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Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI databases for such data, and the ecoinvent database is the largest transparent unit-process LCI database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the database, set the foundation for a truly global database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments. Methods Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the database, and global background data was added, often as an extrapolation from regional data. Results and discussion In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions. Conclusions With version 3, the ecoinvent database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding ecoinvent from a mostly regional into a truly global database and offers many new insights beyond the thousands of new and updated datasets it also introduced.

Hydrogen is a promising energy carrier in the clean energy systems currently being developed. However, its effectiveness in mitigating greenhouse gas (GHG) emissions requires conducting a lifecycle analysis of the process by which hydrogen is produced and supplied. This study focuses on the hydrogen for the transport sector, in particular renewable hydrogen that is produced from wind- or solar PV-powered electrolysis. A life cycle inventory analysis is conducted to evaluate the Well-to-Tank (WtT) GHG emissions from various renewable hydrogen supply chains. The stages of the supply chains include hydrogen being produced overseas, converted into a transportable hydrogen carrier (liquid hydrogen or methylcyclohexane), imported to Japan by sea, distributed to hydrogen filling stations, restored from the hydrogen carrier to hydrogen and filled into fuel cell vehicles. For comparison, an analysis is also carried out with hydrogen produced by steam reforming of natural gas. Foreground data related to the hydrogen supply chains are collected by literature surveys and the Japanese life cycle inventory database is used as the background data. The analysis results indicate that some of renewable hydrogen supply chains using liquid hydrogen exhibited significantly lower WtT GHG emissions than those of a supply chain of hydrogen produced by reforming of natural gas. A significant piece of the work is to consider the impacts of variations in the energy and material inputs by performing a probabilistic uncertainty analysis. This suggests that the production of renewable hydrogen, its liquefaction, the dehydrogenation of methylcyclohexane and the compression of hydrogen at the filling station are the GHG-intensive stages in the target supply chains.

PURPOSE: Source-separated collection of food waste has been reported to reduce the amount of household waste in several cities including Kyoto, Japan. Food waste can be reduced by various activities including preventing edible food loss, draining moisture, and home composting. These activities have different potentials for greenhouse gas (GHG) reduction. Therefore, we conducted a life-cycle inventory analysis of household waste management scenarios for Kyoto with a special emphasis on food waste reduction activities. METHODS: The primary functional unit of our study was “annual management of household combustible waste in Kyoto, Japan.” Although some life-cycle assessment scenarios included food waste reduction measures, all of the scenarios had an identical secondary functional unit, “annual food ingestion (mass and composition) by the residents of Kyoto, Japan.” We analyzed a typical incineration scenario (Inc) and two anaerobic digestion (dry thermophilic facilities) scenarios involving either source-separated collection (SepBio) or nonseparated collection followed by mechanical sorting (MecBio). We assumed that the biogas from anaerobic digestion was used for power generation. In addition, to evaluate the effects of waste reduction combined with separate collection, three food waste reduction cases were considered in the SepBio scenario: (1) preventing loss of edible food (PrevLoss); (2) draining moisture contents (ReducDrain); and (3) home composting (ReducHcom). In these three cases, we assumed that the household waste was reduced by 5%. RESULTS AND DISCUSSION: The GHG emissions from the Inc, MecBio, and SepBio scenarios were 123.3, 119.5, and 118.6 Gg CO2-eq/year, respectively. Compared with the SepBio scenario without food waste reduction, the PrevLoss and ReducDrain cases reduced the GHG emissions by 17.1 and 0.5 Gg CO2-eq/year. In contrast, the ReducHcom case increased the GHG emissions by 2.1 Gg CO2-eq/year. This is because the biogas power production decreased due to the reduction in food waste, while the electricity consumption increased in response to home composting. Sensitivity analyses revealed that a reduction of only 1% of the household waste by food loss prevention has the same GHG reduction effect as a 31-point increase (from 50% to 81%) in the food waste separation rate. CONCLUSIONS: We found that prevention of food losses enhanced by separate collection led to a significant reduction in GHG emissions. These findings will be useful in future studies designed to develop strategies for further reductions in GHG emissions.

PURPOSE: This discussion article aims to highlight two problematic aspects in the International Reference Life Cycle Data System (ILCD) Handbook: its guidance to the choice between attributional and consequential modeling and to the choice between average and marginal data as input to the life cycle inventory (LCI) analysis. METHODS: We analyze the ILCD guidance by comparing different statements in the handbook with each other and with previous research in this area. RESULTS AND DISCUSSION: We find that the ILCD handbook is internally inconsistent when it comes to recommendations on how to choose between attributional and consequential modeling. We also find that the handbook is inconsistent with much of previous research in this matter, and also in the recommendations on how to choose between average and marginal data in the LCI. CONCLUSIONS: Because of the inconsistencies in the ILCD handbook, we recommend that the handbook be revised.

PURPOSE: Life cycle inventory (LCI) data are region-specific because energy fuel mixtures and methods of production often differ from region to region. LCI database examples include US LCI, Ecoinvent v.2, and NIST, each of which is country-specific. Thus, the main aim of this study is to show that Egypt is in need of an Egyptian National LCI (ENLCI) database and to focus on the means of developing a database specific to Egypt. METHODS: Arab countries have thus far engaged in virtually no life cycle assessment (LCA) studies, and a significant neglect of this matter is in evidence for the continent of Africa and, in particular, Egypt. Thus, this study suggests an organizational and managerial framework for the development of a national LCI database and sheds light on the required LCI database categories and data quality for practical solutions reflecting who is equipped to do what in order to keep pace with the world. RESULTS: The results from this review are useful to standardize the study of the life cycle assessment concept in Egypt; to form a foundation for development of an Egyptian database for facilitating a cleaner environment; to encourage stakeholders, such as the environmental agencies, Egyptian Housing and Building Research Center, and the Ministry of Industry; to propose an organizational framework in which they play a central role; and to provide investment to initiate development. CONCLUSIONS: The analysis indicates that the development of a LCI database specific to Egypt is difficult because Egypt has various technical and organizational challenges, but a roadmap of actions to be taken to move ahead is provided. The success of this roadmap depends on the capacity for developing the necessary technical and financial support and on strong partnerships with industry, government, LCA professionals, and academia.

Purpose Version 3 of ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016 ). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. Methods In order to do this, functionally equivalent datasets were matched across database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Results and discussion Differences were observed in between all database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. Conclusions LCIA results continue to change as LCI databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.

Purpose Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004 ). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format. Methods A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach. Results and discussion In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited. Conclusions Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.

Purpose A recent update to the Product Category Rules (PCRs) for Construction Products (of the International EPD System) has triggered a methodological issue for owners and users of Environmental Product Declarations (EPDs). The updated PCR has led to capital goods data being implicitly included in the Life Cycle Inventory (LCI) of EPDs. This paper critically examines the role of capital goods in EPDs and establishes major shortcomings in the current methodology, LCI datasets and interpretation. Methods To evaluate the role of capital goods in EPDs, this paper provides a discourse on the fundamentals of Life Cycle Assessment (LCA) methodology, scope, available LCI data and the impact of capital goods on EPD outcomes. Using the ecoinvent database, we analyse the impact of the inclusion and exclusion of capital goods in selected 38 construction products based on the EN 15804+A2 (2019) Standard. Finally, we estimate the relative contribution of capital goods to a suite of Life Cycle Impact Assessment (LCIA) indicators based on the archetypes of capital goods available in ecoinvent and apply Monte Carlo simulation to establish the range of uncertainties in the capital goods data for the selected construction products. Results and discussion Our research confirms that when capital goods are included based on currently available background LCI data, they mostly have a low effect (<10% increase) on climate change, but they can have an enormous effect (>100% increase) on abiotic depletion (minerals and metals), land use and/or human toxicity indicators. Interestingly, when looking further into the ecoinvent capital goods LCI datasets, it becomes clear that there are inaccuracies, inconsistencies, and possibly incorrect estimates of capital goods and infrastructure data. These findings raise questions about the suitability of the underlying LCI background data and whether non-attributable capital goods should be allowed to define EPD outcomes. Conclusion The requirement for the inclusion of capital goods leads to a major conundrum for LCA practitioners. It is suggested that capital goods be excluded until there is better refinement and improvement of the quality of LCI datasets and EPD programs provide clearer guidance on dealing with capital goods. Alternatively, EPDs could document transparently the inclusion or exclusion of capital goods, so that there is a clear separation of the effects of capital goods on LCIA indicators.

PURPOSE: This report proposes a life-cycle analysis (LCA)-oriented methodology for systematic inventory analysis of the use phase of manufacturing unit processes providing unit process datasets to be used in life-cycle inventory (LCI) databases and libraries. The methodology has been developed in the framework of the CO2PE! collaborative research programme (CO2PE! 2011a) and comprises two approaches with different levels of detail, respectively referred to as the screening approach and the in-depth approach. METHODS: The screening approach relies on representative, publicly available data and engineering calculations for energy use, material loss, and identification of variables for improvement, while the in-depth approach is subdivided into four modules, including a time study, a power consumption study, a consumables study and an emissions study, in which all relevant process in- and outputs are measured and analysed in detail. The screening approach provides the first insight in the unit process and results in a set of approximate LCI data, which also serve to guide the more detailed and complete in-depth approach leading to more accurate LCI data as well as the identification of potential for energy and resource efficiency improvements of the manufacturing unit process. To ensure optimal reproducibility and applicability, documentation guidelines for data and metadata are included in both approaches. Guidance on definition of functional unit and reference flow as well as on determination of system boundaries specifies the generic goal and scope definition requirements according to ISO 14040 (2006) and ISO 14044 (2006). RESULTS: The proposed methodology aims at ensuring solid foundations for the provision of high-quality LCI data for the use phase of manufacturing unit processes. Envisaged usage encompasses the provision of high-quality data for LCA studies of products using these unit process datasets for the manufacturing processes, as well as the in-depth analysis of individual manufacturing unit processes. CONCLUSIONS: In addition, the accruing availability of data for a range of similar machines (same process, different suppliers and machine capacities) will allow the establishment of parametric emission and resource use estimation models for a more streamlined LCA of products including reliable manufacturing process data. Both approaches have already provided useful results in some initial case studies (Kellens et al. 2009; Duflou et al. (Int J Sustain Manufacturing 2:80–98, 2010); Santos et al. (J Clean Prod 19:356–364, 2011); UPLCI 2011; Kellens et al. 2011a) and the use will be illustrated by two case studies in Part 2 of this paper (Kellens et al. 2011b).

Purpose Life cycle inventories (LCI) of electricity generation and supply are among the main determining factors regarding life cycle assessment (LCA) results. Therefore, consistency and representativeness of these data are crucial. The electricity sector has been updated and substantially extended for ecoinvent version 3 (v3). This article provides an overview of the electricity production datasets and insights into key aspects of these v3 inventories, highlights changes and describes new features. Methods Methods involved extraction of data and analysis from several publically accessible databases and statistics, as well as from the LCA literature. Depending on the power generation technology, either plant-specific or region-specific average data have been used for creating the new power generation inventories representing specific geographies. Whenever possible, the parent–child relationship was used between global and local activities. All datasets include a specific technology level in order to support marginal mixes used in the consequential version of ecoinvent. The use of parameters, variables and mathematical relations enhances transparency. The article focuses on documentation of LCI data on the unlinked unit process level and presents direct emission data of the electricity-generating activities. Results and discussion Datasets for electricity production in 71 geographic regions (geographies) covering 50 countries are available in ecoinvent v3. The number of geographies exceeds the number of countries due to partitioning of power generation in the USA and Canada into several regions. All important technologies representing fossil, renewable and nuclear power are modelled for all geographies. The new inventory data show significant geography-specific variations: thermal power plant efficiencies, direct air pollutant emissions as well as annual yields of photovoltaic and wind power plants will have significant impacts on cumulative inventories. In general, the power plants operating in the 18 newly implemented countries (compared to ecoinvent v2) are on a lower technology level with lower efficiencies and higher emissions. The importance of local datasets is once more highlighted. Conclusions Inventories for average technology-specific electricity production in all globally important economies are now available with geography-specific technology datasets. This improved coverage of power generation representing 83 % of global electricity production in 2008 will increase the quality of and reduce uncertainties in LCA studies worldwide and contribute to a more accurate estimation of environmental burdens from global production chains. Future work on LCI of electricity production should focus on updates of the fuel chain and infrastructure datasets, on including new technologies as well as on refining of the local data.