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Climatic Change

, Volume 137, Issue 1–2, pp 15–27 | Cite as

The downside risk of climate change in California’s Central Valley agricultural sector

  • Michael Hanemann
  • Susan Stratton Sayre
  • Larry DaleEmail author
Article

Abstract

Downscaled climate change projections for California, when translated into changes in irrigation water delivery and then into profit from agriculture in the Central Valley, show an increase in conventional measures of variability such as the variance. However, these increases are modest and mask a more pronounced increase in downside risk, defined as the probability of unfavorable outcomes of water supply or profit. This paper describes the concept of downside risk and measures it as it applies to outcomes for Central Valley agriculture projected under four climate change scenarios. We compare the effect of downside risk aversion versus conventional risk aversion or risk neutrality when assessing the impact of climate change on the profitability of Central Valley agriculture. We find that, when downside risk is considered, the assessment of losses due to climate change increases substantially.

Keywords

Risk Aversion Online Appendix Water Delivery Downside Risk Certainty Equivalent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Dr. Hanemann’s research was supported by NSF Award 1204774 to Arizona State University. Our research relies on modeling results provided to us by California Department of Water Resources and CVPM computer code provided by Steven Hatchett. We thank Sydny Fujita and Nathaniel Bush for research assistance. This draft has benefitted from the helpful suggestions of several anonymous reviewers and the associate editor. Any remaining errors are our own.

Supplementary material

10584_2016_1651_MOESM1_ESM.pdf (155 kb)
ESM 1 (PDF 155 kb)
10584_2016_1651_MOESM2_ESM.pdf (120 kb)
ESM 2 (PDF 119 kb)

References

  1. Bawa VS (1975) Optimal rules for ordering uncertain prospects. J Financ Econ 2:95–121CrossRefGoogle Scholar
  2. BECS (Berkeley Economic Consulting). November 2008a. Economic impacts in the Central Valley. Prepared for the San Luis Delta Mendota Water AuthorityGoogle Scholar
  3. BECS (Berkeley Economic Consulting). December 2008b. Economic impacts of the wanger interim order for delta smeltGoogle Scholar
  4. Cayan D, Tyree M, Dettinger M, Hidalgo M, Das T, Maurer E, Bromirski P, Graham N, Flick R. 2009. Climate change scenarios and sea level rise estimates for the California 2009 climate change scenarios Assessment. California Energy Commission, Sacramento, CEC-500-2009-014-F.Google Scholar
  5. CDWR (California Department of Water Resources). 2008. Economic analysis guidebook Google Scholar
  6. Chung F, Anderson J, Arora S et al. 2009. Using future climate projections to support water resources decision making in California. California Energy Commission, Sacramento. CEC-500-2009-052-FGoogle Scholar
  7. Cline WR (1992) The Economics of Global Warming. Peterson Institute for International Economics, Washington, DCGoogle Scholar
  8. Connell-Buck C, Medellín-Azuara J, Lund J, Madani K (2011) Adapting California’s water system to warm vs. dry climates. Clim Chang 109(Suppl 1):133–149CrossRefGoogle Scholar
  9. Dale LL, Dogrul EC, Brush CF, Kadir TN, Chung FI, Miller NL, Vicuna SD (2013) Simulating the impact of drought on California's Central Valley hydrology, groundwater and cropping. Br J Environ Clim Chang 3(3):271CrossRefGoogle Scholar
  10. Draper AJ, Munévar A, Arora SK, Reyes E, Parker NL, Chung FI, Peterson LE (2004). CalSim: generalized model for reservoir system analysis. J Water Resour Plan Manag 130(6):480–489Google Scholar
  11. Ebert U (2005) Measures of Downside risk. Econ Bull 4(16):1–9Google Scholar
  12. Fishburn PC (1977) Mean-risk analysis with risk associated with below-Target returns. Am Econ Rev 67(2):116–126Google Scholar
  13. Franco G, Cayan DR, Moser S, Hanemann M, Jones MA (2011) Second California assessment: integrated climate change impacts assessment of natural and managed systems. guest editorial. Clim Chang 109(Supp 1):1–19CrossRefGoogle Scholar
  14. Hatchett, S. 1997. Description of CVPM. In Central Valley Project Improvement Act Programmatic Environmental Impact Statement, chap. II, pp. II-1–II-23, U.S. Bur. of Reclam., SacramentoGoogle Scholar
  15. Holthausen DM (1981) A risk-return model with risk and return measured as deviations from a target return. Am Econ Rev 71(1):182–188Google Scholar
  16. Howitt RE (1995) Positive mathematical programming. Am J Agric Econ 77:329–342CrossRefGoogle Scholar
  17. IPCC 2007. Climate change 2007: the physical science basis. Contribution to the fourth assessment report by working group I. Cambridge University Press, Cambridge, UK, New York, NY. www.ipcc.ch/ipccreports/ar4-wg1.htm.IPCC 2007Google Scholar
  18. Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica 47:263–291CrossRefGoogle Scholar
  19. Kiparsky (2010) Risk analysis for Water resources under climate change, population growth, and land use change. University of California Berkeley, PhD DissertationGoogle Scholar
  20. Lanzillotti RF (1958) Pricing objectives in large companies. Am Econ Rev 48(5):921–940Google Scholar
  21. Liang X, Wood EF, Lettenmaier DP (1996) Surface soil moisture parametrization of the VIC-21 model: evaluation and modification. Glob Planet Change 13(1–4):195–206CrossRefGoogle Scholar
  22. Mao JC (1970) Models of capital budgeting, EV vs ES. J Financ Quant Anal 4(05):657–675CrossRefGoogle Scholar
  23. Markowitz HM (1952) The utility of wealth. J Polit Econ 60:151–158CrossRefGoogle Scholar
  24. Markowitz HM (1959) Portfolio Selection: Efficient Diversification of Investments. University Press, Yale New Haven ConnecticutGoogle Scholar
  25. Masson RT (1974) Utility functions with jump discontinuities: some evidence and implications from peasant agriculture. Econ Inq 12(4):559–566CrossRefGoogle Scholar
  26. Maurer EP, Hidalgo HG (2008) “Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods.”. Hydrol Earth Syst Sci 12:551–563CrossRefGoogle Scholar
  27. Mearns LO, Rosenzweig C, Goldberg R (1992) Effect of changes in interannual climatic variability on CERES-wheat yields: sensitivity and 2× CO2 general circulation model studies. Agric for Meteorol 62(3):159–189CrossRefGoogle Scholar
  28. Medellín-Azuara J, Howitt RE, MacEwan DJ, Lund JR (2011) Economic impacts of climate-related changes to California agriculture. Clim Chang 109(1):387–405CrossRefGoogle Scholar
  29. Menezes C, Geiss C, Tressler J (1980) Increasing downside risk. Am Econ Rev 70(5):921–932Google Scholar
  30. Rayner S, Lach D, Ingram H (2005) Weather forecasts are for wimps: why water resource managers do not use climate forecasts. Clim Chang 69(2–3):197–227CrossRefGoogle Scholar
  31. Roy AD (1952) Safety first and the holding of assets. Econometrica 20:431–449CrossRefGoogle Scholar
  32. Tobin, J. 1958. “Liquidity preference as behavior towards risk.” Rev Econ Stud February 25:65–85.Google Scholar
  33. Tversky A, Kahneman D (1991) Loss aversion in riskless choice: A reference dependent model. Q J Econ 106:1039–1061CrossRefGoogle Scholar
  34. Waud RN (1976) Asymmetric policymaker utility functions and optimal policy under uncertainty. Econometrica: J Econ Soc 44:53–66CrossRefGoogle Scholar
  35. Zakamouline V (2014) Portfolio performance evaluation with loss aversion. Quant Finan 14(4):699–710CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Michael Hanemann
    • 1
  • Susan Stratton Sayre
    • 2
  • Larry Dale
    • 3
    Email author
  1. 1.Arizona State UniversityTempeUSA
  2. 2.Smith CollegeNorthamptonUSA
  3. 3.Lawrence Berkeley National LaboratoryBerkeleyUSA

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