A Note on the Fisher Ideal Index Decomposition for Structural Change in Energy Intensity

Abstract

Index numbers have been used to decompose aggregate trends in energy intensity, i.e., the ratio of energy use to activity. By making a direct appeal to the theory underlying price index numbers used by the energy decomposition literature, this note proposes the chain weighted Fisher Ideal Index as a formula that solves the 'residual problem.' The connection to index number theory also allows us to illustrate that the measures of activity used to define energy intensity need not be additive across the sectors that are involved in the decomposition. We give an empirical example using recent U.S. manufacturing data of the Fisher Ideal Index, compared to the Törnqvist Divisia index, a popular index in the energy literature.

Keywords

Energy intensity decomposition Fisher Ideal Index Manufacturing US

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References

Andreassen

Chentrens

(1999). “Employment Outlook: 1998-2008 Input-Output 192-Order Documentation”. Private communication

Ang

B. W.

Liu

(2001). “A new energy decomposition method: perfect in decomposition and consistent in aggregation.” Energy 26(6): 537-548.

Ang

B. W.

Zhang

(2000). “A survey of index decomposition analysis in energy and environmental studies.” Energy 25(12): 1149-1176.

Ang

B. W.

Choi

K. H.

(1997). “Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method.” The Energy Journal 18(3): 59-73.

Boyd

G. A.

Laitner

J. A.

(2001). “Recent Trends in the U.S. Energy Intensity: An Index Number Analysis.” International Association for Energy Economics Newsletter 2nd Quarter: 4-9.

Berndt

E. R.

Morrison

C. J.

Watkins

G. C.

(1981). “Dynamic Models of Energy Demand: An Assessment and Comparison.” In Berndt, E. R., and B. C. Field (Eds.) Modeling and Measuring Natural Resource Substitution. Cambridge: The MIT Press.

Boyd

G. A.

McDonald

J. F.

Ross

Hanson

. (1987). “Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach.” The Energy Journal 8(2): 77-96.

Diewert

W. E.

(1974). “Functional Forms for Revenue and Factor Requirements Functions,” International Economic Review 15(1):119-130.

Diewert

W. E.

(2001). The Consumer Price Index and Index Number Theory: A Survey. Vancouver, Canada, Department Of Economics, University Of British Columbia. Web address: http://www.econ.ubc.ca/discpapers/dp0102.pdf

10.

Energy Information Administration (1999). Annual Energy Review. U.S. Department of Energy. Washington, D.C.

11.

Greening

Davis

Schipper

Khrushch

(1997). “Comparison of six decomposition methods: Application to aggregate energy intensity for manufacturing in 10 OECD countries.” Energy Economics 19(3): 375-390.

12.

Lermit

Jonathan

Jollands

Nigel

(2001). Monitoring Energy Efficiency Performance in New Zealand: A Conceptual and Methodological Framework. Prepared for the Energy Efficiency and Conservation Authority. September. Web address: http://www.energywise.co.nz

13.

Myers

Nakamura

(1978). Saving Energy in Manufacturing. Cambridge, MA:: Ballinger.

14.

Padfield

Chris J.

(2001). “The Canadian Decomposition Experience: From 10 to 54 Industries”, paper presented at the 2001 ACEEE Summer Study on Energy Efficiency in Industry, Proceedings volume 1, July, pgs 621-630.

15.

Ross

Thimmapuram

(1993). Long-Term Industrial Energy Forecasting (LIEF) Model (18-Sector Version). Argonne National Laboratory. ANL/EAIS/TM-93

16.

Sun

J.W.

(1998a). “Accounting for energy use in China; 1984-94.” Energy 23(10): 835-949.

17.

Sun

J.W.

(1998b). “Changes in energy consumption and energy intensity: A complete decomposition model.” Energy Economics 20(1): 85-100.

18.

U.S. Department of Energy (DOE) (1989). Energy Conservation Trends: Understanding the Factors that Affect conservation Gains in the U. S. Economy. DOE/PE-0092. Washington, DC.