Hereinafter, an LCIA analysis of the two selected scenarios is reported. The climate change category (by means of the GWP indicator) and the energy efficiency (by means of the NER indicator) are treated, firstly, in view of their relevance for biofuels production, and then other LCIA categories are described. For this purpose, for both the two processing pathways (BD and PVO), the assessment was based on energy allocation. Finally, matters of uncertainty and sensitivity are faced.
3.2.1. Global Warming Potential and Net Energy Ratio (for BD vs. PVO Based on Energy Allocation)
provides the results of the GWP computed for the BD and PVO scenarios, along with the so-called fossil fuel comparator, as described in the Renewable Energy Directive 2018/2001/EU (RED II) [26
]. This latter figure (vertical dashed line in Figure 4
) amounts to 94 gCO2
of fossil fuel.
Camelina PVO shows better performance against the camelina BD scenario (carbon dioxide equivalent saving of about 30%), while both of the camelina scenarios have GWP values markedly lower than the fossil fuel comparator. This corresponds to a GWP mitigation of about 67% for PVO and more than 50% for BD. The computed values have the same magnitude of other literature reports on camelina derived biofuels, although the pure vegetable oil solution is poorly studied. Shonnard et al. [27
] estimated a GWP of about 18 gCO2
of biofuel in USA. This value is somewhat lower than our estimate. However, in that case, the considered biofuel and the related production technology are quite different, because they refer to hydrogenation-derived renewable diesel. This latter biofuel has also been assessed by Miller and Kumar [15
], who calculated a rough averaged value of about 40 gCO2
, i.e., a value close to our estimate of the BD scenario. Khron and Fripp [16
] studied the biodiesel production from camelina in the USA. Their computation amounts to about 40 gCO2
, less than 10% lower than our value. In contrast, Li and Mupondwa [28
] provide GWP values ranging from 7.6 to 24.7 gCO2
eq per megajoule of produced biodiesel. Biodiesel from camelina was also assessed by three more recent studies published in the last 5 years (2015–2020), according to Table 1
. Roughly 30 gCO2
was reported by Dangol et al. [10
], while a very large interval was presented by Tabatabaie et al. [6
], ranging from about 40 to 80 gCO2
. Finally, Bacenetti et al. [7
] compared biodiesel production from flax and camelina. The latter of these was credited of about 150 gCO2
, i.e., a much larger number than that reported in this paper (a discrepancy greater than 100 gCO2
), despite that the country of investigation was the same (Italy). This disagreement is probably due to the lower input approach adopted in the present study. For instance, our nitrogen fertilization amounts to no more than 30 kg ha1
, against 80 kg ha1
found in the study of [7
]. In the same way, the use of no pesticide in our field trials is in contrast to the metazachlor used in the other study.
However, the common element that characterizes all of this literature is the importance of the agricultural step and nitrogen fertilization, and in particular, to determine the GWP score.
In the present work, the contribution analysis for GWP showed that, in the case of the PVO scenario, the major contributing processes are seed drying (mainly owing to electricity use which accounts for about 9% of total GWP) and camelina cultivation (agricultural step). The main entries of the latter process are field machinery operation (i.e., diesel consumption) accounting to about 29% of GWP, nitrogen fertilizer production (about 27%), and dinitrogen monoxide emissions at field test (contributing for about 13% to total GWP) as sum of direct emissions related the use of nitrogen fertilizer, and indirect emissions related to leaching and volatilization phenomena. Potash deserves a certain residual importance, as too do phosphorus fertilizer production processes, with GWP contributions of about 11% and 7%, respectively. In the BD scenario, the fuel conversion (transesterification) plus the oil refining together account for about 9% of GWP, the oil extraction for 9%, seed drying for about 7%, and the agricultural step for about 74%. Therefore, in addition to PVO scenario, the cultivation process of camelina crop was the predominant entry for the BD pathway. Furthermore, in this case, the machinery use for field operation and the nitrogen fertilizer production process are the predominant items, accounting for 24% and 23% of the total GWP, respectively, while dinitrogen monoxide emission was slightly lower than PVO, corresponding to about 11% of the total GWP.
provides the LCIA indicators, other than GWP.
Comparing the two scenarios, the common evidence is a generalized worse performance of the BD scenario, with indicators always scoring greater than PVO and spanning from a percentage variation of +19% of land use, to +89% of resource depletion (mineral, fossils, and renewable). Beside the latter, two other indicators account for a remarkable difference, i.e., human toxicity—carcinogenics (+87%) and freshwater eutrophication (+78%). All of this must be addressed in the refining process, entailing a greater consumption of energy (mainly electricity and natural gas use) and use of chemicals (methanol, sodium hydroxide, and hexane).
The net energy ratio indicator (NER) allows a deep understanding of the efficiency of energy employed in producing a given fuel, the ratio being between the output energy and input fossil energy. For both scenarios, the energy performance is positive with NER values greater than unity, specifically 1.4 and 2.5 for BD and PVO, respectively. Furthermore, in this case, the PVO scenario performs better than BD, because of the more intense energy use in the processes involved in biodiesel production. The literature comparison for NER allows consideration and comparison to GWP. Our estimate lies in the gross interval of the studied literature, being included within the NER scoring 1.3 reported by Shonnard et al. [27
] and the NER scoring 3.5 of Dangol et al. [10
3.2.2. Uncertainty and Sensitivity
The data presented in Table 3
, whose agronomic implications were already discussed in Masella et al. [12
], can now be used to understand the uncertainty and sensitivity of the computed life cycle model. In fact, with all of the agricultural inputs used in those filed trials being constant, the variability showed in the table reflects the uncertainty related to uncontrolled factors, such as the environment and climate. Such variability has the potential to cause uncertainty in environmental impact scores. The field performance variability can be well represented in terms of standard deviation around the mean values, and used to give a reliable estimate of the uncertainty of the computed environmental performances. For this purpose, the mean value and standard deviation computed for the grain yield and seed oil content over the entire set of available data (Table 3
) were used. Three levels of both of the parameters were set by subtracting and adding the standard deviation to the mean value (the data being normally distributed in this interval includes up to about 68% of the recorded values). After testing for the absence of a significant correlation between the yield and oil, the three levels of the two parameters were factorially combined, providing nine different combinations of yield–oil pairs. Then, the product systems (the BD and PVO scenarios) were recalculated for each of the nine pair combinations. The results of this procedure are reported in Figure 5
, focusing on the most relevant indicators, i.e., GWP and NER, and represented by means of boxplot, i.e., quartile distribution around the median. Clearly, the location of the median values is consistent with the results showed in Table 9
; at the same time, however, the partial overlapping of quartiles is evident, which underlines the importance of the grain yield and seed oil content in determining the environmental performances of the two scenarios.
Moreover, in the present work, allocation was solved with partitioning based on energy content and selected as a benchmark alternative for both the PVO and BD scenarios. On the other hand, the results of life cycle studies strongly depend on—that is, they are sensitive to—allocation procedures and choices, which occur whenever a process provides more than one valuable product. Hence, to the study of the sensitivity of the systems under investigation with changing allocation procedures and choices deserves interest. Generally, allocation issues are faced in two major procedures, where, besides the already mentioned partitioning methods, the so-called system expansion is a widespread solution. This procedure consists of expanding the product system to include the additional functions related to the co-products. In other words, where a process provides a valuable co-product, the product system is expanded, including a process providing a product with equivalent function (namely, avoided product). In this way, the product system is credited for the impact related to the included avoided product. In contrast, in the partitioning methods, as above cited, the environmental burdens are mathematically assigned among the co-products by a specified criterion. Hence, in order to analyze the sensitivity of PVO and BD to allocation choices, mass partitioning and system expansion were also computed and compared. In the BD scenario, two main valuable co-products raise the issue of allocation, the meal cake resulting from the oil extraction and glycerol resulting from transesterification of camelina oil; however, in the PVO scenario, only the meal cake resulting from the oil mechanical extraction deserves allocation. In both cases, straw produced during camelina cultivation was not included. In fact, the characteristics of camelina straw are poorly studied and its possible uses as animal bedding or for making fiber products can only be defined as potential. Furthermore, owing to the high value of the straw/grain mass ratio (3–4:1), a proportional partitioning of the environmental burdens, either on a mass or energy basis, would not be a correct allocation, since the straw would loaded too much, providing misleading results. The same holds for the option of system expansion, where the system would be over-credited with the environmental burdens of the selected avoided products. All of this does not represent the real causality of the product system under study. In any case, straw removal from the field was assumed. This leads to energy consumption (diesel needed for bailing operation) on one side, and N2
O emission saving due to the avoided straw decomposition in the soil. Both of these points were considered in the inventory computation. Table 2
provides the coefficient used for allocation, based on partitioning method (mass and energy allocation). For system expansion, camelina cake meal allocation was based on its functionality as animal feed, and its similarity with soybean meal, as proposed by [28
]. The substitution ratio was based on the protein content of the oilseed cakes, deriving the corresponding value from the literature, in the case of soybean (480 g kg−1
dry matter; [29
]). For camelina, the average value from the present field trials was used (341 g kg−1
dry matter; [14
]). Therefore, camelina meal was assumed to replace soybean meal for an equivalent protein content, by including and crediting in the product system the process of soybean meal production. This applies for both the BD and PVO scenarios. With regards to glycerol from transesterification of camelina oil (BD scenario), system expansion was applied by including the process of glycerol production. The latter production process, and also that of soybean meal, was derived from the correspondent processes inventoried in the US NREL database [20
A common pattern for both the BD and PVO scenarios is evident in Figure 6
. Mass allocation provides the best results, either in terms of GWP or NER; however, it is probably the less accurate and reliable choice. By contrast, the most penalizing choice seems to be system expansion, especially for the amount of GWP in the PVO scenario. The same roughly holds for the NER indicator.