Life cycle impact assessment of electric vehicle battery charging in European Union countries

Journal of Cleaner Production 257 (2020) 120476

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Life cycle impact assessment of electric vehicle battery charging in European Union countries Dorota Burchart-Korol a, Simona Jursova b, *, Piotr Fole˛ ga a, Pavlina Pustejovska b a b


skiego 8, 40-019, Katowice, Poland Silesian University of Technology, Faculty of Transport and Aviation Engineering, Krasin VSB - Technical University of Ostrava, ENET CENTRE, 17. listopadu 2172/15, 708 00, Ostrava-Poruba, Czech Republic

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 June 2019 Received in revised form 16 December 2019 Accepted 6 February 2020 Available online 14 February 2020

Energy production is highly diversified across Europe in terms of the energy sources. It leads to diversified environmental impacts of electric vehicles (EV) in European countries. The paper presents life cycle assessment (LCA) on EV battery charging in all the European Union (EU) countries. The analysis builds on the current and the projected structure of electricity production in 28 EU countries for period 2015 e2050. The methodological approach “from cradle to gate” was used for the analysis. The computational LCA model was provided by the SimaPro v. 8.5 packages with the Ecoinvent v. 3 database. The function unit of the system was the amount of electricity in the power grid to charge an electric battery of a passenger car assuming a distance of 100 km and consumption 19.9 kWh/100 km. Three methods were used: method of Intergovernmental Panel on Climate Change (IPCC), Recipe and water footprint assessment. There was provided a multidimensional environmental assessment in impact categories: greenhouse gas emission (GHG), human health (HH), ecosystem quality (EQ), resources depletion (RD), and cumulative water use (CWU). The main determinants for all the studied environmental indicators were shown solids, natural gas and biomass consumption. For 2015, the results showed that EV battery charging had the lowest environmental impact for France which had lowest indicator values on 4 from 5 categories: GHG emission (1765.52), HH (0.09), EQ (0.06), RD (0.06). The lowest CWU value was recorded for Denmark (0.02) while the highest impact on CWU was in Austria (0.11). In view of future perspective to 2050, all the analyzed environmental indicators for EV battery charging are decreasing in most EU countries. It is mainly due to the decline in the share of solids used for electricity production in the EU countries. The analysis of the impact of renewable energy sources (RES) used to generate electricity for EV battery charging showed that the most eco-friendly energy source is wind. © 2020 Elsevier Ltd. All rights reserved.

Handling Editor: Yutao Wang Keywords: Electric vehicle battery charging Current and future electricity generation mix Life cycle assessment European Union

1. Introduction In its official policy, the European Union (EU) emphasizes on clean and emission-free transport, that is substituting fossil fuels with alternatives ones (ec.europa.eu). The main objective in this respect is to reduce the greenhouse gas (GHG) emission from the transport sector. Electric vehicles (EVs) are considered eco-friendly as their use does not lead to dust and gas pollution, or the emission of carbon dioxide (CO2) and other pollutants, such as nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), and particular matter (PM). EVs are also quiet during operation, thus reducing noise and vibration.

* Corresponding author. E-mail address: [email protected] (S. Jursova). https://doi.org/10.1016/j.jclepro.2020.120476 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

The EU’s policy on EVs focuses mainly on optimization of the technological processes and development of the outlet market for these vehicles. According to the data provided by the European Automobile Manufacturers’ Association (ACEA), which presented on the market of electric vehicles in 28 EU member states as well as in Norway and Switzerland, it was established that 2% of the new EVs were registered in the EU, in 2018. Fifty percent of all the EU member states have an EV market share of less than 1%. The share of the EVs sold in Norway is exceptionally high compared to the rest of Europe, that is 49.1%. The market share of EVs in Sweden and the Netherlands is 8% and 6.7%, respectively. The least number of EVs have been sold in Latvia e only 93 vehicles, which constitutes a share of 0.2%. Other countries with extremely low share of electric cars in the EU are Poland (0.2% attributable to EVs), Slovakia (0.3%), Greece (0.3%), the Czech Republic (0.4%) and Lithuania (0.4%). The countries with the highest EV market share in the EU are Germany

2

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

Abbreviations ACEA CWU CO2 EU EVs EEA EQ FU GHG GWP HH IEA IPCC JRC LCA LCI LCIA NMHC NOx PM RD RES SO2 WTW AT BE

European Automobile Manufacturers Association Cumulative water use Carbon dioxide European Union electric vehicles European Environment Agency ecosystem quality functional unit greenhouse gas global warming potential human health International Energy Agency Intergovernmental Panel on Climate Change Joint Research Center Life Cycle Assessment Life Cycle Inventory Life Cycle Impact Assessment non-methane hydrocarbons nitrogen oxides particular matter resources depletion renewable energy sources sulfur dioxide Wheel-To-Wheel Austria Belgium

(2.0%), the United Kingdom (2.5%), France (2.1%), Italy (0.5%), and Spain (0.9%). The implementation of EVs is accompanied by several studies comparing the technological and political conditions in European countries for the use of EVs. Policy interventions to stimulate the transition of vehicle technology are presented by (Davies and Kurani, 2013; Barton and Schütte, 2017; Lucas et al., 2018; Harrison and Thiel, 2017) provided an extensive and up-to-date review on e-mobility in Europe, focusing on the identification and mapping of the incentives for and barriers against the diffusion of electric mobility through three levels of decision-making: Formal Social Units, Collective Decision-Making Units, and Individual Units. Results of the analysis revealed that the main barriers were lack of charging infrastructure, economic restrictions and cost concerns, technical and operational restrictions, lack of trust, information and knowledge, limited supply of electricity and raw materials, and practicability concerns. Various strategic documents of the European Commission stress on the legitimacy of use of EVs. One of the seven flagship projects of the Europe 2020 strategy for smart, sustainable, and inclusive growth (European Commission, 2010) is entitled Resource Efficient Europe. The purpose of this project is to support changes towards a low-carbon and resourceefficient economy, decouple economic growth from the use of resources and energy, and reduce the CO2 emissions. The 2011 White Paper titled Roadmap to a single European transport area e towards a competitive and resource-efficient transport system (European Commission, 2011) highlights the need to decouple transport from oil dependence, including the development of alternative fuels, and forecasts a 60% reduction in GHG emissions from the transport sector by 2050, as compared to 1990. In its communication titled Clean Power for Transport: A European alternative fuels strategy (European Commission, 2013) the European Commission proposed a set of actions and targets for the construction of

BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom European Union

infrastructure for the distribution of alternative fuels (electricity, natural gas, and hydrogen), based on technical standards that would be identical for all member states. The aforementioned EU directives are also important for the development of sustainable transport, especially those concerning the transition to emissionfree means of transport. Emissions from the transport sector are not decreasing enough to limit its environmental and climatic impacts in Europe. GHG emissions from the transport sector have increased over the last decade. The sector remains a significant source of air pollution, especially of PM and NOx, although these emissions have been reduced in the last decade. It also is the main source of environmental noise pollution in Europe (EEA, 2018). According to European Environment Agency (EEA), EVs are anticipated to be key components of Europe’s mobility system, helping in reducing the impacts on the climate and air quality. The largest potential reduction in GHG emissions could be due to the use of the EVs, and it is a developing sector worldwide. According to the latest Information and Forschung (IFO) research, EVs will barely help cut CO2 emissions in Germany over the coming years (Buchal et al., 2019). Almost all the EU countries generate significant CO2 emissions while charging the vehicles’ batteries using their national energy production mixes. As reported in 2015, the EU’s main sources of electric energy included nuclear power (with a share of 26.68%), solid fuels (26.04%) and natural gas (17.41%). The other energy sources combined accounted for 29.87%. According to forecasts, the consumption of nuclear energy and electricity from biomass, wind and solar power will increase by 2050. With regard to the electricity generated from solids, its consumption is expected to significantly decline by 2050. Considerable hard coal and lignite resources are still used to produce electricity in some European countries (Honus et al., 2016), which obviously entail high GHG emissions, therefore

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

one should strive to reduce the use of fossil fuels in power generation. In accordance with the recommendations of the European Commission, this trend should move towards continuous reduction of coal consumption in the structure of electricity production. Detailed information concerning the energy markets in the EU member states is provided in the relevant reports published by the European Commission (ec.europa.eu). Both economic and environmental factors are decisive for the development of new power engineering technologies. Life cycle assessment (LCA) is a one of the sustainability decision support tool (Pelletier et al., 2019; Sala et al., 2013) presented the state of the art of life cycle sustainability assessment (LCSA), giving recommendations for its further development. Environmental, economic and social implications of the life cycle from “cradle to grave” have to be considered to achieve more sustainable production and consumption patterns. Progress toward sustainability requires enhancing the methodologies for integrated assessment and mainstreaming of life cycle thinking from product development to strategic policy support. Life cycle assessment (LCA), life cycle costing (LCC) and social LCA (sLCA) already attempt to cover sustainability pillars, notwithstanding different levels of methodological development. Sustainability assessment according to life cycle sustainability assessment (LCSA) method should encompass all three dimensions - environmental, economic, and social with a life cycle perspective. Life cycle assessment (LCA) is currently the method for assessing environmental impact, whereas life cycle costing (LCC) is used for assessing economic comparisons and method of social life cycle assessment (SLCA) is used for assessing social aspects. Multiple criteria decision making (MCDM) provides integration of indicators representing assessments from various dimensions (Kalbar and Das, 2020). ELECTRE III and non-traditional MCDA approaches can provide a robust decision-making handling the LCSA uncertainties (Angelo and Marujo, 2020). Individual electric power systems differ in terms of their environmental impact, depending on the sources used, including nuclear power and renewable energy sources (RES). One of the methods used to assess the environmental impact of energy systems is the LCA. There are several publications on method of LCA used with regard to electric power systems, including electricity production (Brizmohun et al., 2014; Burchart-Korol et al., 2018b; Peiu, 2007; Rakotoson and Praene, 2017). The studies published to date concerning the LCA for the EU have focused on analyzing the current electric power systems, specifically addressing the GHG emissions as the most important environmental indicator. They have considered the forecasted systems of electric energy production and the impact categories related to cumulative water use, human health, ecosystem quality and depletion of resources to a lesser extent. However, there are diverse aspects of analyses of resource consumption, including water and natural resources (fossil fuels, metals, and minerals), which are important from the perspective of circular economy. The literature on the subject provides results of various environmental impact analyses of individual energy sources, however, there are no reports which present results of environmental analyses of the charging of electric vehicle batteries and considering the breakdown of the forecasted energy sources into different categories of impact and damage. Water management is an important determinant of electromobility development, but, so far, the research presented in literature on environmental impact assessment has been scarce. Water management has become an important part of sustainable transport in recent years. Jursova et al. (2019) presented the water footprint of electric vehicles and batteries charging in view of various sources of power supply in the Czech Republic. The paper (Jursova et al., 2019) can help practitioners and decision makers in the automotive industry to understand and develop a water management strategy.

3

The necessity for a reliable estimation of EV charging profiles of European countries was already mentioned in 2014 by (Pasaoglu et al., 2014). Using European national travel surveys (NTS), they tried to analyze the impact of EVs on the European energy and infrastructure needs from the driving patterns of potential EV users. The development of EVs sector is accompanied by extensive
k, 2017; research and environmental analysis (Rievaj and Syna Muha and Perosa, 2018). Some environmental impact assessment-based studies on EVs have already been performed that differ in terms of the target European town, country, or details of analysis (see Table 1). There are few literature sources providing a comparative LCA of EV charging in chosen European countries. In 2018 European Environmental Agency (EEA) published a report on EVs from life cycle and circular economy perspectives (EEA, 2018). The document presents large LCA analysis of EVs in comparison with ICEVs. Although the report is detailed and summarized the experience of LCA from many sources, the assessment is based on the average European electricity mix. The comprehensive evaluation of EVs impact in particular country is missing (Burchart-Korol et al., 2018a). studied the environmental impact of EVs in Poland and the Czech Republic, particularly focusing on the production of the electricity required to charge EV batteries (Canals Casals et al., 2016). focused on analyzing the sustainability of EVs in chosen European countries regarding EV charging and efficiency. Their study considered different electric energy mix variants in various countries to evaluate the different GHG emissions attributable to EVs generated in the energy production phase in the given countries; moreover, the analysis was expanded by the Monte Carlo method to consider the differences in driving conditions related to EVs. These results show considerable differences in the GHG emission between different European countries, ranging from 20 to 27 gCO2e./kWh, with some uncertainty, in Norway (where the base calculation presented in the study was 23 gCO2e./kWh) and up to 534e815 gCO2e./kWh range in the United Kingdom (where the base calculation was 591 gCO2e./kWh). In the study (Canals Casals et al., 2016), they established the global warming potential (GWP) of the EV battery charging and use for Norway, France, Germany, the UK, the Netherlands, Switzerland, Austria, Italy, Spain, Denmark, Sweden, Belgium, and Portugal. The Eastern European countries as well as those which have only recently joined the EU have not been included in this paper. The aim of this study was to assess the potential impact of the electric energy produced in individual EU member states that was used for charging EV batteries in European countries based on environmental impact categories, such as GHG emission, accumulated water consumption, impact on human health and the ecosystem quality, and resources depletion. The relevant analyses cover the year 2015, and they also include forecasts of energy production in all EU countries for 2020, 2030, and 2050. The research project reported in this paper has been implemented under the Interreg V-A programme for the Czech Republic and Poland, financed under the 2014e2020 Micro-Project Fund for the Silesia Euroregion. 2. Materials and methods 2.1. Goal and scope of analysis LCA enables the identification of environmental burdens related both directly and indirectly to the life cycle of a vehicle. The main elements of an LCA are the identification and quantification of potential environmental burdens associated with the raw materials used, the energy consumed, and the emissions released into the environment. In this study, an LCA was used to assess the potential

4

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

Table 1 Performed environmental impact assessment-based studies on EVs. Source

Objective

Soret et al. (2014) Rangaraju et al. (2015) Tagliaferri et al. (2016) Van Mierlo et al., 2017

Potential air quality improvements resulting from the use of EVs

Automotive technologies focusing on the potential of EVs battery to reduce emissions

Garcia et al. (2018)

Changes in the GHG emissions as EVs are introduced in to the electric power system characterized by relatively high capacities of wind generation and pumped hydro storage. Luca de Tena Different scenarios involving EVs and renewable power generation considering their spatial and temporal characteristics and Pregger, 2018 Paulino et al. Comparison of road passenger transport policies using the basket-of (2018) products methodology Jursova et al. Carbon and waterfootprints due to EV charging on base of shares of the (2019) sources of electricity generation

Marques et al. (2019) Meyera et al., 2019 Onat et al. (2019) Zhang et al. (2019)

Location

Conclusion

Barcelona, EV introduction on level 26e40% is required to significantly improve Madrid air quality The off-peak charging is better option to reduce the life-cycle Influence of charging profile on the well-to tank emissions of EVs with Belgium emissions compared to peak charging reference to hourly emissions and different possible peak and off-peak charging time frames LCA of future electric and hybrid vehicles e The manufacturing is the major impediment to the environmental performance of EVs Brussels Capital Region

Portugal

Germany

Europe Czech Republic

LCA of two types of EVs batteries lithium manganese (LiMn2O4) and lithium ionphosphate (LiFePO4)

e

Noise of road transportations

e

EVs for three electricity production scenarios - marginal electricity mix, USA average electricity mix, 100% solar energy. China LCA of charger for EVs to evaluate the energy consumption and GHG emissions in their manufacturing, use, and end-of-life stages considering the electricity mix, types of chargers and the ration of vehicle and charger quantities

environmental impact of battery charging in an EV. The LCA method was chosen for the purposes of environmental impact assessment because it assesses the entire life cycle of electricity as also considers many other environmental aspects. The life cycle analysis was performed in accordance with ISO 14040:2006 (ISO, 2006a) and ISO 14044:2006 (ISO, 2006b). The LCA was conducted
Sustainability, Amersfoort, the using the SimaPro v.8 software (Pre Netherlands) with the Ecoinvent v3 database (Ecoinvent, 2018). As per the ISO 14040:2006 standard, the aim and scope of the study was defined, including the functional unit, the system boundary, and the basic assumptions for analyses. The second phase consisted of the analysis of input and output data sets (LCI), comprising of an inventory of all data required for the LCA. The third phase, which was the life cycle impact assessment (LCIA), enabled the respective values of the environmental impact categories to be calculated according to the chosen assessment methods. The last phase comprised of an interpretation of the results obtained. The aim of the study was to conduct an environmental LCIA of EV battery charging in European countries. The scope of the study comprised of an analysis of the electricity generation mix structure in Europe, both at present and in the future, as well as an assessment of the impact of this structure on various environmental indicators. Energy production is highly diversified across Europe in terms of the energy sources used, which leads to diversified environmental impacts. This study addresses the environmental impact assessment of the electric energy produced in individual EU countries to charge EV batteries. It defines the most important categories of environmental impact as well as the main sources of

The EV exhibits 31g CO2eq/km (maximum) when charged with the Belgian average electricity mix in 2011. If the vehicle is charged during off-peak hours, the CO2, PM, NOx, SO2emissions per km can be reduced by 12%, 15%, 13% and 12% respectively, when the Belgian electricity mix for 2011 is considered. Introducing EVs charged at times of low demand with absence of energy storage generally increases the penetration of RES resulting in reductions in GHG emission The additional demand of EVs will reach around 20% of total electricity demand in Germany in 2050.

156 modeling processes for the use stage and 22 for the production and end-of-life stage In the Czech Republic, carbon footprint over next 35 years will be decreased from 215,25 g CO2 eq/FU in 2015 to 134,24 CO2 eq/FU in 2050 while the water footprint will increase form 1,34 E-0,3m3/FU in 2050 LiMn2O4 battery has lower production impacts for the same capacity and lower overall live-cycle impact than LiFePO4 battery has. Novel method to include noise impacts of road transportation in LCA which relies on noise emission and propagation models 100% solar charging scenario is the most environmentally friendly because of the environmental impacts in terms of water, energy, and carbon footprint Home charger revealed the lowest cumulative energy demand (CED) and the global warming potential (GWP)

the electricity generation in the EU countries. The function of the system was the amount of electric energy in the power grid used to charge an electric battery of a passenger car assuming a distance of 100 km. Thus, for the sake of comparison, all the analyses referred to the same functional unit (FU) of 100 km. Within its boundaries, the system covered all the technologies encompassing the electricity mix of all countries. The methodological approach used in the study is referred to as from cradle to gate. Cradle to gate is an assessment of a partial product life cycle from resource extraction (cradle) to the factory gate (before it is transported to the consumer). The use phase and disposal phase of the product are omitted in this case. In order to perform the life cycle analysis, the system boundary was defined, and data sets were identified with reference to the structure of electricity generation for individual EU countries. In previous analyses, the authors identified the most important categories of environmental impact as well as the main sources of negative environmental impact of EVs (EEA Report, 2018). It was established that electric energy was the main determinant of the environmental impact of EVs (Burchart-Korol et al., 2018a). 2.2. Assumptions and inventories Detailed specifications of the vehicle for which the analyses were conducted have been provided in the paper by (BurchartKorol et al., 2018a). The electric energy consumption of the vehicle was assumed at 19.9 kWh/100 km (Ecoinvent, 2018; Girardi et al., 2015; Nissan, 2010). The basic assumptions used for the

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

vehicle life cycle were made from the authors’ own analysis (Burchart-Korol et al., 2018b), from databases (Ecoinvent, 2018), and from a paper by Del Duce et al. (2016). The main source of data for the analyses concerning the current and the projected structure of electricity production in all EU countries was the documentation released by the European Commission (EU scenarios). Data pertaining to the energy systems used to charge EV batteries in each European country were developed on the basis of national data (EU Reference Scenario, 2016; Energy Policies, 2018; Eurostat, 2018). The structure of electricity production in the power grid applied in the battery is one of the most important parameters taken into account when analyzing an EV. It was assumed that the basic variable determining the impact of EVs on the environment in EU countries would be the structure of electricity generation for purposes of electric car battery charging. For this reason, environmental analyses of the forecasted changes in energy sources in all EU countries were performed. The analyses provided in this study span from 2015 to 2050 and address the basic assumptions related to the change in the energy sources, as forecasted for individual countries. The analyses considered the types of energy production forecasted in the analyzed countries for the years 2015e2050. They were performed to provide the information required for further analyses concerning the development of electromobility in the EU and its potential environmental impacts. 2.3. Life cycle impact assessment (LCIA) method Problems connected with the reduction of fossil fuel consumption and GHG emissions seem particularly important when considered against the European Commission’s guidelines, and the new challenges identified in the circular economy model. Aspects related to the analysis of GHG emission are also among the key issues to be assessed by application of the Wheel -To-Wheel method (WTW) developed by Joint Research Center (JRC) and recommended by the EU (JEC, 2015). Both the basic assumptions and the set of input and output data used in the various phases of the vehicle life cycle were extracted from the Ecoinvent databases. The data concerning the energy systems used for charging of electric car batteries in all EU countries were processed with reference to the relevant reports of the EU and Eurostat, as well as country-specific data (National Inventory Reports, 2018). The environmental impact analyses were conducted by considering the assessment of the GHG emissions due to electricity generation, taking into account the structure of energy sources in all EU countries. The analyses also pertained to other categories regarded as significant in terms of the environmental impact of EVs, including cumulative water consumption, impact on human health, ecosystem quality, and resources depletion. Three LCIA methods were used to conduct a multi-dimensional environmental impact assessment. The LCA was performed using the LCIA methods: Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2013), Water Footprint, and Recipe Endpoint assuming a hierarchical perspective (Goedkoop et al., 2013). These chosen methods used to assess important impact categories were selected on the basis of the authors’ previous analyses concerning the LCA of EVs. The IPCC method has been developed by the Intergovernmental Panel on Climate Change (IPCC, 2013). The IPCC method is intended to enable GHG assessment over a product’s life cycle. Using the IPCC method, one can calculate a factor based on the GWP, which expresses the radiative forcing of the GHGs released into the environment by converting it into equivalent kgs of carbon dioxide. IPCC 2013 comprises of the climate change factors of IPCC with a

5

timeframe of 100 years. The total amount of carbon footprint has direct and indirect impacts on human activities expressed as a reference unit of kg of CO2. The water footprint indicators were obtained using the LCA based method developed by Hoekstra (Hoekstra et al., 2011, 2012; Hoekstra, 2014). The water footprint was calculated with SimaPro
Sustainability, Amersfoort, the Netherlands), enabling the v. 8 (Pre identification of regions with high water stress. SimaPro v. 8 features a wide range of water footprint impact assessment methods, both at the midpoint and endpoint levels. In this study, the analysis was conducted by applying the method used by (Hoekstra and Chapagain, 2008). Water footprint is a measure of how much water a product uses, and what direct and indirect environmental impacts are involved in the process. The water footprint method makes it possible to establish indirect and direct water consumption. In the study in question, the calculated water footprint was an indicator of depletion of water resources for electricity generation processes in the power grids of individual EU countries for the purposes of EV battery charging. The calculated water footprint includes water consumption and emissions released to fresh water. The water footprint was assessed from a life cycle perspective, which comprised of direct and indirect impacts of the assessed sources of electric energy on water resources. Under the direct water consumption, the inflow of water from the environment, direct water consumption in the technological processes serving the generation electric energy from individual sources, and the consumption of make-up water in cooling systems were taken into consideration. With regard to indirect effects, that is those which occur outside the technology of electricity generation but are related to its utilization, the authors analyzed water consumption and water pollution related to the production of individual materials and raw materials required for electricity production (Galli et al., 2016). The study of water consumption due to EV battery charging was conducted to explore the environmental aspects of the process and to determine its water footprint. Water footprint (WF) methodology is a new concept introduced by (Hoekstra and Chapagain, 2008) to quantify and map indirect water use and demonstrate how important it is to involve consumers and producers in the supply chain of water resource management. In this study, the amount of water used for EV battery charging includes direct and indirect water use. The direct water use corresponds to the water physically consumed during the process, while the indirect use category is the water needed to create something used in the process. This indicator is applied to the consumed water and only assesses the water used. The main purpose of the Recipe method (Goedkoop et al., 2013) is to transform input and output data of the entire life cycle into three damage categories. This method that was adopted to present many different damage categories was the Recipe Endpoint. The main purpose of the Recipe method (Goedkoop et al., 2013) is to present both the damage categories and the environmental impact categories. This Recipe method enabled the environmental impact assessment to be conducted in a comprehensive and complementary manner. Moreover, it increased the possibility to extend the method with a stage of weighing, which made it possible to obtain the result with a single indicator. The Recipe method covers numerous environmental impact categories and has been developed for Europe, in particular. The indicators of damage categories adopted in the Recipe method have been described in detail by (Goedkoop et al., 2013). For the purposes of analyses, the LCIA according to the Recipe method comprised of the following stages:

6

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

Classification: the input and output data collected are assigned to individual impact categories. Characterization: the value of the given category indicator is calculated using the characterization parameter. In this stage, the values of category indicators for different impact categories are obtained. Normalization: this is the stage where the values of impact category indicators are linked with reference information, which according to the Recipe 2008 method is the value of the indicators obtained over a year across Europe per capita. The normalization stage provides the share of the given effect with respect to the total effect as well as information on the indicator’s relative relevance. Grouping: this consists of assigning impact categories to sets; Recipe 2008 allows for the impact categories (intermediate points) to be grouped into three damage categories (end points): ➢ human health e the following impact categories comprise this damage category: GHG emissions, ozone depletion, toxicity to humans, photochemical smog formation, dust formation, ionizing radiation, ➢ ecosystems quality e the category includes GHG emissions, terrestrial acidification, eutrophication of fresh and sea water, terrestrial ecotoxicity, ecotoxicity to fresh and sea water, occupation of agricultural and urban land, and natural land conversion, ➢ resources depletion e covering depletion of fossil fuels and metals. Weighing: this covers conversion of the normalized values using selected weighing factors and aggregation of these values under impact categories and damage categories. The damage result after weighing is expressed in ecopoints (Pt). One ecopoint (1 Pt) represents a thousandth of the annual environmental damage caused by a single inhabitant of Europe. The category of damage to human health is based on an idea that all human beings should be free from diseases, disabilities, and premature death resulting from environmental pollution. Based on the available information concerning emissions, the characterization stage makes it possible to calculate the damage to human health. The category of ecosystem damage assumes that living organisms should not suffer from population decline and geographical distribution. It is expressed as a percentage of species which have disappeared from a certain area as a result of environmental burdens. The analyses comprise of both the current electricity production structure in individual countries and the forecasted electricity production. 3. Results and discussion 3.1. Analysis of energy generation sources in the EU countries For the purpose of the environmental LCA, data for the current and the forecasted electricity generation structure were identified and arranged based on individual EU countries (www.iea.org). For the LCA analyses of energy systems, data from European energy generation databases (electricity generation systems in the EU) were used. The share of individual electric energy sources in the EU has been presented in Fig. 1. Having analyzed the structure of the electricity production share in the EU (Fig. 2) between 2015 and 2050, the authors have identified a significant decline in the share of solids as well as a decrease in the share of nuclear power. In 2015, the accumulated share of nuclear power, solids, and natural gas accounted for 70% (where the individual shares of these sources were 27%, 26% and 17%, respectively), while the share of RES accounted for only 29%. In the coming years, the share of solids

is forecasted to drop to 6% by 2050, and that of nuclear power to 18%, whereas the share of RES is expected to increase to 55%, with the highest share attributable to wind energy (24%). A slight increase in the share of natural gas consumption for the purposes of electricity production is also expected in the EU. A detailed review of the energy policy of individual EU countries has been provided in the publication titled Energy Policies of IEA Countries (www.iea.org). Data concerning electricity production were identified and arranged for individual EU countries. Tables 2e5 illustrate the share of energy production in the years 2015, 2020, 2030, and 2050. Moreover, Figs. 3e6 show the trend observed in terms of utilization of RES, including biomass, hydro, solar, and wind power, in electricity production of all 28 EU countries in 2015, 2020, 2030, and 2050. In 2015, France (76%), Hungary (54%), Slovakia (54%), and Belgium (40%) reported the highest share of nuclear power in electricity generation. Coal was the most significant source of electric energy in Poland (85%) and Estonia (80%), while a high share of coal in the power mix was also observed in the Czech Republic (50%), Greece (49%), Bulgaria (48%), and Germany (42%). The largest share of petroleum in the energy mix was found in Malta (92%) and Cyprus (89%). The highest consumption of natural gas was observed in Luxembourg (83%), Lithuania (60%), the Netherlands (53%), and Ireland (47%), while Austria (81%), Sweden (63%), Latvia (62%), Croatia (58%), and Denmark (58%) displayed the highest share of RES in electricity generation. In 2020, the share of nuclear power is expected to drop in France (66%) and Hungary (45%), whereas it is expected to rise in Slovakia (60%), Belgium (48%), and Finland (42%). The share of coal will remain unaltered in Estonia (80%), whereas it will continue to decrease in Poland (80%), and will continue to remain a significant component of the energy mix in the Czech Republic (53%), Greece (39%), Bulgaria (47%), and Germany (46%). The share of petroleum is assumed to decrease significantly in Malta (92%), Cyprus (9%), and Greece (9%). Natural gas will continue to be used, with its share increasing in the Netherlands (86%), the Czech Republic (70%), and Malta (70%), and its share decreasing in Luxembourg (70%) and Lithuania (31%). The largest share of RES in the overall energy production in 2020 is forecasted for Denmark (80%), Austria (73%), Portugal (70%), Latvia (67%), and Sweden (64%). For 2030, further reduction in the share of nuclear power is forecasted for France (63%), whereas it is expected to rise in Hungary (45%), Slovakia (82%), and Lithuania (65%). The share of coal in the energy mix will continue to decline in Estonia (73%), Poland (65%), the Czech Republic (45%), Greece (22%), Bulgaria (35%), and Germany (38%). Furthermore, natural gas will increase its share in Malta (87%) and Luxembourg (77%). The largest share of RES in the overall energy production in 2030 is expected to be observed in Portugal (86%), Denmark (81%), and Austria (78%). In 2050, the share of nuclear power is expected to increase in some EU countries, with the highest share forecasted for the following EU countries: Slovakia (59%), Hungary (58%), the Czech Republic (54%) and Lithuania (53%). The share of coal will decrease considerably, while it will still be an energy mix component in Estonia (15%), Poland (26%), the Czech Republic (18%), Bulgaria (21%), and Germany (21%). The share of natural gas will continue to be at its highest in Malta (78%) and Luxembourg (82%). The largest share of RES in energy production in 2050 is forecasted for Portugal (96%), Spain (86%), Denmark (80%), and Austria (81%). The share of RES in the energy mix has been and is still increasing in all EU countries, except for Luxembourg where the RES share is expected to decrease from 2020. In Slovakia, Latvia, and Lithuania, this share will decrease in 2030 and increase again in 2050. Austria (81%), Sweden (53%), Latvia (62%), Croatia (58%) and Denmark (58%) reported the highest share of RES in the energy mix

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

7

Fig. 1. Share of individual electric energy sources in the European Union (EU) (based on EU Reference Scenario, 2016).

in 2015. The largest share of RES in 2020 is forecasted for Denmark (80%), Austria (73%), Portugal (70%), Latvia (67%), and Sweden (64%). In 2030, Portugal (86%), Denmark (81%), Austria (78%), Sweden (65%), and Croatia (64%) will be the countries with the largest share of RES, while in 2050, the countries with the largest share of RES will be Portugal (96%), Spain (86%), Austria (81%), and Denmark (80%). The least forecasted share of RES in the electric energy generation mix in 2050 is expected to be observed in the Czech Republic (16%), Luxembourg (18%), Hungary (18%) and in Malta (22%). 3.2. Life cycle assessment of electric vehicle (EV) battery charging in the European Union Based on the analysis of the environmental effects of EV battery charging with respect to electricity consumption from the power grid in the EU, the authors calculated the indicators for GHG emissions (Fig. 7), cumulative water use (Fig. 8), human health (Fig. 9), ecosystem quality (Fig. 10) and resources depletion (Fig. 11), considering 100 km driven by an electric passenger car as the functional unit of measure. Following the analyses, these indicators were then calculated for individual EU countries. With reference to the analysis of the GHG emission effect of EV battery charging, the GHG emission is 9727.67 g of CO2 eq. per 100 km in 2015, 8934.34 g of CO2 eq. per 100 km in 2020, 7579.62 g of CO2 eq. per 100 km in 2030, and 5661.96 g of CO2 eq. per 100 km in the year 2050. As Fig. 7 implies, the highest impact on the GHG emission is attributable to the use of coal (solids) and natural gas in electricity production. Despite the increase in the share of RES, they exert no impact on the GHG emission (the impact of RES on the GHG emission is negligible). Also, despite the large share of nuclear

power in the electric energy mix in the EU, it has no impact on the GHG emission. The CWU for EV battery charging against electricity consumption from the power grid have been calculated based on 100 km driven by an electric passenger car. It has been demonstrated that the ratio of accumulated water consumption will decrease from 0.0579 m3/100 km in 2015, 0.0541 m3/100 km in 2020, and 0.0505 m3/100 km in 2030, to 0.0442 m3/100 km in 2050. As Fig. 8 illustrates, the greatest influence on the CWU coefficient comes from the consumption of hydro power, nuclear power, natural gas, and solids for electricity production. An increase in the share of natural gas in the electric energy production structure triggers increased water consumption. Moreover, despite the increase in the share of other RES (wind, solar and biomass), they still exert no impact on the indicator subject to analysis. High consumption of water that is attributable to electric energy production based on nuclear power, coal and natural gas is associated with the very high consumption of water for cooling processes in these technologies. Further indicators analyzed for environmental efficiency were human health, ecosystem quality and resource depletion. In the case of the human health analysis, this indicator is decreasing for electricity production in the EU, that is from 0.42 Pt/100 km in 2015, 0.39 Pt/100 km in 2020, and 0.32 Pt/100 km in 2030, down to 0.23 Pt/100 km in 2050. As shown in Fig. 9, the greatest impact on the human health indicator is exerted by the consumption of solids and natural gas in electricity production. Based on an analysis of solids (hard coal and lignite) used for electricity generation by Burchart-Korol et al. (2016), it was demonstrated that the impact of lignite-based energy generation is very high and is mainly related to the mining technology.

8

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

Fig. 2. Changes in the share of individual energy sources used for electricity production in the EU from 2015 to 2050 (based on EU Reference Scenario, 2016).

The analysis of the ecosystem quality (EQ) indicator has revealed that it is also declining, that is from 0.27 Pt/100 km in 2015, 0.26 Pt/100 km in 2020, and 0.25 Pt/100 km in 2030, down to 0.23 Pt/100 km in 2050. Fig. 10 clearly illustrates that the greatest impact on the ecosystem is exerted by the consumption of solids, biomass, and natural gas in electricity production. There has been a decrease in the share of solids in energy generation with respect to solid fuels, whereas the EQ indicator for biomass and natural gas consumption has increased. These two energy sources and well as the EQ indicator values are increasing in their share in the energy generation mix structure. Based on the analysis of the resources depletion for EV battery charging, it has been shown that the resources depletion (RD) indicator value is decreasing from 0.29 Pt/100 km in 2015, 0.27 Pt/ 100 km in 2020, and 0.24 Pt/100 km in 2030, to 0.19 Pt/100 km in 2050. Fig. 11 shows that the greatest impact on the consumption of resources is attributable to the consumption of solids and natural gas in electricity production. As the consumption of solids decreases, this indicator declines, while as the consumption of natural gas increases,

there is an increase in the resource depletion indicator. All the indicators with regard to the analyses of all environmental indicators for the production of the electric energy used in the EU to charge batteries of EVs are shown to decrease mainly due to the reduction in the share of solids in the electricity production. However, an increase in the share of natural gas has led to an increase in the environmental efficiency indicators, particularly the depletion of resources (raw materials, i.e. fossil fuels and minerals) and GHG emissions. The analysis of the impact of using RES to generate electricity has shown that the most eco-friendly energy source is wind power. On the other hand, the analysis of hydro power has revealed its negative impact on the cumulative water consumption and human health indicators. Furthermore, biomass consumption entails a negative impact on the EQ indicator, while the use of solar power exerts a negative impact on the GHG emission and water consumption. Compared to the electric energy from wind, the environmental indicators established for the solar power-based electricity production were also high.

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476 Table 2 Analysis of the electricity generation mix in 28 EU countries in 2015.

9

Table 4 Analysis of the electricity generation mix in 28 EU countries in 2030.

2015

Nuclear

Solids

Oil

Natural gas

Total RES

2030

Nuclear

Solids

Oil

Natural gas

Total RES

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0% 40% 32% 0% 0% 34% 0% 0% 32% 76% 15% 0% 54% 0% 0% 0% 0% 0% 0% 4% 0% 0% 18% 54% 36% 21% 36% 18% 27%

7% 4% 48% 22% 0% 50% 24% 80% 12% 2% 42% 49% 23% 25% 20% 1% 0% 0% 0% 27% 85% 30% 33% 15% 32% 21% 1% 27% 26%

0% 0% 1% 1% 89% 0% 1% 0% 1% 0% 0% 9% 0% 0% 3% 0% 4% 0% 92% 1% 0% 2% 1% 1% 0% 2% 0% 1% 1%

11% 34% 6% 19% 0% 7% 17% 6% 11% 4% 14% 16% 12% 47% 38% 36% 60% 83% 0% 53% 2% 19% 12% 6% 0% 19% 0% 33% 17%

81% 21% 13% 58% 11% 9% 58% 14% 44% 18% 28% 25% 10% 28% 36% 62% 37% 17% 8% 16% 13% 49% 37% 24% 32% 37% 63% 21% 29%

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0% 0% 30% 0% 0% 32% 0% 0% 31% 63% 0% 0% 82% 0% 0% 0% 65% 0% 0% 3% 0% 0% 31% 77% 31% 20% 28% 27% 22%

4% 0% 35% 6% 0% 45% 9% 73% 11% 0% 38% 22% 5% 12% 14% 1% 0% 0% 0% 15% 65% 0% 12% 5% 24% 5% 0% 1% 16%

0% 1% 0% 2% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 2% 0% 0% 1% 0% 0% 0% 3% 0% 0% 0% 1% 0% 1% 1%

18% 59% 8% 28% 70% 12% 9% 6% 12% 2% 18% 21% 5% 46% 38% 37% 19% 77% 87% 45% 15% 11% 11% 1% 9% 17% 6% 27% 19%

78% 40% 27% 64% 29% 11% 81% 21% 46% 34% 44% 57% 7% 42% 44% 61% 16% 22% 13% 37% 20% 86% 45% 17% 36% 57% 65% 44% 43%

Table 3 Analysis of the electricity generation mix in 28 EU countries in 2020.

Table 5 Analysis of the electricity generation mix in 28 EU countries in 2050.

2020

Nuclear

Solids

Oil

Natural gas

Total RES

2050

Nuclear

Solids

Oil

Natural gas

Total RES

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0% 48% 31% 0% 0% 35% 0% 0% 42% 66% 6% 0% 45% 0% 0% 0% 0% 0% 0% 3% 0% 0% 17% 60% 34% 21% 31% 17% 23%

7% 0% 47% 19% 0% 53% 18% 80% 13% 2% 46% 39% 15% 20% 21% 2% 0% 0% 0% 19% 80% 7% 31% 14% 32% 20% 1% 7% 23%

0% 1% 0% 0% 9% 0% 0% 0% 0% 0% 0% 9% 0% 0% 2% 0% 0% 0% 0% 0% 0% 4% 1% 0% 0% 0% 0% 1% 1%

20% 24% 8% 28% 70% 5% 2% 6% 9% 4% 12% 24% 29% 40% 40% 31% 67% 70% 86% 38% 5% 18% 15% 3% 1% 20% 4% 31% 17%

73% 27% 14% 52% 21% 8% 80% 14% 37% 28% 36% 28% 10% 40% 34% 67% 33% 30% 14% 40% 14% 70% 36% 24% 34% 39% 64% 44% 36%

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0% 0% 36% 0% 0% 54% 0% 0% 41% 38% 0% 0% 58% 0% 0% 0% 53% 0% 0% 0% 28% 0% 27% 59% 43% 0% 31% 29% 18%

0% 0% 21% 0% 0% 18% 0% 15% 1% 0% 21% 0% 0% 0% 0% 1% 0% 0% 0% 1% 26% 0% 10% 8% 0% 0% 0% 1% 6%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0%

19% 59% 9% 26% 58% 12% 19% 18% 8% 6% 19% 21% 23% 41% 34% 29% 18% 82% 78% 56% 17% 3% 15% 9% 13% 13% 6% 30% 21%

81% 41% 34% 73% 41% 16% 80% 67% 49% 55% 59% 78% 18% 59% 64% 70% 28% 18% 22% 43% 29% 96% 49% 25% 43% 86% 63% 40% 55%

3.3. LCA of EV battery charging in 28 European Union countries The LCA of EV battery charging was additionally conducted by taking into account the changes in the electric energy mix structure in the power grid for the years 2015e2050 in all 28 EU countries. Detailed results of the relevant analyses for individual types of

environmental damage for all EU countries have been presented in Table 5e10. 3.3.1. Analysis of greenhouse gas emission In 2015, Poland, Estonia, Malta, and Cyprus had the largest share of GHG emission among all the countries of the EU. High GHG

10

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

Fig. 3. Share of individual renewable energy sources (RES) of energy production in 28 EU countries in 2015 (based on EU Reference Scenario, 2016).

Fig. 4. Forecasted share of RES in energy production in 28 EU countries in 2020 (based on EU Reference Scenario, 2016).

emissions were also observed in the Netherlands, Luxembourg, and the United Kingdom. The lowest value of the GHG emission indicator from electricity production was reported in France and Sweden. In 2020, the highest GHG emission indicator value was reported for Poland and Estonia. High GHG emissions were also observed for Greece and Malta. France and Sweden are still forecasted to have the lowest GHG emissions. In 2030, the forecasted

Fig. 5. Forecasted share of RES in energy production in 28 EU countries in 2030 (based on EU Reference Scenario, 2016).

value for GHG emission indicator of Estonia is shown to exceed that of Poland, whose GHG emissions are anyway high. High GHG emissions were also found in Malta, Luxembourg, the Czech Republic, Cyprus, and Germany. Low values of GHG emission indicators are invariably observed in France, but in 2030 they were also seen in Hungary, Slovakia, Sweden, and Lithuania. In 2050, the

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

11

Fig. 8. Cumulative water use of EV battery charging in EU.

Fig. 6. Forecasted share of RES in energy production in 28 EU countries in 2050 (based on EU Reference Scenario, 2016).

highest value of GHG emission indicators are reported for Luxembourg and Malta, whereas the emissions are assumed to drop in Poland. The lowest emissions are reported in France, Sweden, Portugal, and Finland. During 2015e2050, the GHG emission indicator values are observed to be declining in most EU countries, except for Belgium, France, Hungary, Luxembourg, and Slovakia. Compared to 2030, the emissions in 2050 are forecasted to increase in Belgium, France, and Hungary. In Luxembourg, emissions are expected to increase between 2020 and 2050. In Slovakia, GHG emission is projected to be higher in 2050 than in 2030. 3.3.2. Analysis of cumulative water use (CWU) Based on the calculations of CWU for individual sources of

Fig. 9. Human health effects of EV battery charging in the EU.

electric energy used in the EU countries in 2015, the highest CWU has been established for Austria, however high values of CWU have also been reported for Sweden, Latvia, and Croatia. The lowest CWU has been calculated for Denmark. In 2020, Austria is forecasted to have the highest CWU, while Sweden, Croatia, Slovenia, and Latvia will also see an increase in this indicator. The lowest value of CWU

Fig. 7. Impact of electrical vehicle (EV) battery charging on greenhouse gas emissions in EU.

12

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

Fig. 10. Ecosystem quality impacts of EV battery charging in the EU.

Table 7 Cumulative water use from EV battery charging in individual European Union countries, m3/FU.

Fig. 11. Impact on resources depletion due to EV battery charging in the EU. Table 6 Impact of greenhouse gas emission from EV battery charging in individual European Union countries, g CO2 eq/FU. GHG emission

2015

2020

2030

2050

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

6394 6959 12640 10413 17794 13023 8790 19564 5888 1766 12524 16420 7604 13792 12471 8451 11129 14063 18397 15273 19942 11168 10714 5577 8693 9050 2304 12128 9728

7393 4601 12515 11021 13480 13121 4975 19532 5260 1723 13023 15328 8435 11401 12768 7546 11608 11964 14487 10992 19482 7114 10871 4487 8613 8617 2901 7374 8934

6362 10264 9864 8433 12030 12612 4127 17970 5504 1110 12262 9246 2312 10609 10818 8268 3512 13088 14579 11209 17628 4170 5794 2085 8215 5113 2979 5267 7580

5435 9979 7006 6261 10214 6473 3732 6848 2673 1913 8694 4580 4216 7138 6801 6620 3528 13642 13245 9861 9084 2541 5763 4016 3548 3501 2625 5436 5662

CWU

2015

2020

2030

2050

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0,11 0,05 0,06 0,09 0,04 0,06 0,02 0,05 0,07 0,07 0,05 0,05 0,06 0,04 0,06 0,09 0,04 0,05 0,04 0,04 0,05 0,05 0,07 0,07 0,08 0,05 0,09 0,05 0,06

0,1 0,05 0,06 0,09 0,04 0,06 0,02 0,05 0,06 0,07 0,04 0,05 0,05 0,04 0,05 0,08 0,04 0,04 0,04 0,03 0,05 0,07 0,07 0,07 0,08 0,05 0,09 0,04 0,05

0,09 0,03 0,06 0,08 0,04 0,06 0,01 0,04 0,06 0,06 0,04 0,04 0,06 0,03 0,05 0,08 0,06 0,04 0,04 0,03 0,04 0,06 0,07 0,08 0,08 0,05 0,08 0,04 0,05

0,08 0,03 0,05 0,07 0,03 0,06 0,01 0,02 0,06 0,05 0,03 0,03 0,06 0,03 0,04 0,07 0,06 0,04 0,04 0,03 0,05 0,06 0,06 0,07 0,08 0,03 0,08 0,04 0,04

will be seen in Denmark. In 2030, the highest CWU value is observed in Austria, Sweden, Slovenia, Slovakia, Latvia, and Croatia. In 2050, the highest values of this indicator are expected to be observed in Sweden, Slovenia, and Austria, while the lowest indicator is expected in Denmark. Based on analyses of the forecasted energy sources used in individual EU countries, Lithuanian CWU indicator will increase from 2020 to 2030, and will remain roughly unaltered until 2050. The CWU indicator of the Czech Republic, Malta, and Slovenia will stay at a comparable level between 2015 and 2050. In Luxembourg and the Netherlands, the CWU indicator decreases from 2015 to 2020, only to stabilize later. Poland’s CWU is forecasted to decrease from 2020 to 2030, and then increase up till 2050. For Slovakia, the CWU indicator increases considerably from 2020 to 2030, followed by a significant reduction in 2050.

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476 Table 8 Effect on human health from EV battery charging in individual European Union countries.

13

Table 10 Effect on resources depletion from EV battery charging in individual European Union countries.

HH, Pt/FU

2015

2020

2030

2050

RD, Pt/FU

2015

2020

2030

2050

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0,32 0,25 0,58 0,48 0,71 0,59 0,37 0,89 0,28 0,09 0,56 0,72 0,33 0,53 0,50 0,35 0,38 0,45 0,73 0,58 0,92 0,49 0,49 0,27 0,43 0,38 0,15 0,49 0,42

0,34 0,16 0,57 0,48 0,44 0,60 0,25 0,89 0,25 0,09 0,58 0,65 0,33 0,43 0,51 0,32 0,39 0,39 0,46 0,43 0,89 0,32 0,49 0,23 0,42 0,37 0,17 0,28 0,39

0,30 0,34 0,45 0,36 0,39 0,56 0,19 0,82 0,26 0,07 0,53 0,39 0,11 0,38 0,42 0,33 0,13 0,42 0,46 0,42 0,78 0,20 0,26 0,12 0,39 0,21 0,17 0,19 0,32

0,25 0,32 0,32 0,26 0,34 0,29 0,14 0,29 0,14 0,10 0,37 0,18 0,15 0,24 0,26 0,27 0,14 0,43 0,42 0,33 0,39 0,15 0,25 0,19 0,17 0,15 0,15 0,19 0,23

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0,21 0,24 0,35 0,32 0,69 0,36 0,27 0,53 0,18 0,06 0,36 0,49 0,22 0,44 0,41 0,29 0,40 0,51 0,71 0,49 0,53 0,34 0,31 0,17 0,24 0,28 0,08 0,38 0,29

0,24 0,17 0,35 0,35 0,49 0,36 0,15 0,52 0,16 0,06 0,37 0,47 0,27 0,37 0,41 0,26 0,42 0,43 0,52 0,36 0,52 0,24 0,32 0,13 0,24 0,27 0,10 0,25 0,27

0,21 0,37 0,28 0,29 0,44 0,35 0,14 0,48 0,17 0,04 0,36 0,29 0,07 0,36 0,36 0,29 0,13 0,47 0,53 0,37 0,49 0,15 0,18 0,06 0,24 0,18 0,10 0,19 0,24

0,19 0,36 0,21 0,23 0,37 0,19 0,14 0,22 0,09 0,07 0,27 0,17 0,15 0,26 0,25 0,23 0,13 0,49 0,48 0,36 0,27 0,09 0,19 0,13 0,13 0,13 0,09 0,20 0,19

3.3.3. Analysis of effect on human health (HH) For 2015 and 2020, the highest HH indicator values have been found with respect to Poland and Estonia, while the lowest values have been found for France and Sweden. In 2030, the HH indicator

Table 9 Effect on ecosystem quality from EV battery charging in individual European Union countries. EQ, Pt/FU

2015

2020

2030

2050

AT BE BG HR CY CZ DK EE FI FR DE EL HU IE IT LV LT LU MT NL PL PT RO SK SI ES SE UK EU

0,21 0,23 0,26 0,23 0,38 0,29 0,32 0,49 0,35 0,06 0,36 0,33 0,24 0,29 0,32 0,29 0,37 0,33 0,38 0,38 0,47 0,30 0,23 0,17 0,19 0,20 0,18 0,32 0,27

0,23 0,14 0,26 0,25 0,26 0,28 0,44 0,49 0,30 0,07 0,33 0,31 0,24 0,24 0,33 0,28 0,36 0,29 0,26 0,37 0,47 0,23 0,24 0,17 0,21 0,20 0,21 0,31 0,26

0,21 0,27 0,21 0,21 0,26 0,30 0,37 0,48 0,39 0,07 0,35 0,20 0,10 0,24 0,31 0,30 0,15 0,30 0,27 0,37 0,45 0,17 0,14 0,08 0,21 0,14 0,22 0,30 0,25

0,22 0,27 0,19 0,20 0,22 0,22 0,34 0,44 0,36 0,10 0,31 0,13 0,17 0,21 0,32 0,33 0,17 0,29 0,26 0,35 0,28 0,16 0,17 0,17 0,21 0,12 0,23 0,24 0,23

is expected to rise in Estonia. The lowest HH indicator value is reported for France. In 2050, the highest values of the HH indicator associated with electricity generation are to be found in Luxembourg, Malta, Poland, and Germany. France reported the lowest HH indicator value to date. In 2050, the HH indicator value is expected to rise in Hungary and Croatia, as compared to 2030. In Luxembourg, the HH indicator value will continue to rise up to 2050. In Slovakia, the value of HH indicator will decrease from 2020 to 2030, and then increase until 2050. 3.3.4. Analysis of ecosystem quality (EQ) Based on the LCA analyses, the highest EQ values for the years 2015 have been found in Estonia and Poland, while the lowest have been found in France. Similar results have been obtained for 2020 and 2030. In 2050, Estonia, Finland, and the Netherlands are expected to report the highest EQ values, while France and Spain e the lowest. The findings formulated with reference to the analyses performed by the authors are that Austria’s EQ values will increase from 2015 to 2020, and then decrease until 2030, and then increase again by 2050. In Belgium and Latvia, the EQ values will increase after 2020. In France, Italy, and Slovakia, the EQ values will increase after 2030 and until 2050. 3.3.5. Analysis of resources depletion (RD) For the year 2015, the highest RD indicator was established for Malta and Cyprus, while the lowest e for France and Sweden. In 2020, Poland, Malta, Estonia, and Cyprus will have the highest RD rates due to electricity generation, while France and Sweden e the lowest. In 2030, the highest value of the RD indicator is in Malta, and the lowest value is seen in France and Hungary. In 2050, highest results have been established for Luxembourg and Malta, while France, Sweden, Finland, and Portugal have reported the lowest rates. Furthermore, the RD indicator will increase in Belgium

14

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476

and Luxembourg past 2020, while in France and Slovakia, it will increase after 2030 and until 2050. The literature on the subject provides very little data enabling the water consumption attributable to EVs to be analyzed by taking forecasts of changes in the respective electric power systems into account, but numerous publications have already been published addressing the GHG emissions due to EVs considering different energy sources used for charging of EV batteries. The literature also lacks an analysis of specific categories of damage, including damage to human health, ecosystem quality, and resources depletion, from electric energy production in all EU countries. Thus, this study provides a comprehensive overview of the impact exerted by different electric energy sources used for charging of electric car batteries on GHG emissions, consumption of water, resources depletion, human health, and ecosystem quality.

8. EU countries should definitely rely on RES for the purposes of EV battery charging. This will reduce the impact of EVs on the environment in its broad sense by taking many different kinds of environmental damage into account. The most ecofriendly energy source is wind power. On the other hand, the analysis of hydro power has revealed its negative impact on the CWU and HH indicators. 9. The analyses addressed in this study have provided new knowledge to be used in further analyses concerning the development of electromobility in the EU as well as its potential environmental impact. 10. The performed analyses were limited to environmental aspects of EVs charging. The subsequent publications will be aimed also at socio-economic analysis of EVs charging taking into account all three aspects of sustainable development (environmental, economic and social).

4. Conclusions Author contributions section The LCA on EV battery charging in 28 EU countries was conducted. The purpose of the analysis was to present environmental impacts of EV charging by highly diversified energy production across EU countries. Using IPCC, Recipe method and water footprint assessment, the impact categories GHG emission, CWU, HH, EQ, and RD were analyzed from 2015 with the perspective up to 2050 based on data of the EU Commission. Having analyzed the life cycle of electric energy generation in the power grid for purposes of charging EV batteries, the authors conclude the following: 1. The main determinants for all the studied environmental indicators are solids, natural gas and biomass consumption. 2. In most EU countries, the analyzed environmental indicators have been found to decline in the successive years of the analyses that is from 2015 to 2050. 3. In 2015, Poland, Estonia, Malta and Cyprus had the largest share of GHG emission on EV battery charging among all the countries of the EU with calculated impact 19 942.25, 19563.84, 18 396.71 and 17 793.72, respectively. The most significant change in future perspective of the environmental indicator is recorded in Belgium with calculated impact 6959.28 in 2015 and 9978.83 in 2050. 4. The EV battery charging has the lowest impact on CWU in Denmark with stable value from 0.02 in 2015 to 0.01 in 2050. For 2015e2050, the highest CWU values have been observed for Austria (0.11 in 2015 and 0.08 in 2050), Sweden (0.09 in 2015 and 0.08 in 2050) and Slovenia (0.08 from 2015 to 2050). 5. In 2015, the highest HH indicator values have been found for Poland (0.92) and Estonia (0.89), while the lowest values are seen in France (0.09) and Sweden (0.15). In 2015, the average value for EU countries has been calculated 0.47. In 2050, the highest values of HH indicator associated with electricity generation for EV battery charging are in Luxembourg (0.43), Malta (0.42), Poland (0.39) and Germany (0.37). 6. The EV battery charging has the lowest impact on EQ for France with EQ indicator 0.06 in 2015, 0.07 in 2020 and 2030, and 0.10 in 2050. The highest EQ values for the year 2015 have been found in Estonia (0.49) and Poland (0.47). Similar results for these countries have been obtained for 2020, 2030 and 2050. 7. The electricity generation for EV battery charging is associated with lowest impact on RD in France (0.06 in 2015) and Sweden (0.08 in 2015) with stable values up to 2050. In 2015, highest results have been established for Malta (0.71), Poland (0.53) and Luxembourg (0.51).

Dorota Burchart-Korol conceived the idea, designed the framework of the paper, performed the software assessment and prepared the draft. Simona Jursova edited the draft, verified the calculated data, contributed to analysis tools and results conclusions. Pavlina Pustejovska and Piotr Folega cooperated on the results interpretation and article review. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The study was prepared in the frame of the project “Electromobility in Czech-Polish Cross-border Area”, reg. no. CZ.11.4.120/ 0.0/0.0/16_013/0001585 which is co-financed by the European Union fund for regional development, programme Interreg V-A Czech Republic-Poland, Fund of microprojects 2014e2020, conducted within Centre for research of low-carbon energetic technologies, no. CZ 02.1.01/0.0/0.0/16_019/0000753, and supported by the projects no. 12/020/RGP19/0145. References Angelo, A.C., Marujo, L.G., 2020. Life cycle sustainability assessment and decisionmaking under uncertainties. In: Jingzheng, R. (Ed.), Life Cycle Sustainability Assessment for Decision-Making, Methodologies and Case Studies. Elsevier, pp. 253e268. Barton, B., Schütte, P., 2017. Electric vehicle law and policy: a comparative analysis. J. Energy Nat. Resour. Law 35 (2), 147e170. Brizmohun, R., Ramjeawon, T., Azapagic, A., 2014. Life cycle assessment of electricity generation in Mauritius. J. Clean. Prod. 16, 1727e1734. Buchal, Ch, Hans-Dieter, K., Hans-Werner, S., 2019. Kohlemotoren, Windmotoren und Dieselmotoren: was zeigt die CO2-Bilanz? Ifo Schnelld. 72, 40e54 (08). Burchart-Korol, D., Korol, J., Czaplicka-Kolarz, K., 2016. Life cycle assessment of heat production from underground coal gasification. Int. J. Life Cycle Assess. 21, 1391e1403. Burchart-Korol, D., Jursova, S., Fole˛ ga, P., Korol, J., Pustejovska, P., Blaut, A., 2018a. Environmental life cycle assessment of electric vehicles in Poland and the Czech Republic. J. Clean. Prod. 202, 476e487. Burchart-Korol, D., Pustejovska, P., Blaut, A., Jursova, S., Korol, J., 2018b. Comparative life cycle assessment of current and future electricity generation systems in the Czech Republic and Poland. Int. J. Life Cycle Assess. 23 (11), 2165e2177. Canals Casals, L., Martinez-Laserna, E., Amante Garcia, B., Nieto, N., 2016. Sustainability analysis of the electric vehicle use in Europe for CO2 emissions reduction. J. Clean. Prod. 127, 425e437. Davies, J., Kurani, K.S., 2013. Moving from assumption to observation: implications for energy and emissions impacts of plug-in hybrid electric vehicles. Energy Pol. 62, 550e560.

D. Burchart-Korol et al. / Journal of Cleaner Production 257 (2020) 120476 Del Duce, A., Gauch, M., Althaus, H.J., 2016. Electric passenger car transport and passenger car life cycle inventories in Ecoinvent version 3. Int. J. Life Cycle Assess. 21, 1314e1326. Ecoinvent, 2018. Swiss Centre for Life Cycle Inventories. Ecoinvent 2018 dEcoinvent Database V 3, 2018. Available online: www.ecoinvent.org. (Accessed 29 April 2019). EEA, 2018. Greenhouse Gas Emissions from Transport, 2018. The European Environment Agency Report available. https://www.eea.europa.eu. (Accessed 8 March 2019). EEA Report, 2018. Electric Vehicles from Life Cycle and Circular Economy Perspectives TERM 2018: Transport and Environment Reporting Mechanism (TERM) Report. https://www.eea.europa.eu/publications/electric-vehiclesfrom-life-cycle. (Accessed 11 November 2019). Energy Policies of IEA Countries, 2018. https://www.iea.org/publications/ countryreviews/. (Accessed 10 March 2018). EU Reference Scenario, 2016. Energy, Transport and GHG Emissions Trends to 2050. https://ec.europa.eu/. (Accessed 8 April 2019). European Commission, 2010. Europe 2020: A Strategy for Smart, Sustainable and Inclusive Growth, COM(2010) 2020 Final. Brussels. https://www.eea.europa.eu/ policy-documents/com-2010-2020-europe-2020. (Accessed 12 March 2018). European Commission, 2011. White Paper: Roadmap to a Single European Transport Area e towards a Competitive and Resource-Efficient Transport System, COM(2011) 144 Final. Brussels. https://www.eea.europa.eu/policy-documents/ com-2011-144-roadmap-to. (Accessed 10 March 2018). European Commission, 2013. Communication from the Commission to the European Parliament, the Council, the European Social and Economic Committee and the Committee of the Regions: Clean Power for Transport: A European Alternative Fuels Strategy, COM (206) 017 Final. Brussels. https://eur-lex.europa. eu. (Accessed 12 March 2018). Eurostat, 2018. Energy, Transport and Environment Indicators, Eurostat Statistical Book. European Union. https://www.ec.europa.eu. (Accessed 12 April 2019). Galli, A., Wiedmann, T., Ercin, E., Knoblauch, D., Ewing, B., Giljum, S., 2016. Integrating Ecological, Carbon and Water footprint into a “Footprint Family” of indicators: definition and role in tracking human pressure on the planet. Ecol. Indicat. 16, 100e112. Garcia, R., Freire, F., Clift, R., 2018. Effects on greenhouse gas emissions of introducing electric vehicles into an electricity system with large storage capacity. J. Ind. Ecol. 22, 288e299. Girardi, P., Gargiulo, A., Brambilla, P., 2015. A comparative LCA of an electric vehicle and an internal combustion engine vehicle using the appropriate power mix: the Italian case study. Int. J. Life Cycle Assess. 20, 1127e1142. Goedkoop, M., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., Van Zelm, R., 2013. ReCiPe 2008, A Life Cycle Impact Assessment Method Which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level (Report I: Characterisation). Harrison, G., Thiel, C., 2017. An exploratory policy analysis of electric vehicle sales competition and sensitivity to infrastructure in Europe. Technol. Forecast. Soc. Change 114, 165e178. Hoekstra, A.Y., Chapagain, A.K., 2008. Globalization of Water : Sharing the Planet’s Freshwater Resources. Blackwell Publishing, Oxford. Hoekstra, A.Y., Chapagain, A.K., Aldaya, M.M., Mekonnen, M.M., 2011. Setting the global standard. In: The Water Footprint Assessment Manual. Earthscan, London. Hoekstra, A.Y., Mekonnen, M.M., Chapagain, A.K., Mathews, R.E., Richter, B.D., 2012. Global monthly water scarcity: blue water footprints versus blue water availability. PloS One 7. https://doi.org/10.1371/journal.pone.0032688. Hoekstra, A.Y., 2014. Sustainable, efficient and equitable water use: the three pillars under wise freshwater allocation. WIREs Water 1, 31e40. m Honus, S., Kumagai, S., Ne cek, O., Yoshioka, T., 2016. Replacing conventional fuels in USA, Europe, and UK with plastic pyrolysis gases - part I: experiments and graphical interchangeability methods. Energy Convers. Manag. 126, 1118e1127. IPCC - Intergovernmental Panel on Climate Change, 2013. IPCC fifth assessment report. In: The Physical Science Basis. http://www.ipcc.ch. (Accessed 8 March 2019). ISO, 2006. ISO 14040:2006 Environmental Management - Life Cycle Assessment Principles and Framework. International Organization for Standardization, Geneva, Switzerland, 2006. ISO, 2006. ISO 14044:2006 Environmental Management-Life Cycle AssessmentRequirements and Guidelines. International Organization for Standardization, Geneva, Switzerland, 2006. JEC, 2015. Well-to-wheels Analysis of Future Automotive Fuels and Powertrains in

15

the European Context. CONCAWE, EUCAR, JRC, (JEC) 2015. http://et.jrc.ec. europa.eu. (Accessed 20 December 2017). Jursova, S., Burchart-Korol, D., Pustejovska, P., 2019. Carbon footprint and water footprint of electric vehicles and batteries charging in view of various sources of power supply in the Czech Republic. Environments 6, 38e49. Kalbar, P.P., Das, D., 2020. Advancing life cycle sustainability assessment using multiple criteria decision making. In: Jinghzheng, R. (Ed.), Life Cycle Sustainability Assessment for Decision-Making, Methodologies and Case Studies, pp. 205e224. Luca de Tena, D., Pregger, T., 2018. Impact of electric vehicles on a future renewable energy-based power system in Europe with a focus on Germany. Int. J. Energy Res. 42, 2670e2685. Lucas, A., Prettico, G., Flammini, M.G., Kotsakis, E., Fulli, G., Masera, M., 2018. Indicator-based methodology for assessing EV charging infrastructure using exploratory data analysis. Energies 11, 1869e1887. Marques, P., Garcia, R., Kulay, L., Freire, F., 2019. Comparative life cycle assessment of lithium-ion batteries for electric vehicles addressing capacity fade. J. Clean. Prod. 229, 787e794. Meyera, R., Benetto, E., Mauny, F., Lavandier, C., 2019. Characterization of damages from road traffic noise in life cycle impact assessment: a method based on emission and propagation models. J. Clean. Prod. 231, 121e131. Muha, R., Perosa, A., 2018. Energy consumption and carbon footprint of an electric vehicle and a vehicle with an internal combustion engine. Transport Probl. 13, 49e58. National Inventory Reports, 2018. Submission under the UNFCCC and under the Kyoto Protocol, Reported Inventories 1990-2015. In: https://unfccc.int/processand-meetings/transparency-and-reporting/reporting-and-review-under-theconvention/greenhouse-gas-inventories-annex-i-parties/submissions/nationalinventory-submissions-2018. (Accessed 6 April 2019). Nissan, 2010. Nissan Leaf. Nissan North America. https://www.nissanusa.com. (Accessed 20 December 2017). Onat, N.C., Kucukvar, M., Afshar, S., 2019. Eco-efficiency of electric vehicles in the United States: a life cycle assessment based principal component analysis. J. Clean. Prod. 212, 515e526. Pasaoglu, G., Zubaryeva, A., Fiorello, D., Thiel, C., 2014. Analysis of European mobility surveys and their potential to support studies on the impact of electric vehicles on energy and infrastructure needs in Europe. Technol. Forecast. Soc. Change 87, 41e50. Paulino, F., Pina, A., Baptista, P., 2018. Evaluation of alternatives for the passenger road transport sector in Europe: a life-cycle assessment approach. Environments 5, 21. Peiu, N., 2007. Life cycle inventory study of the electrical energy production in Romania. Int. J. Life Cycle Assess. 12, 225e229. ~o, M., 2019. Interpreting life cycle assessment results Pelletier, N., Bamber, N., Branda for integrated sustainability decision support: can an ecological economic perspective help us to connect the dots? J. Life Cycle Assess. 24 (9), 1580e1586. Rakotoson, V., Praene, J.P., 2017. A life cycle assessment approach to the electricity generation of French overseas territories. J. Clean. Prod. 168, 755e763. Rangaraju, S., De Vroey, L., Messagie, M., Mertens, J., Van Mierlo, J., 2015. Impacts of electricity mix, charging profile, and driving behavior on the emissions performance of battery electric vehicles: a Belgian case study. Appl. Energy 148, 496e505.
k, F., 2017. Does electric car produce emissions? Scientific Journal of Rievaj, V., Syna Silesian University of Technology. Ser. Transport 94, 187e197. Sala, S., Farioli, F., Zamagni, A., 2013. Life cycle sustainability assessment in the context of sustainability science progress (part 2). J. Life Cycle Assess. 18 (9), 1686e1697. Soret, A., Guevara, M., Baldasano, J.M., 2014. The potential impacts of electric vehicles on air quality in the urban areas of Barcelona and Madrid (Spain). Atmos. Environ. 99, 51e63. Tagliaferri, C., Evangelisti, S., Acconcia, F., Domenech, T., Ekins, P., Barletta, D., Lettieri, P., 2016. Life cycle assessment of future electric and hybrid vehicles: a cradle-to-grave systems engineering approach. Chem. Eng. Res. Des. 112, 298e309. Van Mierlo, J., Messagie, M., Rangaraju, S., 2017. Comparative environmental assessment of alternative fueled vehicles using a life cycle assessment. Transport. Res. Procedia 25, 3435e3445. Zhang, Z., Sun, X., Ding, N., Yang, J., 2019. Life cycle environmental assessment of charging infrastructure for electric vehicles in China. J. Clean. Prod. 227, 932e941.