How empirical uncertainties influence the stability of climate coalitions

Abstract

International climate agreements are negotiated in the face of uncertainties concerning the costs and benefits of abatement and in the presence of incentives for free-riding. Numerical climate coalition models provide estimates of the challenges affecting cooperation, but often resort to assuming certainty with respect to the values of model parameters. We study the impact of uncertainty on the stability of coalitions in the Model of International Climate Agreements using the technique of Monte Carlo analysis. We extend the existing literature by (1) calibrating parametric uncertainty about damages and abatement costs to estimates from meta-studies and by (2) explicitly considering uncertainty in the curvature of the damage function. We find that stability is more sensitive to uncertainty in damages than in abatement costs and most sensitive to uncertainty about the regional distribution of damages. Our calculations suggest that heterogeneity can increase stability of coalitions; however, this depends on the availability of transfers.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    For detailed definition of the regions in MICA and a full model description, see Kornek et al. (2017).

  2. 2.

    The computational burden of Monte Carlo analysis for coalition stability is substantial. Altogether, this manuscript is based on four Monte Carlo ensembles: for parameters in mitigation costs, marginal damages (perfectly correlated and independent) and the slope of marginal damages. We execute the model 500 times per parameter and for 21 coalition equilibria. Thus, we arrive at a total of 4 × 500 × 21 = 42,000 Monte Carlo runs and, at approximately 1 min CPU time per shot, 42,000 min or 700 h, or about 30 days of CPU time. To explore, for example, the stability of the grand coalition would add its ten subcoalitions to the list, raising the computation time by 50%.

  3. 3.

    For a discussion of the basic model structure, see Lessmann et al. (2009).

  4. 4.

    Regional Integrated Model of Climate and the Economy.

  5. 5.

    For a description of the calibration procedure, see Kornek et al. (2017).

  6. 6.

    These scenarios are 650 CO2e/full participation/no overshoot and 550 CO2e/full participation/overshoot with a CV of 0.27 and 0.21, respectively.

  7. 7.

    An exponent of 4 equals the 90% percentile of Nordhaus’s (1994) subjective cumulative probability function in the explorative stage of his sensitivity analysis.

  8. 8.

    Abatement costs are measured by an abatement cost index, which is defined as the reciprocal of the cumulative abatement over the twenty-first century by each region in the coalition of all regions compared to the all singletons scenario. The rationale for this approach is that within the grand coalition abatement is done where it is less costly and if marginal abatement costs are well behaved, abatement costs are inversely related to the abatement done in the grand coalition. This approach was developed by Lessmann et al. (2015).

  9. 9.

    Kornek et al. (2014) define a measure for the surplus in the non-transferable utility framework and develop an algorithm to compute it, which we describe here. For a coalition \(S\), consider the transfer scheme \(\tau\) that redistributes consumption so that the pay-off of each signatory \(k\) is at least at its free-rider level. The surplus can then be defined as the maximal consumption per coalition member every signatory \(j\) could lose, still having a positive incentive to stay, discounted at rate \(r_{t}\) over time \(t_{0}\) to \(t_{m}\):

    \({\text{lo}}\left( {S,\;d} \right) = \mathop {\hbox{max} }\limits_{{\tau_{{{\text{k}},{\text{t}}}} ,\Delta C_{t} }} \left( { \mathop \sum \limits_{{t = t_{0} }}^{{t_{n} }} \frac{1}{{1 + r_{t} }} \Delta C_{t} \left( {S,\;d} \right)} \right)\)

    subject to \({\pi}_{k} \left({c_{j} \left({S,d} \right) + {\tau}_{{{\text{k}},{\text{t}}}} \left({S,d} \right) - \Delta C_{t} \left({S,d} \right)} \right) \ge {\pi}_{k} \left({S\backslash \left\{j \right\} ,d} \right), \forall k \in S\).

    The surplus aggregates the consumption streams of all members of the coalition that are available for arbitrary redistribution when the coalition is internally stable after transfers. To gain an indicator of how much additional transfers each member could receive, we divide the sum of consumption by the number of members. If the surplus is positive, each member has an incentive to stay inside the coalition after redistribution and the magnitude of the surplus indicates how strong the incentives to stay are. If the surplus is negative, the coalition is not PIS and the negative magnitude of the surplus indicates how much more consumption would be necessary inside the coalition in order for the incentive for members to stay to become positive.

References

  1. Barrett, S. (1994). Self-enforcing international environmental agreements. Oxford Economic Papers, 46, 878–894.

    Article  Google Scholar 

  2. Barrett, S. (2003). Environment and statecraft: The strategy of environmental treaty-making. Oxford: Oxford University Press.

    Google Scholar 

  3. Barrett, S. (2005). The theory of international environmental agreements. In K.-G. Mäler & J. R. Vincent (Eds.), Handbook of environmental economics (pp. 1457–1516). Amsterdam: Elsevier.

    Google Scholar 

  4. Barrett, S. (2013). Climate treaties and approaching catastrophes. Journal of Environmental Economics and Management, 66(2), 235–250.

    Article  Google Scholar 

  5. Bosetti, V., Carraro, C., De Cian, E., Massetti, E., & Tavoni, M. (2013). Incentives and stability of international climate coalitions: An integrated assessment. Energy Policy, 55, 44–56.

    Article  Google Scholar 

  6. Bosetti, V., Carraro, C., Galeotti, M., Massettti, E. & Tavoni, M. (2006). WITCH: A world induced technical change hybrid model. The Energy Journal, Special Issue, Hybrid modelling of energy—environmental policies: Reconciling bottom-up and top-down, (Vol. 27, pp 13–37).

  7. Bréchet, T., Gerard, F., & Tulkens, H. (2011). Efficiency vs. stability in climate coalitions: A conceptual and computational appraisal. The Energy Journal, 31(1), 49–75.

    Google Scholar 

  8. Bréchet, T., Thénié, J., Zeimes, T., & Zuber, S. (2012). The benefits of cooperation under uncertainty: The case of climate change. Environmental Modelling and Assessment, 17(1), 149–162.

    Article  Google Scholar 

  9. Carraro, C., Eyckmans, J., & Finus, M. (2006). Optimal transfers and participation decisions in international environmental agreements. The Review of International Organizations, 1(4), 379–396.

    Article  Google Scholar 

  10. Carraro, C., & Siniscalco, D. (1993). Strategies for the international protection of the environment. Journal of Public Economics, 52(3), 309–328.

    Article  Google Scholar 

  11. Clarke, L., Edmonds, J., Krey, V., Richels, R., Rose, S., & Tavoni, M. (2009). International climate policy architectures: Overview of the EMF 22 international scenarios. Energy Economics, 31, 564–581.

    Google Scholar 

  12. Crost, B., & Traeger, C. P. (2011). Risk and aversion in the integrated assessment of climate change. CUDARE working papers. Department of Agricultural & Resource Economics, University of Carlifornia, Berkeley.

  13. d’Aspremont, C. & Gabszewicz, J. J. (1986). On the stability of collusion. In J. E. Stiglitz & G. F. Mathewson (Eds.), New developments in the analysis of market structure. International economic association series (Vol. 77). London: Palgrave Macmillan.

  14. Dellink, R. (2011). Drivers of stability of climate coalitions in the STACO model. Climate Change Economics, 2(2), 105–128.

    Article  Google Scholar 

  15. Dellink, R., Altamirano-Cabrera, J. C., Finus, M., von Ireland, E. & Weikard, H. (2004). Empirical background paper of the STACO model. http://www.wageningenur.nl/web/file?uuid=9115e403-7832-48d6-ac1c-eb61f67edbec&owner=d1bd6906-08fd-4139-956f-b8004307a16e. Accessed 01 Aug 2013.

  16. Dellink, R., Dekker, T., & Ketterer, J. (2013). The fatter the tail, the fatter the climate agreement. Simulating the influence of fat tails in climate change damages on the success of international climate negotiations. Environmental & Resource Economics, 56(2), 277–305.

    Article  Google Scholar 

  17. Dellink, R., & Finus, M. (2012). Uncertainty and climate treaties: Does ignorance pay? Resource and Energy Economics, 34(4), 565–584.

    Article  Google Scholar 

  18. Dellink, R., Finus, M., & Olieman, N. (2008). The stability likelihood of an international climate agreement. Environmental & Resource Economics, 39(4), 337–377.

    Article  Google Scholar 

  19. Dietz, S. (2011). High impact, low probability? An empirical analysis of risk in the economics of climate change. Climatic Change, 108(3), 519–541.

    Article  Google Scholar 

  20. Eyckmans, J. & Bréchet, T. (2012). Coalitions in the 18 region stochastic CWS model, presented at the workshop on modeling climate coalitions, Potsdam Institute for Climate Impact Research, February 8-9, 2012.

  21. Eyckmans, J. & Finus, M. (2006). Coalition formation in a global warming game: how the design of protocols affects the success of environmental treaty-making. Natural Resource Modeling, 19(3), 323–358.

    Article  Google Scholar 

  22. Finus, M. (2008). Game theoretic research on the design of international environmental agreements: Insights, critical remarks, and future challenges. International Review of Environmental and Resource Economics, 2(1), 29–67.

    Article  Google Scholar 

  23. Finus, M., & Pintassilgo, P. (2013). The role of uncertainty and learning for the success of international climate agreements. Journal of Public Economics, 103, 29–43.

    Article  Google Scholar 

  24. Hoel, M. (1992). International environmental conventions: the case of uniform reductions of emissions. Environmental & Resource Economics, 2(2), 141–159.

    Google Scholar 

  25. Hwang, I. C., Reynès, F., & Tol, R. S. J. (2013). Climate policy under fat-tailed risk: An application of dice. Environmental & Resource Economics, 56(3), 415–436.

    Article  Google Scholar 

  26. IPCC. (2001). Climate change 2001: Impacts, adaptation, and vulnerability. Cambridge: Cambridge University Press.

    Google Scholar 

  27. Jakob, M., Lessmann, K., & Wildgrube, T. (2014). The role of emissions trading and permit allocation in international climate agreements with asymmetric countries. Strategic Behavior and the Environment, 4(4), 361–392.

    Article  Google Scholar 

  28. Kolstad, C. D. (2007). Systematic uncertainty in self-enforcing international environmental agreements. Journal of Environmental Economics and Management, 53, 68–79.

    Article  Google Scholar 

  29. Kolstad, C. D., & Ulph, A. (2008). Learning and international environmental agreements. Climatic Change, 89, 125–141.

    Article  Google Scholar 

  30. Kolstad, C. D., & Ulph, A. (2011). Uncertainty, learning and heterogeneity in international environmental agreements. Environmental & Resource Economics, 50, 389–403.

    Article  Google Scholar 

  31. Kornek U., Lessmann K. & Tulkens H. (2014). Transferable- and non-transferable utility implementations of coalitional stability in integrated assessment models. CORE discussion paper, No. 35

  32. Kornek, U., Steckel, J., Lessmann, K., & Edenhofer, O. (2017). The climate rent curse: New challenges for burden sharing. International Environmental Agreements. doi:10.1007/s10784-017-9352-2.

    Google Scholar 

  33. Leimbach, M., Bauer, N., Baumstark, L., & Edenhofer, O. (2010). Mitigation costs in a globalized world: climate policy analysis with REMIND-R. Environmental Modelling and Assessment, 15(3), 155–173.

    Article  Google Scholar 

  34. Lessmann, K., Kornek, U., Bosetti, V., Dellink, R., Emmerling, J., Eyckmans, J., et al. (2015). The stability and effectiveness of climate coalitions: A comparative analysis of multiple integrated assessment models. Environmental & Resource Economics, 62(4), 811–836.

    Article  Google Scholar 

  35. Lessmann, K., Marschinski, R., & Edenhofer, O. (2009). The effects of tariffs on coalition formation in a dynamic global warming game. Economic Modelling, 26(3), 641–649.

    Article  Google Scholar 

  36. Na, S., & Shin, H. S. (1998). International environmental agreements under uncertainty. Oxford Economic Papers, 50(2), 173–1785.

    Article  Google Scholar 

  37. Nordhaus, W. D. (1994). Managing the global commons. The economics of climate change. London: MIT Press.

    Google Scholar 

  38. Nordhaus, W. D. (2008). A question of balance: Weighting the options on global warming. New Heaven: Yale University Press.

    Google Scholar 

  39. Nordhaus, W. D. (2010). Economic aspects of global warming in a post-Copenhagen environment. PNAS, 107(26), 11721–11726.

    CAS  Article  Google Scholar 

  40. Nordhaus, W. D. (2011). The economics of tail events with an application to climate change. Review of Environmental Economics and Policy, 5(2), 240–257.

    Article  Google Scholar 

  41. Nordhaus, W. D. (2012). Economic Policy in the face of severe tail events. Journal of Public Economic Theory, 14(2), 197–219.

    Article  Google Scholar 

  42. Nordhaus, W. D., & Yang, Z. (1996). A regional dynamic general-equilibrium model of alternative climate-change-strategies. The American Economic Review, 86(4), 741–765.

    Google Scholar 

  43. Olieman, N. J., & Hendrix, E. M. T. (2006). Stability likelihood of coalitions in a two-stage cartel game: An estimation method. European Journal of Operational Research, 174(1), 333–348.

    Article  Google Scholar 

  44. Tavoni, M., & Tol, R. S. J. (2010). Counting only the hits? The risk of underestimating the cost of stringent climate policy. A letter. Climatic Change, 100, 769–778.

    Article  Google Scholar 

  45. Tol, R. S. T. (1995). The damage costs of climate change toward more comprehensive calculations. Environmental & Resource Economics, 5(4), 353–374.

    Article  Google Scholar 

  46. Tol, R. S. J. (2012). On the uncertainty about the total economic impact of climate change. Environmental & Resource Economics, 53, 97–116.

    Article  Google Scholar 

  47. Tol, R. S. J. (2013). Targets for global climate policy: An overview. Journal of Economic Dynamics & Control, 37, 911–928.

    Article  Google Scholar 

  48. Tol, R. S. J. (2014). Correction and update: The economic effects of climate change. The Journal of Economic Perspectives, 28(2), 221–225.

    Article  Google Scholar 

  49. Weikard, H. P. (2009). Cartel stability under optimal sharing rule. The Manchester School, 77, 575–593.

    Article  Google Scholar 

  50. Weitzman, M. L. (2007). A review of the stern review on the economics of climate change. Journal of Economic Literature, 45(3), 703–724.

    Article  Google Scholar 

  51. Weitzman, M. L. (2009). On modelling and interpreting the economics of catastrophic climate change. The Review of Economics and Statistics, 91(1), 1–19.

    Article  Google Scholar 

  52. Weitzman, M. L. (2010). What is the “damage function” for global warming—and what difference might it make? Climate Change Economics, 1(1), 57–69.

    Article  Google Scholar 

  53. Weitzman, M. L. (2012). GHG targets as insurance against catastrophic climate damages. Journal of Public Economic Theory, 14(2), 221–244.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Achim Hagen, Andrew Halliday as well as conference participants at ICP 2015, EAERE 2016 and especially two anonymous reviewers for helpful comments on earlier drafts of this paper.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jasper N. Meya.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Appendix

Appendix

See Tables 6 and 7.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Meya, J.N., Kornek, U. & Lessmann, K. How empirical uncertainties influence the stability of climate coalitions. Int Environ Agreements 18, 175–198 (2018). https://doi.org/10.1007/s10784-017-9378-5

Download citation

Keywords

  • International environmental agreements
  • Climate coalition formation
  • Uncertainty
  • Monte Carlo analysis
  • Numerical modelling

JEL Classification

  • C72
  • D80
  • H87
  • Q54