Managing dependencies in forest offset projects: toward a more complete evaluation of reversal risk

  • David M. Cooley
  • Christopher S. Galik
  • Thomas P. Holmes
  • Carolyn Kousky
  • Roger M. Cooke
Original Article

Abstract

Although forest carbon offsets can play an important role in the implementation of comprehensive climate policy, they also face an inherent risk of reversal. If such risks are positively correlated across projects, it can affect the integrity of larger project portfolios and potentially the entire offsets program. Here, we discuss three types of risks that could affect forest offsets—fat tails, micro-correlation, and tail dependence—and provide examples of how they could present themselves in a forest offset context. Given these potential dependencies, we suggest several new risk management approaches that take into account dependencies in reversal risk across projects and which could help guard the climate integrity of an offsets program. We also argue that data collection be included as an integral part of any offsets program so that disturbance-related dependencies may be identified and managed as early and to the greatest extent possible.

Keywords

Carbon offsets Climate policy Forestry Natural disturbance Risk 

References

  1. Brumelle S, Stanbury WT, Thompson WA et al (1990) Framework for the analysis of risks in forest management and silvicultural investments. For Ecol Manag 36:279–299CrossRefGoogle Scholar
  2. Chomitz KM, Lecocq F (2004) Temporary sequestration credits: an instrument for carbon bears. Clim Pol 4:65–74Google Scholar
  3. Dale VH, Joyce LA, McNulty S et al (2001) Climate change and forest disturbances. BioSci 51:723–734CrossRefGoogle Scholar
  4. Ellis J (2001) Forestry projects: Permanence, credit accounting and lifetime. OECD Environment Directorate International Energy Agency, ParisGoogle Scholar
  5. EPA (2005) Greenhouse gas mitigation potential in U.S. Forestry and Agriculture. US Environmental Protection Agency, Office of Atmospheric Programs, Washington, DCGoogle Scholar
  6. EPA (2010) EPA analysis of the American Power Act in the 111th Congress. US Environmental Protection Agency, Office of Atmospheric Programs, Washington, DCGoogle Scholar
  7. Galik CS, Jackson RB (2009) Risks to forest carbon offset projects in a changing climate. For Ecol Manag 257:2209–2216CrossRefGoogle Scholar
  8. Gamarra JGP, He F (2008) Spatial scaling of mountain pine beetle infestations. J Animal Ecol 77:796–801CrossRefGoogle Scholar
  9. Gardiner BA, Quine CP (2000) Management of forests to reduce the risk of abiotic damage—a review with particular reference to the effects of strong winds. For Ecol Manag 135:261–277CrossRefGoogle Scholar
  10. Holmes TP, Huggett RJ, Westerling AL (2008) Statistical analysis of large wildfires. In: Holmes TP et al (eds) The economics of forest disturbances: Wildfires, storms, and invasive species. Springer, DordrechtGoogle Scholar
  11. Hultman NE (2006) Geographic diversification of carbon risk—a methodology for assessing carbon investments using eddy correlation measurements. Glob Env Change-Human and Policy Dimensions 16:58–72CrossRefGoogle Scholar
  12. Kent G, Thoumi G (2010) Forest carbon is in the climate bill, but how do we insure it? With Trees! Ecosystem MarketplaceGoogle Scholar
  13. Kim M-K, McCarl BA, Murray BC (2008) Permanence discounting for land-based carbon sequestration. Ecol Econ 64:763–769CrossRefGoogle Scholar
  14. Kousky C, Cooke RM (2009) Climate change and risk management: Challenges for insurance, adaptation, and loss estimation. Resources for the Future, Washington, DCGoogle Scholar
  15. Laurikka H, Springer U (2003) Risk and return of project-based climate change mitigation: a portfolio approach. Glob Env Change-Human and Policy Dimensions 13:207–217CrossRefGoogle Scholar
  16. Malamud BD, Millington JDA, Perry GLW (2005) Characterizing wildfire regimes in the United States. PNAS 102:4694–4699CrossRefGoogle Scholar
  17. Mignone B, Hurteau M, Chen Y, Sohngen B (2009) Carbon offsets, reversal risk and US climate policy. Carbon Bal and Manag 4:3CrossRefGoogle Scholar
  18. Millar CI, Stephenson NL, Stephens SL (2007) Climate change and forests of the future: Managing in the face of uncertainty. Ecol Appl 17:2145–2151CrossRefGoogle Scholar
  19. Murray BC, Olander LP (2008) Addressing impermanence risk and liability in agriculture, land use change, and forest carbon projects. Nicholas Institute For Environmental Policy Solutions, DurhamGoogle Scholar
  20. Romme WH, Despain DG (1989) Historical-perspective on the yellowstone fires of 1988. Bioscience 39:695–699CrossRefGoogle Scholar
  21. Routledge RD (1980) The effect of potential catastrophic mortality and other unpredictable events on optimal forest rotation policy. For Sci 26:389–399Google Scholar
  22. Seidl R, Rammer W, Jager D, Lexer MJ (2008) Impact of bark beetle (Ips typographus L.) disturbance on timber production and carbon sequestration in different management strategies under climate change. For Ecol Manag 256:209–220CrossRefGoogle Scholar
  23. Spring D, Kennedy J, Mac Nally R (2005) Optimal management of a flammable forest providing timber and carbon sequestration benefits: an Australian case study. Austr J Agri Res Econ 49:303–320CrossRefGoogle Scholar
  24. Strauss D, Bednar L, Mees R (1989) Do one percent of forest fires cause 99-percent of the damage. For Sci 35:319–328Google Scholar
  25. Subak S (2003) Replacing carbon lost from forests: an assessment of insurance, reserves, and expiring credits. Clim Pol 3:107–122Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • David M. Cooley
    • 1
  • Christopher S. Galik
    • 1
  • Thomas P. Holmes
    • 2
  • Carolyn Kousky
    • 3
  • Roger M. Cooke
    • 3
  1. 1.Nicholas Institute for Environmental Policy SolutionsDuke UniversityDurhamUSA
  2. 2.United States Forest ServiceSouthern Research StationResearch Triangle Park (RTP)USA
  3. 3.Resources for the FutureWashingtonUSA

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