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Environmental Modeling & Assessment

, Volume 17, Issue 1–2, pp 39–49 | Cite as

Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

  • Jana Szolgayová
  • Sabine FussEmail author
  • Nikolay Khabarov
  • Michael Obersteiner
Article

Abstract

Concerning the stabilization of greenhouse gases, the UNFCCC prescribes measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects. Such measures should be cost-effective and scientific uncertainty should not be used as a reason for postponing them. However, in the light of uncertainty about climate sensitivity and other underlying parameters, it is difficult to assess the importance of different technologies in achieving robust long-term climate risk mitigation. One example currently debated in this context is biomass energy, which can be used to produce both carbon-neutral energy carriers, e.g., electricity, and at the same time offer a permanent CO2 sink by capturing carbon from the biomass at the conversion facility and permanently storing it. We use the GGI Scenario Database IIASA [3] as a point of departure for deriving optimal technology portfolios across different socioeconomic scenarios for a range of stabilization targets, focusing, in particular, on new, low-emission scenarios. More precisely, the dynamics underlying technology adoption and operational decisions are analyzed in a real options model, the output of which then informs the portfolio optimization. In this way, we determine the importance of different energy technologies in meeting specific stabilization targets under different circumstances (i.e., under different socioeconomic scenarios), providing valuable insight to policymakers about the incentive mechanisms needed to achieve robust long-term climate risk mitigation.

Keywords

Robust energy portfolios Climate policy Socioeconomic scenarios 

Notes

Acknowledgment

The work described in this paper has been conducted within the project “Climate Risk Management Modeling” of IIASA’s Greenhouse Gas Initiative (http://www.iiasa.ac.at/Research/GGI/). The authors also acknowledge funding from the EU projects CC-TAME (grant no. 212535, http://www.cctame.eu), PASHMINA (grant no. 244766, http://www.pashmina-project.eu/), LC-IMPACT (grant no. 243827, http://www.lc-impact.eu/), and PROSUITE (grant no. 227078, http://www.prosuite.org/).

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jana Szolgayová
    • 1
    • 2
  • Sabine Fuss
    • 1
    Email author
  • Nikolay Khabarov
    • 1
  • Michael Obersteiner
    • 1
  1. 1.International Institute for Applied Systems AnalysisLaxenburgAustria
  2. 2.Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and InformaticsComenius UniversityBratislavaSlovakia

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