Incorporating Investment Uncertainty into Greenhouse Policy Models

Abstract

Greenhouse gas policy decisions require comprehensive understanding of atmospheric, economic, and social impacts. Many studies have considered the effects of atmospheric uncertainty in global warming, but economic uncertainties, have received Less analysis. We consider a key component of economic uncertainty: the return on investments in new technologies. Using a mathematical! programming model, we show that ignoring uncertainty in technology investment policy may lead to decreases as great as 2 percent in overall expected economic activity in the U.S. with even higher losses in possible future scenarios. These results indicate that both federal and private technology investment policies should be based on models explicitly incorporating uncertainty.

Keywords

Greenhouse gas policy uncertainty investment technology change stochastic model

Get full access to this article

View all access options for this article.

References

Ausubel

J.H.

(1991). “Does Climate Still Matter?” Nature 350: 649-652.

Benders

J.F.

(1962). “Partitioning Procedures for Solving Mixed-Variables Programming Problems.” Numerische Mathematik 4: 238-252.

Birge

J.R.

(1982). “The Value of the Stochastic Solution in Stochastic Linear Programs with Fixed Recourse.” Mathematical Programming 24: 314-325.

Birge

J.R.

(1985). “Decomposition and Partitioning Methods for Multi-Stage Stochastic Linear Programs.” Operations Research 33: 989-1007.

Birge

J.R.

(1988). “Exhaustible Recourse Models with Uncertain Returns from Exploration Investment.” In Numerical Techniques for Stochastic Optimization, Y. Ermoliev, R. Wets, eds., Berlin: Springer, pp. 481-488.

Birge

J.R.

Rosa

C.H.

(1996). “Parallel Decomposition of Large-Scale Stochastic Nonlinear Programs.” Annals of Operations Research, forthcoming.

Birge

J.R.

J-B Wets

(1986). “Designing Approximation Schemes for Stochastic Optimization Problems, in particular, for Stochastic Programs with Recourse.” Mathematical Programming Study 27: 54-102.

Chao

H.P.

(1981). “Exhaustible Resource Models: the Value of Information.” Operations Research 29: 903-923.

Dantzig

G. B.

Madansky

(1961). “On the Solution of Two-Stage Linear Programs under Uncertainty.” In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, J. Neyman, ed., Berkeley: University of California Press, pp. 165-176.

10.

Louveaux

F.V.

(1980). “A Solution Method for Multistage Stochastic Programs with Recourse with Application to an Energy Investment Problem.” Operations Research 28: 889-902.

11.

Manne

A.S.

Richels

R.G.

(1991). “Global CO₂ Emission Reductions â€” the Impacts of Rising Energy Costs.” The Energy Journal 12(1): 87-107.

12.

Manne

A.S.

Richels

R.G.

(1992). Buying Greenhouse Insurance: the Economic Costs of CO₂ Emission Limits. Cambridge: MIT Press.

13.

Noël

M.C.

Smeers

(1987). “Nested Decomposition of Multistage Nonlinear Programs with Recourse.” Mathematical Programming 37: 131-152.

14.

Nordhaus

W.D.

(1992). “An Optimal Transition Path for Controlling Greenhouse Gases.” Science 258: 1315-1319.

15.

Nordhaus

W.D.

(1994). Managing the Global Commons: the Economics of Climate Change. Cambridge: MIT Press.

16.

O’Neill

R.P.

(1976). “Nested Decomposition of Multistage Convex Programs.” SIAM Journal on Control and Optimization 14: 409-418.

17.

Peck

S.C.

Teisberg

T.J.

(1993). “Global Warming Uncertainties and the Value of Information: An Analysis using CETA.” Resource and Energy Economics 15: 71-97.

18.

Raiffa

Schlaifer

(1961). Applied Statistical Decision Theory. Boston: Harvard Business School.

19.

Schlesinger

M.E.

(1993). “Greenhouse Policy.” Research and Exploration 9: 159-172.

20.

Van Slyke

J-B Wets

(1969). “Lâ€”shaped Linear Programs with Application to Optimal Control and Stochastic Programming.” SIAM Journal on Applied Mathematics 17: 638-663.

21.

J-B Wets

(1980). “Stochastic Multipliers, Induced Feasibility and Nonanticipativity in Stochastic Programming.” In Stochastic Programming, M.A.H. Dempster, ed., New York: Academic Press, chap. 8, pp. 137-146.