The final step in performing an LCA is interpretation of the results to highlight principal themes emerging from the study. In the process of conducting an LCA the analyst should develop a deep understand of the relationship between the model structure, assumptions, inputs, and the model outputs. The analyst should highlight the most important relationships for readers who lack the time or expertise to repro­duce the analysis. The analyst is also tasked with developing broad conclusions from the analysis. Clearly, this process lends itself to subjective interpretation of results and must be handled carefully to ensure the results are as transparent and useful as possible.

One of the most common methods for minimizing subjectivity in data interpreta­tion is to perform a sensitivity analysis in which the connection between modeling inputs and outputs is quantified. For example, Clarens et al., used a sensitivity anal­ysis to report the top five input parameters driving energy use and greenhouse gas emissions during algae cultivation [8] (Fig. 5). The results, shown in Fig. 5, illus­trate how the model outputs respond to a change of ±10% on the input parameters in turn. From these results it is clear that algae high heating value (i. e., lipid con­tent), fertilizer production and application, and CO2 production and application are driving the burdens.

An important element of data interpretation is understanding how errors in the model could propagate and impact final results. Errors can be introduced into the model in several ways, including inaccurate or poorly transcribed data sources, inaccurate relationships in the model, or unrealistic modeling assumptions. A com­mon source of error in LCA models is double counting in which one emission is

incorporated into multiple metrics. Some reactive nitrogen species, for example, can contribute to eutrophication of surface waters and global warming.

One of the most effective ways to interpret the results of an LCA and understand whether there are sources of error is to benchmark the results to related studies. In the case of algae, this step has been largely ignored, probably because there was little prior literature until recently. This does not mean that comparing data to analo­gous systems is not worthwhile. To illustrate this, Fig. 6 shows the energy use required to produce biodiesel from algae from four different studies (one paper has two cases). Following adjustment to a standardized functional unit of 1,000-L algae biodiesel (Fig. 6a), the values are compared to the results from conventional soy biodiesel, a thoroughly characterized process (dotted line) as reported by Hill et al. [12]. A preliminary comparison of the results (Fig. 6a) suggests that algae are either much better or much worse than conventional soy biodiesel. Based on this compari­son alone, it would be difficult to say anything definitive about how favorable algae biodiesel may be relative to soy biodiesel.

Figure 6b summarizes the same results following adjustment of functional unit and system boundaries. As expected, these adjustments make the results of the four algae LCA papers more consistent. This increase in uniformity among stud­ies can be quantified using coefficients of variation (CV), where CV is defined as the ratio or standard deviation to mean value. CV in Fig. 6a, reflecting only nor­malization of the functional unit, is 1.39. From Fig. 6b, we see that CV is dramati­cally reduced, to 0.46, following manual adjustment for system boundaries. This decrease emphasizes the substantial impact of systems boundaries selection, here standardization of upstream nutrient burdens and coproduct allocations, on the outcome of algae LCA studies. A third and final normalization can be carried out in which key model assumptions regarding algae attributes and separations/drying parameters are made uniform across all studies. These parameters have been identified by one or more authors as model inputs that are especially critical dur­ing LCA of energy production from algae. Results from this final step of the assimilation analysis are presented in Fig. 6c. This increase in uniformity among selected studies enables more meaningful comparison between algae biodiesel and an external benchmark, as shown visually in the figure. In Fig. 6c, the various estimates for algae biodiesel, derived independently, then normalized, are very close to the estimate for soy biodiesel.

During data interpretation, it is common to incorporate other elements that are exogenous to the LC model but which could inform analysis of the results. One common example of this is the incorporation of economic drivers into the model. Campbell et al., for example, performed a combined economic and environmental life cycle analysis of producing biodiesel from algae grown in near-shore salt-water ponds in Australia [5]. The results of this study suggest that, based on GHG emis­sions alone, algae perform favorably relative to conventional terrestrial crops. This study is noteworthy because it is the only one to consider growing the algae in salt water. Given the tremendous potential to grow salt water species on marginal, near coastal waters, this is an approach that has been experimentally proposed in several papers by Chisti but for which little life cycle modeling results exist [6] .

8 Conclusions

This chapter surveyed some of the key challenges associated with utilizing LCA methodologies for studying algae-to-energy technology. These challenges have emerged over the last two years as a large number of systems-level life cycle studies of proposed algae-based energy technologies have appeared in the academic literature. Before 2009, only a few algae-to-energy LCAs had been published and even these were only nominally LCAs [ 15]. The assumptions about cultivation and drying that were used in these studies were not highly representative of previ­ously published reports. The recent work better reflects the way that the industry expects algae-to-energy systems will be deployed in the field, but the results are difficult to compare directly because of the varied boundaries and assumption specified by the authors. This chapter has highlighted most of the normative judg­ments faced by LC practitioners and discussed each in the context of algae-to-energy systems in order to support future work in this area.

From the existing literature, several themes begin to emerge that will assist in designing future analyses. One of the most common is that recent LCAs echo many of the conclusions of the first-generation algae research conducted in the 1980s and 1990s. These studies suggested many of the system’s-level implications of large-scale algae deployment [35]. Benneman’s report to the United States Department of Energy concluded that open ponds would be the only economically viable way to grow algae for sequestering CO2 from power plant flue gases [2]. Similarly, Votolina and others have suggested that algae-based wastewater treatment would be a technically com­petitive approach for conducting tertiary treatment of wastewater [32]. In both cases, these conclusions are well aligned with the results of more recent LCA studies, even though these early reports never use the term “LCA.” A second important theme is that algae-to-energy systems have a long way to go technologically before they are viable from an environmental burden standpoint. In this regard, LCA is a powerful design tool because it allows for a focus of R&D on those processes that will have the most significant impact on reducing the burdens of the processes as a whole.

In light of the large amount of investment in the algae-to-energy field, it is likely that LC tools will continue to be used to understand and assess the impacts of these emerging technologies. In order for these studies to be more immediately compa­rable, it is important that the community develop nominal assumptions about how to handle algae systems. This chapter can serve as a first step toward developing these norms.

Acknowledgments The authors gratefully acknowledge funding for this study from a UVA Fund for Excellence in Science and Technology Grant.