Transcript Climate Change, Uncertainty and Precaution

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
[email protected]
www.jvds.nl
Copernicus Institute for Sustainable Development and Innovation
Utrecht University
&
Centre d'Economie et d'Ethique pour l'Environnement
et le Développement,
Université de Versailles Saint-Quentin-en-Yvelines, France
Principles in Environmental Policy
• curative model
Polluter Pays Principle
• ‘prevention is better than cure’ model
Prevention Principle
• ‘better safe than sorry’ model
Precautionary 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)
UNESCO-COMEST Expert
Group 2005 report
Available at:
www.jvds.nl
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.
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
Framing uncertainty
Some examples
Terra Incognita
Atmospheric
concentrations of the
greenhouse gases CO2
and CH4 over the last
four glacial-interglacial
cycles from the Vostok ice
core record. The presentday values and estimates
for the year 2100 are also
shown.
Adapted from Petit et al. (1999)
Nature 399, 429-436 and the IPCC
(Intergovernmental Panel on
Climate Change) Third Assessment
Report by the PAGES (Past Global
Changes) International Project
Office.
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)
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
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 Hindex) 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
Figure SPM.5
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)
CDFs Climate Sensitivity
1
Cumulative distribution function
0.9
0.8
0.7
0.6
0.5
Uniform Forest et al. (2002)
Expert Forest et al. (2002)
Gregory et al. (2002)
Andronova & Schlesinger (2001)
Knutti et al. (2002)
Tol & de Vos (1998)
Unweighted Murphy et al. (2004)
Weigthed Murphy et al. (2004)
IPCC TAR GCMs (2001)
IPCC range (1990-2001)
0.4
0.3
0.2
0.1
0
0
1
2
3
4
5
6
Climate sensitivity (ºC)
7
8
9
10
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 EemSangamon 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.
Thermo-haline circulation
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.
Species committed to extinction
Climate scenario
2050
> 2.0 °C
universal
dispersal
21–32%
no dispersal
1.8–2.0 °C
15–20%
26–37%
0.8–1.7 °C
9–13%
22–31%
38–52%
(Thomas et al., 2004)
Ice components and their sea
level equivalents
Ice volume
106 km3
sea level equivalent
(m)
land ice
East Antarctica
25.92
64.8
West Antarctica
3.40
8.5
3.0
7.6
0.12
0.3
0.03 - 0.7
0.08-0.17
Arctic
0.02-0.05
-
Antarctic
0.01-0.06
-
Greenland
Small ice caps and mountain
glaciers
Permafrost
Sea ice
(Titus, 1986)
Arctic Climate Impact
Assessment 2004:
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)
“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.
“
Extreme weather in a changing climate
Small shift in the mean =
Huge change in frequency of extremes.
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 0.08
CP.net
G
G+
W
W+
0.12
Probability
0.1
0.08
0.06
G+
0.05
W
0.04
W+
0.03
0.02
0.02
0.01
0
-25
G
0.06
0.04
-50
CP.net
0.07
Probability
0.14
0
0
25
50
Precipitation change (%)
(Dessai & Van der Sluijs, 2007)
75
100
-100
-75
-50
-25
0
Precipitation change (% )
25
50
www.air-worldwide.com/_public/html/air_currentsitem.asp?ID=632
(R. Kerr, Science, 16 September 2005)
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 sealevel.
• Temperature:
maximum rate of increase of
temperature of 0.1°C/ decade
maximum total increase of 1°C or 2°C
above pre-industrial global mean
temperature.
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
Source: IPCC 2007 synthesis report
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
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
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
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
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
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), 6379. 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