Abstract
International climate negotiations take place in a setting where uncertainties regarding the impacts of climate change are very large. In this paper, we examine the influence of increasing the probability and impact of large climate change damages, also known as the ‘fat tail’, on the formation of an international mitigation agreement. We systematically vary the shape and location of the distribution of climate change damages using the stochastic version of the applied game-theoretical STACO model. Our aim is to identify how changes to the distributional form affect the stability of coalitions and their performance. We find that fatter upper tails increase the likelihood that more ambitious coalitions are stable as well as the performance of these stable coalitions. Fatter tails thus imply more successful, or ‘fatter’, international climate agreements.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Uncertainties and risk are inherent in the climate system. In the STACO model we track the influence of risk and uncertainty by performing Monte Carlo simulations, specifying different distributional forms and parameters. We do not make a strict distinction between the terms uncertainty and risk, but use both terms to refer to an unknown impact of climate change.
Weitzman (2009a, b) initiated the discussion on ‘fat tails’ by criticising Integrated Assessment models (IAMs) for underestimating climate change damages. With reference to Nordhaus (2009), we assume explicitly that IAMs remain a valid tool as Weitzman’s invalidating conditions do not apply to the wide range of climate scenarios investigated here, as long as the model allows for some mitigation action and carefully specifies the distributional forms to represent uncertainty. Within these boundaries we apply alternative scenarios and examine their impact on the stability and performance of an IEA. The expression ‘fat tail’ is used to describe a distribution in which high impacts have a higher probability than can be expected based on a normal distribution.
In the STACO model benefits are characterised by a stream of prevented climate damages due to mitigation efforts.
Note that there are \(2^{N}\) announcement vectors, but there are only \(2^{N}-N\) different coalitions as coalitions of only one member are trivial.
Note that the payoff function is purely a monetary measure and not a fully specific utility function. A non-linear utility function would affect the preferences of countries to join the coalition, but is beyond the scope of the current paper.
We check the stability condition by changing the announcement vector of one player at a time. Due to these single deviations, multiple stable equilibria are possible. In the case of multiple stable coalitions, we assume that each of them is equally likely to occur. There is a probability that one of them is (Pareto-)dominated by another stable coalition. The STACO model controls for this by assigning the set of Pareto-dominated coalitions a zero probability of occurring. The remaining set of stable coalitions is used to evaluate the success of coalition formation.
The interested reader is referred to Weikard (2009) for more details on the sharing scheme. Note that when coalitional payoff is insufficient to compensate all free-rider payoffs, the coalition becomes internally unstable.
The STACO model described in the next section is linear in parameters (but not in abatement levels). Hence, the expected payoff is equal to the payoff based on the expected parameter vector (Dellink et al. 2008).
We are not aware of any paper that provides analytical solutions of stable coalitions in the context of heterogeneous players even in the absence of uncertainty.
While this may ignore much of the interactions that take place in the climate system, it suffices for our goal of valuing the benefits of abatement activities.
Projections for GDP are taken from the MIT-EPPA model (Paltsev et al. 2005).
During the Monte Carlo sampling procedure 20,000 samples are generated from their respective probability density functions. The discount rate is not specified as a stochastic parameter, since raising the discount rate in the STACO model has an equivalent impact as a lower mean value of gamma (Weikard et al. 2006).
Note that regional benefit shares are bounded from below at zero. This precludes situations where some countries have positive damages, while others have negative damages. While this is clearly a limitation of the model that affects the outcomes of the simulations, Dellink et al. (2008) present a sensitivity analysis on the gamma distribution by replacing it with a normal distribution, which implies that regional shares can become negative. They find that the impact on the stability analysis is very limited. Moreover, given the focus of our paper on the high end of the damage function, it is not unreasonable to assume that damages will be positive in all regions.
However, increasing mean benefits also increases free-riding incentives.
Following the meta-study by Tol (2009) the lower bound of climate damages is set at \(-80\) $/tC in all scenarios, based on the 1st percentile for global damages.
Whether such a set of international transfers is politically realistic remains to be seen, but there is no doubt that countries with high benefits have an economic incentive to finance mitigation in other countries.
By definition, the incentive not to join a coalition (i.e. the incentive for a current free-rider) is the inverse of the incentive to free-ride for the enlarged coalition, where the same player is a member. The overarching idea of the ‘incentive’ column is that negative values indicate a contribution to stability, while positive values undermine stability.
Note that our scenario selection mainly represents cases with high kurtosis when distributions are right-skewed. This affects the impact of the kurtosis on performance negatively.
From the total of 4,096 coalitions, all but one of the thirteen possible AS coalitions are excluded. We keep the AS coalition with no members. Using a definition for the AS with a single member would only affect the underlying incentives to change announcement, but not the performance of the coalition,
We prefer to work with the sample moments, since this allows obtaining the same moments for all distributions.
Negative draws imply that countries benefit from climate change and form coalitions without mitigating. The SSL is defined in the following way: \(SSL=SL - \%\) of negative draws. Effectively, this implies that trivial coalitions are not counted as strictly stable.
Cooperation between regions increases with higher marginal benefits from abatement as the additional benefits from mitigation can be used to finance transfers to keep low-marginal-cost countries within the coalition, thereby increasing the stability of the coalition. Simultaneously, the outside option for all countries increases with increasing marginal benefits, thus inducing additional free-riding incentives. The net effect is a priori ambiguous.
A distribution with a fat upper tail is negatively skewed.
References
Anthoff D, Tol R (2010) Climate policy under fat-tailed risk: an application of FUND. ESRI working paper 348, Dublin
Barrett S (1994) Self-enforcing international environmental agreements. Oxf Econ Pap 46:878–894
Barrett S (2003) Environment and statecraft: the strategy of environmental treaty making. Oxford University Press, Oxford
Carraro C, Siniscalco D (1993) Strategies for the international protection of the environment. J Public Econ 52(3):309–328
Carraro C, Eyckmans J, Finus M (2006) Optimal transfers and participation decisions in international environmental agreements. Rev Int Organ 1(4):379–396
Chander P, Tulkens H (1995) A core-theoretic solution for the design of cooperative agreements on transfrontier pollution. Int Tax Public Financ 2(2):279–293
d’Aspremont C, Jaquemin A, Gabszewicz JJ, Weymark JA (1983) On the stability of collusive price leadership. Can J Econ 16(1):17–25
Dellink RB (2011) Drivers of stability of climate coalitions in the STACO model. Clim Change Econ 2(2) :105–128
Dellink RB, Finus M, Olieman N (2008) The stability likelihood of an international climate agreement. Environ Resource Econ 39:357–377
Dellink RB, Nagashima M, van Ierland EC, Hendrix EMT, Sáiz E, Weikard H-P (2009) STACO Technical Document 2: Model description and calibratrion of STACO 2.1, mimeo, Wageningen University, available at www.enr.wur.nl/uk/staco
Dellink RB, Finus M (2012) Uncertainty and climate treaties: does ignorance pay. Resource Energy Econ 34:565–584
Dietz S (2009) High impact, low probability? An empirical analysis of risk in the economics of climate change. Grantham research institute on climate change working paper 9, London
Ellerman AD, Decaux A (1998) Analysis of post-Kyoto CO2 emissions trading using marginal abatement curves, Joint program on the science and policy of global change. Report no. 40, MIT, Cambridge
Finus M, van Ierland EC, Dellink RB (2006) Stability of climate coalitions in a cartel formation game. Econ Gov 7(3):271–291
Ikefuji M, Laeven RJA, Magnus JR, Muris C (2010) Expected utility and catastrophic risk in a stochastic economy-climate model. CentER working paper no. 2010–122, University of Tilburg, The Netherlands
IPCC (2007) Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Cambridge University Press, Cambridge
Kolstad CD (2007) Systematic uncertainty in self-enforcing international environmental agreements. J Environ Econ Manag 53(1):68–79
Kolstad CD, Ulph A (2008) Learning and international environmental agreements. Clim Change 89(1–2) :125–141
Kolstad CD, Ulph A (2011) Uncertainty, learning and heterogeneity in international environmental agreements. Environ Resource Econ 50(3):389–403
Lancaster T (2000) The incidental parameter problem since 1948. J Econom 95(2):391–413
Na S, Shin HS (1998) International environmental agreements under uncertainty. Oxf Econ Pap 50:173–185
Nagashima M, Dellink R, van Ierland E, Weikard HP (2009) Stability of international climate coalitions—a comparison of transfer schemes. Ecol Econ 68(5):1476–1487
Nordhaus WD, Yang Z (1996) A regional dynamic general-equilibrium model of alternative climate-change strategies. Am Econ Rev 86(4):741–765
Nordhaus W (2009) An analysis of the dismal theorem. Department of Economics, Yale University, New Haven, Cowles Foundation Discussion paper
OECD (2012) Environmental Outlook to 2050: the consequences of inaction. Organisation for Economic Co-operation and Development, Paris
Olieman NJ, Hendrix EMT (2006) Stability likelihood of coalitions in a two-stage cartel game: an estimation method. Eur J Oper Res 174(1):333–348
Paltsev S, Reilly JM, Jacoby HD, Eckaus RS, McFarland J, Sarofim M, Asadoorian M, Babiker M (2005) The MIT emissions prediction and policy analysis (EPPA) model: Version 4. MIT joint program report series 125, Cambridge
Commission Productivity (2011) Carbon emission policies in key economies. Research report, Canberra
Tol RSJ (2009) The economic effects of climate change. J Econ Perspect 23:29–51
Ulph A (2004) Stable international environmental agreements with a stock pollutant, uncertainty and learning. J Risk Uncertain 29(1):53–73
UNEP (2010) The emissions gap report: Are the Copenhagen Accord pledges sufficient to limit global warming to \(2^{\circ }\text{ C }\) or \(1.5^{\circ }\text{ C }?\) UNEP, Geneva
Weikard H-P, Finus M, Altamirano-Cabrera JC (2006) The impact of surplus sharing on the stability of international climate coalitions. Oxf Econ Pap 58:209–232
Weikard H-P (2009) Cartel stability under optimal sharing rule. Manches School 77(5):575–593
Weitzman M (2009) Additive damages, fat-tailed climate dynamics and uncertain discounting. Harvard economics discussion paper 26, Cambridge
Weitzman M (2009) On modelling and interpreting the economics of catastrophic climate change. Rev Econ Stat 91(1):1–19
Author information
Authors and Affiliations
Corresponding author
Additional information
The authors would like to thank Santiago Rubio and Michael Finus for inspiring us to write this paper and for stimulating discussion on coalition formation.
Appendices
Appendix 1
see Table 8.
Appendix 2
see Table 9.
Appendix 3
The countries are classified with respect to benefit and cost characteristics:
High benefit | Medium benefit | Low benefit | |
|---|---|---|---|
High cost | JPN | BRA | |
Medium cost | EEC | IND, ROW | OOE, EET, EEC, DAE |
Low cost | USA | FSU, CHN |
Then we sort the 4,083 coalitions (all except the AS which is used as the reference coalition in the regressions) in nine different classes, based on contribution to effective coalitions, i.e. starting with high benefits and low costs and working through the columns of the matrix:
High benefit (HB) | Medium benefit (MB) | Low benefit (LB) | |
|---|---|---|---|
Low cost (LC) | 3,199 coalitions | 382 | 0 |
Medium cost (MC) | 381 | 94 | 26 |
High cost (HC) | 1 | 0 | 0 |
HB/LC specifies a coalition with at least one high-benefit and one low-cost country. HB/MC describes a coalition with at least one high-benefit and one medium-cost country and no LC country in the coalition. HB/HC describes a coalition that includes at least one high-benefit and one high-cost country, and no MC or LC countries. Using the same procedure, we classified medium-benefit and low-benefit coalitions. Therefore, the nine categories are mutually exclusive and coalitions not double-counted.
If a coalition consists of for instance JPN (HB/HC) and OOE (LB/MC) and CHN (MC/LC), it would be classified as HB/LC, because JPN has high benefits and CHN low costs and it thus has the most favourable combination for effective cooperation. It does not matter how many countries are in a coalition, for the classification we just take into account the most “extreme” members.
Rights and permissions
About this article
Cite this article
Dellink, R., Dekker, T. & Ketterer, J. The Fatter the Tail, the Fatter the Climate Agreement. Environ Resource Econ 56, 277–305 (2013). https://doi.org/10.1007/s10640-013-9642-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10640-013-9642-2