Ex-Ante LCA for Microalgae-Based Veterinary Molecules in Finfish Aquaculture: How to Assess the Environmental Performance of Unknown Molecules ?
Abstract
Diseases have been identified as a key limiting factor to the global growth of aquaculture. The sector is experiencing massive losses due to a large diversity of health issues. At the same time, a new paradigm in health management systems should fight antibioresistance, reduce fish farms environmental impacts, and lower losses. In this context, the EU-funded AquaHealth consortium intends to identify and develop new veterinary molecules produced by microalgae to treat finfish health issues. To orient the development of this early-stage technology, we perform an ex-ante and consequential LCA (with system expansion and marginal supplier identification) on a product system where microalgae production and aquaculture work in synergy. Our study attempts to delimit the range of possibilities for this future synergy by identifying and then modelling quantitatively the most relevant scenarios. The challenge is due to the high epistemic uncertainty in determining the microalgae candidates and the different types of bioactive molecules, while modelling the effect of the molecule on the fish farm’s production and emissions is challenged by both epistemic and aleatory uncertainty. To address such complexity, we implement a stepwise approach which progressively tackles the foreseen non-linearity within the synergy. The final step is the building of a fish farm dynamic model in which different types of molecules affect distinct variables such as Feed Conversion Ratio, Disease outbreak frequency, resistance to disease. These biologic parameters then affect the farm operational choices and associated production and emissions. The outcomes of our fictive farm simulations are then used as input/output data for the life cycle inventory. The expected result of this approach and models is to provide a parameterized and dynamic LCA which allows to answer questions such as: Which type of molecule could provide the best environmental performance compared to alternatives? What are the maximal environmental impacts associated with the microalgae production for the synergy to compete with other alternatives? The conference presentation includes then preliminary results answering these questions, as well as a reflection on how these methods have the potential to be generalized and used to study other systems of emerging technologies in which a product is used to increase the efficiency of another process delivering the functional unit. Our approach particularly tackles the case of emerging techno-biological systems and constitute an attempt to address their complexity and uncertainty.
Info
Conference Abstract, 2021
UN SDG Classification
DK Main Research Area
Science/Technology
