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Efficient climate policies under technology and climate uncertainty

Author

Listed:
  • Held, Hermann
  • Kriegler, Elmar
  • Lessmann, Kai
  • Edenhofer, Ottmar

Abstract

This article explores efficient climate policies in terms of investment streams into fossil and renewable energy technologies. The investment decisions maximise social welfare while observing a probabilistic guardrail for global mean temperature rise under uncertain technology and climate parameters. Such a guardrail constitutes a chance constraint, and the resulting optimisation problem is an instance of chance constrained programming, not stochastic programming as often employed. Our analysis of a model of economic growth and endogenous technological change, MIND, suggests that stringent mitigation strategies cannot guarantee a very high probability of limiting warming to 2 °C since preindustrial time under current uncertainty about climate sensitivity and climate response time scale. Achieving the 2 °C temperature target with a probability P* of 75% requires drastic carbon dioxide emission cuts. This holds true even though we have assumed an aggressive mitigation policy on other greenhouse gases from, e.g., the agricultural sector. The emission cuts are deeper than estimated from a deterministic calculation with climate sensitivity fixed at the P* quantile of its marginal probability distribution (3.6 °C). We show that earlier and cumulatively larger investments into the renewable sector are triggered by including uncertainty in the technology and climate response time scale parameters. This comes at an additional GWP loss of 0.3%, resulting in a total loss of 0.8% GWP for observing the chance constraint. We obtained those results with a new numerical scheme to implement constrained welfare optimisation under uncertainty as a chance constrained programming problem in standard optimisation software such as GAMS. The scheme is able to incorporate multivariate non-factorial probability measures such as given by the joint distribution of climate sensitivity and response time. We demonstrate the scheme for the case of a four-dimensional parameter space capturing uncertainty about climate and technology.

Suggested Citation

  • Held, Hermann & Kriegler, Elmar & Lessmann, Kai & Edenhofer, Ottmar, 2009. "Efficient climate policies under technology and climate uncertainty," Energy Economics, Elsevier, vol. 31(Supplemen), pages 50-61.
  • Handle: RePEc:eee:eneeco:v:31:y:2009:i:supplement1:p:s50-s61
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, Open Access Journal, vol. 4(10), pages 1-33, October.
    2. Tamaki, Tetsuya & Nozawa, Wataru & Managi, Shunsuke, 2017. "Evaluation of the ocean ecosystem: Climate change modelling with backstop technologies," Applied Energy, Elsevier, vol. 205(C), pages 428-439.
    3. Jin, Wei & Zhang, ZhongXiang, 2016. "On the mechanism of international technology diffusion for energy technological progress," Resource and Energy Economics, Elsevier, vol. 46(C), pages 39-61.
    4. Fan, Lin & Norman, Catherine S. & Patt, Anthony G., 2012. "Electricity capacity investment under risk aversion: A case study of coal, gas, and concentrated solar power," Energy Economics, Elsevier, vol. 34(1), pages 54-61.
    5. Elnaz Roshan & Mohammad M. Khabbazan & Hermann Held, 2019. "Cost-Risk Trade-Off of Mitigation and Solar Geoengineering: Considering Regional Disparities Under Probabilistic Climate Sensitivity," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 263-279, January.
    6. Hermann Held, 2019. "Cost Risk Analysis: Dynamically Consistent Decision-Making under Climate Targets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 247-261, January.
    7. Gren, Ing-Marie & Carlsson, Mattias & Elofsson, Katarina & Munnich, Miriam, 2012. "Stochastic carbon sinks for combating carbon dioxide emissions in the EU," Energy Economics, Elsevier, vol. 34(5), pages 1523-1531.
    8. Matthias Schmidt & Alexander Lorenz & Hermann Held & Elmar Kriegler, 2011. "Climate targets under uncertainty: challenges and remedies," Climatic Change, Springer, vol. 104(3), pages 783-791, February.
    9. Markus Ohndorf & Julia Blasch & Renate Schubert, 2015. "Emission budget approaches for burden sharing: some thoughts from an environmental economics point of view," Climatic Change, Springer, vol. 133(3), pages 385-395, December.
    10. Tamaki, Tetsuya & Nozawa, Wataru & Managi, Shunsuke, 2017. "Evaluation of the ocean ecosystem: climate change modelling with backstop technology," MPRA Paper 80549, University Library of Munich, Germany.
    11. Delf Neubersch & Hermann Held & Alexander Otto, 2014. "Operationalizing climate targets under learning: An application of cost-risk analysis," Climatic Change, Springer, vol. 126(3), pages 305-318, October.
    12. A. Lopez & E. Suckling & F. Otto & A. Lorenz & D. Rowlands & M. Allen, 2015. "Towards a typology for constrained climate model forecasts," Climatic Change, Springer, vol. 132(1), pages 15-29, September.
    13. Anthony G. Patt & Elke U. Weber, 2014. "Perceptions and communication strategies for the many uncertainties relevant for climate policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 5(2), pages 219-232, March.
    14. Alfred Endres & Bianca Rundshagen, 2013. "Incentives to Diffuse Advanced Abatement Technology Under the Formation of International Environmental Agreements," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(2), pages 177-210, October.

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