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PurposeMuch progress has recently been made in modelling future background systems for LCA by including future scenario data, e.g. from Integrated Assessment Models (IAMs), into life cycle inventory (LCI) databases. A key problem is, however, that this yields potentially dozens of scenario LCI databases (i.e. LCI databases that represent different scenarios and reference years), instead of a single background database, which is very impractical for LCA modelling purposes. This paper proposes an approach to overcome this problem.MethodsThe approach consists of transforming all scenario LCI databases into a single superstructure database and an associated scenario difference file. The superstructure database is also a regular LCI database, but is constructed to contain all unique exchanges (elementary and intermediate flows) and processes that exist across all scenario LCI databases. The scenario difference file stores the differences between all scenarios and can be used to turn the superstructure into a specific scenario LCI database. This is very fast as it can be done in memory during LCA calculations.Results and discussionA key advantage of the superstructure approach is that a single LCI database can be used to represent different background systems. Therefore, the practitioner does not need to re-link a foreground system to multiple LCI databases, which is work-intensive and invites modelling errors. LCA results for all scenarios and reference years can be calculated automatically. We also illustrate how the superstructure approach has been implemented in the Activity Browser open source LCA software. Although this paper introduces the superstructure approach for background scenarios, it can also be used to model foreground scenarios, and even, as implemented in the Activity Browser, combinations of background and foreground scenarios. Finally, we briefly discuss further challenges that need to be addressed for a more widespread use of background scenarios in LCA.ConclusionsThe superstructure approach presents a practical solution for making the use of future background scenarios more wide-spread and, therefore, to overcome the problem of performing prospective LCA with temporally inconsistent foreground and background systems. The implementation in the Activity Browser makes the approach available for anyone and may serve as inspiration for other LCA software to implement the superstructure approach or a similar concept. While this may be an important technical milestone, additional coordination between data providers, scenario generators, LCA practitioners, and software developers will be required to further facilitate the use of background scenarios in prospective LCA studies.

Building stock growth around the world drives extensive material consumption and environmental impacts. Future impacts will be dependent on the level and rate of socioeconomic development, along with material use and supply strategies. Here we evaluate material-related greenhouse gas (GHG) emissions for residential and commercial buildings along with their reduction potentials in 26 global regions by 2060. For a middle-of-the-road baseline scenario, building material-related emissions see an increase of 3.5 to 4.6 Gt CO2eq yr-1 between 2020–2060. Low- and lower-middle-income regions see rapid emission increase from 750 Mt (22% globally) in 2020 and 2.4 Gt (51%) in 2060, while higher-income regions shrink in both absolute and relative terms. Implementing several material efficiency strategies together in a High Efficiency (HE) scenario could almost half the baseline emissions. Yet, even in this scenario, the building material sector would require double its current proportional share of emissions to meet a 1.5 °C-compatible target. Building construction causes large material-related emissions which present a serious decarbonization challenge. Here, the authors show that the building material sector could halve emissions by increasing efficiency until 2060 but even then its emissions would be twice as high as needed to meet the 1.5 °C target.

The world is shifting to electric vehicles to mitigate climate change. Here, we quantify the future demand for key battery materials, considering potential electric vehicle fleet and battery chemistry developments as well as second-use and recycling of electric vehicle batteries. We find that in a lithium nickel cobalt manganese oxide dominated battery scenario, demand is estimated to increase by factors of 18–20 for lithium, 17–19 for cobalt, 28–31 for nickel, and 15–20 for most other materials from 2020 to 2050, requiring a drastic expansion of lithium, cobalt, and nickel supply chains and likely additional resource discovery. However, uncertainties are large. Key factors are the development of the electric vehicles fleet and battery capacity requirements per vehicle. If other battery chemistries were used at large scale, e.g. lithium iron phosphate or novel lithium-sulphur or lithium-air batteries, the demand for cobalt and nickel would be substantially smaller. Closed-loop recycling plays a minor, but increasingly important role for reducing primary material demand until 2050, however, advances in recycling are necessary to economically recover battery-grade materials from end-of-life batteries. Second-use of electric vehicles batteries further delays recycling potentials. Lithium-ion-based batteries are a key enabler for the global shift towards electric vehicles. Here, considering developments in battery chemistry and number of electric vehicles, analysis reveals the increasing amounts of lithium, cobalt and nickel that could be needed.

The energy transition will require a rapid deployment of renewable energy (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding of many socio-technical parameters and constraints. We quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment, battery degradation, and market participation. We include both in-use and end-of-vehicle-life use phases and find a technical capacity of 32–62 terawatt-hours by 2050. Low participation rates of 12%–43% are needed to provide short-term grid storage demand globally. Participation rates fall below 10% if half of EV batteries at end-of-vehicle-life are used as stationary storage. Short-term grid storage demand could be met as early as 2030 across most regions. Our estimates are generally conservative and offer a lower bound of future opportunities. Renewable energy and electric vehicles will be required for the energy transition, but the global electric vehicle battery capacity available for grid storage is not constrained. Here the authors find that electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030.

PurposeThe objective of this paper was to provide LCA practitioners with recommendations and a framework for upscaling emerging technologies by reviewing upscaling methods applied so far in ex ante life cycle assessment (LCA).MethodsWeb of Science was searched for articles published between 1990 and 2019 (April) using different variations of the term “ex ante LCA” as keywords. Suitable studies were reviewed to understand the key characteristics and main methodological principles of upscaling methods.Results and discussionA total of 18 studies were selected for literature review. Review results showed that most studies reported what a hypothetical upscaled technology would look like in the future. All studies described how they estimated data; they applied different data estimation methods, using process simulation, manual calculations, molecular structure models (MSMs) and proxies. Since the review results showed that most ex ante LCA studies followed similar upscaling steps, we developed a framework for the upscaling of emerging technologies in ex ante LCA consisting of three main steps: (1) projected technology scenario definition, (2) preparation of a projected LCA flowchart, and (3) projected data estimation. Finally, a decision tree was developed based on the review results that provides recommendations for LCA practitioners regarding the upscaling procedure in ex ante LCA.ConclusionsOur findings can be useful for LCA practitioners aiming at upscaling in ex ante LCA. We provide an overview of upscaling methods used in ex ante LCA and introduce a framework describing the steps involved in the upscaling process and a decision tree recommending an up-scaling procedure. The results show that in theory all data estimation methods described in this paper can be applied to estimate material flows, energy flows, and elementary flows (emissions and natural resource use). Finally, since different kinds of expertise are required for upscaling in ex ante LCA, we recommend that technology experts from different fields are involved in performing ex ante LCA, e.g., technology developers, LCA practitioners, and engineers.

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.

Purpose Some future-oriented life cycle assessment (LCA) terms, particularly prospective and ex-ante, show notable increase in use in publications over the last decade. However, scholars have pointed out that it is currently unclear exactly what these terms mean and how they are related. This paper aims to explain defining differences between future-oriented LCA terms and provide terminology recommendations. Methods Existing definitions of future-oriented LCA terms were reviewed and analyzed. Workshops were held where defining differences of future-oriented LCA terms were discussed. Results Temporal positionality and technology maturity appear to be two critical aspects of future-oriented LCA. Prospective and ex-ante LCA are similar, with the possible difference that ex-ante LCA always involves an increase in technology maturity in the future. Considering the notable similarities, it seems reasonable to converge terms to mitigate field fragmentation and avoid terminology confusion. Conclusions To denote LCA studies with a future temporal positionality, we recommend using the term prospective LCA, defined as “LCA that models the product system at a future point in time relative to the time at which the study is conducted”. Furthermore, since technology maturity is clearly a critical aspect for prospective LCA, we recommend prospective LCA studies to clearly define the maturity of the technologies modeled in the production system.

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.

Sustainable forest management and harvested wood products together can create a growing carbon sink by storing carbon in long-lived products. The role of wood products in climate change mitigation has been studied from several perspectives, but not yet from a consumer’s view. In this study, we examine the impact of wooden housing on consumer carbon footprints in Finland. We use the 2016 Finnish Household Budget Survey and Exiobase 2015, a global multi-regional input-output model. The sample size is 3700 households, of which 45% live in a wooden house. We find that residents of wooden houses have a 12(±3)% (950 kg CO 2 -eq/year) lower carbon footprint on average than residents of non-wooden houses, when income, household type, education of the main income provider, age of the house, owner-occupancy and urban zone are controlled in regression analysis. This is not fully explained by the impact of the construction material, which suggests that the residents of wooden houses may have some features in their lifestyles that lower their carbon footprints further. In addition, we find that an investment in a new wooden house in an urban area has a strong reducing impact on a consumer’s carbon footprint, while investments in other types of housing have a weaker or no reducing impact. Our findings support wooden housing as a meaningful sustainable consumption choice.