Risk Hedging Strategies Under Energy System and Climate Policy Uncertainties

  • Volker KreyEmail author
  • Keywan Riahi
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 199)


The future development of the energy sector is rife with uncertainties. They concern virtually the entire energy chain, from resource extraction to conversion technologies, energy demand, and the stringency of future environmental policies. Investment decisions today need thus not only to be cost-effective from the present perspective, but have to take into account also the imputed future risks of above uncertainties. This chapter introduces a newly developed modeling decision framework with endogenous representation of above uncertainties. We employ modeling techniques from finance and in particular modern portfolio theory to a systems engineering model of the global energy system and implement several alternative representations of risk. We aim to identify salient characteristics of least-cost risk hedging strategies that are adapted to considerably reduce future risks and are hence robust against a wide range of future uncertainties. These lead to significant changes in response to energy system and carbon price uncertainties, in particular (i) higher short- to medium-term investments into advanced technologies, (ii) pronounced emissions reductions, and (iii) diversification of the technology portfolio. From a methodological perspective, we find that there are strong interactions and synergies between different types of uncertainties. Cost-effective risk hedging strategies thus need to take a holistic view and comprehensively account for all uncertainties jointly. With respect to costs, relatively modest risk premiums (or hedging investments) can significantly reduce the vulnerability of the energy system against the associated uncertainties. The extent of early investments, diversification, and emissions reductions, however, depends on the risk premium that decision makers are willing to pay to respond to prevailing uncertainties and remains thus one of the key policy variables.


Risk Measure Risk Premium Carbon Price Latin Hypercube Sampling Total System Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ascough II JC, Maier HR, Ravalico JK, Strudley MW (2008) Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecol Model 219(3–4):383–399CrossRefGoogle Scholar
  2. 2.
    Babonneau F, Kanudia A, Labriet M, Loulou R, Vial J-P (2012) Energy security: a robust optimization approach to design a robust european energy supply via tiam-world. Environ Model Assess 17(1-2):19–37CrossRefGoogle Scholar
  3. 3.
    Beale EML (1955) On minimizing a convex function subject to linear inequalities. J R Stat Soc Series B (Methodological) 17(2):173–184Google Scholar
  4. 4.
    Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust optimization. Princeton University Press, PrincetonGoogle Scholar
  5. 5.
    Carnell R (2006) lhs: Latin hypercube samples. R package version 0.3Google Scholar
  6. 6.
    Charnes A, Cooper W (1959) Chance constrained programming. Manag Sci 6:73–89CrossRefGoogle Scholar
  7. 7.
    Dantzig GB (1955) Linear programming under uncertainty. Manag Sci 1: 197–206CrossRefGoogle Scholar
  8. 8.
    de Vries BJM, van Vuuren DP, Hoogwijk MM (2007) Renewable energy sources: their global potential for the first-half of the 21st century at a global level: an integrated approach. Energy Policy 35(4):2590–2610CrossRefGoogle Scholar
  9. 9.
    Dessai S, Hulme M (2007) Assessing the robustness of adaptation decisions to climate change uncertainties: a case study on water resources management in the east of england. Glob Environ Change 17(1):59–72CrossRefGoogle Scholar
  10. 10.
    Ermoliev Y, Wets RJ-B (eds) (1988) Numerical techniques for stochastic optimization, vol 10 of Springer series in computational mathematics. Springer, BerlinGoogle Scholar
  11. 11.
    Fisher B, Nakicenovic N, Alfsen K, Morlot JC, de la Chesnaye F, Hourcade J-C, Jiang K, Kainuma M, Rovere EL, Matysek A, Rana A, Riahi K, Richels R, Rose S, Vuuren Dv, Warren R (2007) Issues related to mitigation in the long term context. In: Metz B, Davidson O, Bosch P, Dave R, Meyer L (eds) Climate change 2007: mitigation. Contribution of working group III to the fourth assessment report of the inter-governmental panel on climate change. Cambridge University Press, Cambridge, pp 169–250Google Scholar
  12. 12.
    Gritsevskyi A, Nakićenović N (2000) Modeling uncertainty of induced technological change. Energy Policy 28:907–921CrossRefGoogle Scholar
  13. 13.
    Grübler A, Gritsevskyi A (2002) A model of endogenous technological change through uncertain returns on innovation. In: Grübler A, Nakićenović N, Nordhaus W (eds) Technological change and the environment. Resources for the Future Press, WashingtonGoogle Scholar
  14. 14.
    Grübler A, Nakicenovic N, Riahi K, Wagner F, Fischer G, Keppo I, Obersteiner M, O’Neill B, Rao S, Tubiello F (2007) Integrated assessment of uncertainties in greenhouse gas emissions and their mitigation: introduction and overview. Technol Forecast Soc Change 74(7):873–886CrossRefGoogle Scholar
  15. 15.
    Hanaoka T, Kawase R, Kainuma M, Matsuoka Y, Ishii H, Oka K (2006) Greenhouse gas emissions scenarios database and regional mitigation analysis. Technical report, National Institute of Environmental StudiesGoogle Scholar
  16. 16.
    IIASA GGI (2007) IIASA GGI scenario database.
  17. 17.
    Iman RL, Conover WJ (1980) Small sample sensitivity analysis techniques for computer models with an application to risk assessment. Commun Stat Theor Methods 9(17):1749–1842CrossRefGoogle Scholar
  18. 18.
    Iman R, Conover W (1982) A distribution-free approach to including rank correlation among input variables. Commun Stat B 11:311–334CrossRefGoogle Scholar
  19. 19.
    Kann A, Weyant J (2000) Approaches for performing uncertainty analysis in large-scale energy/economic policy models. Environ Model Assess 5(1):29–46CrossRefGoogle Scholar
  20. 20.
    Kanudia A, Loulou R (1998) Robust responses to climate change via stochastic MARKAL: the case of Québec. Eur J Oper Res 106:15–30CrossRefGoogle Scholar
  21. 21.
    Koomey J, Hultman NE (2007) A reactor-level analysis of busbar costs for us nuclear plants, 1970–2005. Energy Policy 35(11):5630–5642CrossRefGoogle Scholar
  22. 22.
    Kouvaritakis N, Panos V (2005) Modelling the two factor learning curve equations - guidelines on implementation. Technical report, E3M Lab, School of Electrical and Computer Engineering, National Technical University of AthensGoogle Scholar
  23. 23.
    Krey V, Martinsen D, Wagner H-J (2007) Effects of stochastic energy prices on long-term energy-economic scenarios. Energy 32(12):2340–2349CrossRefGoogle Scholar
  24. 24.
    Labriet M, Kanudia A, Loulou R (2012) Climate mitigation under an uncertain technology future: a tiam-world analysis. Energy Econ 34, Suppl 3(0): S366–S377CrossRefGoogle Scholar
  25. 25.
    Lempert RJ, Groves DG, Popper SW, Bankes SC (2006) A general, analytic method for generating robust strategies and narrative scenarios. Manag Sci 52(4):514–528CrossRefGoogle Scholar
  26. 26.
    Loulou R, Kanudia A (1999) Minimax regret strategies for greenhouse gas abatement: methodology and application. Oper Res Lett 25(5):219–230CrossRefGoogle Scholar
  27. 27.
    Loulou R, Labriet M, Kanudia A (2009) Deterministic and stochastic analysis of alternative climate targets under differentiated cooperation regimes. Energy Econ 31(Supplement 2):S131–S143. doi: 10.1016/j.eneco.2009.06.012Google Scholar
  28. 28.
    Manne A (1996) Hedging strategies for global carbon dioxide abatement: a summary of poll results emf 14 subgroup – analysis for decision making under uncertainty. In: Nakicenovic N, Nordhaus WD, Richels R, Toth FL (eds) Climate change: integrating science, economics and policy, vol CP-96-1 of Conference proceedings. International Institute for Applied Systems Analysis, Laxenburg, pp 207–228Google Scholar
  29. 29.
    Manne AS, Richels RG (1992) Buying greenhouse insurance: the economic costs of carbon dioxide emission limits. The MIT Press, CambridgeGoogle Scholar
  30. 30.
    Markowitz HM (1952) Portfolio selection. J Financ 7(1):77–91Google Scholar
  31. 31.
    Marti K, Ermoliev Y, Pflug G (eds) (2004) Dynamic stochastic optimization, vol 532 of Lecture notes in economics and mathematical systems. Springer, BerlinGoogle Scholar
  32. 32.
    Messner S, Schrattenholzer L (2000) Message-macro: linking an energy supply model with a macroeconomic module and solving it iteratively. Energy 25(3):267–282CrossRefGoogle Scholar
  33. 33.
    Messner S, Strubegger M (1995) User’s guide for MESSAGE III. IIASA working paper WP-95-69, International Institute for Applied Systems Analysis (IIASA), LaxenburgGoogle Scholar
  34. 34.
    Messner S, Golodnikov A, Gritsevskii A (1996) A stochastic version of the dynamic linear programming model MESSAGE III. Energy 21(9):775–784CrossRefGoogle Scholar
  35. 35.
    Metz B, Davidson O, Swart R, Pan J (eds) (2001) Climate Change 2001: Mitigation. Third assessment report of the IPCC. Cambridge University Press, Cambridge.
  36. 36.
    Nakicenovic N, Riahi K (2001) An assessment of technological change across selected energy scenarios. Report, World Energy Council (WEC), LondonGoogle Scholar
  37. 37.
    Nakićenović N, Swart R (eds) (2000) IPCC Special Report on Emissions Scenarios. IPCC special reports. Cambridge University Press, Cambridge.
  38. 38.
    Nakićenović N, Grübler A, McDonald A (eds) (1998) IIASA-WEC global energy perspectives. Cambridge University Press, Cambridge.
  39. 39.
    Palmquist J, Rockafellar RT, Uryasev S (1999) Portfolio optimization with conditional value-at-risk objective and constraints. Technical report, Center for Applied Optimization, Department of Industrial and Systems Engineering, University of FloridaGoogle Scholar
  40. 40.
    Peterson S (2006) Uncertainty and economic analysis of climate change: a survey of approaches and findings. Environ Model Assess 11(1):1–17CrossRefGoogle Scholar
  41. 41.
    Pflug G, Roemisch W (2007) Modeling, measuring and managing risk. World Scientific Publishing Company, LondonCrossRefGoogle Scholar
  42. 42.
    Pizer WA (1999) The optimal choice of climate change policy in the presence of uncertainty. Resour Energy Econ 21(3–4):255–287CrossRefGoogle Scholar
  43. 43.
    R Development Core Team (2008) R: a language and environment for statistical computing. R foundation for statistical computing. Vienna, Austria. ISBN 3-900051-07-0.
  44. 44.
    Rao S, Riahi K (2006) The role of non-CO2 greenhouse gases in climate change mitigation: long-term scenarios for the 21st century. Energy J 27(Special Issue Nov):177–200Google Scholar
  45. 45.
    Riahi K, Roehrl RA (2000) Greenhouse gas emissions in a dynamics-as-usual scenario of economic and energy development. Technol Forecast Soc Change 63(2–3):175–205CrossRefGoogle Scholar
  46. 46.
    Riahi K, Grübler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol Forecast Soc Change 74(7):887–935CrossRefGoogle Scholar
  47. 47.
    Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J. Risk 2(3):21–41Google Scholar
  48. 48.
    Rogner HH (1997) An assessment of world hydrocarbon resources. Ann Rev Energy Environ 22(1):217–262CrossRefGoogle Scholar
  49. 49.
    Rokityanskiy D, Benítez PC, Kraxner F, McCallum I, Obersteiner M, Rametsteiner E, Yamagata Y (2007) Geographically explicit global modeling of land-use change, carbon sequestration, and biomass supply. Technol Forecast Soc Change 74(7):1057–1082CrossRefGoogle Scholar
  50. 50.
    Stirling A (1994) Diversity and ignorance in electricity supply investment – addressing the solution rather than the problem. Energy Policy 22(3):195–216CrossRefGoogle Scholar
  51. 51.
    Stirling A (1998) On the economics and analysis of diversity. Electronic working paper 28. University of Sussex, BrightonGoogle Scholar
  52. 52.
    Wigley T (2003) MAGICC/SCENGEN 4.1: Technical manual. Technical report, National Center for Atmospheric ResearchGoogle Scholar
  53. 53.
    Yan J (2007) Enjoy the joy of copulas: with a package copula. J Stat Softw 21(4):1–21Google Scholar
  54. 54.
    Yohe G, Andronova N, Schlesinger M (2004) Climate: to hedge or not against an uncertain climate future? Science 306(5695):416–417CrossRefGoogle Scholar
  55. 55.
    Zhang Y, Pinder G (2004) Latin hypercube lattice sample selection strategy for correlated random hydraulic conductivity fields. Water Resour Res 39(8):1226Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.IIASALaxenburgAustria

Personalised recommendations