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.
No abstract
In the EU, the transport sector is the only sector with increasing GHG emissions compared to 1990. While harmful emissions have decreased due to successful regulation, transport performance, fossil fuel consumption and thus CO 2 emissions have continued to increase, despite powertrain efficiency improvements. Meaningful regulation, which can be market-based (MBI) and non-market-based (NMBI) by nature, is needed to meet climate targets. To understand the mechanisms, effects and limitations of MBI and NMBI, this study investigates and evaluates selected regulations in the German road transportation sector until 2020. Therefore, this study identifies, describes, and categorizes environmental policy instrument types. Based on this step, selected instruments in the road transportation sector are identified by their type and implemented policies are described and assessed. Furthermore, an assessment methodology is developed to evaluate and score target achievement, cost-efficiency and practical feasibility by linking the outcomes of instruments to its goals. Based on the findings of this assessment, conclusions and recommendations are developed and discussed. Finally, results and general properties of policies and their type of instruments are extrapolated, and general statements about market and non-market-based instruments in a broader context for future regulation and market designs are projected. The study discovers that fuel producers and distributors, vehicle manufacturers and sellers are directly regulated by non-marked-based instruments, despite the EU Emissions Trading Scheme (ETS). On the customer side, primarily market-based implemented except for low-emission zones, which are direct regulations. The study finds that holistic representation and realistic internalization of external effects in a market is complex and will never be complete. Still, sufficient representation can be enough to drive transformation in the transport sector. The CO 2 price itself is not sufficiently representing the consequential costs of climate change induced by road transport, but it helps to make low-carbon alternatives economically viable. Overall, the study finds that most implemented regulations in the German road transport sector were successful in relation to their goals. Supplementary Information The online version contains supplementary material available at 10.1186/s12302-022-00663-7.
No abstract
The quantification of Greenhouse Gas (GHG) inventories and its associated uncertainty is a relevant activity often requested by authorities. Accurate methods to calculate both inventories and the involved uncertainty are convenient for close monitoring purposes. Using Monte Carlo simulations, correlations of high accuracy between emission factors (EFs), lower heating value (LHV), and density were built for refinery fuel gas, natural gas and fuel/residual oil. In all cases, the data generated by the simulations also served the purpose of building correlations for upper and lower bounds of the EF that can be readily used to estimate the EF estimation uncertainty. The correlations were tested against actual refinery data and the results show that more accurate estimations were obtained compared with EF obtained from laboratory composition methods and from methods that estimate EF as proportional to LHV only. In the case of fuel and residual oils, the correlations developed are a function of LHV only but were improved by using a cubic polynomial. The calculation of upper and lower bounds for EF offer a convenient method to estimate EF uncertainties that are required in official GHG emissions inventory calculations. In conclusion, in addition to LHV, the use of one additional readily available fuel property, namely fuel density is sufficient to reduce uncertainty of estimation of GHG (in this case CO 2 ) from combustion to acceptable levels.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.