As discussed in the last post in this series, the energy return on energy invested in renewable sources of energy will be a critical measure of whether it is possible to transition on a large scale from a fossil-fuel powered economy, or whether a global "powerdown" is eventually inevitable. If EROEI, the net-energy ratio of a renewable energy source, is high--say 40:1--then it should be possible to rapidly transition our fossil-fuel driven economy to a renewable energy base, and to support ongoing economic growth that requires ever more energy. If this ratio, however, is low--say 4:1--then at a minimum a transition to renewable energy will be extremely challenging, and may be effectively impossible. As a result, the actual EROEI value of the various renewable energy options available to us is plainly critical. There are lots of measures, lots of studies, and lots of figures floated about for the EROEI value of solar photovoltaics, wind turbines, etc. There is not, however, a universally accepted methodology for calculating EROEI. In fact, I don't think it's a stretch to say that EROEI figures are more likely to be marketing copy intended to secure venture capital than the result of rigorous inquiry.
In my opinion, understanding the reality of our society's ability to transition to a renewable energy basis for our economy is one of, if not THE most important issue to be resolved. If this transition is a realistic possibility, then it should be our society's primary and immediate focus. In addition, improving our understanding of just how realistic such a societal transition is will help us understand the necessary rate of investment in renewables, as well as the nature and degree of the challenges to be accomplished. If it is not realistic, then we must not waste what little surplus energy we have on a fools errand. In addition, the present understanding that such a transition is unrealistic will allow us to both develop and focus on those societal options that are realistic. Given the importance of accurate EROEI calculations, this post will discuss the current methodology issues with EROEI calculation and make recommendations for proceeding.
There are two generally used methods for calculating EROEI: process-analysis and input-output analysis. Both basically boil down to a brute-force accounting of energy used in various component processes of producing a renewable energy source, with the key differences being how wide a net is cast in counting energy inputs. For example, is the diesel fuel required to deliver the turbine blades to the installation site accounted for? What about the energy required to build the truck, divided by the percentage of that truck's useful life used in that delivery? What about fraction of the energy required to build the machine tools used in the manufacture of that truck?
This highlights the problem main problem with current system boundary calculations: you can regress these energy inputs infinitely far (e.g. what about the energy used to grow the rice eaten by the merchant marine captain who piloted the ship that delivered the metal ores used in manufacturing the bolts that hold together the turbine tower), and it's fundamentally impossible to use a brute-force accounting methodology to account for all energy inputs. If one hopes to use such a brute force approach (as used in both process-analysis and input-output analysis methods of EROEI calculation), then one must draw an artificial boundary for what is counted, and what is not. Is it acceptable to artificially constrain the accounted system? Clearly any artificial system boundary results in an artificially high EROEI, but how artificially high? Does this long-tail of non-accounted-for system inputs make the resulting EROEI figure 1% too high? 10%? 100%? 10 times too high? It's easy to dismiss, but how do we know if we are completely ignoring these long-tail energy inputs? I think there's great cause for concern that our EROEI is significantly over-estimated. For example, in a paper by Prof. Cutler Cleveland and others, the EROEI of wind-power is assessed by looking at over 100 separate EROEI studies. These studies are broken down into process-analysis and input-output methodologies. Prof. Cleveland notes that process-analysis generally draws a tighter system boundary than input-output analysis--that is, it counts fewer inputs. In that survey, the process-analysis EROEI measurements for wind average 24:1, and the input-output measurements average 12:1. That's a 100% difference based on where the artificial system boundary is drawn. In light of that significant difference, how can we be sure that a truly inclusive system boundary wouldn't result in a further 100% (or more) decrease in the measured EROEI? The take-away here is that we simply can't trust the accuracy of currently available EROEI calculations. Further, it seems unreasonable to place any credence in any brute-force (e.g process-analysis or input-output analysis) approach to EROEI calculation.
How can we get around the accounting difficulties and arrive at an accurate EROEI calculation--a calculation that can do more than just provide a comparison between renewables, and can actually provide a self-contained assessment of whether a given technology can facilitate a societal energy-transition? Odum has proposed what he calls an "Emergy" measurement that intends to account for a true EROEI measurement. However, while Odum recognizes the importance of an inclusive calculation, Odum's methodology does nothing to address these accounting issues, and the end result is still a brute-force estimate that suffers from the same methodological failings as traditional EROEI calculations (even if it tends to arrive at lower EROEI figures).
Rather than a brute-force approach that literally attempts to count all the energy inputs, I think it will be necessary to use a proxy to calculate "true" EROEI. One methodology that I've proposed for this task is to use price as a proxy for EROEI. I'll discuss briefly the theory of how this would work, as well as the clear problems with this approach.
It always struck me as fishy that various EROEI claims (especially for wind) result in an energy payback time of less than a year. In other words, these figures suggest that it would only take a few months to pay back all the energy required to build a wind turbine, and then that wind turbine would go on generating electricity for decades more. Why, then, didn't we already transition the vast majority of our energy base to wind if it's so efficient? The answer is that the financial payback isn't nearly so rosy. What accounts for the difference between the rapid energy payback (only months) and the much longer financial payback (often an order of magnitude or more longer)? Intuitively, it seems that at least part of the answer is that the EROEI wasn't accounting for many inputs that were counted in the financial analysis. For example, the financial analysis accounted for the high salaries--derivatives of the long years of training--that must be paid to the engineers, the financiers, the technicians, the managers, the materials scientists, etc. that are involved in the production of a wind turbine. These long years of education certainly represent an energy input, but aren't accounted for in either process-analysis or input-output analysis EROEI calculations. Similarly, the cost of raw materials represents, at least in theory, the full spectrum of energy, machinery, personnel, and support systems needed to extract, refine, transport, and market it--a great deal of which lies outside the traditional artificial system boundaries drawn in traditional EROEI calculations. It seemed to me that the financial cost of a renewable was a better proxy for the energy inputs to that renewable than were any of the accepted EROEI calculation methodologies. This is the core of what I've called "price-estimated EROEI," which uses financial cost as a proxy for energy cost. The basic calculation assumes that the entire cost of a renewable is made up--eventually, if one regresses far enough--by energy, so divides that cost by an average energy cost to arrive at the energy input, and then compares that as a ratio to the amount of energy the renewable will produce over its lifetime. Not surprisingly, this form of calculation tends to produce a far lower EROEI than any of the accepted EROEI methodologies.
Of course, there are acknowledged flaws with this price-estimated EROEI methodology. Just to name a few, it's difficult to account for the differing values of the various types of input energies and the resulting output energy; there are market distortions, tax-incentive distortions, geopolitical distortions, etc. That said, I think this type of proxy calculation at least directly addresses the need to calculate a truly inclusive EROEI, and may well be much closer to the "truth" of the required energy inputs than any traditional methodology.
In the next two post I'll address two other potential methods for measuring "true" EROEI: asymptote location and worker-year calculation (as suggested by Neil Howes). Then, I'll look at the EROEI of wind power and solar power from both traditional and proxy methods of calculation.