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The Threat of Carbon Regulation & Business Hedging Strategy

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. Motivation.

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The Threat of Carbon Regulation & Business Hedging Strategy

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  1. The Threat of Carbon Regulation & Business Hedging Strategy • A. Golub¹, S. Fuss² • J. Szolgayova² & M. Obersteiner² • ¹EDF, Washington²IIASA,Laxenburg, Austria • International Energy Workshop • June 17-19, 2009, Venice, Italy

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

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

  5. 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. 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. Modeling approach • Technology adoption under uncertainty: dynamic programming • One representative coal-fired power plant (PC), retrofittable with CCS • Binomial trees for the development of CO2 prices (regulation) and CCS costs (new technology)

  8. Data: CO2 price scenarios Source: GGI Scenario Database (IIASA, 2009)

  9. Regulatory uncertainty • Run the model for different CO2 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. Data: CCS costs Source: v.d.Broek et al (2008), International Journal of Greenhouse Gas Control 2:105–129.

  11. 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. Modeling framework

  13. Results Value of R&D as a function of the CO2 price

  14. Results II • Less strict policy  high target  low CO2 price  investment triggered late or not at all  R&D value lower or zero • Low target  high CO2 price: if unexpected  high prices trigger CCS investment early  no scope to reap benefits from technical change • Strong signal that low CO2 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. 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. 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. 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 CO2 prices in the future are possible, transparency about this will lead to more R&D.

  18. 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 CO2 price turns out high and CCS is still expensive. • Do not only focus on structural uncertainty, but also address short-term volatility by making CO2 price stochastic.

  19. 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. 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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