Environmental life cycle assessment of algae systems: Critical review of modelling approaches

生命周期评估 温室气体 环境科学 藻类 生物过程 生化工程 环境影响评价 富营养化 范围(计算机科学) 环境经济学 环境工程 营养物 环境资源管理 工程类 计算机科学 生态学 生产(经济) 生物 经济 程序设计语言 宏观经济学 化学工程
作者
L. Braud,Kevin McDonnell,Fionnuala Murphy
出处
期刊:Renewable & Sustainable Energy Reviews [Elsevier BV]
卷期号:179: 113218-113218 被引量:1
标识
DOI:10.1016/j.rser.2023.113218
摘要

Recent advances in algal biotechnology and bioprocess engineering have been supported by techno-economic and life cycle assessment (LCA) methodologies to develop cost-effective and sustainable algae-based products. Over the past 20 years, LCA studies have shown that the use of energy and nutrients are major contributors to the environmental impacts of algae systems. In this study, we investigate four mitigation strategies commonly used to reduce these impacts: considering multiple products, accounting for carbon capture and nutrient uptake, and including recirculation loops. The modelling approaches used by LCA practitioners to test these mitigation strategies were analysed to identify potential methodological challenges. We reviewed 158 LCA studies published between 2010 and 2022 with a focus on the goal & scope and life cycle inventory (LCI) stages. The results show that the consideration of multiple products in algae systems is directly related to the choice of method(s) used to avoid or solve multifunctionality. The use of a substitution approach leads to the avoidance of greenhouse gas emissions compared to benchmark systems. The net negated emissions from carbon capture and nutrient uptake during algae growth result in reduced impacts on climate change and eutrophication. Finally, the use of waste streams and the consideration of recirculation loops reduce the need for external inputs of water, inorganic fertilisers, and energy. This review provides detailed information on algae system modelling approaches to facilitate the interpretation of LCA results from existing studies and serves as a basis for the development of transparent and reproducible LCI frameworks.

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