Nutritional profile of microalgae and fish
The nutritional profiles of the considered microalgae and fish fillet are presented per kg of dry matter content (90% DM) in Table 2. The two microalgae species investigated showed a similar or slightly smaller calorific value and fat content than the fish species. Concerning the dry matter fat content, only Alaska pollack, codfish, and carp exhibited a smaller share of fat than microalgae, which also affected the share of EPA+DHA. These three fish species, as well as pangasius, had the lowest share of EPA+DHA, ranging from 3.91 g kg−1 DM for pangasius to 13.71 g kg−1 DM for Alaska pollack. While the two microalgae species are characterized by relatively high amounts of EPA of 31.1 g kg−1 DM for P. tricornutum and 42 g kg−1 DM for Nannochloropsis sp., which exceeds the EPA content of all of the fish species analyzed, their share of DHA is rather low. This difference results in an EPA+DHA dry matter content of microalgae that lies in the medium range compared with that of fish fillet. Concerning the dry matter protein content, fish contained significantly higher amounts than microalgae, which even amounted to more than twofold of the protein content found in microalgae.
Energy- and nutrient-specific environmental assessment
All environmental impact values were depicted in three functional units: 100 kcal, 0.5 g EPA+DHA, and 50 g protein. Values were always depicted with a logarithmic scale in order to provide more precise values in the illustration. Additionally, all figures are presented with a normal scaling in the supplementary material (Figs. S1-S6). The fish fillet products were subdivided into capture production and aquaculture production. Microalgae cultivation was divided into scenarios with avoided CO2 and scenarios with the burden of CO2 production. Thus, five microalgae scenarios each with and without the burden of CO2 production were considered comprising borosilicate glass tubes with 36 and 40 mm diameter, PMMA (polymethyl methacrylate) tubes with a 3- and 7-year lifespan and P. tricornutum as an alternative species. For the analysis of the fish and microalgae values, the median values were utilized as a basis for comparison. Exact LCIA values for fish species are included in the supplementary material (Tabs. S8-S13).
Global warming potential
Concerning the global warming potential of the fish products examined (Fig. 3), the emissions depend highly on the fish species and production type. It appears that aquaculture production tends to have higher CO2eq emissions than capture production of fish fillets across all FUs. In particular, Pangasius showed high values, with its median reaching a peak of over 3711 g CO2eq 0.5 g−1 EPA+DHA (see supplementary material), which makes it an unfavorable option as a source for these nutrients. Fish fillet production caused greenhouse gas (GHG) emissions from 25 to 471 g 100 kcal−1, with capture production having its highest median at 135 g CO2eq 100 kcal−1 and aquaculture production having a peak at 471 g CO2eq 100 kcal−1. The microalgae scenarios with avoided CO2 burden caused GHG emissions from 33 g 100 kcal−1 for the 36 mm borosilicate glass scenario to 103 g 100 kcal−1 for the 3-year PMMA scenario. When accounting for the burden of CO2 production, scenarios showed emissions between 93 and 163 g 100 kcal−1. The baseline scenario (Na.sp., glass, 40 mm) with the burden of CO2 had approximately the same amount of GHG emissions per 100 kcal as the worst scenario with avoided CO2 burden (Na.sp., PMMA, 3 years). Concerning the nutritional energy values, all borosilicate glass scenarios with avoided CO2 burden had lower GHG emissions than capture and aquaculture fish production (except wild-caught herring). Even though microalgae scenarios with the included burden of CO2 had higher GHG emissions than those without, the borosilicate glass scenarios of this category still outperformed all aquaculture fish per 100 kcal.
With regard to EPA+DHA, the microalgae borosilicate glass scenarios with avoided CO2 burden (16–26 g CO2eq 0.5 g−1 EPA+DHA) were responsible for lower GHG emissions than the median values of all fish scenarios (33–3711 g CO2eq 0.5 g−1 EPA+DHA), except herring and mackerel. Again, even the microalgae scenario with the highest GHG emissions (3-year PMMA with CO2 burden, 82 g CO2eq 0.5 g−1 EPA+DHA) was preferable over most of the aquaculture fish production cases (median, 109–3710 g CO2eq 0.5 g−1 EPA+DHA) except salmon (54 g CO2eq 0.5 g−1 EPA+DHA). However, compared with capture production, the scenarios including PMMA as the reactor material were rather unfavorable, similar to the scenarios with the included burden of CO2, which were only able to compete with Alaska pollack.
Regarding protein, borosilicate glass scenarios with avoided CO2 burden (234–272 g CO2eq 50 g−1 protein) showed similar or higher emissions than those for capture production (111–283 g CO2eq 50 g−1 protein). Only wild-caught tuna caused higher GHG emissions, with a median value of 493 g CO2eq 50 g−1 protein. Considering the median GHG emissions of aquaculture production (529–1725 g 50 g−1 protein), all microalgae scenarios with avoided CO2 burden showed similar or lower GHG emissions. In general, all microalgae scenarios caused similar or lower GHG emissions than those of aquaculture fish fillet production. Microalgae scenarios with the burden of CO2 had GHG emissions from 654 to 1148 g 50 g−1 protein, which were similar or lower than those in aquaculture fish production. In comparison with capture fish fillet production, only the microalgae borosilicate glass scenarios with avoided CO2 burden were able to compete.
Wild-caught herring was the only fish that outperformed even the most favorable microalgae scenarios in all FUs. Concerning protein, wild-caught salmon and Alaska pollack also had lower GHG emissions than all microalgae scenarios. For both the fish and microalgae scenarios, the highest values were obtained in relation to the daily protein intake of 50 g. Most GHG emissions from fish arose from feed production for aquaculture, and diesel usage for boats in the case of capture production. GHG emissions in microalgae cultivation were mostly due to energy-intensive processes that were carried out using nonrenewable energy sources. Additionally, the values showed that the production burden of CO2 added a significant amount of GHG emissions to the microalgae scenarios. On average, this burden caused GHG emissions of the microalgae scenarios to double per FU.
In terms of the acidification potential of fish products (Fig. 4), the distribution is rather heterogeneous across the different species and production methods for all FUs. Concerning nutritional energy, both the lowest and highest emissions result from fish products originating from aquaculture production. Carp from aquaculture production had the lowest SO2eq emissions of 0.00002 g SO2eq 100 kcal−1, while tilapia from aquaculture had a value of 3.5 g SO2eq 100 kcal−1. Nevertheless, wild-caught herring showed a low median emission value of 0.00009 g SO2eq 100 kcal−1. All microalgae scenarios had a lower acidification potential than most fish, except herring and cod from capture production and carp from aquaculture. The microalgae borosilicate glass scenarios with avoided CO2 burden were responsible for 0.08–0.11 g SO2eq 100 kcal−1, whereas the PMMA scenarios with avoided CO2 burden had more than twofold higher emissions, with a range from 0.21–0.36 g SO2eq 100 kcal−1. Microalgae cultivation with the burden of CO2 production had emissions values ranging from 0.18–0.21 g SO2eq 100 kcal−1 for the borosilicate glass scenarios and from 0.31–0.46 g SO2eq 100 kcal−1 for the PMMA scenarios.
Considering the daily intake recommendation of EPA+DHA, the distribution of values for microalgae and fish scenarios was similar. The SO2eq emissions of fish production ranged from 0.00003–18.4 g SO2eq 0.5 g−1 EPA+DHA. Microalgae with avoided CO2 burden had emissions of 0.04–0.07 g SO2eq 0.5 g−1 EPA+DHA for the borosilicate glass scenarios and 0.10–0.18 g SO2eq 0.5 g−1 EPA+DHA for the PMMA scenarios, which indicates that even the acrylic glass scenarios performed better than most fish production scenarios. Microalgae with the burden of CO2 had an acidification potential ranging from 0.09–0.14 g SO2eq 0.5 g−1 EPA+DHA for the borosilicate glass scenarios and 0.16–0.23 g SO2eq 0.5 g−1 EPA+DHA for the PMMA scenarios. Even the microalgae scenarios with the burden of CO2 production could compete with most wild-caught and aquaculture fish species (exceptions: herring, codfish, mackerel, and carp). Similar to the global warming potential, the protein requirement on average also caused the highest acidification potential compared with the other FUs. The acidification potential of fish production ranged from 0.00006–10.1 g SO2eq 50 g−1 protein. Wild-caught herring and codfish as well as carp from aquaculture again outperformed all microalgae scenarios, exhibiting emissions from 0.58–0.71 g SO2eq 50 g−1 protein for glass scenarios with avoided CO2 burden and 1.48–2.52 g SO2eq 50 g−1 protein for acrylic glass scenarios with avoided CO2 burden. Microalgae cultivation with the burden of CO2 had emissions ranging from 1.21–1.41 g SO2eq 50 g−1 protein for the borosilicate glass scenarios and 2.21–3.25 g SO2eq 50 g−1 protein for the PMMA scenarios. Moreover, Pangasius from aquaculture had a lower acidification potential than the 3-year PMMA scenario with avoided CO2 burden and the PMMA scenarios with the burden of CO2. Apart from PMMA being responsible for acidification, other critical points for SO2eq emissions during microalgae cultivation comprised electricity use from nonrenewable resources, hydrogen peroxide use, and the utilization of ammonium fertilizer. CO2 production also resulted in a significantly higher acidification potential and approximately doubled the emissions of SO2eq per FU. Concerning the fish scenarios, different hotspots were responsible for the heterogeneous distribution of values. In capture production, the major contributors were vessel operations and diesel. Bottom trawlers generated higher emissions through accelerated fuel use and cooling agent leakage than purse seine capture, which overall had hotspots regarding ice production and anti-fouling paint for boats (Vázquez-Rowe et al. 2010; Cavadas et al. 2013). In aquaculture production, feed poses the highest threat in regard to SO2eq emissions (d’Orbcastel et al. 2009; Cavadas et al. 2013), but electricity for recirculation for certain systems is also critical (Dekamin et al. 2015).
The highest eutrophication potential (Fig. 5) was observed for aquaculture production. Concerning the nutritional value, fish scenarios emitted 0.00001–4.6 g PO4−eq 100 kcal−1. In particular, salmon, trout, and Pangasius, which all derived from aquaculture, exhibited great emissions here. Microalgae with avoided CO2 burden were responsible for 0.19–0.23 g PO4−eq 100 kcal−1 and thus were only favorable over aquaculture fish production (except tilapia) and wild-caught tuna. Microalgae with the burden of CO2 production emitted 0.25–0.28 g PO4−eq 100 kcal−1 and could compete well with aquaculture fish, except tilapia. Moreover, the borosilicate glass scenarios performed only slightly better than the PMMA scenarios.
In terms of EPA+DHA, the eutrophication potential of the fish scenarios had an extreme range between 0.000006 g PO4−eq 0.5 g−1 EPA+DHA for wild-caught herring and 14.9 g PO4−eq 0.5 g−1 EPA+DHA for pangasius from aquaculture. The microalgae scenarios with avoided CO2 caused similar or slightly higher PO4−eq emissions than capture production but were overall favorable over aquaculture production. Microalgae scenarios with avoided CO2 burden had a eutrophication potential ranging from 0.09–0.13 g PO4−eq 0.5 g−1 EPA+DHA. Microalgae scenarios with the burden of CO2 production had emissions ranging from 0.12–0.17 g PO4−eq 0.5 g−1 EPA+DHA whereas here the alternative microalgae species scenario with P. tricornutum caused the highest emissions. In contrast, the P. tricornutum scenario was most favorable in terms of protein, with 1.20 g PO4−eq 50 g−1 protein for avoided CO2, whereas the 3-year PMMA scenario with the burden of CO2 was responsible for 2.01 g PO4−eq 50 g−1 protein. Microalgae with the avoided burden of CO2 were thus again preferable over aquaculture production (except tilapia) but inferior to capture production. When the burden of CO2 production during microalgae cultivation was accounted for, scenarios had eutrophication potentials similar to those of aquaculture fish production and still lower emissions than with trout and Pangasius. Overall, the eutrophication potential of fish products in terms of protein reached values ranging from 0.00004–15.1 g PO4−eq 50 g−1 protein. Similar to the situation for other emissions, feed production for aquaculture fish generated the highest share of PO4−eq emissions (Bosma et al. 2009), although on-farm emissions, mainly due to nutrient emissions, were also a critical point (Chen et al. 2015; Dekamin et al. 2015; Biermann and Geist 2019). In capture production, vessel operations were a hotspot (Vázquez-Rowe et al. 2010; Ramos et al. 2011), as was diesel, which is especially critical in capture by fuel-intensive fleets (Cavadas et al. 2013).
Cumulative energy demand
The distribution of the cumulative energy demand (CED) (Fig. 6) is also fairly heterogeneous for all FUs and no clear trend is visible concerning a potentially better performance of any fish production method. In terms of nutritional value, fish products had the lowest CED at 0.08 MJ 100 kcal−1 for carp from aquaculture and the highest at 2.85 MJ 100 kcal−1 for trout from aquaculture. For the microalgae scenarios with avoided CO2 burden, the borosilicate glass scenarios were favorable over the PMMA scenarios, with a range of 0.74–0.87 MJ 100 kcal−1 for glass and 1.25–1.80 MJ 100 kcal−1 for PMMA. When CO2 production was accounted for, the glass scenarios used 1.46–1.60 MJ 100 kcal−1 and PMMA scenarios used 1.98–2.53 MJ 100 kcal−1. Accordingly, the energy use of microalgae cultivation is comparable with the energy demand of fish production. The same can be observed concerning the EPA+DHA content. No general statement can be made regarding whether microalgae perform better than certain fish production methods. Microalgae with avoided CO2 burden consumed 0.37–0.55 MJ 0.5 g−1 EPA+DHA for glass and 0.63–0.91 MJ 0.5 g−1 EPA+DHA for PMMA whereas microalgae with the burden of CO2 production used 0.73–1.02 MJ 0.5 g−1 EPA+DHA for glass and 0.99–1.27 MJ 0.5 g−1 EPA+DHA for PMMA. The energy demand of fish products ranged between 0.04 MJ 0.5 g−1 EPA+DHA for wild-caught mackerel and 9.69 MJ 0.5 g−1 EPA+DHA for pangasius from aquaculture production. Again, all scenarios showed the highest values concerning protein content. The microalgae borosilicate glass scenarios with avoided CO2 burden (4.92–5.95 MJ 50 g−1 protein) corresponded to the average values of fish production (0.29–9.76 MJ 50 g−1 protein). However, the PMMA scenarios on average had a higher energy demand (8.76–12.62 MJ 50 g−1 protein) than most fish. Microalgae with the full burden of CO2 production used 9.07–17.78 MJ 50 g−1 protein, which exceeded all fish scenarios, except that P. tricornutum used slightly less energy than herring and trout. Whereas the energy demand for microalgae cultivation again predominantly arose from energy-intensive processes and PMMA, the CED of fish was mainly due to the exact method of capture and aquaculture production. Thus, demersal fisheries were described to use more fuel than pelagic fisheries, representing the main contributor in capture production (Ziegler et al. 2012). In aquaculture production, feed is generally the highest consumer of energy, more precisely, the milling of feed, although the energy for this process is also highly dependent on the energy source used (Pelletier et al. 2009). Further critical processes here were on-farm electricity use for pumps, aeration, water treatment, etc., during the farming and hatchery stages (Smárason et al. 2017).
The usage of blue water in fish production is only relevant for aquaculture, where the largest share of blue water arises from irrigation during crop cultivation (especially rice) for feed, while the remaining consumption is largely due to rearing production (Mungkung et al. 2013; Hognes et al. 2014). However, even for aquaculture fish production, the water use was not consequently assessed, which is why values were presented as a range across all analyzed fish species. Compared with fish production in aquaculture, microalgae consumed far less blue water (Fig. 7). Based on the nutritional energy value, aquaculture fish production on average used 62.90 L 100 kcal−1, whereas the microalgae scenarios with avoided CO2 burden only needed 0.95–1.30 L 100 kcal−1, and those with the full burden of CO2 needed 1.26–1.61 L 100 kcal−1. Even though the PMMA scenarios were the least favorable, their water use was only marginally higher than that in the borosilicate glass scenarios. With regard to EPA+DHA, microalgae had a water consumption from 0.48–0.73 L 0.5 g−1 EPA+DHA for scenarios with avoided CO2 burden and 0.63–0.93 L 0.5 g−1 EPA+DHA for scenarios with the burden of CO2 production. Aquaculture fish used an amount that was more than 50-fold higher, with the average at 47.01 L 0.5 g−1 EPA+DHA. The highest values for both microalgae and fish were again reached for protein. Aquaculture fish consumed 162.78 L 50 g−1 protein. In contrast, microalgae only used a fraction of this value, with 6.56–11.33 L 50 g−1 protein. P. tricornutum with avoided CO2 burden was the most favorable scenario, which can be traced back to its protein content being higher than that of Nannochloropsis sp. as used in the other scenarios.
Concerning the land use of fish products (Fig. 8), values for both aquaculture and capture production were available. As the land use values of microalgae were directly analyzed from the life cycle inventory, scenarios with the burden of CO2 did not differ from those with avoided CO2 burden. Consequently, these scenarios were not presented separately. The land use of capture production was clearly lower than the values for aquaculture fish and equal to the values for microalgae. Where capture production had a land use rate of 0.04 m2 (100 kcal)−1 (probably resulting from refineries and pipelines for diesel production), aquaculture production needed an approximately fourfold higher amount, at 0.16 m2 (100 kcal)−1. In contrast, microalgae only presented values from 0.004–0.0046 m2 100 kcal−1. Similar trends can be observed for the remaining FUs. Concerning EPA+DHA, fish showed average values of 0.03 to 0.12 m2 0.5 g−1 EPA+DHA for capture and aquaculture production, respectively. Microalgae land use values in terms of EPA+DHA ranged from 0.002 to 0.0028 m2 0.5 g−1 EPA+DHA. The daily recommendation of protein again was characterized by the highest values for all scenarios, ranging from 0.025–0.032 m2 50 g−1 protein, whereas P. tricornutum again was the most favorable scenario here. Fish used 0.11–0.42 m2 50 g−1 protein. Even the unfavorable microalgae scenarios outperformed fish production in all FUs. Concerning aquaculture, feed production is clearly the dominant factor in the high land use rates (Mungkung et al. 2013; Seves et al. 2016). No explanation could be obtained from the literature on where the land use of capture fish production comes from. Microalgae had a relatively low land use rate due to the vertical dimension of the reactor and the relatively high yields of microalgae. As an additional advantage, the photobioreactor (PBR) was built on non-arable land.