Abstract
Decision making for climate change management seldom incorporates uncertainty in the analysis that underpins the policy process. First, uncertainty is seldom characterised fully, and attempts to reduce uncertainty—when this is possible—are rare. Second, scientists are ill-equipped to communicate about uncertainty with policy makers, and policy makers most often favour pretended certainty over nuance and detail. Third, the uncertainty analysis that may have been conducted most often fails to actually influence policy in a significant manner. The case is made for (i) characterising and, to the extent possible, reducing uncertainty, (ii) communicating uncertainty, and (iii) reflecting uncertainty in the design of policy initiatives for climate change management. Possible elements for a research agenda on each of these areas are proposed.
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Notes
Guidance exists for dealing with uncertainty in the area of climate change management. Yet, this guidance is insufficient in most cases, and under-used at best. In light of this, the article (i) makes the case for using this guidance to incorporate uncertainty in the analysis that underpins the policy process, and (ii) proposes possible elements for a research agenda in this area.
This interest stems from the critical role that the individual emission reduction commitments by parties to the United Nations Framework Convention on Climate Change (UNFCCC) play in the Paris Agreement.
In most countries, policy making for climate change management is informed by the results of consultations with key stakeholders. This refers to all stages in the policy process, from identifying priorities and setting objectives, to defining and implementing potential actions, to monitoring progress with implementation. In so far as these consultations help identify uncertainties, the consultations contribute to reflecting uncertainty in the policy-making process. Similarly, it is now customary for modelling results to benefit from sensitivity analyses, which help evaluate the extent to which projections of a variable of interest may change, depending on which assumption is used, across the full range of plausible future values for an uncertain variable. Whilst these and other similar practices constitute relevant efforts to reflect uncertainty in climate change-management policies, they are far from comprehensive, given the much broader set of uncertainties that reasonably could be considered (Walker et al. 2013).
When the resources needed to obtain ‘the best available evidence’ are not on hand, it is the government’s duty of care to explicitly acknowledge this, whilst adopting a no-regrets approach to policy making. In this setting, a ‘no-regrets approach’ to policy making refers to adopting measures that meet two requirements: they are consistent with the information about which there is a high degree of certainty, and they preclude as few future courses of action as possible (Kwakkel et al. 2016). Ideally, this approach to policy making should be complemented with regular evaluations of performance against the policy’s intended objective.
In the Paris agreement, the accounting of future emission levels relies on the UNFCCC parties’ deterministic estimates of future emission-reduction volumes by the individual parties. If those estimates turn out to be overly optimistic (or pessimistic), a key pillar of the negotiations is compromised. In light of this, it has been suggested that national-level emission reduction targets should be attached to scenarios, and expressed in probabilistic terms (Puig et al. 2017).
Model refers to a computer-based representation of reality, simple as it may be, as opposed to a less explicit or tangible alternative.
It is worth noting that uncertainty reduction may, in some instances, hamper uncertainty communication (Puig and Bakhtiari 2017). This observation only strengthens the case for developing uncertainty reduction protocols, and gaining experience with uncertainty communication (for example, by applying the Fischhoff and Davis protocol referred to above).
References
Benveniste H, Boucher O, Guivarch C, Le Treut H, Criqui P (2018) Impacts of nationally determined contributions on 2030 global greenhouse gas emissions: uncertainty analysis and distribution of emissions. Environ Res Lett 13(1):014022
Broomell SB, Kane PB (2017) Public perception and communication of scientific uncertainty. J Exp Psychol Gen 146(2):286–304
Dessai S, O’Brien K, Hulme M (2007) On uncertainty and climate change. Glob Environ Chang 17(7):1–3
Enserink B, Kwakkel JH, Veenman S (2013) Coping with uncertainty in climate policy making: (mis) understanding scenario studies. Futures 53:1–12
Fischhoff B (2012) Communicating uncertainty fulfilling the duty to inform. Issues Sci Technol 28(4):63–70
Fischhoff B, Davis AL (2014) Communicating scientific uncertainty. Proc Natl Acad Sci 111(Supplement 4):13664–13671
Funtowicz S, Ravetz J (1993) Science for the post-normal age. Futures 25(7):739–755
Gluckman P (2016) Science advice to governments: an emerging dimension of science diplomacy. Science Diplomacy 5(2):9
Heal G, Kriström B (2002) Uncertainty and climate change. Environ Resour Econ 22(1):3–39
IPCC (2006) 2006 IP CC guidelines for national greenhouse gas inventories. IntergovernmentalPanel on Climate Change, Geneva
Katz RW (2002) Techniques for estimating uncertainty in climate change scenarios and impact studies. Clim Res 20(2):167–185
Knaggård Å (2014) What do policy-makers do with scientific uncertainty? The incremental character of Swedish climate change policy-making. Policy Stud 35(1):22–39
Kwakkel JH, Walker WE, Marchau VA (2010) Classifying and communicating uncertainties in model-based policy analysis. Int J Technol Policy Manag 10(4):299–315
Kwakkel JH, Eker S, Pruyt E (2016) How robust is a robust policy? Comparing alternative robustness metrics for robust decision-making. In: Robustness Analysis in Decision Aiding, Optimization, and Analytics. Springer, Cham, pp 221–237
Mathijssen J, Petersen A, Besseling P et al (2008) Dealing with uncertainty in policymaking. PBL publication 550032011. Netherlands Environmental Assessment Agency, Bilthoven
Montibeller G, von Winterfeldt D (2015) Biases and debiasing in multi-criteria decision analysis. In System Sciences (HICSS), 2015 48th Hawaii International Conference on System Sciences. IEEE, pp 1218-1226
Morgan MG (2009) Best practice approaches for characterizing, communicating and incorporating scientific uncertainty in climate decision making. U.S. Climate Change Science Program Synthesis and Assessment Product 5.2. DIANE Publishing, Collingdale
Petersen AC, Cath A, Hage M, Kunseler E, van der Sluijs JP (2011) Post-normal science in practice at the Netherlands environmental assessment agency. Sci Technol Hum Values 36(3):362–388
Puig D, Bakhtiari F (2017) The impact of debiasing on uncertainty communication: an application to multi-criteria decision analysis in the area of climate change. UNEP DTU Partnership, Copenhagen
Puig D, Morales-Nápoles O, Bakhtiari F, Landa G (2017) The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico. Clim Pol 18(6):742–751
Rogelj J, Fricko O, Meinshausen M, Krey V, Zilliacus JJ, Riahi K (2017) Understanding the origin of Paris agreement emission uncertainties. Nat Commun 8:15748
UNFCCC (2016) Aggregate effect of the intended nationally determined contributions: an update. Synthesis report by the secreatariat (FCCC/CP/2016/2). United Nations Framework Convention on Climate Change, Bonn
Unwin SD, Moss RH, Rice JS et al (2011) Characterizing uncertainty for regional climate change mitigation and adaptation decisions (PNNL report 20788). Pacific Northwest National Laboratory, Richland
Walker WE, Marchau VA, Kwakkel JH (2013) Uncertainty in the framework of policy analysis. In public policy analysis. Springer, Boston, pp 215–261
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Puig, D., Bakhtiari, F. Incorporating uncertainty in national-level climate change-mitigation policy: possible elements for a research agenda. J Environ Stud Sci 9, 86–89 (2019). https://doi.org/10.1007/s13412-018-0514-5
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DOI: https://doi.org/10.1007/s13412-018-0514-5