Comparing World Economic and Net Energy Metrics, Part 1: Single Technology and Commodity Perspective

Energies

Carey W. King¹  Alyssa Donovan²  John P. Maxwell³ 

[1] Energy Institute, the University of Texas at Austin, 2304 Whitis Ave., C2400, Austin, TX 78712, USA;Jackson School of Geosciences, the University of Texas at Austin, 2275 Speedway, C9000, Austin, TX 78712, USA;Senate Fiscal Agency, P.O. Box 30036 Lansing, MI 48909-7536, USA;

关键词: energy;    net energy;    price;    cost;    economics;    life cycle assessment;   

DOI  :  10.3390/en81112346

来源: DOAJ

【 摘 要 】

We translate between biophysical and economic metrics that characterize the role of energy in the economy. Specifically, using data from the International Energy Agency, we estimate the energy intensity ratio (EIR), a price-based proxy for a power return ratio (PRR∼P out/P invested). The EIR is a useful metric, because for most countries and energy commodities, it can indicate the biophysical trends of net energy when data are too scarce to perform an original net energy analysis. We calculate EIR for natural gas, coal, petroleum and electricity for forty-four countries from 1978 to 2010. Global EIR values generally rise from 1978 to 1998, decline from 1998 to 2008 and then slightly rebound. These trends indicate one interpretation of the net energy of the world economy. To add perspective to our recent, but short, time series, we perform the same calculations for historical England and United Kingdom energy prices to demonstrate that a given energy price translates to different PRRs (EIR in this case) depending on the structure of the economy and technology. We review the formulation of PRRs and energy return ratios (ERR∼E out/E invested) to indicate why PRRs translate to (the inverse of) energy prices and ERRs translate to (the inverse of) energy costs. We show why for any given value of an ERR or PRR, there is not a single corresponding energy cost or price, and vice versa. These principles in turn provide the basis to perform better modeling of future energy scenarios (e.g., low-carbon transition) by considering the relationship between economic metrics (cost and price) and biophysical metrics (energy and power return ratios) based on energy, material and power flows.

【 授权许可】

Unknown