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IEW 2009 Venice1 The Threat of Carbon Regulation & Business Hedging Strategy A. Golub ¹, S. Fuss ² J. Szolgayova ² & M. Obersteiner ² ¹ EDF, Washington.

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Presentation on theme: "IEW 2009 Venice1 The Threat of Carbon Regulation & Business Hedging Strategy A. Golub ¹, S. Fuss ² J. Szolgayova ² & M. Obersteiner ² ¹ EDF, Washington."— Presentation transcript:

1 IEW 2009 Venice1 The Threat of Carbon Regulation & Business Hedging Strategy A. Golub ¹, S. Fuss ² J. Szolgayova ² & M. Obersteiner ² ¹ EDF, Washington ² IIASA, Laxenburg, Austria International Energy Workshop June 17-19, 2009, Venice, Italy

2 IEW 2009 Venice2 Motivation Barrier for adopting less emission- intensive or emission-saving energy technology  regulatory uncertainty: post-2012 agreement? Government commitment? Business response to uncertainty? Investment behavior? Policy recommendations?

3 IEW 2009 Venice3 Share of climate-related patents in total number of patents Source: Dechezlepretre et al., 2009

4 IEW 2009 Venice4 Innovation trend in climate technologies compared to all other sectors Source: Dechezlepretre et al., 2009

5 IEW 2009 Venice5 Emission reductions Carbon offsets (e.g. credits for avoided deforestation)  REDD option (Golub et al, 2009) R&D to ensure option on clean technology: e.g. carbon capture and storage (CCS)  technology option (focus of this paper)

6 IEW 2009 Venice6 Questions What is an option on carbon capture and storage (CCS) technology worth? How much is the firm willing to pay for cost-reducing R&D? How does this change over CO2 price scenarios? Does the inclusion of options on offsets make a difference?

7 IEW 2009 Venice7 Modeling approach Technology adoption under uncertainty: dynamic programming One representative coal-fired power plant (PC), retrofittable with CCS Binomial trees for the development of CO 2 prices (regulation) and CCS costs (new technology)

8 IEW 2009 Venice8 Data: CO 2 price scenarios Source: GGI Scenario Database (IIASA, 2009)

9 IEW 2009 Venice9 Regulatory uncertainty Run the model for different CO 2 price scenarios: (1) deterministic (2) two scenarios in combination with 50-50 chance of transition Compare results to deterministic scenarios, where the price is the average

10 IEW 2009 Venice10 Data: CCS costs PCPC-CCS Availability factor89%85% Capture ratio00.9 Storage cost (EUR/ton CO2)01.465 Investment cost (EUR/MW 2010)11821851 Investment cost (EUR/MW 2040)10001400 CO2 (g/kWh)0.740.142 Efficiency46%36% Variable O&M cost (EUR/GJ)0.260.96 Fixed O&M cost (EUR/kW)6175 Source: v.d.Broek et al (2008), International Journal of Greenhouse Gas Control 2:105– 129.

11 IEW 2009 Venice11 R&D process Investment in R&D leads to cost-reducing innovations; firms that do not invest into R&D can only install the expensive module Cost reductions mainly concern investment costs: embodied technical change Arrival of innovations is uncertain  technological uncertainty

12 IEW 2009 Venice12 Modeling framework

13 IEW 2009 Venice13 Results Value of R&D as a function of the CO 2 price

14 IEW 2009 Venice14 Results II Less strict policy  high target  low CO 2 price  investment triggered late or not at all  R&D value lower or zero Low target  high CO 2 price: if unexpected  high prices trigger CCS investment early  no scope to reap benefits from technical change Strong signal that low CO 2 prices will be rising high in the future  invest into R&D to preserve opportunity to install CCS in the future if necessary  value of flexibility

15 IEW 2009 Venice15 Results III Trees with two scenarios vs the average (deterministic) price: uncertainty about the stabilization target can also imply potentially high prices, triggering R&D.

16 IEW 2009 Venice16 Results IV Inclusion of an option on REDD  no impact on the firm’s decisions and the value of R&D. Reason: REDD option priced under no arbitrage condition – no benefits from options for a risk-neutral investor Role of REDD can still be important if the firm is risk-averse  portfolio effect

17 IEW 2009 Venice17 Conclusions R&D is a “hedging” strategy for businesses facing regulatory uncertainty.  explains stylized facts by Dechezlepretre et al., 2009 R&D helps to reduce the firm’s costs and its exposure to risk. The wrong signal by policymakers can lead to too early investment and thus loss of the opportunity to improve the technology first, but also to failure to invest in R&D. If high CO 2 prices in the future are possible, transparency about this will lead to more R&D.

18 IEW 2009 Venice18 Outlook: current work Test different technologies Include risk aversion in order to properly investigate the importance of REDD options: these can be exercised to buy time when the CO 2 price turns out high and CCS is still expensive. Do not only focus on structural uncertainty, but also address short-term volatility by making CO 2 price stochastic.

19 IEW 2009 Venice19 References A. Dechezlepretre, M. Glachant, I. Hascic, N. Johnstone, Y. Meniere, “Invention and Transfer of Climate Change Mitigation Technologies on a Global Scale: A Study Drawing on Patent Data,” Report, CERNA, Paris, 2009. Available at http://www.cerna.ensmp.fr/images/stories/final_report _090224.pdf v.d.Broek et al (2008), "Planning for an electricity sector with carbon capture and storage Case of the Netherlands," International Journal of Greenhouse Gas Control 2:105–129.

20 IEW 2009 Venice20 References II A. Golub, S. Fuss, J. Szolgayova and M. Obersteiner, “Effects of low-cost offsets on energy investment: new perspectives on REDD,” FEEM Nota di Lavoro 17.09, March 2009, available at http://www.feem.it/NR/rdonlyres/80EAC8FD-07E5- 4135-8642-5D959AE95C25/2832/1709.pdf International Institute of Applied Systems Analysis. GGI scenario database. Available at http://www.iiasa.ac.at/Research/GGI/DB/; 2009.


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