Towards a more holistic sustainability assessment framework for agro-bioenergy systems — A review

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• 作者：Oludunsin Arodudu^a ; ^b ; ^{Oludunsin.Arodudu@zalf.de} ; Katharina Helming^a ; ^d ; Hubert Wiggering^a ; ^b ; Alexey Voinov^c
• 关键词：LCA ; Life cycle thinking ; Life cycle sustainability assessment ; Sustainability assessment ; Agronomic factors ; Agro-ecological
• 刊名：Environmental Impact Assessment Review
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：62
• 期：Complete
• 页码：61-75
• 全文大小：1656 K
• 卷排序：62

文摘

The use of life cycle assessment (LCA) as a sustainability assessment tool for agro-bioenergy system usually has an industrial agriculture bias. Furthermore, LCA generally has often been criticized for being a decision maker tool which may not consider decision takers perceptions. They are lacking in spatial and temporal depth, and unable to assess sufficiently some environmental impact categories such as biodiversity, land use etc. and most economic and social impact categories, e.g. food security, water security, energy security. This study explored tools, methodologies and frameworks that can be deployed individually, as well as in combination with each other for bridging these methodological gaps in application to agro-bioenergy systems. Integrating agronomic options, e.g. alternative farm power, tillage, seed sowing options, fertilizer, pesticide, irrigation into the boundaries of LCAs for agro-bioenergy systems will not only provide an alternative agro-ecological perspective to previous LCAs, but will also lead to the derivation of indicators for assessment of some social and economic impact categories. Deploying life cycle thinking approaches such as energy return on energy invested-EROEI, human appropriation of net primary production-HANPP, net greenhouse gas or carbon balance-NCB, water footprint individually and in combination with each other will also lead to further derivation of indicators suitable for assessing relevant environmental, social and economic impact categories. Also, applying spatio-temporal simulation models has a potential for improving the spatial and temporal depths of LCA analysis.