Analysing the water and greenhouse gas effects of soya bean‐based biodiesel in five different regions

• Main Authors: Marlinde M. J. Knoope, Christoph H. Balzer, Ernst Worrell
• Format: Article
• Language: English
• Published: Wiley 2019-02-01
• Series: GCB Bioenergy
• Online Access: https://doi.org/10.1111/gcbb.12558

Abstract Bioenergy may have significant lower greenhouse gas (GHG) emission intensities compared to fossil alternatives, but concerns are raised that bioenergy would contribute to additional water scarcity. Therefore, the GHG intensity, water intensity and water‐related risks are analysed simultaneously for conventional diesel and soya bean‐based biodiesel from Argentina, Brazil, Unites States (U.S.), Thailand and Iran on a life cycle basis. The water‐related risks are estimated with a water scarcity—consumption matrix, which was recently developed. Results show that a significant share (9%‐38%) of the GHG emissions in all biodiesel cases is caused by soil N2O emissions. In addition, the ranges in water consumption intensity for soya bean‐based biodiesel are considerably larger than for fossil fuels. However, whether this leads to high water‐related risks depends on the local water scarcity. Soya bean‐based biodiesel from Argentina has low water‐related risks to all nodes of the supply chain due to low local water stress combined with a low direct water consumption intensity (20 L/GJfuel). In addition, high GHG emission reduction (71%) and a low‐specific eutrophication potential (0.04 kg PO43−/GJfuel) are achieved. The indirect water consumption intensity is estimated at 120–420 L/GJ for soya bean‐based biodiesel, which is significant if the soya beans are rainfed, like in Argentina and Brazil. If irrigation is required, indirect water consumption is dwarfed by irrigation water. Overall, it is concluded that soya bean‐based biodiesel can have significant lower GHG emission intensity than fossil diesel, without causing additional water stress in the supply chain if they are produced in water abundant areas and good agricultural practices are used. The used method shows disaggregated water‐related risks for the different nodes of the supply chain to acknowledge the regional nature of water scarcity and enables decision makers to identify “hot spots” and take targeted actions.