SINGLE CELL PROTEIN – THE RIGHT INGREDIENT FOR A SUSTAINABLE AQUACULTURE?

Introduction

In European aquaculture, protein feeds are essential for the growth and health of farmed fish. Sustainability concerns have led to a shift from fishmeal towards plant-based proteins like soybean meal. Soy production has, however, raised concerns related to pesticide use and deforestation. This has prompted a search for alternative protein sources within Europe. Novel alternatives, such as microbial-derived proteins like Single Cell Protein (SCP), present an innovative solution. In this study, SCP is produced via pyrolysis, converting low-value feedstocks into high-value bioproducts. This study aims to assess the potential environmental and social impacts of this new SCP value chain in aquaculture feed (Figure 1).

Figure 1. Single Cell Protein for aquafeed production

Methods

The study employed a "cradle to gate" attributional environmental and social life cycle assessment (respectively E-LCA and S-LCA) approach, considering a SCP production plant located in Norway, following ISO 14044:2006 guidelines for E-LCA and ISO 14075:2024 for S-LCA. SimaPro 9.1 software and the ReCiPe 2016 Midpoint (H) V1.11 method were used for the life cycle impact assessment (LCIA). For the E-LCA, the main database in use was ecoinvent 3.11, while for S-LCA, the social footprint and risks were analyzed using the Social Hotspot Database and its risk map tool, using the Social Hotspots Index (SHI) methodology. In both assessments, expert judgment was applied iteratively to recognize key processes and collect data in critical areas. The selected functional unit was 1 kg of protein, assuming a protein content of 60% in SCP. Mass-based allocation was used when necessary. Considering the maturity of the innovation, a prospective LCA was carried out starting from a screening LCA at an early stage of innovation, using data from laboratory experiments for identifying the major environmental and social "hotspots", and moving through an intermediate LCA at pilot scale towards a full LCA (data from industrial scale). A literature review using the snowballing methodology identified key features to establish a baseline for scaling up the system. A parameterized model was created to easily adjust contributing inputs as the study progressed from screening to a complete E-LCA. For the industrial scale assessment, the data came from one reference and one optimized scenario, using the SuperPro optimization tool. The model included all the input and output mass fluxes, energy use, and emissions. SuperPro also includes monetary values for materials and utilities, which were used for the S-LCA modelling. The study developed a specific S-LCA framework through several steps. First, social subcategories dependent on the technology were identified, following Hannouf et al. (2024). These were then prioritized using a Responsible Research and Innovation (RRI) approach (Thorstensen & Forsberg, 2016), where stakeholders across two workshops scored the subcategories by perceived importance. A decision matrix was built from these scores, and subcategories rated medium to high importance were included for further scrutiny. Indicators for each subcategory were sourced from existing SLCA frameworks or developed using relevant statistics and expert opinion.

Results and discussion

For the screening LCA, the results show that the SCP final production step is the main contributor to the total climate change emissions, with the other processing steps contributing only to a minor extent. The burdens are mainly linked to energy requirements. According to experts' feedback, however, the energy requirements may be 10-100 times lower under optimized conditions. For the syngas conditioning and fermentation processes, the climate change contribution is mainly associated with chemicals and energy use, while for the sawdust pre-treatment, drying, and pyrolysis steps, it is linked to softwood debarking and processing. Across different SCP protein value chains given in literature, climate change impacts are primarily driven by energy inputs. Bacterial SCP is mainly influenced by natural gas (25%) and electricity use (12%), whereas yeast-based SCP is strongly affected by enzyme production (48.3%) and wheat grain inputs (32%). For the pilot and full-scale E-LCA, the comparison with a generic benchmark can be done by using a prospective data source for energy use simulation of the future energy mix, since energy use has been highlighted as the main driver in the SCP production. For the pilot-scale scenario, shifting the electricity mix from the Netherlands to a Norwegian scenario reduces greenhouse gas emissions (GHG) by 45.8% of the final product. Using a generic market for heat in the future decreased by 46.3% the total GHG emissions related to the base-case scenario. In both models, almost all the indicators present the same trend. The S-LCA results indicate that the main social hotspot is the electricity used for producing the SCP (60% of the total contribution). The social footprint identifies Health and Safety as the main social risk along the supply chain, due to potential occupational risks such as workplace injuries and fatalities​, exposure to hazardous substances​ and unsafe working environment. For the specific assessment, the framework based on the stakeholder engagement activities and literature prioritizes eight social subcategories: the highest score being Health and Safety and the lowest score being Ethical Treatment of Animals. The latest category has not been included in any previous S-LCA studies in literature, and not in the field of aquaculture. Three key indicators were selected for assessing ethical treatment of animals: Feed Conversion Ratio (FCR), Mortality rate, and Gut health.

Conclusions

The environmental and social LCAs have identified key hotspots of the SCP value chain from an early phase of development to a pilot scale following the maturity of the innovation. The results should be used for improving the design of the innovative processes to minimize environmental and social impact before full-scale operation, as well as to provide recommendations to industrial partners during the implementation phase, aiming towards potential benefits for feed security and resource efficiency. The application of S-LCA including animal welfare performance thresholds, and RRI, is important for raising awareness and involving the relevant partners, and it extends beyond the state of the art by including social sustainability in the development of novel bio-based value chains. The inclusion of ethical treatment of animals as a subcategory represents a meaningful extension of traditional S-LCA frameworks, recognizing that animal welfare is a relevant social concern in bio-based value chains such as single cell protein (SCP) production.

Acknowledgements

SynoProtein: the project is supported by the Circular Bio-based Europe Joint Undertaking (CBE-JU) and its members under Grant Agreement No. 101112345