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Environmental impacts of key metals' supply and low‐carbon technologies are likely to decrease in the future

2021; Wiley; Volume: 25; Issue: 6 Linguagem: Inglês

10.1111/jiec.13181

1530-9290

Carina Harpprecht, Lauran van Oers, Stephen Northey, Yongxiang Yang, Bernhard Steubing,

Recycling and Waste Management Techniques

Journal of Industrial EcologyVolume 25, Issue 6 p. 1543-1559 RESEARCH AND ANALYSISOpen Access Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future Carina Harpprecht, Corresponding Author Carina Harpprecht c.i.harpprecht@cml.leidenuniv.nl orcid.org/0000-0002-2878-0139 Department of Energy Systems Analysis, Institute of Networked Energy Systems, German Aerospace Center (DLR), Stuttgart, Germany Institute of Environmental Sciences (CML), Leiden University, Leiden, The Netherlands Correspondence Carina Harpprecht, Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518, 2300 RA Leiden, The Netherlands. Email: c.i.harpprecht@cml.leidenuniv.nlSearch for more papers by this authorLauran van Oers, Lauran van Oers orcid.org/0000-0002-7383-604X Institute of Environmental Sciences (CML), Leiden University, Leiden, The NetherlandsSearch for more papers by this authorStephen A. Northey, Stephen A. Northey orcid.org/0000-0001-9001-8842 Institute for Sustainable Futures, University of Technology Sydney, Ultimo, AustraliaSearch for more papers by this authorYongxiang Yang, Yongxiang Yang orcid.org/0000-0003-4584-6918 Department of Materials Science and Engineering, Delft University of Technology (TU Delft), Delft, The NetherlandsSearch for more papers by this authorBernhard Steubing, Bernhard Steubing orcid.org/0000-0002-1307-6376 Institute of Environmental Sciences (CML), Leiden University, Leiden, The NetherlandsSearch for more papers by this author Carina Harpprecht, Corresponding Author Carina Harpprecht c.i.harpprecht@cml.leidenuniv.nl orcid.org/0000-0002-2878-0139 Department of Energy Systems Analysis, Institute of Networked Energy Systems, German Aerospace Center (DLR), Stuttgart, Germany Institute of Environmental Sciences (CML), Leiden University, Leiden, The Netherlands Correspondence Carina Harpprecht, Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518, 2300 RA Leiden, The Netherlands. Email: c.i.harpprecht@cml.leidenuniv.nlSearch for more papers by this authorLauran van Oers, Lauran van Oers orcid.org/0000-0002-7383-604X Institute of Environmental Sciences (CML), Leiden University, Leiden, The NetherlandsSearch for more papers by this authorStephen A. Northey, Stephen A. Northey orcid.org/0000-0001-9001-8842 Institute for Sustainable Futures, University of Technology Sydney, Ultimo, AustraliaSearch for more papers by this authorYongxiang Yang, Yongxiang Yang orcid.org/0000-0003-4584-6918 Department of Materials Science and Engineering, Delft University of Technology (TU Delft), Delft, The NetherlandsSearch for more papers by this authorBernhard Steubing, Bernhard Steubing orcid.org/0000-0002-1307-6376 Institute of Environmental Sciences (CML), Leiden University, Leiden, The NetherlandsSearch for more papers by this author First published: 05 September 2021 https://doi.org/10.1111/jiec.13181Citations: 2 Editor Managing Review: Ichiro Daigo AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The environmental benefits of low-carbon technologies, such as photovoltaic modules, have been under debate because their large-scale deployment will require a drastic increase in metal production. This is of concern because higher metal demand may induce ore grade decline and can thereby further intensify the environmental footprint of metal supply. To account for this interlinkage known as the “energy-resource nexus”, energy and metal supply scenarios need to be assessed in conjunction. We investigate the trends of future impacts of metal supplies and low-carbon technologies, considering both metal and electricity supply scenarios. We develop metal supply scenarios for copper, nickel, zinc, and lead, extending previous work. Our scenarios consider developments such as ore grade decline, energy-efficiency improvements, and secondary production shares. We also include two future electricity supply scenarios from the IMAGE model using a recently published methodology. Both scenarios are incorporated into the background database of ecoinvent to realize an integrated modeling approach, that is, future metal supply chains make use of future electricity and vice versa. We find that impacts of the modeled metal supplies and low-carbon technologies may decrease in the future. Key drivers for impact reductions are the electricity transition and increasing secondary production shares. Considering both metal and electricity scenarios has proven valuable because they drive impact reductions in different categories, namely human toxicity (up to −43%) and climate change (up to −63%), respectively. Thus, compensating for lower ore grades and reducing impacts beyond climate change requires both greener electricity and also sustainable metal supply. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges 1 INTRODUCTION Although low-carbon technologies are considered essential for climate change mitigation (Bruckner et al., 2014), their environmental benefits are under debate because of their high metal intensity (Alonso et al., 2012; Fizaine & Court, 2015; Kleijn et al., 2011). Therefore, it is expected that a large-scale deployment of low-carbon technologies will lead to a drastic increase of metal demand in the future (de Koning et al., 2018; Roelich et al., 2014; Tokimatsu et al., 2018). This is of concern since metal production has severe environmental implications. It is not only highly energy intensive, consuming around 10% of global primary energy (Fizaine & Court, 2015; Rankin, 2011), and therefore a major contributor to global greenhouse gas (GHG) emissions. It also adds to other environmental pressures, such as ecosystem degradation or human health impacts (UNEP, 2013). These environmental pressures could be further intensified in the future were there a continuation of declining mined ore grades as documented for copper, nickel, zinc, and lead (Crowson, 2012; Mudd, 2010; Mudd et al., 2017). Lower mined ore grades mean that more ore needs to be processed to produce the same amount of metal, leading to a rise in energy requirements and thus GHG emissions (Norgate & Haque, 2010; Norgate & Rankin, 2000). A decline in mined ore grades may result from various factors, such as, altered economic conditions, technology improvements (Ericsson et al., 2019; West, 2011), or from a depletion of higher grade ores due to rising metal demand as possibly induced by large-scale production of low-carbon technologies in the future. Thus, metal and energy supply systems are closely interlinked, which is commonly referred to as the “energy-resource nexus” (Bleischwitz et al., 2017; Graedel & van der Voet, 2010; Le Blanc, 2015). Therefore, it is crucial to consider both systems when investigating future impacts of metal production and of low-carbon technologies in order to capture the interplay of the two systems and to avoid problem shifting. A widely applied environmental assessment tool to analyze “potential impacts associated with a product” is life cycle assessment (LCA) (ISO, 2006). LCA models are often divided into so-called foreground and background systems. The foreground system typically consists of specific processes that are modeled by the practitioners. The background system typically consists of many more processes and is drawn from a life cycle inventory (LCI) database, for example, ecoinvent (Wernet et al., 2016). This background database provides the inputs to the foreground system such that the practitioners do not have to model all processes themselves. While current product systems are in general analyzed using LCA, impacts of future systems are assessed using prospective LCA (Arvidsson et al., 2017; Pesonen et al., 2000). For prospective LCA, LCA models are adapted according to scenarios. To ensure consistency, scenarios are incorporated ideally into both fore- and background systems. While the foreground systems usually do reflect future scenarios, adapting the (much more numerous) processes in the background typically is not feasible. This is a prevalent shortcoming of prospective LCAs and is referred to as a “temporal mismatch” between the foreground and the background system (Arvidsson et al., 2017; Nordelöf et al., 2014; Sandén, 2007; Vandepaer & Gibon, 2018). Metal supply systems in particular are mostly investigated regarding their current characteristics and current environmental performance (Elshkaki et al., 2016; Kuipers et al., 2018; Norgate & Haque, 2010; Norgate & Rankin, 2000; Nuss & Eckelman, 2014; Paraskevas et al., 2016). Yet, metal supply and its related impacts have been changing continually in the past, and are expected to continue doing so in the future (Rötzer & Schmidt, 2020). These changes are not only due to ore grade decline, which leads to higher energy intensity of mining activities, but also to technological innovation, which may lead to increased energy efficiencies, to regional differences between production locations (Northey et al., 2013), and to changes in secondary production shares or in shares of different production routes. For example, environmental impacts of pyrometallurgical copper production differ considerably from the hydrometallurgical copper production route (Azadi et al., 2020; Norgate & Haque, 2010; Norgate & Jahanshahi, 2010). Van der Voet et al. (2019) developed detailed supply scenarios for seven major metals (copper, nickel, zinc, lead, iron, aluminum, and manganese) considering various relevant future developments, such as ore grade decline, energy-efficiency improvements, or changes in secondary production shares. They model future electricity systems by adapting electricity mixes in the background according to different energy scenarios (IEA, 2012). Thereby, all processes in the back- and foreground which have electricity as inputs receive the adapted future electricity, or the “futurized” electricity. However, their future metal supply chains are not integrated in the background database but modeled in the foreground, “on top” of the background database. This means that all other processes of the background database still make use of the non-future metal supply chains, such as, the future electricity supply sector (see Supporting Information S8, Section B.1 for a comparison of scenarios in foreground and background systems). Other work investigated future impacts of low-carbon technologies taking an integrated scenario incorporation approach. Mendoza Beltran et al. (2020) and Cox et al. (2018) recently pioneered the integration of comprehensive model data into an LCA background database. They developed a Python-based software, Wurst (Mutel & Vandepaer, 2019), to incorporate comprehensive electricity supply scenarios from the integrated assessment model (IAM) from IMAGE (Integrated Model to Assess the Global Environment) into the background database (ecoinvent v3.3) (Stehfest et al., 2014). They confirm that electricity supply systems, or background systems in general, can be the decisive factors for environmental benefits of low-carbon technologies. To date, a few studies combined future electricity and metal supply scenarios within an LCI database. The New Energy Externalities Development for Sustainability (NEEDS) project generated prospective LCIs by incorporating energy supply and material production scenarios into ecoinvent version 1.3. The most comprehensive and recent work is THEMIS (Technology Hybridized Environmental-Economic Model With Integrated Scenarios) (Gibon et al., 2015; Hertwich et al., 2015). Using hybrid input–output LCA models, THEMIS integrates various scenarios, such as NEEDS, future electricity mixes from the International Energy Agency (IEA), and material production scenarios, into ecoinvent v2.2 to build prospective LCIs. The material production scenarios assume one development, namely a reduction of energy inputs during productions due to technological-efficiency improvements. Metal supply scenarios considering possible future developments, such as ore grade decline and shares of different production routes, have not been incorporated into a recent background database yet, despite the substantial environmental contributions of metal supply to impacts of technology productions. Most of the research so far focused on incorporating detailed energy scenarios, yet did not model diverse changes in future metal production systems (Arvesen et al., 2018). Moreover, comprehensive metal supply scenarios have not been incorporated into an LCI database in combination with electricity supply scenarios to create a more consistent background database suitable for accounting for interdependencies, for instance, due to the energy-resource nexus. This study aims to incorporate metal supply scenarios, which model several future developments, as well as scenarios for an energy transition directly into the ecoinvent 3.5 database. This integrated scenario incorporation allows for interactions between these two modified supply chains, and therefore accounts for the energy-resource nexus. We aim to answer the following research questions: 1. What are the environmental impacts of the future production of copper, nickel, zinc, and lead? 2. How do future metal supply changes and electricity supply changes influence future impacts of metal supply and of low-carbon technologies? To achieve this, we build on approaches and scenarios from previous research as follows. We use the work of Mendoza Beltran et al. (2020) to incorporate electricity scenarios from IMAGE. For the metal supply scenarios, we build on and extend the study of van der Voet et al. (2019), which provides comprehensive supply scenarios for seven metals. We choose four metals whose global GHG emissions are among the top 10 of all metals (Nuss & Eckelman, 2014) and for which ore grade decline has been documented: copper, nickel, zinc, and lead. We further extend the scenarios of van der Voet et al. (2019), adapt them from ecoinvent version 2.2 to version 3.5, and integrate them into the background database. The metal supply scenarios form the main focus of our work. It is important to stress that our scenarios should not be seen as predictions but rather as an exploration of possible future developments and their role for future environmental performances of a product system. 2 METHODS 2.1 Approach overview We modeled future metal supply (MS) scenarios for four metals until 2050: copper (Cu), nickel (Ni), zinc (Zn), and lead (Pb). To estimate future developments in metal supply, we chose key factors influencing future changes, and describe them via five variables: (1) mined ore grade, (2) primary production locations, (3) energy-efficiency improvements of metal refining, (4) shares of primary production routes, and (5) shares of primary and secondary production. Furthermore, we added electricity supply (ES) scenarios which describe possible future energy systems using a recently published approach by Mendoza Beltran et al. (2020). Considering both metal and electricity supply scenarios, we investigated how environmental impacts of future metal supply and low-carbon technologies may develop in the future, and examined the key drivers for those future impact changes. Furthermore, we also assessed the effect of metal and electricity supply changes on key applications of a low-carbon economy, such as electricity production from photovoltaics (PV) and wind, as well as the production of Li-ion batteries, and transport with an EV. The scenarios were assessed for the time period of 2010–2050 in intervals of 5 years using Brightway2 (Mutel, 2017a, 2018). They were modeled by modifying the background database, that is, ecoinvent version 3.5, allocation, cut-off by classification (Ecoinvent Center, 2018; Wernet et al., 2016). This means that already existing activities in ecoinvent were changed and/or new activities were added according to scenario data (see Supporting Information S8, Section B.1). Thereby, future versions of ecoinvent are created for each scenario year representing future systems. This method increases temporal consistency through the creation of future background databases, and it realizes an integrated approach since process modifications become effective in the whole database. Hence, this approach allows for interactions between the metal and electricity supply systems: future metal supply chains use future electricity and vice versa, thereby accounting for interlinkages due to the energy-resource nexus. 2.2 Metal supply scenarios The five variables of our metal supply scenarios address different production stages of metal supply chains, from mining (variable 1, ore grade decline) over refining (e.g., variable 3, energy-efficiency improvements) to global market shares (e.g., variable 5, primary/secondary production shares). Figure 1 illustrates how ecoinvent represents metal supply chains at the example of copper and at which production stage the variables are incorporated. It distinguishes between three stages: (1) mining and mineral processing which produces copper concentrates of 30%; (2) metal production which comprises copper smelting, converting, and refining, to supply refined copper; and (3) a global market. Furthermore, we distinguish between pyrometallurgical and hydrometallurgical primary production of copper, and between primary and secondary production shares. FIGURE 1Open in figure viewerPowerPoint Structure of the copper supply chain in ecoinvent 3.5 and the modeled variables at each supply stageStructure of the copper supply chain in ecoinvent 3.5, the included metallurgical processes, and the modeled variables at each supply stage. Copper mine operation produces a copper concentrate of 30%. Primary copper production refines this concentrate producing refined copper. The supply chains of the other metals are given in Supporting Information S8 (Figures B.3– B.7). Cu, copper; SX-EW, solvent extraction and electro-winning; V, variable The supply chains of the other metals are described in Supporting Information S8 (Section B.2). For nickel, we model two different types which cover the majority of the nickel market (van der Voet et al., 2019). Those are “nickel” with a purity of 99.5%, and the less pure “ferronickel,” which contains 25% nickel (see Supporting Information S8, Section B.2.2). Primary metal supply (PMS) changes are represented by variables 1 to 4, while variable 5 models secondary metal supply (SMS) changes. The main focus of our metal supply scenario lies on ore grade decline (variable 1). Therefore, this variable is modeled for all four metals, while the rest of the primary supply variables, variables 2–4, are only modeled for copper. Copper is of special interest given its expected demand growth and relevance for low-carbon technologies (Deetman et al., 2018; Hertwich et al., 2015). Variable 5 is modeled for copper, nickel, and lead. Zinc and ferronickel are excluded for variable 5 as their ecoinvent models do not include secondary supply activities. The data sources used for each variable are shown in Table 1. Differences to the scenarios of van der Voet et al. (2019) mostly lie in the addition of regionalized copper scenarios for variables 1 and 2, and in the adaptation of the variable models to the newer supply chains in ecoinvent v3.5. Each variable is further explained in the following paragraphs with its data being accessible via a repository (Harpprecht et al., 2021). The generated scenarios are then illustrated in the results section in Figure 2. TABLE 1. Variables and data sources for the generation of metal supply scenarios Variable Metal Data source Information 1. Ore grade decline Ni, FeNi Mudd and Jowitt 2014 Historical ore grades to create a regression model to project future global ore grades Norgate and Jahanshahi 2006 Ore grade-energy requirement relation Zn, Pb Mudd, Jowitt, and Werner 2017 Historical ore grades to create a regression model to project future global ore grades Valero, Valero, and Domınguez 2011 Ore grade-energy requirement relation Cu Mudd and Jowitt 2018 Regionalized instead of global ore grades, historical data Northey et al. 2014 Regionalized instead of global ore grade scenarios based on supply–demand models Northey, Haque, and Mudd 2013 Ore grade-energy requirement relation 2. Market shares of production locations Cu Northey et al. 2014 Regionalized future production scenarios based on supply–demand models 3. Energy efficiency improvements Cu Kulczycka et al. 2016 Future energy inputs for pyrometallurgical Cu production 4. Market shares of primary production routes Cu International Copper Study Group 2018 More recent historical data on hydro- and pyrometallurgical production shares 5. Market shares of primary, secondary production Cu, Ni, Pb Elshkaki et al. 2018 Global shares of primary, secondary supply Crucial updates compared to the models of van der Voet et al. (2019) are highlighted in italics. Cu, copper; FeNi, ferronickel; Ni, nickel; Pb, lead; Zn, zinc. 2.2.1 Stage 1: Metal mining Variable 1: Ore grade decline and energy requirements For all metals, we calculate future ore grade decline, the caused change in energy requirements and in other inputs/outputs in two steps, similarly to van der Voet et al. (2019) and Kuipers et al. (2018). Detailed explanations are provided in Supporting Information S8 (Section B.3.1). 1. Defining current, , and future ore grades, : We estimate current, , and future ore grades, , with an ore grade model, . is the year for each ecoinvent mining process. For nickel, zinc, and lead, is defined via metal-specific regression models of van der Voet et al. (2019), which are based on historical data (Table 1). For copper, future ore grades, , are defined using data from regionalized models of Northey et al. (2014), specifically their “country-dynamic” scenario. They model copper production amounts and ore grades for 83 regions from 2010 to 2100 with the Geologic Resources Supply–Demand Model (GeRS-DeMo) developed by Mohr (2010). We match their 83 regions to the 6 pyrometallurgical copper production regions in ecoinvent, and use the production shares of the individual countries as weighing factors to derive an average ore grade per region (see Supporting Information S8, Equation B.10 and Harpprecht et al. (2021)). For , historic ore grade data is taken from Mudd and Jowitt (2018). 2. Defining current, , and future energy requirements, , with an ore grade–energy relation, : The ore grade–energy relations are taken from van der Voet et al. (2019), who generated them from the literature (Table 1) for each metal. With , , and , we define and as: (1) (2) Subsequently, we define a factor, , which describes how future energy requirements, , will change relative to current energy requirements, (see Supporting Information S8, Section B.3.1). As a simplification, which was also used by van der Voet et al. (2019), we assume that this factor, , can be applied as a proxy to also model the increase and decrease of all other in- and outflows of the mining process (see Supporting Information S8, Section D.1 for a discussion). 2.2.2 Stage 2: Primary metal production Variable 2: Market shares of primary production locations Since production characteristics, such as energy sources or waste treatments, are country-specific, environmental impacts associated with primary copper production vary largely between countries (Beylot & Villeneuve, 2017) (Supporting Information S8, Figure B.15). We apply the future production shares modeled by Northey et al. (2014) to the production shares per ecoinvent region of copper primary production using the regional match from variable 1 (see Supporting Information S8, Section B.3.2). Variable 3: Energy-efficiency improvements during smelting and refining We model a decrease of required electricity and natural gas inputs (-1.77% and -1.5% per year) during smelting and reduction processes within the pyrometallurgical primary production route (Supporting Information S8, Figure B.16) with an exponential regression of van der Voet et al. (2019), which was based on projections of Kulczycka et al. (2016). 2.2.3 Stage 3: Market shares of global metal markets Variable 4: Market shares of primary production routes Copper is predominantly produced in two primary production routes, pyrometallurgy and hydrometallurgy. Since their environmental impacts differ considerably (Norgate & Haque, 2010; Norgate & Rankin, 2000), we build a scenario for their future market shares. While Kuipers et al. (2018) applied a linear regression model based on historic data showing increasing hydrometallurgical shares, we apply an exponential regression model taking into account the recent continuous declines of hydrometallurgical shares (International Copper Study Group, 2018). Thus, we assume a decrease over time in the share of copper production from hydrometallurgical processing of oxide ores, in contrast to the increase in Kuipers et al. (2018). This is in line with recent forecasts for Chile (COCHILCO, 2019), globally the largest copper miner (see Supporting Information S8, Section B.3.4). Variable 5: Market shares of primary and secondary production Primary and secondary production shares are projected using the models of Elshkaki et al. (2018) (see Supporting Information S8, Section B.3.5), which they based on the Fourth Global Environmental Outlook scenario set (GEO-4) by the United Nations Environmental Programme (UNEP) (UNEP, 2007). In line with van der Voet et al. (2019), we select the “Market First” scenario of Elshkaki et al. (2018), since it is a business-as-usual scenario. The scenario is incorporated into the global markets of copper, nickel (99.5%), and lead. 2.3 Electricity supply scenarios The electricity supply scenarios are taken from Mendoza Beltran et al. (2020), who use IMAGE 3.0 as scenario source (Stehfest et al., 2014) (see Supporting Information S8, Section B.4). As an integrated assessment model (IAM), IMAGE models the human system with a focus on energy and land use systems. Mendoza Beltran et al. (2020) use the Shared Socioeconomic Pathways (SSPs) of IMAGE (O'Neill et al., 2014). Each pathway consists of a baseline scenario, that is, how the future develops without additional climate policies, and various mitigation scenarios (Riahi et al., 2017). From those pathways, we select SSP2, the “middle-of-the-road” pathway in which current trends continue without considerable change (Fricko et al., 2017; van Vuuren et al., 2017). From SSP2, we take its baseline and its strongest mitigation scenario, SSP2 and SSP2-2.6. They represent the two extremes within SSP2 (Fricko et al., 2017). SSP2-2.6 describes the strongest mitigation efforts to reach the two-degree target of 450 ppm CO2eq. 2.4 Incorporating metal and electricity supply scenarios To analyze the effect of the MS variables and ES scenarios, we adapt the background database, that is, ecoinvent, with the scenarios described in Table 2. The scenario data is incorporated with Presamples (Lesage, 2019; Lesage et al., 2018) and Wurst (Mutel, 2017b) for the MS and ES scenarios, respectively (see Supporting Information S8, Section B.5). TABLE 2. Future scenarios modeled for the prospective LCAs from 2010 to 2050 in time steps of five years Description MS variables ES scenario Scenario MS 1–5 n.a. MS MS, only primary production changes 1–4 n.a. PMS MS, only secondary production changes 5 n.a. SMS ES n.a. SSP2 ES-BAU ES n.a. SSP2-2.6 ES-Mitigation ES + MS 1–5 SSP2 MS + ES-BAU ES + MS 1–5 SSP2-2.6 MS + ES-Mitigation BAU, business-as-usual; ES, electricity supply; MS, metal supply; PMS, primary metal supply; SMS, secondary metal supply; SSP, shared socioeconomic pathway. 2.5 Scenario evaluation 2.5.1 Functional units The effect of our scenarios on the future environmental performances of the five metals' supply as well as of electricity supply and low-carbon technologies are assessed using functional units from ecoinvent (Table 3). We present results for two out of the five low-carbon technology examples: electricity production from PV and production of a Li-ion battery (see Supporting Information S8, Section B.6.1). The functional units use ecoinvent, updated with the scenario data, as background. TABLE 3. Functional units taken from ecoinvent 3.5 for metal supply and metal applications Category Reference flow Process Region Global metal markets 1 kg of copper Market for copper GLO 1 kg of nickel, 99.5% Ni Market for nickel, 99.5% GLO 1 kg of ferronickel, 25% Ni Market for ferronickel, 25% Ni GLO 1 kg of zinc Market for zinc GLO 1 kg of lead Market for lead GLO Metal applications 1 kWh electricity, high voltage Market group for electricity, high voltage GLO 1 kWh electricity, low voltage Electricity production, PV, 3 kWp slanted-roof installation, multi-Si CH 1 kg of Li-ion battery prismatic Battery production, Li-ion, prismatic GLO CH, Switzerland; GLO, global; kWp, kilowatt peak; Li, lithium; Ni, nickel; PV, photovoltaics. 2.5.2 Impact assessment Impacts are assessed for six impact categories: climate change (CC); cumulat