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Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

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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.

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Notes

  1. Note that the decision maker in this problem could be a region or a country, but we prefer to think of this as being a large energy company: even in liberalized electricity markets, typically a few large firms are dominant and seek to diversify their portfolios of utilities to hedge against a multitude of risks.

  2. [39] call this the worst case conditional value at risk for portfolios of financial assets.

  3. For a technical lifetime of 30 years, for example, there are then six distributions for profits from year 1–5, 6–10, 11–15, 16–20, 21–25, and 26–30.

  4. [17] examines the impact of adapting climate policy (and thus CO2 prices) more frequently, but therefore less dramatically to gain more insight into the influence of policy uncertainty on investment decisions in the electricity sector, whereas [18] investigates the issues of price ceilings, or “safety valves,” to constrain CO2 price spikes.

  5. The restrictions on actions are that the investor can invest into the CCS module only if it has not been built yet and only once the CCS module has been built, it can be switched off and on.

  6. As an assumption, a power plant will produce continuously throughout the year, i.e., we have a fixed coefficients production function in the style of Leontieff mimicking output contracts between distributors and generators.

  7. Alternative methods are the formulation of partial differential equations, which are then solved numerically, or the setup of binomial lattices.

  8. This can be achieved by the discretization of the carbon price, so by a possible realization of price in a specific year we mean a point in the discretized grid between a predefined maximum and minimum price. The maximum and minimum price levels considered are chosen so that they encompass 95% of all possible price paths.

  9. We will present results for both the minimization of CVaR as indicated here and for the minimization of expected costs (subject to a risk constraint). In both cases, the constraints will be chosen to be nonbinding, so as to observe the full diversification effect without excluding low-profit technologies, which might be attractive mainly on account of their risk profile.

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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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Authors and Affiliations

  1. International Institute for Applied Systems Analysis, Schlossplatz 1, 2361, Laxenburg, Austria

    Jana Szolgayová, Sabine Fuss, Nikolay Khabarov & Michael Obersteiner

  2. Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia

    Jana Szolgayová

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  1. Jana Szolgayová
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  2. Sabine Fuss
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  3. Nikolay Khabarov
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  4. Michael Obersteiner
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Correspondence to Sabine Fuss.

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Szolgayová, J., Fuss, S., Khabarov, N. et al. Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty. Environ Model Assess 17, 39–49 (2012). https://doi.org/10.1007/s10666-011-9274-2

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