by Dev Shrestha and Jon Van Gerpen, Bioediesel Education Program, University of Idaho

In a recent Tech Note, we clarified the differences between attributional life cycle analysis (ALCA) and consequential life cycle analysis (CLCA). Briefly, ALCA is a “business-as-usual” scenario analyzing current practices, and does not include indirect effects such as indirect land use change. CLCA analyzes potential changes in a system, and can include indirect effects.

In the Tech Note, we explained why the Environmental Protection Agency came out in 2009 and 2010 with two sets of dramatically different numbers for greenhouse gas emissions of soy biodiesel. In May of 2009, the EPA said that soy biodiesel achieved a 22% reduction of greenhouse gases over petro-diesel. In February of 2010, the EPA raised this number to 57%. The Tech Note points out that changing the assumptions of a consequential life cycle analysis can dramatically change the results.

Some have suggested that inherent uncertainties in consequential life cycle analysis demonstrate its lack of scientific rigor. These people suggest that CLCA should not be used to calculate the effects of indirect land use change.

We think that if it is used carefully, there is a place for CLCA in the process of making decisions about environmental options. However, it is important to emphasize the appropriate use of consequential life cycle analysis. Consequential life cycle analysis should not be used for regulatory purposes, to pass judgment on certain biofuels, or to prevent certain biofuels from being able to participate in government mandates such as the RFS2 program. Instead, CLCA should be used as a warning system.

Currently, many people do not realize how uncertain and variable a consequential life cycle analysis can be. For example, the incorporation of indirect land use change into CLCA typically requires models to forecast economic interactions between countries. These economic interactions are very difficult to estimate. This kind of consequential LCA is similar to such challenging activities as predicting the performance of the stock market.

People may take the numbers from a consequential life cycle analysis as though these numbers were set in stone, and compare numbers from one CLCA to another CLCA without understanding what was involved in deriving these numbers. Then, they use the numbers to pass judgment on a particular fuel, saying that, for example, biodiesel from a particular feedstock is “bad” or “good” based on these numbers.

The main thing to understand is that it may not be appropriate to compare numbers from two consequential life cycle analyses to decide which is “better.” For example, a consequential LCA for soybean biodiesel production is often compared to a consequential LCA for algae biodiesel, which is not yet commercially produced. This is not necessarily a valid comparison, because there are so many unknowns related to the indirect land use changes from soy biodiesel. There are also unknowns associated with the production of algae biodiesel, since no commercial production exists. A consequential life cycle analysis of algae biodiesel is based on a hypothetical production scenario from smaller model plants, theoretical process kinetics, and upscaled laboratory data.

If people conclude from this that algae biodiesel is better, this may not be a valid conclusion, because the CLCA numbers for both soy biodiesel and algae biodiesel are derived very differently based on different sets of assumptions. Instead of comparing two perhaps highly unreliable CLCAs to decide which is “better,” policy makers should use each CLCA separately to assess the potential benefits and drawbacks of each. If an option looks promising based on a CLCA, policy makers might cautiously proceed to support it and re-evaluate the scenario as more reliable numbers are available.

Two CLCAs can be appropriately compared when they both deal with the same system, with a single variable changed. For example, a CLCA for soy biodiesel based on one level of soy yield can be compared to a CLCA for soy biodiesel based on a different soy yield. This kind of comparison can help policy makers understand the potential impact of their decisions.

Just as it may not be appropriate to compare two CLCAs to decide which is better, it also may not be appropriate to compare numbers from an attributional LCA to a consequential LCA. While the EPA included indirect effects in the biofuels life cycle analysis, they did not include indirect effects in the petroleum fuels life cycle analysis. The EPA states that they considered including indirect land use change caused by the development of Canadian tar sands, but decided that this land change would have a “negligible” effect on overall GHG emissions, and therefore did not include it (p. 467, Renewable Fuel Standard Program Regulatory Impact Analysis). The EPA also did not consider other indirect effects such as oil spills. Therefore, an attributional LCA of petroleum-based fuels is being compared to a consequential LCA of biofuels, with potentially misleading results.

The problem is, Congress has mandated that the CLCA for soy biodiesel (and biodiesel from other feedstocks) be compared to the ALCA for petroleum diesel. The EPA is endeavoring to comply with this law. We think the law should be changed to take out the requirement to incorporate indirect land use change into the biofuels analyses. The ALCA for biodiesel should be compared to the ALCA for petroleum diesel.

However, we don’t want to inadvertently harm the world’s forests and grasslands by ignoring potential threats. So, how can we avoid this? A consequential life cycle analysis for a biofuel should be used as a warning about possible outcomes, rather than a tool to kill policies or technologies not favored by the analyst. Results of consequential LCA should always be posed as “if-then” statements. “If we do this, then we need to take some action to ensure that the undesirable consequences don’t happen.”

If consequential LCA were seen as a source of warning signals rather than the final word on the energy and environmental impact of a particular course of action, it would gain more support and credibility. Indirect land use change arguments, which are inherently based on consequential LCA, rather than being seen as obstacles to progress, would be seen as triggers for actions to monitor and protect sensitive lands around the world. It would be more effective to take steps to directly protect the world’s rainforests and other sensitive lands, instead of relying on the elimination of biofuels mandates to somehow indirectly save the forests.

By Jon Van Gerpen, Project Director, Biodiesel Education Program, University of Idaho

It has become popular lately to refer to biofuels as "first generation," "second generation," "third generation" and so on. First generation biofuels consist of ethanol from sugar or starch (such as from corn) and biodiesel from animal fats and vegetable oils. Second generation biofuels are those produced from ligno-cellulosic materials such as switchgrass or wood chips. At this point the definitions get a little less well defined, but third generation is usually defined as fuels from algae, and fourth generation status is claimed by every new technology seeking to promote itself as the next big thing.

The term "generation" carries a connotation of a sequence of time. It implies that higher generation fuels are superior to lower generation fuels and will replace them over time. First generation fuels are criticized for competing with food, for negative land use changes, for taking too much energy to produce, and for only being economically viable with government subsidies. Higher generation fuels are supposed to be free of these disadvantages and will provide greater supplies of low-cost fuel while helping to reduce climate-changing greenhouse gases.

Unfortunately, this understanding of the development of biofuels is based on false assumptions and leads to incorrect conclusions.

A better model for how the various biofuel options should be compared can be drawn from the petroleum industry and the manner in which petroleum is extracted. In a new oil field, about 5 to 15% of the petroleum rises to the surface under its own pressure and is known as primary recovery.

The amount of oil that can be produced by primary recovery is limited, so the desire for more petroleum leads to the implementation of more expensive and more technically sophisticated techniques such as water or CO2 injection to raise the pressure of the petroleum so it can be removed. These techniques are known as secondary recovery, and they allow extraction of an additional 30 to 50% of the oil.

The desire for even more extraction leads to tertiary recovery techniques such as steam injection that are based on lowering the viscosity of the petroleum so it can flow more easily toward the well.

The lesson to be learned from the petroleum model is that the desire to increase the supply of the product is met by introducing more expensive and more sophisticated technology.

In the same manner, biofuels should be categorized as primary, secondary, and tertiary. Primary biofuels are those which are least expensive and which require the least technology to produce. These are the low-hanging fruit, and include sugar and starch-based ethanol and biodiesel. Although requiring subsidies to compete with petroleum (which is itself subsidized, of course), these fuels have already achieved commercial status and market acceptance.

Ultimately, it should be expected that their success will cause the price of corn and soybean oil to increase to the point where additional production of the primary biofuel is not economically viable. To further displace petroleum with additional biofuel will require a move to secondary biofuels. These fuels will be more difficult to produce and more expensive. Cellulosic ethanol is likely to be the first fuel to enter the marketplace in serious volumes using straw or corn cobs/stover as feedstock. Although the feedstock is cheaper than corn, the processing is much more extensive, making cellulosic ethanol more expensive. If this were not the case, then this approach would already be the primary source for biofuel and corn would return to its traditional use as food for cattle, hogs and chickens.

Tertiary biofuels from algae will also be needed if we are to replace a major fraction of our current petroleum consumption. These fuels are even more expensive and involve even more difficult technical problems.

What we can learn from this model is that secondary and tertiary biofuels do not replace primary Biofuels—they supplement those fuels. Primary oil extraction is always preferred, but meeting the demand for petroleum requires supplemental oil from secondary and tertiary recovery. Primary biofuels will always be around and they will become increasingly profitable as higher fuel prices draw secondary and tertiary fuels into the market.

A criticism of primary biofuels is that the increase in demand for feedstocks causes an increase in food prices and land use changes. These impacts have been exaggerated in many cases or do not recognize positive benefits such as rural revitalization and higher income for farmers. It should be recognized that secondary and tertiary biofuels will be subject to these same criticisms if they achieve any level of success. Growing switchgrass to produce ethanol will be criticized for displacing food crops and requiring fertilizer. Gathering straw and stover will lower soil quality. Growing camelina on rangeland and algae in the desert will destroy delicate eco-systems. Biofuels do not come without drawbacks.

As a society, we have to make choices about how we want to allocate the costs of different fuel options. It seems clear we have opted to maintain our mobility. Now the question is whether the economic, environmental and societal costs of biofuels are sufficiently below those of petroleum to justify continued support.