
  • R. J. Nicholls
  • J. O’Hanley
  • T. J. Osborn
  • T. Osborne
  • J. Price
  • S. C. B. Raper
  • G. Rose
  • J. Vanderwal


Quantitative simulations of the global-scale benefits of climate change mitigation are presented, using a harmonised, self-consistent approach based on a single set of climate change scenarios. The approach draws on a synthesis of output from both physically-based and economics-based models, and incorporates uncertainty analyses. Previous studies have projected global and regional climate change and its impacts over the 21st century but have generally focused on analysis of business-as-usual scenarios, with no explicit mitigation policy included. This study finds that both the economics-based and physically-based models indicate that early, stringent mitigation would avoid a large proportion of the impacts of climate change projected for the 2080s. However, it also shows that not all the impacts can now be avoided, so that adaptation would also therefore be needed to avoid some of the potential damage. Delay in mitigation substantially reduces the percentage of impacts that can be avoided, providing strong new quantitative evidence for the need for stringent and prompt global mitigation action on greenhouse gas emissions, combined with effective adaptation, if large, widespread climate change impacts are to be avoided. Energy technology models suggest that such stringent and prompt mitigation action is technologically feasible, although the estimated costs vary depending on the specific modelling approach and assumptions.


Climate Change Impact Integrate Assessment Model Mitigation Scenario Transient Climate Response Simple Climate Model 
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.



This work was supported by the AVOID programme (DECC and Defra) under contract GA0215.


  1. Ackerman F, Stanton EA (2012) Climate Risks and Carbon Prices: Revising the Social Cost of Carbon. Economics: The Open Access, Open Assessment E-Journal 6 (2012–10)Google Scholar
  2. Ackerman F, DeCanio SJ, Howarth RB, Sheeran K (2009) Limitations of integrated assessment models of climate change. Clim Chang 95:297–315CrossRefGoogle Scholar
  3. Arnell NW, Lowe JA, Brown S, Gosling SN, Gottschalk P, Hinkel J, Lloyd-Hughes B, Nicholls RJ, Osborn TJ, Osborne TM, Rose GA, Smith P, Warren R (2013) A global assessment of the effects of climate policy on the impacts of climate change. Nat Clim Chang. doi: 10.1038/nclimate1793
  4. Bosello, F., Carraro, C. and de Cian E. (2010), “Climate Policy and the Optimal Balance between Mitigation, Adaptation and Unavoided Damage”. Climate Change Economics 1(2):71–92Google Scholar
  5. Bowen, A. (2010): The economic costs of mitigation: Results from Year 1 of the AVOID programme. Work stream 2, Report 9 of the AVOID programme (AV/WS2/D1/R09). Available at
  6. Challinor AJ, Wheeler TR, Slingo JM, Craufurd PQ, Grimes DIF (2004) Design and optimisation of a large-area process-based model for annual crops. Agric For Meteorol 124(1–2):99–120CrossRefGoogle Scholar
  7. Ciscar J-C, Igleseias A, Feyen L, Szabo L, Van Regemorter D, Amelung B, Nicholls R, Watkiss P, Christensen OB, Dankers R, Garotte L, Goodess CM, Hunt A, Moreno A, Richards J, Sonia A (2011) Physical and economic consequences of climate change in Europe. PNAS 108:2678–2683CrossRefGoogle Scholar
  8. Climate Change Committee (2008) Building a low-carbon economy: The UK’s contribution to tackling climate change. LondonGoogle Scholar
  9. ClimateCost (2012) Available at
  10. den Elzen MGJ, van Vuuren DP, van Vliet J (2010) Postponing emission reductions from 2020 to 2030 increases climate risks and long-term costs. Clim Chang 99:313–320CrossRefGoogle Scholar
  11. Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ (2010) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57CrossRefGoogle Scholar
  12. Gambhir et al. (2011) China’s energy technology options to 2050 (AV/WS2/D1/R26) available at
  13. Gambhir et al. (2012) India’s CO2 emissions pathway to 2050 (AV/WS2/D1/R26 R4) available at (
  14. Gosling SN, Arnell NW (2011) Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis. Hydrol Process 25:1129–1145CrossRefGoogle Scholar
  15. Gosling SN, Warren R, Arnell NW, Good P, Caesar J, Bernie D, Lowe JA, van der Linden P, O’Hanley JR, Smith SM (2011) A review of recent developments in climate change science. Part II: the global-scale impacts of climate change. Prog Phys Geogr 35:443–464. doi: 10.1177/0309133311407650 CrossRefGoogle Scholar
  16. Hinkel J, Klein RJT (2009) Integrating knowledge to assess coastal vulnerability to sea-level rise: the development of the DIVA tool. Glob Environ Chang 19:384–395CrossRefGoogle Scholar
  17. Hope C (2008a) Discount rates, equity weights and the social cost of carbon. Energy Econ 30:1011–1019CrossRefGoogle Scholar
  18. Hope CW (2008b) Optimal carbon emissions and the social cost of carbon over time under uncertainty. Integr Assess J 8(1):107–122Google Scholar
  19. Hougton JT et al (eds) (2001) Climate change 2001: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  20. Huntingford C, Lowe JA, Gohar LK, Bowerman NHA, Allen MR, Raper SCB, Smith SM (2012) The link between a global 2 °C warming threshold and emissions in years 2020, 2050 and beyond. Environ Res Lett 7:014039Google Scholar
  21. Johns TC, Royer J-F, Hoeschel I et al (2011) Climate change under aggressive mitigation: the ENSEMBLES multi-model experiment. Clim Dyn 37(9–10):1975–2003. doi: 10.1007/s00382-011-1005-5 CrossRefGoogle Scholar
  22. Lowe JA, Hewitt CD, van Vuuren DP, Johns TC (2009a) New study for climate modelling, analyses and scenarios. Eos 90:181–182CrossRefGoogle Scholar
  23. Lowe JA, Huntingford C, Raper SCB, Jones CD, Liddicoat SK, Gohar LK (2009b) How difficult is it to recover from dangerous levels of global warming? Environ Res Lett 4:014012CrossRefGoogle Scholar
  24. Manne A, Richels R (1995) MERGE––A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23:17–34CrossRefGoogle Scholar
  25. Meehl GA et al. (2007) The WCRP CMIP3 multimodel dataset - A new era in climate change research. Bulletin of the American Meteorological Society 88: 1383–+Google Scholar
  26. Moss RH et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756. doi: 10.1038/nature08823 CrossRefGoogle Scholar
  27. Nakicenovich N et al (2000) Special Report on Emission Scenarios. Cambridge University Press, CambridgeGoogle Scholar
  28. Nordhaus WD (2008) A question of balance: weighing the options on global warming policies. Yale University PressGoogle Scholar
  29. Nordhaus WD (2010) Economic aspects of global warming in a post-Copenhagen environment. PNAS 107(26):11721–11726CrossRefGoogle Scholar
  30. Nordhaus WD, Boyer M (2000) Warming the world: Economic models of global warming. MIT, CambridgeGoogle Scholar
  31. O’Hanley JR (2009) NeuralEnsembles: a neural network based ensemble forecasting program for habitat and bioclimatic suitability analysis. Ecography 32:89–93CrossRefGoogle Scholar
  32. O’Neill BC, Riahi K, Keppo I (2010) Mitigation implications of midcentury targets that preserve long-term climate policy options. Proc Natl Acad Sci 107(3):1011–1016CrossRefGoogle Scholar
  33. Ramankutty N, Foley JA, Norman J, McSweeney K (2009) The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Glob Ecol Biogeogr 11:377–392CrossRefGoogle Scholar
  34. Roson R and van der Mensbrugge D (2012) Climate change and economic growth: impacts and interactions. Int. J. Sustainable Economy 4:270–285Google Scholar
  35. Schneider S (1997) Integrated assessment modelling of global climate change: transparent rational tool for policy making or opaque screen for hiding value-laden assumptions? Environ Monit Assess 2:229–249CrossRefGoogle Scholar
  36. Smith JB, Schneider SH, Oppenheimer M et al (2009) Assessing dangerous climate change through an update of the Intergovernmental Panel on Climate Change (IPCC) ‘reasons for concern’. Proc Natl Acad Sci U S A 106:4133–4137CrossRefGoogle Scholar
  37. Solomon S et al (eds) (2007) Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  38. Tol RSJ (1999) Spatial and temporal efficiency in climate policy: applications of FUND. Environ Resour Econ 14:33–49CrossRefGoogle Scholar
  39. Tol RSJ (2009) The economic effects of climate change. J Econ Perspect 23(2):29–51CrossRefGoogle Scholar
  40. UNEP (2010) The Emissions Gap Report: Are the Copenhagen Pledges sufficient to limit global warming to 2°C or 1.5°C?, November 2010.Google Scholar
  41. Van Vuuren D, Lowe J, Stehfest E, Gohar L, Hof AF, Hope C, Warren R, Meinshausen M, Plattner G-K (2011) How well do integrated models simulate climate change? Clim Chang 104:255–285CrossRefGoogle Scholar
  42. Warren R, Mastrandrea M, Hope C, Hof A (2010) Variation in the climatic response of integrated models. Clim Chang 102(3–4):671–685CrossRefGoogle Scholar
  43. Warren R, Yu RMS, Osborn TJ, Santos SD (2012) European drought regimes under mitigated and unmitigated climate change: application of the Community Integrated Assessment System (CIAS). Clim Res 51:105–123Google Scholar
  44. Warren R, VanDerWal J, Price J, Welbergen JA, Atkinson I, Ramirez-Villegas J, Osborn TJ, Jarvis A, Shoo LP, Williams SE, Lowe J (2013) Quantifying the benefit of early mitigation in avoiding biodiversity lossGoogle Scholar
  45. Wigley TML, Raper SCB (2001) Interpretation of high projections for global-mean warming. Science 293:451–454CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • R. Warren
    • 1
    Email author
  • J. A. Lowe
    • 2
  • N. W. Arnell
    • 3
  • C. Hope
    • 4
  • P. Berry
    • 5
  • S. Brown
    • 6
  • A. Gambhir
    • 7
  • S. N. Gosling
    • 8
  • R. J. Nicholls
    • 6
  • J. O’Hanley
    • 9
  • T. J. Osborn
    • 10
  • T. Osborne
    • 3
  • J. Price
    • 11
  • S. C. B. Raper
    • 12
  • G. Rose
    • 3
  • J. Vanderwal
    • 13
  1. 1.School of Environmental SciencesTyndall CentreNorwichUK
  2. 2.Department of MeteorologyMet Office Hadley CentreReadingUK
  3. 3.Walker InstituteUniversity of ReadingReadingUK
  4. 4.Judge Business SchoolCambridgeUK
  5. 5.Oxford University Centre for the EnvironmentEnvironmental Change InstituteOxfordUK
  6. 6.Dept. of Engineering and the EnvironmentSouthamptonUK
  7. 7.Imperial CollegeGrantham Institute for Climate ChangeLondonUK
  8. 8.School of GeographyUniversity of NottinghamNottinghamUK
  9. 9.Kent Business SchoolKentUK
  10. 10.School of Environmental SciencesClimatic Research UnitNorwichUK
  11. 11.School of Environmental SciencesTyndal l CentreNorwichUK
  12. 12.Centre for Air Transport and the Environment (CATE)ManchesterUK
  13. 13.School of Marine and Tropical BiologyCentre for Tropical Biodiversity and Climate ChangeQueenslandAustralia

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