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, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
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.
Influence Diagrams, commonly used in decision analysis, are Bayesian Networks with decision nodes.
The 4 is for the opportunity cost.
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.
Baker, E. (2006). Increasing risk and increasing informativeness: Equivalence theorems. Operations Research, 54, 26–36.
Baker, E. (2009). Uncertainty and learning in climate change. Journal of Public Economic Theory, 11, 721–747.
Baker, E., & Adu-Bonnah, K. (2008). Investment in risky R&D programs in the face of climate uncertainty. Energy Economics, 30, 465–486.
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.
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.
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.
Baker, E., Clarke, L., & Weyant, J. (2006). Optimal technology R&D in the face of climate uncertainty. Climatic Change, 78, 157–180.
Baker, E., & Shittu, E. (2006). Profit-maximizing R&D in response to a random carbon tax. Resource and Energy Economics, 28, 160–180.
Baker, E., & Shittu, E. (2008). Uncertainty and endogenous technical change in climate policy models. Energy Economics, 30, 2817–2828.
Baker, E., & Solak, S. (2011). Climate change and optimal energy technology R&D policy. European Journal of Operations Research, 213, 442–454.
Bickel, J. E. (2008). The relationship between perfect and imperfect information in a two-action risk-sensitive problem. Decision Analysis, 3, 116–128.
Blanford, G. J. (2009). R&D investment strategy for climate change: A numerical study. Energy Economics, 31, S27–S36.
Blanford, G. J., & Weyant, J. P. (2007). Optimal investment portfolios for basic R&D. Working Paper, Stanford University.
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.
Bosetti, V., & Gilotte, L. (2007). The impact of carbon capture and storage on overall mitigation policy. Climate Policy, 7, 3–12.
Bosetti, V., & Tavoni, M. (2009). Uncertain R&D, backstop technology and GHGs stabilization. Energy Economics, 31, S18–S26.
Brenkert, A. S., Smith, S., Kim, S., & Pitcher, H. (2003). Model documentation for the MiniCAM. Technical Report PNNL-14337, Pacific Northwest National Laboratory.
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.
Clarke, L., Weyant, J., & Birky, A. (2006). On the sources of technological advance: assessing the evidence. Energy Economics, 28(5–6), 579–595.
Clarke, L., Weyant, J., & Edmonds, J. (2006). On the sources of technological advance: what do the models assume? Energy Economics, (in press).
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.
Clemen, R., & Winkler, R. (2002). Multiple experts vs. multiple methods: combining correlation assessments. Durham: Duke University.
Clemen, R. T., & Kwit, R. C. (2001). The value of decision analysis at Eastman Kodak Company, 1990–1999. Interfaces, 31, 74–92.
Clemen, R. T., & Winkler, R. L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis, 19, 187–203.
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.
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).
Farzin, Y. H., & Kort, P. M. (2000). Pollution abatement investment when environmental regulation is uncertain. Journal of Public Economic Theory, 2, 183–212.
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.
Goeschl, T., & Perino, G. (2009). On backstops and boomerangs: Environmental R&D under technological uncertainty. Energy Economics, 31(437), 800–809.
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.
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.
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.
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.
Keith, D. W. (1996). When is it appropriate to combine expert judgments? Climatic Change, 33, 139–143.
Linville, C. (1998). Mathematical and computational techniques for research prioritization with an application to global climate change research. Ph.D. thesis, Carnegie Mellon University.
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.
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.
Nordhaus, W. (2008). A question of balance: Weighing the options on global warming policies. Connecticut: Yale University Press.
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.
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.
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.
Peng, Y. (2010). A stochastic R&D portfolio model under climate uncertainty. Master’s thesis, University of Massachusetts Amherst
Pizer, W. A., & Popp, D. (2008). Endogenizing technological change: matching empirical evidence to modeling needs. Energy Economics, 30, 2754–2770.
Popp, D. (2006). ENTICE-BR: The effects of backstop technology R&D on climate policy models. Energy Economics, 28, 188–222.
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.
Rothschild, M., & Stiglitz, J. (1970). Increasing risk I: A definition. Journal of Economic Theory, 2, 225–243.
Schlaifer, R. (1959). Probability and statistics for business decisions. New York: McGraw-Hill.
Schorpp, G. (2009). Optimal energy R&D decision making under climate change uncertainty. Master’s thesis, University of Massachusetts Amherst
Sharpe, P., & Keelin, T. (1998). How smithkline beecham makes better resource-allocation decisions. Harvard Business Review, 76, 45–57.
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.
Wing, I. S. (2006). Representing induced technological change in models for climate policy analysis. Energy Economics, 28, 539–562.
Viscusi, K. (1983). Frameworks for analyzing the effects of risk and environmental regulations on productivity. American Economic Journal, 73, 793–801.
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.
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
- Value of information
- Technology R&D
- Environmental policy