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Centre d'Economie et d'Ethique pour l'Environnement et le Développement, Université de Versailles Saint-Quentin-en-Yve

Conference: Ethics and Politics of Climate Change - Challenges for Human Rights? Utrecht, 23 and 24 January 2009 Climate Change, Uncertainty and the Precautionary Principle. Dr. Jeroen P. van der Sluijs j.p.vandersluijs@uu.nl www.jvds.nl.

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Centre d'Economie et d'Ethique pour l'Environnement et le Développement, Université de Versailles Saint-Quentin-en-Yve

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  1. Conference: Ethics and Politics of Climate Change - Challenges for Human Rights? Utrecht, 23 and 24 January 2009Climate Change, Uncertainty and the Precautionary Principle Dr. Jeroen P. van der Sluijsj.p.vandersluijs@uu.nl www.jvds.nl Copernicus Institute for Sustainable Development and InnovationUtrecht University & Centre d'Economie et d'Ethique pour l'Environnement et le Développement, Université de Versailles Saint-Quentin-en-Yvelines, France

  2. Principles in Environmental Policy • curative model Polluter PaysPrinciple • ‘prevention is better than cure’ model Prevention Principle • ‘better safe than sorry’ modelPrecautionary Principle paradigmatic shift from a posteriori control (civil liability as a curative tool) to the level of a priori control (anticipatory measures) of risks Now perverted by Emission Trading Systems: • Polluter buys the right to continue polluting (in stead of polluter pays to clean up the mess)

  3. UNESCO-COMEST Expert Group 2005 report Available at: www.jvds.nl

  4. New working definition PP UNESCO COMEST When human activities may lead to morally unacceptable harm that is scientifically plausible but uncertain, actions shall be taken to avoid or diminish that harm. Morally unacceptable harm refers to harm to humans or the environment that is • threatening to human life or health, or • serious and effectively irreversible, or • inequitable to present or future generations, or • imposed without adequate consideration of the human rights of those affected. The judgment of plausibility should be grounded in scientific analysis. Analysis should be ongoing so that chosen actions are subject to review. Uncertainty may apply to, but need not be limited to, causality or the bounds of the possible harm. Actions are interventions that are undertaken before harm occurs that seek to avoid or diminish the harm. Actions should be chosen that are proportional to the seriousness of the potential harm, with consideration of their positive and negative consequences, and with an assessment of the moral implications of both action and inaction. The choice of action should be the result of a participatory process.

  5. Complex - uncertain - risks Typical characteristics (Funtowicz & Ravetz): • Decisions will need to be made before conclusive scientific evidence is available; • Potential impacts of ‘wrong’ decisions can be huge • Values are in dispute • Knowledge base is characterized by large (partly irreducible, largely unquantifiable) uncertainties, multi-causality, knowledge gaps, and imperfect understanding; • More research  less uncertainty; unforeseen complexities! • Assessment dominated by models, scenarios, assumptions, extrapolations • Many (hidden) value loadings reside in problem frames, indicators chosen, assumptions made Knowledge Quality Assessment is essential

  6. Framing uncertainty Some examples

  7. Terra Incognita Atmospheric concentrations of the greenhouse gases CO2and CH4 over the last four glacial-interglacial cycles fromthe Vostok ice core record. The present-day values andestimates for the year 2100 are also shown. Adapted from Petit et al. (1999) Nature 399, 429-436 and theIPCC(Intergovernmental Panel on Climate Change) Third AssessmentReport by the PAGES (Past Global Changes) International ProjectOffice.

  8. A practical problem: Protecting a strategic fresh-water resource 5 scientific consultants addressed same question: “which parts of this area are most vulnerable to nitrate pollution and need to be protected?” (Refsgaard, Van der Sluijs et al, 2006)

  9. 3 framings of uncertainty 'deficit view' • Uncertainty is provisional • Reduce uncertainty, make ever more complex models • Tools: quantification, Monte Carlo, Bayesian belief networks 'evidence evaluation view' • Comparative evaluations of research results • Tools: Scientific consensus building; multi disciplinary expert panels • focus on robust findings 'complex systems view / post-normal view' • Uncertainty is intrinsic to complex systems • Uncertainty can be result of production of knowledge • Acknowledge that not all uncertainties can be quantified • Openly deal with deeper dimensions of uncertainty (problem framing indeterminacy, ignorance, assumptions, value loadings, institutional dimensions) • Tools: Knowledge Quality Assessment • Deliberative negotiated management of risk

  10. How to act upon such uncertainty? • Bayesian approach: 5 priors. Average and update likelihood of each grid-cell being red with data (but oooops, there is no data & we need decisions NOW) • IPCC approach: Lock the 5 consultants up in a room and don’t release them before they have consensus • Nihilist approach: Dump the science and decide on an other basis • Precautionary robustness approach: protect all grid-cells • Academic bureaucrat approach: Weigh by citation index (or H-index) of consultant. • Select the consultant that you trust most • Real life approach: Select the consultant that best fits your policy agenda • Post normal: explore the relevance of our ignorance: working deliberatively within imperfections

  11. Figure SPM.5

  12. Probability distributions of climate sensitivity. Obtained using linear statistical estimation of GCM predictions likely to result from a large “perturbed physics ensemble” sampling the model parameter space comprehensively, with (red) and without (blue) weighting according to the estimated reliability of model versions based on correspondence to observations. (Murphy et al., Nature, 11 Aug 2004)

  13. CDFs Climate Sensitivity

  14. Global average warming: putting degrees in context: • 1 -1.5°C warmer than it ever was since 6,000 years ago in the Holocene period, which was roughly the beginning of agricultural societies. • 2-2.5°C a climate not experienced since the so-called Eem-Sangamon interglacial period some 125,000 years ago. At that time, human society consisted of hunter gather societies and the West Antarctic ice sheet had partially disintegrated, raising sea levels by up to 5-7 meters. • 3-4°C warming would represent a climate not experienced since humans appeared on Earth (about 2 million years ago). The last time the Earth was this warm was in the Pliocene period (5 to 3 million years ago) • 5°C and above corresponds to a climate not experienced for tens of millions of years. In that period there were no glaciers in the Antarctic and Greenland.

  15. Thermo-haline circulation

  16. Millennium Ecosystem Assessment, 2005 by the end of the century, climate change and its impacts may be the dominant direct driver of biodiversity loss and changes in ecosystem services globally. It will increase the risk of extinction for many species, especially those already at risk due to factors such as low population numbers, restricted or patchy habitats and limited climatic ranges.

  17. Species committed to extinction (Thomas et al., 2004)

  18. Ice components and their sea level equivalents (Titus, 1986)

  19. Arctic Climate Impact Assessment 2004: “At least half the summer sea ice in the Arctic is projected to melt by the end of this century, along with a significant portion of the Greenland Ice Sheet, as the region is projected to warm an additional 4-7 C by 2100. “ NASA 1997: “The area impacted by recent summer melting on Greenland is significantly larger than that previously observed. It appears that climate changes over the last two decades have influenced patterns of snow accumulation and melting on Greenland.” (M. Drinkwater, NASA, 1997)

  20. Extreme weather in a changing climate Small shift in the mean = Huge change in frequency of extremes.

  21. Scenarios versus Perturbed Physics Statistical uncertainty in “predicted” change in 2050 precipitation over The Netherlands according to climateprediction.net, compared to range of KNMI scenarios Winter Summer (Dessai & Van der Sluijs, 2007)

  22. www.air-worldwide.com/_public/html/air_currentsitem.asp?ID=632www.air-worldwide.com/_public/html/air_currentsitem.asp?ID=632 (R. Kerr, Science, 16 September 2005)

  23. Villach-Bellagio 1987 proposed long term climate targets • Sea level: maximum rate 2 - 5 cm / decade maximum total rise of between 0.2 and 0.5 m above the 1990 mean global sea-level. • Temperature:maximum rate of increase of temperature of 0.1°C/ decademaximum total increase of 1°C or 2°C above pre-industrial global mean temperature.

  24. EU long term target • Max 2°C above pre-industrial global mean temperature • 550 ppmv for CO2 equivalents, meaning 450 ppmv for CO2 only • Requires 70% reduction of emissions compared to 1990 • Leading scientists call for 350 ppmv target: tipping points

  25. Source: IPCC 2007 synthesis report

  26. Emission Trading – some serious problems • Ethical question: is it moral to grant a RIGHT to pollute? Present-day caps are highly unsustainable! • Market Failures • Transaction costs higher than assumed • If speculators enter the carbon market, the market may become highly instable (Matsumoto 2008) • Tendency to grant too much emission rights • Export the problem by buying emission rights abroad • Promotes lock-in to unsustainable technology, inhibits and delays transition • Trading has so far not led to a net decrease in emissions • Stable carbon tax much better

  27. Adaptation under uncertainty:Resilience! • Even when magnitude and nature of climate change are highly uncertain and unpredictable, often we do know how the impacted system can me made more resilient to unknown changes. • Resilience: the extent to which a system can cope with stress and shocks without collapsing into an undesired state. • Principes: • Homeostasis • Omnivory • High flux • Flatness • Buffering • Redundance

  28. Weiss 2003/2006 evidence scale 10. Virtually certain 9. Beyond a reasonable doubt 8. Clear and Convincing Evidence 7. Clear Showing 6. Substantial and credible evidence 5. Preponderance of the Evidence 4. Clear indication 3. Probable cause: reasonable grounds for belief 2. Reasonable, articulable grounds for suspicion 1. No reasonable grounds for suspicion 0. Insufficient even to support a hunch or conjecture

  29. Disagreement has 2 dimensions: - How do we appraise the level of evidence of risk - What level of intervention is justified given a the level of evidence Attitudes: 1. Environmental absolutist 2. Cautious environmentalist 3. Environmental centrist 4. Technological optimist 5. Scientific absolutist C. Weiss, 2003, “Scientific Uncertainty and Science-Based Precaution”, Politics, Law and Economics 3: 137–166

  30. Conclusions & recommendations • Climate risks can not be governed with polluter pays and prevention principle alone, but requires primarily a strong application of the precautionary principle • Need for strong international regime to enforce transition and avoid lock in to present day’s unsustainable fossil energy system Implications for science-policy interface: • Science should better reflect uncertainty, complexity and non-linear risks • Enhance the role of vulnerability science: systematic search for surprises and ways to constrain them • Enhance the role of monitoring and empirical research • Search for robust solutions that increase resilience • Be more realistic about the role and potential of science in assessment of complex risks • Increase societies capacity to act upon uncertain early warnings • Knowledge partnerships for precaution and sustainable development

  31. Further reading • J.P. van der Sluijs and W.C. Turkenburg (2006), Climate Change and the Precautionary Principle, In: Elizabeth Fisher, Judith Jones and René von Schomberg, Implementing The Precautionary Principle, Perspectives and Prospects, ELGAR, pp 245-269. • Jeroen P. van der Sluijs (2006), Uncertainty, assumptions, and value commitments in the knowledge-base of complex environmental problems, in: Ângela Guimarães Pereira, Sofia Guedes Vaz and Sylvia Tognetti, Interfaces between Science and Society, Green Leaf Publishing, pp. 67-84. • Van der Sluijs, J.P., M. Kaiser, S. Beder, V. Hosle, A. Kemelmajer de Carlucci, A. Kinzig, The Precautionary Principle, UNESCO, Paris Cedex, Paris, France, March 2005, 54 pp. http://unesdoc.unesco.org/images/0013/001395/139578e.pdf • J.P. van der Sluijs, A.C. Petersen, P.H.M. Janssen, James S Risbey and Jerome R. Ravetz (2008) Exploring the quality of evidence for complex and contested policy decisions, Environmental Research Letters, 3 024008 (9pp)http://dx.doi.org/10.1088/1748-9326/3/2/024008 • J.A. Wardekker, J.P. van der Sluijs, P.H.M. Janssen, P. Kloprogge, A.C. Petersen, (2008). Uncertainty Communication in Environmental Assessments: Views from the Dutch Science-Policy Interface, Environmental Science and policy, 11, 627-641. http://dx.doi.org/10.1016/j.envsci.2008.05.005 • J-C. Refsgaard; J.P. van der Sluijs; A.L. Højberg; P.A Vanrolleghem (2007), Uncertainty in the environmental modelling process: A framework and guidance, Environmental Modelling & Software, 22 (11), 1543-1556.http://dx.doi.org/10.1016/j.envsoft.2007.02.004 • J.P. van der Sluijs (2007), Uncertainty and Precaution in Environmental Management: Insights from the UPEM conference, Environmental Modelling and Software, 22, (5), 590-598. http://dx.doi.org/10.1016/j.envsoft.2005.12.020 • P. Kloprogge and J.P. van der Sluijs (2006), The inclusion of stakeholder knowledge and perspectives in integrated assessment of climate change. Climatic Change, 75 (3) 359-389. http://dx.doi.org/10.1007/s10584-006-0362-2 • J.P. van der Sluijs (2005), Uncertainty as a monster in the science policy interface: four coping strategies. Water science and technology, 52 (6) 87–92. http://www.iwaponline.com/wst/05206/wst052060087.htm • J.P. van der Sluijs, M. Craye, S. Funtowicz, P. Kloprogge, J. Ravetz, and J. Risbey (2005), Experiences with the NUSAP system for multidimensional uncertainty assessment in Model based Foresight Studies, Water science and technology, 52 (6), 133–144. http://www.iwaponline.com/wst/05206/wst052060133.htm • Risbey, J., J.P. van der Sluijs, P. Kloprogge, J. Ravetz, S. Funtowicz, and S. Corral Quintana (2005): Application of a Checklist for Quality Assistance in Environmental Modelling to an Energy Model. Environmental Modeling & Assessment, 10 (1), 63-79. http://dx.doi.org/10.1007/s10666-004-4267-z • M. Craye, J.P. van der Sluijs and S. Funtowicz (2005), A reflexive approach to dealing with uncertainties in environmental health risk science and policy, International Journal for Risk Assessment and Management, 5 (2), p. 216-236 http://dx.doi.org/10.1504/IJRAM.2005.007169 S. Dessai and J.P. van der Sluijs, 2007, Uncertainty and Climate Change Adaptation - a Scoping Study, report NWS-E-2007-198, Department of Science Technology and Society, Copernicus Institute, Utrecht University. 95 pp. http://www.nusap.net P. Kloprogge, J.P. van der Sluijs and A. Wardekker, 2007, Uncertainty communication: issues and good practice, report NWS-E-2007-199, Department of Science Technology and Society, Copernicus Institute, Utrecht University. 60 pp. http://www.nusap.net

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