Abstract
PurposeThe long-term marginal electricity supply mixes of 40 countries were generated and integrated into version 3.4 of the ecoinvent consequential database. The total electricity production originating from these countries accounts for 77% of the current global electricity generation. The goal of this article is to provide an overview of the methodology used to calculate the marginal mixes and to evaluate the influence of key parameters and methodological choices on the results.MethodsThe marginal mixes are based on public energy projections from national and international authorities and reflect the accumulated effect of changes in demand for electricity on the installation and operation of new-generation capacities. These newly generated marginal mixes are first examined in terms of their compositions and environmental impacts. They are then compared to several sets of alternative electricity supply mixes calculated using different methodological choices or data sources.Results and discussionRenewable energy sources (RES) as well as natural gas power plants show the highest growth rates and usually dominate the marginal mixes. Nevertheless, important variations may exist between the marginal mixes of the different countries in terms of their technological compositions and environmental impacts. The examination of the modeling choices reveals substantial variations between the marginal mixes integrated into the ecoinvent consequential database version 3.4 and marginal mixes generated using alternative modeling options. These different modeling possibilities include changes in the methodology, temporal parameters, and the underlying energy scenarios. Furthermore, in most of the impact categories, average (i.e., attributional) mixes cause higher impact scores than marginal mixes due to higher shares of RES in marginal mixes.ConclusionsAccurate and consistent data for electricity supply is integrated into a consequential database providing a strong basis for the development of consequential Life Cycle Assessments. The methodology adopted in this version of the database eliminates several shortcomings from the previous approach which led to unrealistic marginal mixes in several countries. The use of energy scenarios allows the evolution of the electricity system to be considered within the definition of the marginal mixes. The modeling choices behind the electricity marginal mix should be adjusted to the goal and scope of individual studies and their influence on the results evaluated.
Highlights
Electricity supply is a central parameter in the life cycle assessments (LCA) of numerous products and services (Reinhard et al 2016; Steubing et al 2016; Astudillo et al 2017; Gibon et al 2017; Cox et al 2018)
The approach adopted to obtain the marginal electricity supply mixes prior to the ecoinvent database version 3.4 had several drawbacks leading to inaccurate and unrealistic results. These results increased the risk of poor decisions and were an obstacle to the realization of Consequential life cycle assessment (CLCA) and the wider adoption of the methodology (Treyer and Bauer 2016)
It would be beneficial to the field of CLCA to replicate this approach to other sectors in the ecoinvent database
Summary
Electricity supply is a central parameter in the life cycle assessments (LCA) of numerous products and services (Reinhard et al 2016; Steubing et al 2016; Astudillo et al 2017; Gibon et al 2017; Cox et al 2018). The approach adopted to obtain the marginal electricity supply mixes prior to the ecoinvent database version 3.4 had several drawbacks leading to inaccurate and unrealistic results. These results increased the risk of poor decisions and were an obstacle to the realization of CLCAs and the wider adoption of the methodology (Treyer and Bauer 2016)
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