Contribution-based prioritization of LCI database improvements: Method design, demonstration, and evaluation

Purpose The 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. Methods The 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 discussion Renewable 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. Conclusions Accurate 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. Keywords CLCA. Ecoinvent. Life cycle inventory (LCI) database. Long-term marginal electricity supply mix 1 Using the IPCC Global Warming Potentials (GWP) characterization factors with a time horizon of 100 years and the calculation approach detailed in Mutel (2018).

Section: Introductionmentioning

confidence: 99%

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

Vandepaer

Treyer

Mutel

et al. 2018

“…Tracing and measuring the exchange flows of and between these activities is realized on the basis of nodes called unit processes. A unit process represents one specific activity or a group of activities allocated to one unique output and records (i) the exchanges with environment, i.e., the input of natural resources and output of emissions; (ii) the intermediate exchanges from and to the technosphere, i.e., the input of usable energy and raw materials and the output of products and waste (Reinhard et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…A typical product system covers thousands of unit processes, each of which needs to be described with exchange flow values (Bourgault et al 2012). This information cannot usually be gathered as primary data within a specific project due to the high cost that would be involved in data collection (Reinhard et al 2016). It is therefore a common practice to focus data collection efforts on selected activities that reflect the space for action-these activities are together called the foreground system-and to use generic data from Life Cycle Inventory (LCI) databases, 2 such as ecoinvent (Weidema et al 2013), to model the remaining activities, called the background system (Tillman 2000;Bourgault et al 2012;Reinhard et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This information cannot usually be gathered as primary data within a specific project due to the high cost that would be involved in data collection (Reinhard et al 2016). It is therefore a common practice to focus data collection efforts on selected activities that reflect the space for action-these activities are together called the foreground system-and to use generic data from Life Cycle Inventory (LCI) databases, 2 such as ecoinvent (Weidema et al 2013), to model the remaining activities, called the background system (Tillman 2000;Bourgault et al 2012;Reinhard et al 2016). Even when 100 processes are modeled with primary data, the foreground system would still not exceed 5% of the entire product system as it typically involves thousands of unit processes (Steubing et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Even when 100 processes are modeled with primary data, the foreground system would still not exceed 5% of the entire product system as it typically involves thousands of unit processes (Steubing et al 2016). Bearing this in mind, background data from LCI databases can be considered the backbone of any LCA study (Reinhard et al 2016). The unit processes in such background databases therefore form the basic building blocks required by all LCA applications (Sonnemann and Vigon 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent

Reinhard

Wernet²,

Zah

et al. 2019

Self Cite

Purpose Improving the quality and quantity of unit process datasets in Life Cycle Inventory (LCI) databases affects every LCA they are used in. However, improvements in data quality and quantity are so far rather directed by the external supply of data and situation-driven requirements instead of systematic choices guided by structural dependencies in the data. Overall, the impact of current data updates on the quality of the LCI database remains unclear and maintenance efforts might be ineffective. This article analyzes how a contribution-based prioritization approach can direct LCI update efforts to datasets of key importance. Methods A contribution-based prioritization method has been applied to version 3 of the ecoinvent database. We identified the relevance of unit processes on the basis of their relative contributions throughout each product system with respect to a broad range of Life Cycle Impact Assessment (LCIA) indicators. A novel ranking algorithm enabled the ranking of unit processes according to their impact on the LCIA results. Finally, we identified the most relevant unit processes for different sectors and geographies. Results and discussion The study shows that a relatively large proportion of the overall database quality is dependent on a small set of key processes. Processes related to electricity generation, waste treatment activities, and energy carrier provision (petroleum and hard coal) consistently cause large environmental impacts on all product systems. Overall, 300 datasets are causing 60% of the environmental impacts across all LCIA indicators, while only 3 datasets are causing 11% of all climate change impacts. In addition, our analysis highlights the presence and importance of central hubs, i.e., sensitive intersections in the database network, whose modification can affect a large proportion of database quality. Conclusions Our study suggests that contribution-based prioritization offers important insights into the systematic and effective improvement of LCI databases. The presented list of key processes in ecoinvent version 3.1 adds a new perspective to database improvements as it allows the allocation of available resources according to the structural dependencies in the data.

Prioritizing regionalization efforts in life cycle assessment through global sensitivity analysis: a sector meta-analysis based on ecoinvent v3

Patouillard

Collet

Lesage

et al. 2019

Purpose Regionalization in life cycle assessment (LCA) aims to increase the representativeness of LCA results and reduce the uncertainty due to spatial variability. It may refer to adapting processes to better account for regional technological specificities (inventory regionalization) or adding of spatial information to the elementary flows (inventory spatialization) which allow using more regionalized characterization factors. However, developing and integrating regionalization requires additional efforts for LCA practitioners and database developers that must be prioritized. Methods We propose a stepwise methodology for LCA practitioners to prioritize data collection for regionalization based on global sensitivity analysis (GSA) using Sobol indices. It involves several GSA to select the impact categories (ICs) that require further inventory data collection (IC ranking), prioritize between inventory regionalization and inventory spatialization (LCA phase ranking), and target specific data to collect. Then we propose a method to derive sector-specific recommendations using statistical tests to prioritize inventory regionalization versus spatialization and the ICs on which to focus inventory data collection. These recommendations are meant to help LCA practitioners and database developers define their strategy for regional data collection by focusing on data that have the highest potential to reduce the uncertainty of the results. Results and discussionThe applicability of the methodology is illustrated through three case studies using the ecoinvent v3 database and the regionalized impact methodology IMPACT World+: one on prioritizing data collection in a single biofuel product system and two meta-analyses of all product systems in two distinct economic sectors (biofuel production and land passenger transport). Recommendations for regionalization can be derived for an economic sector and appear to be different from one economic sector to another. GSA seems to be more relevant to prioritize regionalization efforts than an impact contribution analysis (ICA) approach often used to prioritize data collection in LCA. However, further improvements, such as accounting for spatial correlations and better computational times for GSA, are required to implement it in LCA. Conclusions We recommend using the methodology based on GSA to efficiently prioritize regionalization efforts between ICs and between inventory regionalization and inventory spatialization. We proved that the implementation of IC ranking and LCA phase ranking is computationally feasible and therefore invite current LCA software providers to unlock this new horizon in LCA interpretation. We also invite to expand the meta-analysis to all sectors in an LCA database.