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Net Energy Of Ethanol Minimal: Study

By David Murphy

28 July, 2010
The Oil Drum: Net Energy

New perspectives on the energy return on (energy) investment (EROI) of corn ethanol: Part 1 of 2

The following is the first of two posts based on a recent paper published under the same title in the journal Environment, Development, and Sustainability. The paper is divided into five sections, and to keep each post succinct, we have divided the paper into two posts. The first post will present the first two sections of the research and the second post will present the last three sections and the conclusions of the research.

Fig. 2. Map of the EROI of corn ethanol production for counties within states that produced at least 1% of the corn harvest in 2005, and biorefinery locations.


Over the past decade there has been considerable debate on corn ethanol, most focused on whether it is a net energy yielder. The argument is generally that “if the Energy Return on Investment (EROI) of corn ethanol is positive then it should be pursued. On one side are Pimentel (2003) and Patzek (2004) who claim that corn ethanol has an EROI below one energy unit returned per energy unit invested, and on the other side are a number of studies claiming that the EROI is positive, reported variously as between 1.08 and 1.45 (Wang et al. 1997; Wang 2001; Shapouri et al. 2002; Graboski 2004; Shapouri 2004; Oliveira et al. 2005; Farrell et al. 2006; Wang et al. 2007). Even with numerous publications on this issue, disagreement remains as to whether corn ethanol is a net energy yielder.

We believe that focus within the literature on whether or not corn ethanol yields a positive net energy gain has diverted attention from more fundamental issues. The following is a brief description of some of these issues and how we addressed them in this research.

First, none of the major studies of the EROI of corn ethanol account for statistical error within their analysis. Error is associated with all measurements, and we should expect there to be error associated with EROI as well. Yet each of Farrell et al. (2006), Wang et al. (2007), Patzek (2004), Pimentel (2003), and Shapouri et al. (2002) fail to report even general error statistics associated with their calculation of EROI. Considering that the range of published values for the EROI of corn ethanol is so small (from 0.8 to 1.5) one would expect that even a relatively small amount of error could be meaningful. In response to these concerns, we performed an error analysis for the calculation of the EROI of corn ethanol.

Second, most analyses to date, including those referenced above, use optimal (i.e. Iowa) values for corn yield, fertilizer, and irrigation, despite the fact that each of these have large geographical (as well as other) variation. Because of this they fail to represent the variable nature of corn production across space, and by extension the subsequent variability in the EROI of corn ethanol. Our spatial analysis addressed this issue by examining the impacts of the natural geographic variability of corn inputs and yields on the EROI of corn ethanol production within the U.S.


We performed four major analyses in this research. The first was a meta-error analysis, in which we quantified the error associated with the calculation of EROI of corn ethanol based on various estimates of the energy inputs and outputs found in the literature. This analysis was based on the five main studies in corn ethanol: Wang et al. (1997), Shapouri et al. (2002), Pimentel (2003), Patzek (2004), and Farrell et al. (2006). The second was a spatial analysis of the EROI of corn ethanol. It is these two items that are discussed in Part 1.

The third was a sensitivity analysis; wherein we assess the degree to which corn yields and co-product credits impact the EROI of corn ethanol. Fourth, we combined the results of our EROI analysis with the data of biorefinery production to assess how much net energy was delivered to society by ethanol in 2009. These items are discussed in Part 2, which is a separate post.


The results from our meta-error analysis indicated that the average EROI for corn ethanol was 1.07 with a standard error of 0.1. The 95% confidence interval was 1.07 ± 0.2. This result is interpreted as follows: there is a 95% chance that the true value of the EROI of corn ethanol is contained within 0.2 of 1.07. Alternatively, this calculation means that we are unable to assert whether the true value of the EROI of corn ethanol is greater than one.

EROI values calculated in the spatial analysis ranged from 0.36 in less optimal corn-growing areas, for example southern Texas, to 1.18 in optimal areas, for example Nebraska (Fig. 2). If we apply the same confidence calculated in the meta-error analysis to the results of the county EROI analysis, we find that none of the counties had an EROI that was high enough (1.20) to conclude that corn ethanol was produced at an energy profit. The average EROI value across all counties was 1.01, which was 0.06 less than the average calculated across the literature. This supports the idea that the literature used optimal values for corn ethanol inputs and outputs and as such has underestimated costs, overestimated benefits, or both. The distribution of EROI values followed a normal distribution skewed slightly left (Fig. 3). The vast majority of counties had EROIs that fell within either the 1.01–1.05 or 1.06–1.10 category.

Our spatial analysis indicated diminishing returns to EROI as distance from the Corn Belt increased. Counties with high EROI values were located in Nebraska and other Corn Belt states, while the lower EROI values were located in counties toward the northwest or southeast of the area analyzed, essentially northwestern South and North Dakota, and southeastern Texas, respectively (Fig. 2). As expected, the counties with EROI values within the top 10% had a combination of higher yields and lower agricultural inputs, while the counties within the lowest 10% of EROIs had lower yields and higher agricultural inputs on average (Fig. 4). We can conclude that even with a precision of ±0.2, 48 counties have EROIs below 1, as the EROI calculated for each of these counties was <0.80 (Fig. 3).

Fig. 3.Histogram of number of counties vs. EROI.

Fig. 4.Average values for spatial agricultural inputs and corn yield for counties with EROI values within the top and bottom 10% of all counties.

An analysis at the state-level indicated a similar geographic pattern, as the Corn Belt states, i.e. Nebraska, Minnesota, Iowa, Illinois, had EROI values in the upper half of the states analyzed and states further from the optimal corn-growing lands were located in the bottom half, e.g. Kentucky, Texas, Missouri. EROIFG (EROI "Farm Gate") ranged from 3.81 to 6.25, while EROIRG (EROI "Refinery Gate") ranged from 0.96 to 1.14 (Table 4). Since much of the costs of the agricultural phase of corn production were constant across all states in this study (i.e. non-spatial), the range in EROIFG reflects the corn yields and fertilizer inputs in different environments rather than differences in the energy cost of planting and harvesting an acre of corn. On the other hand, the small range in EROIRG indicated that the off-farm costs dwarfed the energy costs on-farm. We calculated that 65% of the costs of producing ethanol from corn originated in the biorefinery phase (Fig. 5).

Table 4. Summary statistics of the costs and gains of the agricultural phase of corn ethanol production for states that produced at least 1% of the 2005 corn harvest in the United States, ranked by decreasing EROIRG.

Fig. 5. Relative mix of inputs (spatial agricultural inputs, non-spatial agricultural inputs, biorefinery phase) and outputs (ethanol and co-products) of corn ethanol production.

According to Eq. 2, to deliver one liter of ethanol as net energy at an EROI of 1.18 (max found in the spatial analysis), 7.5 liter of ethanol must be produced; 1 liter as net energy and 6.5 liter (or its energy equivalent) to be reinvested to produce more ethanol. If we assume that the average we calculated across all counties (1.01) was the actual value for EROI, then producing ethanol is virtually a zero sum game; i.e. energy produced equals energy consumed.

Equation 2. Gross amount of energy required to deliver one unit of net energy = EROI/(EROI-1)

Applying Eq. 2 to our spatial analysis reveals other interesting results. Eight liters of ethanol must be produced to deliver one unit of net energy in Minnesota, using an EROI of 1.14. Another way, only 13% of the ethanol produced in Minnesota is net energy because the energy equivalent of 87% of the ethanol produced must be reinvested to produce more ethanol. The energy reinvested is in many forms, including, but not limited to, the fossil energy required to generate corn, fertilizer, lime, gasoline, natural gas, diesel, etc. For states with an EROI below 1.0 (Texas and Missouri), the production of ethanol is acting as a drain on the energy system, requiring more energy to produce ethanol than the energy contained in the ethanol product.

The EROI values for counties with biorefineries ranged from 0.64 in Stark, North Dakota, to 1.18 in Phillips, Kansas. Our analysis of 127 biorefineries indicated that of 31.6 billion liters of ethanol produced in the United States, only 1.6 billion liters were net energy (roughly 5%). As a point of comparison, of the 136 billion liters of gasoline consumed in 2009, roughly 122 billion liters (90%) were net energy, assuming that the 136 billion liters were produced at an EROI of 10 (Cleveland 2005). Adjusting for the lower energy content of ethanol (21.46 MJ/L etoh vs. 34.56 MJ/L gasoline = 0.62), we calculated that the net energy from ethanol is roughly 0.99 billion ‘‘gasoline-equivalent’’ liters.

Dividing the net energy supplied to society from ethanol by that from gasoline, we calculated that the supply of net energy to society from ethanol is only 0.8% of that from gasoline (0.99/122 = 0.8%). Thus comparing simply the gross production of gasoline-equivalent liters of both ethanol and gasoline is misleading, as one would conclude that the US production of ethanol is 14% of gasoline consumption (19.6/136 = 14%).