Skip to main content

Advertisement

SpringerLink
Log in

The Value of Better Information on Technology R&D Programs in Response to Climate Change

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

Expert elicitations are a promising method for determining how R&D investments are likely to have an impact on technological advance in climate change energy technologies. But, expert elicitations are time consuming and resource intensive. Thus, we investigate the value of the information gained in expert elicitations. More specifically, given baseline elicitations from one study, we estimate the expected value of better information (EVBI) from revisiting and improving these assessments. We find that the EVBI is very large in comparison with the cost of performing expert elicitations. We also find that EVBI is higher on technologies with larger budgets and with net values that are not too high or too low.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Note that we do not address the initial value of information gained from the original elicitations. This is for two reasons. First, since the elicitations have already been done, it would be an odd exercise requiring us to pretend that we did not know the outcome of the elicitations. Second, it is very hard to determine how one would go about deriving an a priori probability distribution without performing some form of elicitation.

  2. http://www.icarus-project.org/

  3. http://belfercenter.ksg.harvard.edu/project/10/energy_technology_innovation_policy.html?page_id=213

  4. http://cdmc.epp.cmu.edu/

  5. Influence Diagrams, commonly used in decision analysis, are Bayesian Networks with decision nodes.

  6. More detailed discussions of our methods and assumptions on related technologies are included in [5–7].

  7. The 4 is for the opportunity cost.

References

  1. Ambrosi, P., Hourcade, J.-C., Hallegatte, S., Lecocq, F., Dumas, P., & Duong, M. H. (2010). Optimal control models and elicitation of attitudes towards climate damages. In J. A. Filar, & A. Haurie (Eds.), Uncertainty and environmental decision making (pp. 177–209). New York: Springer.

    Google Scholar 

  2. Baker, E. (2006). Increasing risk and increasing informativeness: Equivalence theorems. Operations Research, 54, 26–36.

    Article  Google Scholar 

  3. Baker, E. (2009). Uncertainty and learning in climate change. Journal of Public Economic Theory, 11, 721–747.

    Article  Google Scholar 

  4. Baker, E., & Adu-Bonnah, K. (2008). Investment in risky R&D programs in the face of climate uncertainty. Energy Economics, 30, 465–486.

    Article  Google Scholar 

  5. Baker, E., Chon, H., & Keisler, J. (2008). Advanced Nuclear Power: Combining expert elicitations with economic analysis to inform climate policy. Available at SSRN: http://ssrn.com/abstract=1407048.

  6. Baker, E., Chon, H., & Keisler, J. (2009). Advanced solar R&D: Combining economic analysis with expert elicitations to inform climate policy. Energy Economics, 31, S37–S49.

    Article  Google Scholar 

  7. Baker, E., Chon, H., & Keisler, J. (2009). Carbon capture and storage: combining expert elicitations with economic analysis to inform climate policy. Climatic Change, 96(3), 379.

    Article  CAS  Google Scholar 

  8. Baker, E., Clarke, L., & Weyant, J. (2006). Optimal technology R&D in the face of climate uncertainty. Climatic Change, 78, 157–180.

    Article  CAS  Google Scholar 

  9. Baker, E., & Shittu, E. (2006). Profit-maximizing R&D in response to a random carbon tax. Resource and Energy Economics, 28, 160–180.

    Article  Google Scholar 

  10. Baker, E., & Shittu, E. (2008). Uncertainty and endogenous technical change in climate policy models. Energy Economics, 30, 2817–2828.

    Article  Google Scholar 

  11. Baker, E., & Solak, S. (2011). Climate change and optimal energy technology R&D policy. European Journal of Operations Research, 213, 442–454.

    Article  Google Scholar 

  12. Bickel, J. E. (2008). The relationship between perfect and imperfect information in a two-action risk-sensitive problem. Decision Analysis, 3, 116–128.

    Article  Google Scholar 

  13. Blanford, G. J. (2009). R&D investment strategy for climate change: A numerical study. Energy Economics, 31, S27–S36.

    Article  Google Scholar 

  14. Blanford, G. J., & Weyant, J. P. (2007). Optimal investment portfolios for basic R&D. Working Paper, Stanford University.

  15. Bosetti, V., & Drouet, L. (2004). Accounting for uncertainty affecting technical change in an economic-climate model. Technical Report FEEM Working Paper 147, Fondazione Eni Enrico Mattei, Milan.

  16. Bosetti, V., & Gilotte, L. (2007). The impact of carbon capture and storage on overall mitigation policy. Climate Policy, 7, 3–12.

    Article  Google Scholar 

  17. Bosetti, V., & Tavoni, M. (2009). Uncertain R&D, backstop technology and GHGs stabilization. Energy Economics, 31, S18–S26.

    Article  Google Scholar 

  18. Brenkert, A. S., Smith, S., Kim, S., & Pitcher, H. (2003). Model documentation for the MiniCAM. Technical Report PNNL-14337, Pacific Northwest National Laboratory.

  19. Clarke, L., Kyle, P., Wise, M. A., Calvin, K., Edmonds, J. A., Kim, S. H., et al. (2008). CO2 emissions mitigation and technological advance: an updated analysis of advanced technology scenarios. Technical Report PNNL-18075, Pacific Northwest National Laboratory.

  20. Clarke, L., Weyant, J., & Birky, A. (2006). On the sources of technological advance: assessing the evidence. Energy Economics, 28(5–6), 579–595.

    Article  Google Scholar 

  21. Clarke, L., Weyant, J., & Edmonds, J. (2006). On the sources of technological advance: what do the models assume? Energy Economics, (in press).

  22. Clarke, L. E., & Weyant, J. P. (2002). Modeling induced technological change: An overview. In A. Grubler, N. Nakicenovic, & W. D. Nordhaus (Eds.), Technological change and the environment. Washington, DC: Resources for the Future.

    Google Scholar 

  23. Clemen, R., & Winkler, R. (2002). Multiple experts vs. multiple methods: combining correlation assessments. Durham: Duke University.

    Google Scholar 

  24. Clemen, R. T., & Kwit, R. C. (2001). The value of decision analysis at Eastman Kodak Company, 1990–1999. Interfaces, 31, 74–92.

    Google Scholar 

  25. Clemen, R. T., & Winkler, R. L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis, 19, 187–203.

    Google Scholar 

  26. Cooke, R. M., & Probst, K. N. (2006). Highlights of the expert judgment policy symposium and technical workshop. Technical Report Conference Summary, Resources for the Future.

  27. Edmonds, J. A., Clarke, J. F., Dooley, J. J., Kim, S. H., & Smith, S. J. (2004). Stabilization of CO2 in a B2 world: insights on the roles of carbon capture and storage, hydrogen, and transportation technologies. In J. Weyant, & R. Tol (Eds.), Special issue, Energy Economics (Vol 26(4), pp. 517–537).

  28. Farzin, Y. H., & Kort, P. M. (2000). Pollution abatement investment when environmental regulation is uncertain. Journal of Public Economic Theory, 2, 183–212.

    Article  Google Scholar 

  29. Gillingham, K., Newell, R., & Pizer, W. (2007). Modeling endogenous technological change for climate policy analysis. RFF Discussion Paper 07-14. Washington, DC: Resources For the Future.

  30. Goeschl, T., & Perino, G. (2009). On backstops and boomerangs: Environmental R&D under technological uncertainty. Energy Economics, 31(437), 800–809.

    Article  Google Scholar 

  31. Gritsevskyi, A., & Nakicenovic, N. (2002). Modeling uncertainty of induced technological change. In A. Grubler, N. Nakicenovic, & W. D. Nordhaus (Eds.), Technological change and the environment (pp. 251–279). Washington, DC: RFF.

    Google Scholar 

  32. Grubb, M., Kohler, J., & Anderson, D. (2002). Induced technical change in energy and environmental modeling: Analytic approaches and policy implications. Annual Review of Energy and the Environment, 27, 271–308.

    Article  Google Scholar 

  33. Grubler, A., & Gritsevskyi, A. (2002). A model of endogenous technological change through uncertain returns on innovation. In A. Grubler, N. Nakicenovic, & W. D. Nordhaus (Eds.), Technological change and the environment (pp. 280–319). Washington, DC: RFF.

    Google Scholar 

  34. Kanudia, A., & Loulou, R. (1998). Robust responses to climate change via stochastic MARKAL: the case of Quebec. European Journal of Operations Research, 106, 15–30.

    Article  Google Scholar 

  35. Keith, D. W. (1996). When is it appropriate to combine expert judgments? Climatic Change, 33, 139–143.

    Article  Google Scholar 

  36. Linville, C. (1998). Mathematical and computational techniques for research prioritization with an application to global climate change research. Ph.D. thesis, Carnegie Mellon University.

  37. Loschel, A. (2004). Technological change, energy consumption, and the costs of environmental policy in energy-economy-environment modeling. International Journal of Energy Technology and Policy, 2(3), 250–261.

    Article  Google Scholar 

  38. National Research Council (2007). Prospective evaluation of applied energy research and development at DOE (phase two). Washington: The National Academies Press. http://www.nap.edu/catalog/11806.html.

  39. Nordhaus, W. (2008). A question of balance: Weighing the options on global warming policies. Connecticut: Yale University Press.

    Google Scholar 

  40. Nordhaus, W. D. (2002). Modeling induced innovation in climate change policy. In A. Grubler, N. Nakicenovic, & W. D. Nordhaus (Eds.), Technological change and the environment (pp. 182–209). Washington: RFF/IIASA.

    Google Scholar 

  41. Nordhaus, W. D., & Popp, D. (1997). What is the value of scientific knowledge? An application to global warming using the PRICE model. The Energy Journal, 18, 1–45.

    Article  Google Scholar 

  42. Peerenboom, J. P., Buehring, W. A., & Joseph, T. W. (1989). Selecting a portfolio of environmental programs for a synthetic fuels facility. Operations Research, 37, 689–699.

    Article  Google Scholar 

  43. Peng, Y. (2010). A stochastic R&D portfolio model under climate uncertainty. Master’s thesis, University of Massachusetts Amherst

  44. Pizer, W. A., & Popp, D. (2008). Endogenizing technological change: matching empirical evidence to modeling needs. Energy Economics, 30, 2754–2770.

    Article  Google Scholar 

  45. Popp, D. (2006). ENTICE-BR: The effects of backstop technology R&D on climate policy models. Energy Economics, 28, 188–222.

    Article  Google Scholar 

  46. Rao, A. B., Rubin, E. S., Keith, D. W., & Morgan, M. G. (2006). Evaluation of potential cost reductions from improved amine-based CO2 capture systems. Energy Policy, 34, 3765–3772.

    Article  Google Scholar 

  47. Rothschild, M., & Stiglitz, J. (1970). Increasing risk I: A definition. Journal of Economic Theory, 2, 225–243.

    Article  Google Scholar 

  48. Schlaifer, R. (1959). Probability and statistics for business decisions. New York: McGraw-Hill.

    Google Scholar 

  49. Schorpp, G. (2009). Optimal energy R&D decision making under climate change uncertainty. Master’s thesis, University of Massachusetts Amherst

  50. Sharpe, P., & Keelin, T. (1998). How smithkline beecham makes better resource-allocation decisions. Harvard Business Review, 76, 45–57.

    CAS  Google Scholar 

  51. Titus, J. G., & Narayanan, V. (1996). A delphic monte carlo analysis in which twenty researchers specify subjective probability distributions for model coefficients within their respective areas of expertise. Climatic Change, 33, 151–212.

    Article  CAS  Google Scholar 

  52. Wing, I. S. (2006). Representing induced technological change in models for climate policy analysis. Energy Economics, 28, 539–562.

    Article  Google Scholar 

  53. Viscusi, K. (1983). Frameworks for analyzing the effects of risk and environmental regulations on productivity. American Economic Journal, 73, 793–801.

    Google Scholar 

Download references

Acknowledgements

The research leading to these results was completed while Baker was visiting the Precourt Energy Efficiency Center at Stanford University and was partially supported by NSF under award number SES-0745161 and by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement n° 240895—Project ICARUS “Innovation for Climate Change Mitigation: a Study of energy R&D, its Uncertain Effectiveness and Spillovers”. We thank Haewon McJeon for providing the GCAM results.

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Massachusetts, 220 Elab, Amherst, MA, 01003, USA

    Erin Baker & Yiming Peng

Authors
  1. Erin Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiming Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erin Baker.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Baker, E., Peng, Y. The Value of Better Information on Technology R&D Programs in Response to Climate Change. Environ Model Assess 17, 107–121 (2012). https://doi.org/10.1007/s10666-011-9278-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-011-9278-y

Keywords

Search

Navigation