Skip to main content

Advertisement

Log in

Climate targets under uncertainty: challenges and remedies

A letter

  • Letter
  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

We start from the observation that climate targets under uncertainty should be interpreted as safety constraints on the probability of crossing a certain threshold, such as 2°C global warming. We then highlight, by ways of a simple example, that cost-effectiveness analysis for such probabilistic targets leads to major conceptual problems if learning about uncertainty is taken into account and the target is fixed. Current target proposals presumably imply that targets should be revised in the light of new information. Taking this into account amounts to formalizing how targets should be chosen, a question that was avoided by cost-effectiveness analysis. One way is to perform a full-fledged cost-benefit analysis including some kind of monetary damage function. We propose multi-criteria decision analysis including a target-based risk metric as an alternative that is more explicite in its assumptions and more closely based on given targets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  • Azar C, Lindgren K (2003) Editorial commentary: catastrophic events and stochastic cost-benefit analysis of climate change. Clim Change 56(3)

  • Blau RA (1974) Stochastic programming and decision analysis: an apparent dilemma. Manage Sci 21(3):271–276

    Article  Google Scholar 

  • Bordley RF, Pollock SM (2009) A decision-analytic approach to reliability-based design optimization. Oper Res 57(5):1262–1270

    Article  Google Scholar 

  • Bosetti V, Carraro C, Sgobbi A, Tavoni M (2009) Delayed action and uncertain stabilisation targets: how much will the delay cost? Clim Change 96(3):299–312

    Article  Google Scholar 

  • Charnes A, Cooper WW (1959) Chance constrained programming. Manage Sci 5:73–79

    Article  Google Scholar 

  • Charnes A, Cooper WW (1975) A comment on Blau’s dilemma in stochastic programming and bayesian decision analysis. Manage Sci 22(4):498–500 .

    Article  Google Scholar 

  • Charnes A, Cooper WW (1983) Response to “Decision problems under risk and chance constrained programming: dilemmas in the transition”. Manage Sci 29(6):750–753

    Article  Google Scholar 

  • den Elzen MGJ, Meinshausen M (2005) Meeting the EU 2°C climate target: global and regional emission implications. Report 728001031:2005

  • den Elzen MGJ, Meinshausen M, van Vuuren DP (2007) Multi-gas emission envelopes to meet greenhouse gas concentration targets: costs versus certainty of limiting temperature increase. Glob Environ Change Human Policy Dimensions 17(2):260–280

    Google Scholar 

  • den Elzen MGJ, van Vuuren DP (2007) Peaking profiles for achieving long-term temperature targets with more likelihood at lower costs. Proc Natl Acad Sci 104(46):17931

    Article  Google Scholar 

  • Eisner MJ, Kaplan RS, Soden JV (1971) Admissible decision rules for the E-model of chance-constrained programming. Manage Sci 17(5):337–353

    Article  Google Scholar 

  • European Council (2005) Presidency conclusions. European Council, Brussels

  • Held H, Kriegler E, Lessmann K, Edenhofer O (2009) Efficient climate policies under technology and climate uncertainty. Energy Econ 31:S50–S61

    Article  Google Scholar 

  • Hogan AJ, Morris JG, Thompson HE (1981) Decision problems under risk and chance constrained programming: dilemmas in the transition. Manage Sci 27(6):698–716

    Article  Google Scholar 

  • Hogan AJ, Morris JG, Thompson HE (1984) Reply to professors charnes and cooper concerning their response response to “Decision problems under risk and chance constrained programming”. Manage Sci 30(2):258–259

    Article  Google Scholar 

  • Jagannathan R (1985) Use of sample information in stochastic recourse and chance-constrained programming models. Manage Sci 31(1):96–108

    Article  Google Scholar 

  • Jagannathan R (1987) Response to ‘On the “bayesability” of chance-constrained programming problems’ by Lavalle Manage Sci 33:1229–1231

    Google Scholar 

  • Johansson DJA, Persson UM, Azar C (2008) Uncertainty and learning: implications for the trade-off between short-lived and long-lived greenhouse gases. Clim Change 88(3–4):293–308. ISSN 0165-0009

    Google Scholar 

  • Keppo K, O’ BC, Riahi K (2007) Probabilistic temperature change projections and energy system implications of greenhouse gas emission scenarios. Technol Forecast Soc Choice 74(7):936–961. ISSN 0040-1625

    Article  Google Scholar 

  • Lange A, Treich N (2008) Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions. Clim Change 89(1):7–21

    Article  Google Scholar 

  • LaValle IH (1986) On information augmented chance-constrained programs. Oper Res Lett 4(5):225–230

    Article  Google Scholar 

  • LaValle IH (1987) Response to “Use of sample information in stochastic recourse and chance-constrained programming models”: on the ‘bayesability’ of CCP’s. Manage Sci 33(10):1224–1228

    Article  Google Scholar 

  • Machina MJ (1989). Dynamic consistency and non-expected utility models of choice under uncertainty. J Econ Lit 27(4):1622–1668

    Google Scholar 

  • Mastrandrea MD, Schneider SH (2004) Probabilistic integrated assessment of “dangerous” climate change. Science 304:571–575

    Article  Google Scholar 

  • Meinshausen M, Hare B, Wigley TML, Van Vuuren D, Den Elzen MGJ, Swart R (2006) Multi-gas emissions pathways to meet climate targets. Clim Change 75(1–2):151–194

    Article  Google Scholar 

  • Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, Frame DJ, Allen MR (2009) Greenhouse-gas emission targets for limiting global warming to 2°C. Nature 458(7242):1158–1162

    Article  Google Scholar 

  • Morgan DR, Eheart JW, Valocchi AJ (1993) Aquifer remediation design under uncertainty using a new chance contrained programming technique. Water Resour Res 29(3):551–569

    Article  Google Scholar 

  • O’Neill B, Ermoliev Y, Ermolieva T (2006) Endogenous risks and learning in climate change decision analysis. Springer, Berlin, Germany, pp 283–300

  • Rive N, Torvanger A, Berntsen T, Kallbekken S (2007) To what extent can a long-term temperature target guide near-term climate change commitments? Clim Change 82(3):373–391

    Article  Google Scholar 

  • Schaeffer M, Kram T, Meinshausen M, van Vuuren DP, Hare WL (2008) Near-linear cost increase to reduce climate-change risk. Proc Natl Acad Sci USA 105(52):20621–20626

    Article  Google Scholar 

  • Schneider SH, Mastrandrea MD (2005) Probabilistic assessment of “dangerous” climate change and emissions pathways. Proc Natl Acad Sci USA 102(44):15728–15735

    Article  Google Scholar 

  • UNFCCC (1992) Article 2. http://unfccc.int/essential_background/convention/background/items/1353.php. Accessed 12 November 2010

  • UNFCCC (2009) Copenhagen accord. http://unfccc.int/documentation/documents/advanced_search/items/3594.php?rec=j&priref=600005735#beg. Accessed 12 November 2010

  • Watanabe T, Ellis H (1993) Stochastic programming models for air quality management. Comput Oper Res 20(6):651–663

    Article  Google Scholar 

  • Webster M, Jakobovits L, Norton J (2008) Learning about climate change and implications for near-term policy. Clim Change 89(1):67–85

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matthias G. W. Schmidt.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

PDF (10.8 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schmidt, M.G.W., Lorenz, A., Held, H. et al. Climate targets under uncertainty: challenges and remedies. Climatic Change 104, 783–791 (2011). https://doi.org/10.1007/s10584-010-9985-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-010-9985-4

Keywords

Navigation