A Comparative Life Cycle Assessment of an Electric and a Conventional Mid-Segment Car: Evaluating the Role of Critical Raw Materials in Potential Abiotic Resource Depletion

Abstract

Electric passenger vehicles are set to dominate the European car market, driven by EU climate policies and the 2035 ban on internal combustion engine production. This study assesses the sustainability of this transition, focusing on global warming potential and Critical Raw Material (CRM) extraction throughout its life cycle. The intensive use of CRMs raises environmental, economic, social, and geopolitical concerns. These materials are scarce and are concentrated in a few politically sensitive regions, leaving the EU highly dependent on external suppliers. The extraction, transport, and refining of CRMs and battery production are high-emission processes that contribute to climate change and pose risks to ecosystems and human health. A Life Cycle Assessment (LCA) was conducted, using OpenLCA software and the Ecoinvent 3.10 database, comparing a Peugeot 308 in its diesel and electric versions. This study adopts a cradle-to-grave approach, analyzing three phases: production, utilization, and end-of-life treatment. Key indicators included Global Warming Potential (GWP100) and Abiotic Resource Depletion Potential (ADP) to assess CO₂ emissions and mineral resource consumption. Technological advancements could mitigate mineral depletion concerns. Li-ion battery recycling is still underdeveloped, but has high recovery potential, with the sector expected to expand significantly. Moreover, repurposing used Li-ion batteries for stationary energy storage in renewable energy systems can extend their lifespan by over a decade, decreasing the demand for new batteries. Such innovations underscore the potential for a more sustainable electric vehicle industry.

1. Introduction

The topics explored in this study regarding mobility, energy production and storage, sustainable raw material use, and the circular economy are just a few of the key issues driving the industrial and economic transformation expected to unfold globally in the coming decades.

As highlighted in the IPCC 6th Assessment Report [1], global warming is undeniably driven by human activities, which have significantly contributed to an average global temperature increase of 1.1 °C compared to the pre-industrial era. These significant data further support what the scientific community has advocated for over fifty years: the urgent need for a profound transformation in our economic and production models, resource exploitation, and overall lifestyle. As a result, national governments are increasingly prioritizing environmental issues by setting regulations and targets, pushing entire industries toward greater sustainability, and promoting the adoption of new “green” solutions. This shift is paving the way for what can be seen as a new industrial revolution.

The transportation sector is a major contributor to greenhouse gas (GHG) emissions, primarily due to conventional vehicles powered by fossil fuels, which release substantial amounts of carbon dioxide (CO₂), along with smaller quantities of methane (CH₄) and nitrous oxide (N₂O), into the atmosphere [2]. In contrast, electric mobility presents a more sustainable alternative by significantly lowering or eliminating these emissions, thereby playing a vital role in improving air quality and addressing climate change. Since electric vehicles produce zero tailpipe emissions of particulate matter and pollutants, they contribute to cleaner air in densely populated areas. Additionally, electric mobility helps shift pollution away from urban centres to energy production sites. Moreover, the expansion of charging infrastructure supports the transition to cleaner transportation, further encouraging the adoption of sustainable mobility solutions in cities.

Recent technical assessments further clarify the potential environmental gains from electrification, depending on powertrain type and regional energy mix. For instance, a comprehensive analysis by the International Council on Clean Transportation (ICCT) demonstrates that battery electric vehicles (BEVs) significantly reduce life cycle greenhouse gas (GHG) emissions compared to internal combustion engine vehicles (ICEVs). The study covers global markets and powertrain types, showing that BEVs emit approximately 50%–70% fewer GHGs over their lifetime, even when charged from high-emission electricity grids. These advantages are projected to increase as electricity production decarbonizes further. The report also underscores the importance of including upstream emissions, such as those from fuel production and battery manufacturing, in a complete life cycle assessment to accurately capture the environmental performance of vehicle technologies [3].

Complementing this global perspective, a recent report by the Italian Ministry of the Environment and Energy Security (MASE) focuses on the national electricity mix and its implications for electric mobility. The study highlights a steady decline in the carbon intensity of electricity generation in Italy, currently averaging around 300 g CO₂ per kWh. This improvement significantly enhances the environmental performance of electric vehicles within the country. As a result, the report confirms that EVs used in Italy already provide meaningful reductions in GHG emissions compared to ICEVs, especially considering the full life cycle perspective. The analysis also reinforces the importance of regional energy contexts in evaluating the sustainability of vehicle technologies [4].

Expanding on this regional insight, a 2024 Fédération Internationale de l’Automobile (FIA) report further emphasizes the need for geographically specific and usage-based life cycle assessments when evaluating electric vehicles. The study shows that factors such as local electricity mix, intensity of vehicle use, and battery origin critically affect the overall carbon footprint of EVs. In particular, the FIA highlights that the environmental advantages of electric mobility become most evident when EVs are powered by low-carbon electricity and are used over long distances. Moreover, the report calls for greater transparency and methodological harmonization in LCA studies to support both policymaking and consumer awareness in the transition to sustainable transportation [5].

The European Union (EU) has taken a leading role in tackling climate change through strong legislative measures. The European Green Deal [6] and its associated policies [7] prioritize electric mobility as a key strategy to decarbonize the transportation sector, driving the transition toward a greener and more sustainable future. Strict emission regulations and incentives for electric vehicles are already in place to support the ambitious goal of reducing GHG emissions from new cars by 55% by 2030 [8]. Most notably, the commitment to phasing out the sale of internal combustion engine vehicles by 2035 highlights the European Commission’s strong dedication to mitigating climate change and improving air quality.

A major challenge associated with the projected exponential growth in electric vehicle (EV) production in the coming years is the rising demand for batteries, which, in turn, drives the need for Critical Raw Materials (CRMs). The European Commission defines CRMs as “Raw materials of high importance for the EU economy and whose supply is associated with a high risk” [9].

The sustainable future that the EU envisions through the European Green Deal relies on digitalization, electric mobility, and renewable energy. These sectors will require vast amounts of new electronic equipment, instruments, and components in the coming years. However, this transition to a zero-carbon economy is significantly more resource-intensive, particularly regarding specific materials and minerals. Unfortunately, these resources are not evenly distributed across the Earth’s crust. Europe lacks reserves for most of them, relying entirely on imports of refined materials or finished products from other countries that control their extraction and processing.

However, despite these benefits, electric vehicles also present new environmental and geopolitical challenges related to their reliance on batteries. The production of lithium-ion batteries requires substantial quantities of Critical Raw Materials such as lithium, cobalt, nickel, and graphite, resources that are often extracted under environmentally damaging conditions and with significant social and ethical concerns, especially in regions with weak labour and environmental protections. Furthermore, the global supply chains for these materials are highly concentrated, with a few countries and corporations controlling a significant market share, potentially leading to new forms of resource dependency and strategic vulnerabilities. As demand for electric vehicles grows, addressing these supply chain issues will ensure that the transition to electric mobility is both environmentally responsible and socially equitable. For this reason, it is essential to begin by assessing the environmental sustainability of electric vehicles, particularly through a direct comparison with conventional fossil fuel-powered cars. This analysis is undertaken in this study.

Within this background, the EU will inevitably undergo a radical transformation in its mobility and energy systems in the coming years. The transition from conventional fossil fuel-based to renewable energy systems marks a profound shift in global resource dependencies. While fossil fuel systems rely on continuous oil, coal, and gas extraction, renewable technologies increasingly depend on Critical Raw Materials (CRMs). This change brings new challenges related to resource availability, supply chain resilience, and the environmental and social impacts of CRM extraction and processing. Rare Earth Elements such as copper, nickel, cobalt, and manganese are essential for producing batteries, wind turbines, and other green technologies. However, their extraction and refinement are often highly polluting and energy-intensive, with reserves frequently located in politically unstable regions where environmental protections and human rights are weak or poorly enforced. As a result, CRMs are expected to remain at the heart of policy, industrial, and scientific attention in the years to come, as their responsible sourcing and sustainable management are crucial for ensuring a just and successful energy transition.

A market review by the International Energy Agency (IEA) [10] highlights a sharp rise in the extraction and trade of Critical Raw Materials. Between 2016 and 2021, CRM consumption driven solely by the clean technology sector grew by an estimated 20%. The upper section of Figure 1 highlights a steep acceleration in clean energy deployment from 2019 to 2023: annual solar PV installations increased from about 80 GW to nearly 300 GW, while combined onshore and offshore wind capacity increased from roughly 60 GW to 120 GW. Electric vehicle sales grew even more dramatically, increasing from around 3 million units in 2019 to an estimated 13 million in 2023.

As illustrated in the lower section of Figure 1, this surge has driven a sharp rise in Critical Raw Material demand. Worldwide lithium usage jumped from roughly 40 kt to 130 kt, with its clean energy application share increasing from 30% to 56%. Cobalt demand increased by about 70%, its clean-energy share increasing from 17% to 40%, while nickel demand rose by 40% and the portion used in clean energy technologies expanded from 6% to 16%. These changes highlight how the energy transition is rapidly transforming global mineral markets.

A foresight study on raw materials by the European Commission [11] examines the projected demand growth for various raw materials in both the EU and globally by 2030 and 2050. Figure 2 illustrates these projections, with the left side of the charts representing a low-demand scenario (LDS) and the right side depicting a high-demand scenario (HDS). In the EU, for a low-demand scenario, two essential battery production materials, lithium and graphite, are forecast to see demand multiply roughly 10× by 2030 and increase to 15× (lithium) and 18× (graphite) by 2050. Under a HDS, these figures increase to about 14× by 2030 and reach 21× for lithium and 26× for graphite by the mid-century. Globally, the LDS predicts lithium and graphite requirements growing 13× and 14× by 2030 and increasing to 65× and 80× by 2050. In the HDS, these same materials increase roughly 20× by 2030 and increase to approximately 90× for lithium and 110× for graphite by 2050.

The projected increase in EV production is expected to drive nearly the entire increase in demand for lithium, graphite, and cobalt.

To meet this rapidly growing demand, the EU relies heavily on outside countries, particularly China, across various supply chain stages. For instance, the Rare Earth Elements required for permanent magnets in wind turbines are mined, refined, and processed in China [9]. Similarly, most solar photovoltaic modules and cells are imported from China, which also dominates every stage of the battery supply chain. The control and management of mining operations in outside countries present another significant challenge. For instance, nearly 70% of cobalt mines in the Democratic Republic of Congo are owned by Chinese companies and supply exclusively to Chinese refineries [12]. This monopolistic control poses a significant risk to the stability and security of the European raw materials supply chain [10]. As illustrated in Figure 3 (2019 data), the global distribution of essential minerals for the energy transition is far more geographically concentrated than fossil fuels [13]. In 2019, nearly 100% of global lithium and Rare Earth Element production was supplied by just three countries, while about 80% of cobalt and roughly 55–60% of nickel and copper output were similarly dominated by the top three producers. This extreme clustering magnifies geopolitical and supply chain risks, from export curbs to political instability, well beyond those typical in oil and gas markets. This highlights the need to develop new and more diversified supply sources to secure a resilient clean-energy rollout. Without strategically expanding mining and processing capacities, Europe’s transition to low-carbon energy risks being bottlenecked by critical material shortages.

Dependencies and vulnerabilities exist at multiple stages of the value chain. The challenges extend beyond just the supply of raw materials; they also encompass these materials’ processing, refining, and manufacturing. In some cases, such as solar photovoltaic panels and digital technologies, dependencies permeate the entire value chain. For other technologies, such as wind power, the EU has maintained global leadership in overall production, but lacks self-sufficiency in the mining and processing of raw materials. Despite being self-sufficient in manufacturing, the EU has also experienced a gradual loss of competitiveness and a decline in production capacity. In the value chain of wind turbine construction, China holds dominant positions in the upstream stages, particularly in Rare Earth Elements extraction, refining, and permanent magnet production. At the same time, the EU remains active mainly in downstream assembly [14]. This asymmetry creates strategic dependencies and exposes European industries to supply and price volatility. A lack of processing capacity and limited access to raw materials undermine the EU’s industrial resilience. Strengthening domestic capabilities in magnet materials and refining is essential to reduce risks. Strategic investments are needed to secure a competitive and sustainable wind energy sector.

From a geopolitical perspective, the risk of supply disruptions in various value chains and for specific materials and components is significantly heightened. This risk stems from the likelihood of disruptive natural and environmental events, such as epidemics or damage to facilities, as well as geopolitical factors like wars, blockades, or market restrictions. The concern intensifies when these critical value chains are geographically concentrated. Should any adverse event occur in China, the repercussions for the global economy could be substantial and potentially severe.

In light of these multifaceted challenges, ranging from emissions reduction and sustainable mobility to Critical Raw Material dependency and supply chain vulnerabilities, there is a clear and urgent need to assess the true environmental sustainability of electric vehicles.

This study responds to that need by conducting a comprehensive Life Cycle Assessment (LCA) comparing a diesel and an electric version of the Peugeot 308, one of Europe’s best-selling compact cars. Using the OpenLCA software and the Ecoinvent 3.10 database, the analysis follows a cradle-to-grave approach, encompassing the vehicle production, use, and end-of-life phases. Two key sustainability indicators are examined: Global Warming Potential (GWP100) to measure climate impact, and Abiotic Resource Depletion Potential (ADP) to quantify the pressure on mineral resources.

This research aims to quantify the environmental advantages of electric mobility in terms of reduced greenhouse gas emissions and critically investigate the trade-offs associated with increased CRM consumption. The results are intended to inform policymakers, industry stakeholders, and consumers about the actual sustainability of electric vehicles, supporting evidence-based decisions as Europe accelerates its transition to a low-carbon economy. Through this analysis, the study aims to contribute to a more nuanced and balanced understanding of the benefits and limitations of electric mobility in the context of global decarbonization goals and resource security.

1.1. Are EU Green Deal Goals in Jeopardy Due to CRMs’ Supply Risk?

The EU’s policies for a sustainable future are highly ambitious, and yet they rely heavily on resources largely not found within Europe. While it is challenging to predict definitively whether the EU Green Deal will succeed or fail, it is clear that enhancements in supply chains, import policies, production capacity, and end-of-life management of lithium-ion batteries are essential for its success.

The primary goal of the Green Deal extends beyond merely ensuring cleaner air in European cities, although that is a significant benefit. It aims to achieve a more ambitious vision: making Europe the first continent to attain net-zero greenhouse gas (GHG) emissions. Relocating emission sources contributing to European growth, from internal combustion engine vehicles or conventional power plants to Critical Raw Material (CRM) mines and refining operations in other parts of the world, does not resolve the climate change issue. Climate change is a global phenomenon with extensive consequences, necessitating a comprehensive and sustainable approach to effectively tackle it.

Moreover, the raw materials supply chain represents a critical consideration, as the European market is becoming increasingly reliant on imports of raw materials and refined products from external sources, many of which pose varying degrees of supply risks. Depending on only a limited number of foreign countries for the global supply of raw materials means that any disruption in one of these nations could lead to significant disturbances throughout the entire global value chain. This situation could result in fluctuating prices and material availability, causing economic, diplomatic, and social challenges for both public and private entities, much like the disruptions experienced in the European energy market following the onset of the Ukraine war and the subsequent impact on Russian natural gas supplies. This may lead to physical shortages, extended delivery times, rising prices, and heightened geopolitical tensions, which could significantly impact battery and car manufacturers and complicate the widespread adoption of electric vehicles (EVs).

This study will perform a Life Cycle Assessment (LCA) to determine how the electrification of European mobility affects net greenhouse gas (GHG) emissions. It will also identify potential vulnerabilities and areas for enhancement within the current system, particularly in relation to the supply and sustainable management of Critical Raw Materials.

1.2. The Role of Critical Raw Materials in Assessing the Potential for Abiotic Resource Depletion During LCIA

In this study, raw material consumption will be evaluated using an LCA. The key to obtaining reliable and meaningful results is conducting a robust Life Cycle Impact Assessment (LCIA) phase, incorporating the most appropriate and up-to-date impact indicators and calculation methods.

Every LCA database package includes a wide range of impact indicators, enabling users to obtain detailed insights into global warming potential, human ecotoxicity, eutrophication, and ozone layer depletion. However, one of the main challenges when conducting an LCA on electric vehicles is the lack of dedicated impact indicators for Critical Raw Materials (CRMs). Furthermore, existing methodologies account for only a limited subset of Critical Raw Materials, underscoring the disconnect between resource scarcity assessments and criticality evaluation frameworks [15].

The most commonly used impact category for characterizing raw material use is Abiotic Resource Depletion Potential (ADP), measured in kg Sb eq. However, this category focuses solely on resource and mineral depletion or scarcity. So far, security of supply is not explicitly considered in impact assessment methodologies [16], but the only focus is on the ecological burdens of extractive activities regarding resource depletion relative to their known reserves. Moreover, the list of raw materials covered by these impact indicators is insufficient, making it difficult to directly calculate the consumption of Critical Raw Materials.

There are also academic debates regarding the appropriate methodology for criticality assessment, particularly in determining the list of Critical Raw Materials. New methods incorporating environmental implications are being proposed [17], as there is no universally accepted definition of raw material criticality. Some studies assess the criticality of raw materials in the context of national defence or a country’s economy, while others focus on specific technologies, companies, or products [18]. Others concentrate on the potential causes of value chain disruptions or the scarcity of reserves related to market demand.

Despite the uncertainties and challenges that characterize Critical Raw Materials, particularly in accurately evaluating their impacts, new approaches are beginning to emerge. One such method is the Abiotic Resource Expected Dissipation Potential (AEDP), which offers a way to assess how current resource use affects the future accessibility of abiotic resources [19]. Another approach that more specifically targets Critical Raw Materials is the Integrated Method to Address Resource Efficiency (ESSENZ). Developed by the Technical University of Berlin, ESSENZ is a criticality assessment method designed to evaluate the socioeconomic availability and the social and environmental dimensions of sustainability. It investigates how material criticality can shape and enhance our understanding of the drivers, complexities, and considerations involved in the responsible sourcing of battery minerals [20]. Currently, this method is not available in major commercial databases, but it exists as an open-source Excel file that assigns a criticality weight to each CRM.

Recognizing the central role of criticality assessment in driving global technological, social, and economic progress, this research team has recently completed a study to address the existing methodological gaps. A corresponding manuscript is currently being prepared for submission to an international peer-reviewed journal. This study introduces two novel characterization indicators, the Raw Material Extraction/Reserve Index and the Gini Index, specifically developed to improve the evaluation of Critical Raw Materials within the LCA framework. These indicators provide a more nuanced understanding of resource depletion risk and supply vulnerability by quantifying global availability and the geographical concentration of CRM production.

1.3. Potential and Limits of LCA

The tool selected to assess the climate change and raw material criticality impacts of electric vehicles is the LCA methodology. LCA is a holistic approach used to evaluate the sustainability of a product or service. Thanks to its specificity and flexibility, this methodology enables the assessment of the environmental impacts associated with each production process. This comprehensive assessment helps identify the areas with the most significant environmental burdens, guiding efforts to mitigate these impacts and ultimately reduce the overall environmental footprint of human activities.

LCA supports informed decision-making by offering detailed insights into the environmental performance of products and processes. Companies that utilize LCA to develop more sustainable products, optimize processes, and reduce environmental impacts are more likely to attract investors and clients by demonstrating their commitment to sustainability. Similarly, policymakers who rely on LCA to shape regulations and standards that promote sustainability have a greater chance of successfully achieving key objectives, such as reducing greenhouse gas emissions, minimizing resource consumption, and decreasing waste generation.

The main downsides of Life Cycle Assessment stem from data quality issues and the significant effort required to obtain accurate results. Collecting comprehensive and reliable data can be particularly challenging for complex product systems involving multiple suppliers across different geographical locations. Tracing the entire value chain is sometimes difficult due to missing information or the challenges of establishing contact with every supplier. As a result, each LCA study involves methodological choices and limitations, such as defining system boundaries, selecting impact categories, and choosing allocation methods—all of which can significantly increase the variability and uncertainty of the results. Conducting a detailed LCA from scratch, without relying on process databases or statistical values and using only primary, site-specific data, can be highly time-consuming and costly, particularly for small- and medium-sized enterprises (SMEs).

LCA is a powerful tool for assessing the environmental impacts of products, processes, and services. Despite its limitations, LCA’s comprehensive approach supports sustainable decision-making, identifies areas for improvement, and enhances environmental communication. The environmental, social, cultural, and economic advantages of integrating LCA in the decision-making process of both policymakers and companies are so significant that they far outweigh the inevitable approximations in the results. As sustainability gains prominence in global agendas, the adoption of LCA will continue to expand, driving the transition toward more sustainable production and consumption patterns. It enables researchers to develop comprehensive, in-depth, and customized models, providing valuable and reliable insights based on big data about the life cycle of any product or service. Due to its ability to analyze thousands of diverse production processes, LCA is the most suitable approach for effectively assessing environmental impacts across complex systems.

1.4. Objectives of This Study

This study aims to evaluate the overall environmental impacts of the transition to electric mobility, considering both global warming potential and the depletion of Critical Raw Material reserves. Additionally, this study aims to analyze the state of the art in various key areas, including legislative frameworks, technological transformation pathways, and the development of impact assessment methods, to provide a comprehensive outlook on future challenges and expectations.

Beyond determining which type of vehicle is most sustainable, the primary goal of this study is to assess whether we have adequate tools to properly evaluate Critical Raw Materials, which are essential to the environmental, economic, and social sustainability of electric vehicles.

2. Materials and Methods

2.1. Goal and Scope Definition

This study highlights the differences in environmental impacts throughout the life cycle of an electric passenger car and a diesel passenger car. As the debate continues regarding the EU’s decision to phase out internal combustion engine vehicles by 2035, this research aims to raise awareness and provide analytical and critical insights into the sustainability of electric vehicles (EVs).

To achieve this, the study will analyze various scenarios, assessing the potential benefits of technological advancements in battery lifespan, green energy production, and circular economy solutions for end-of-life battery management—all of which could significantly reduce the environmental footprint of EVs throughout their life cycle.

2.1.1. Data Quality and System Parameters

The entire LCA modelling and calculation were conducted using OpenLCA v.2.2.0 software. The authors utilized the latest version of Ecoinvent v3.10 for the database. Process data and flux ratios have been derived from this database to ensure a comprehensive and consistent Life Cycle Inventory (LCI). Each process extracted from Ecoinvent will be detailed in Section 2.2, focusing on the Life Cycle Inventory Analysis.

Some external parameters have been incorporated to adapt this LCA to a specific case study of an existing vehicle. The selection criteria for choosing a commercial passenger car for this study are as follows:

• The vehicle must be currently in production.
• The vehicle must be available in both diesel and electric versions.
• The vehicle must belong to the C-segment (with a length between 4.30 m and 4.40 m), aligning with all “passenger car”-related process descriptions in the Ecoinvent database.

Once the Peugeot 308 was chosen as the reference vehicle, data from specialized websites was incorporated into OpenLCA. In particular, the following aspects were considered:

• Diesel vehicle total weight: 1361 kg (source: Al Volante—Italian automotive magazine, AlVolante.it).
• Electric vehicle total weight: 1684 kg (source: Al Volante—Italian automotive magazine, AlVolante.it).
• Diesel vehicle estimated fuel consumption: 5 L/100 km (source: Peugeot).
• Electric vehicle estimated consumption: 15 kWh/100 km (source: Peugeot).
• Electric vehicle Li-ion battery pack information (source: ev-database.org):

  -

  Cathode material: NMC811.

  -

  N. of cells: 102.

  -

  Nominal capacity: 54.0 kWh.

• Electric vehicle Li-ion battery pack weight: According to some sources from the literature [21,22], NMC811 batteries have a cell energy density ranging from 244 to 300 Wh/kg. For this study, we assume that this proportion applies to the case study battery pack as well. To standardize our calculations, we adopt a 300 Wh/kg value for NMC811 batteries, considering that some Li-ion battery manufacturers report even higher values. Based on this assumption, we estimate a battery weight proportional to its nominal capacity, resulting in approximately 180 kg.
• Diesel vehicle glider and powertrain weight, electric vehicle powertrain: The glider and powertrain weight for diesel vehicles has been determined using the Ecoinvent process “passenger car production, diesel,” which provides a weight distribution of 69.5% for the glider and 30.5% for the internal combustion engine (ICE) relative to the total vehicle weight. Since the case study selection criteria require that the vehicle be available in both electric and diesel versions, we assume that the glider remains identical in both models in terms of composition and weight. This allows us to calculate the EV powertrain weight by simply subtracting the glider weight and the lithium-ion battery (LIB) weight from the total weight of the electric vehicle.

While this study adopts reasonable estimates for energy consumption (5 litres per 100 km for the diesel vehicle and 15 kWh per 100 km for the electric vehicle), it does not explicitly address the fundamental differences in energy content and conversion efficiency between diesel fuel and electricity. Five litres of diesel contain approximately 178 MJ of chemical energy, whereas 15 kWh of electricity correspond to about 54 MJ. Despite this notable difference in total energy content, electric vehicles typically require far less input energy to travel the same distance because of their superior efficiency. Internal combustion engines convert only about 20–30% of the chemical energy in fuel into useful kinetic energy, with the rest lost as heat. In contrast, electric drivetrains convert 85–95% of electrical energy into motion, resulting in significantly lower energy losses. This technological advantage is one of the key reasons electric vehicles exhibit better environmental performance during the use phase. The comparison of energy consumption values, therefore, reflects not only differences in fuel type, but also the efficiency with which each vehicle transforms input energy into propulsion. Understanding this distinction is critical for accurately interpreting the life cycle environmental impacts of diesel and electric vehicles.

2.1.2. System Boundaries

The overall boundary of the proposed LCA study follows a “cradle-to-grave” approach that considers all processes from the extraction of primary raw materials, both for refined materials and energy production, through the manufacturing, distribution, and utilization of the product, and finally to the end-of-life (EoL) processes downstream.

Regarding the geographical aspects, the car manufacturing is assumed to take place in Europe, while the vehicle utilization phase is considered to occur in Italy, using the Italian national energy mix. As for the end-of-life, it is assumed to take place in Europe as well.

Several recent studies in the field of LCA for electric vehicles support the adoption of a vehicle lifespan of 200,000 km. Chen et al. [23] compare LCA models for lithium-ion batteries during the use phase, highlighting that 200,000 km is a realistic estimate based on battery degradation data and manufacturer warranties. Similarly, Zackrisson et al. [24] consider a lifespan range between 150,000 and 200,000 km for plug-in hybrid electric vehicles, which remains a relevant benchmark for electric vehicles today. More recently, Shen et al. [25] also adopt a 200,000 km lifespan in their assessment of lithium-ion battery production and usage, confirming that this distance reflects current expectations of battery and vehicle durability.

However, while 200,000 km is commonly used as the baseline in LCA studies, emerging evidence suggests that this value may underestimate the true operational lifespan of electric vehicles. For instance, an EV with a nominal battery capacity of 54 kWh and an energy consumption of 15 kWh/100 km achieves an approximate range of 360 km per full charge, corresponding to about 555 full charging–discharging cycles over 200,000 km. Yet, according to some scientific studies [26], lithium-ion batteries typically undergo only around 10–15% capacity degradation even after 1500–2000 full cycles. Using the conservative lower bound of 1500 cycles, this would imply a potential total driving distance of approximately 540,000 km (1500 cycles × 360 km). Therefore, the adoption of a 540,000 km lifespan is proposed as an additional scenario, alongside the baseline 200,000 km, to better reflect the actual durability of EV batteries. By incorporating this longer service life into the LCA framework, we can achieve a more thorough and realistic evaluation of EVs’ environmental impacts and enable a stronger, more meaningful comparison with internal combustion vehicles.

2.1.3. Functional Units

The functional unit selected for this LCA is one standard mid-sized passenger car (C-segment), evaluated over a reference distance of 200,000 km (baseline), corresponding to its assumed technical lifespan. This functional unit applies consistently to both the vehicle production and end-of-life phases. The choice is aligned with the available datasets in the Ecoinvent 3.10 database, which model components such as the glider, internal combustion engine, and electric powertrain based on a generic C-segment vehicle with a typical length between 4.30 and 4.40 m. This approach ensures consistency and comparability across both vehicle types assessed in the study.

Within this segment, a market investigation was conducted to identify a car model available in diesel and electric versions, with accessible data on weight and battery type. The Peugeot 308 was selected as the functional unit, with the Peugeot e-308 representing its electric counterpart. This choice ensures that the glider of both versions has the same weight and material composition, enhancing the consistency and comparability of this study.

Regarding the car utilization phase, the functional unit considered is 1 km of travelled distance. This approach allows for a direct and fair comparison of the environmental impacts of covering the same distance using an electric vehicle (EV) and a conventional internal combustion engine vehicle (ICEV).

2.2. Life Cycle Inventory (LCI)

2.2.1. Model Overview

The LCI model is structured in three main phases: passenger car production (cradle-to-gate analysis), utilization (cradle-to-use analysis), and end-of-life (cradle-to-grave analysis). For each phase, the electric and diesel vehicles are modelled separately, creating two parallel LCIs. These LCIs share the same overall structure and complexity, but differ in specific processes due to the distinct characteristics of each vehicle type.

The LCI framework is structured as follows (Figure 4):

The system expansion approach was adopted for this inventory. Since this study follows a cradle-to-grave approach, no cut-offs were applied. Estimating the quantities of recycled materials used as secondary raw materials is highly complex, and establishing a closed-loop system would be inaccurate, given that the EV value chain is not yet fully circular. Under the system expansion approach, the benefits of generating secondary raw materials from waste are accounted for by treating these outputs as “avoided waste”. This reflects their contribution to the circular economy and the reduced demand for primary raw materials in other processes. Additionally, secondary raw materials used as process inputs often have lower environmental impacts compared to their primary counterparts.

2.2.2. Phase 1: Production (Cradle-to-Gate)

The production phase of both electric and diesel cars encompasses all upstream processes involved in manufacturing the vehicle’s components, the final assembly of the car, and its distribution to the dealership.

Electric and diesel cars are therefore composed of the following main elements:

• Electric passenger car: glider + powertrain + Li-ion battery.
• Diesel passenger car: glider + internal combustion engine.

Specific processes model each one of these components on the Ecoinvent 3.10 database, which has been specifically adjusted to fit our case study based on the parameters detailed in Section 2.1.1.

All upstream processes (e.g., raw material extraction, electricity generation, logistics, and the production of components such as the chassis, tyres, cockpit, gearbox, printed circuit boards, etc.) have been kept as default input processes. They are not specific to this case study, as the regionalization is generic, but they are based on reliable Ecoinvent data. They have been included in this study to ensure a more comprehensive assessment; nonetheless, this study primarily emphasizes the downstream processes. The following paragraph provides a detailed explanation of each production process, including descriptions from the Ecoinvent database:

• Passenger car production, electric, without battery: This dataset describes the production of an electric passenger car, excluding its battery. The data is based on a per-kg approach, optimized for a vehicle weighing approximately 1200 kg, including the battery. It is subdivided into two modules: the glider and the drivetrain. Each module accounts for the specific materials, production efforts, and emissions associated with its components. The dataset includes as input the two modules (glider and drivetrain) representing the vehicle assembly without the battery. The corresponding production efforts, emissions, and manufacturing infrastructure are embedded within each module, and the process concludes with the assembly of the battery-free electric vehicle.
• Battery production, Li-ion, NMC811, rechargeable, prismatic: This dataset represents the production of 1 kg of a Li-ion battery pack, typically used for the mechanical drive of an electric vehicle. The battery cells feature a nickel–manganese–cobalt (NMC811) cathode, a silicon-coated graphite-based anode, a liquid electrolyte, and a porous plastic separator. Infrastructure is also accounted for in this dataset, modelled as an “electronic component factory”.
• Passenger car production, diesel: This dataset represents the production of a compact diesel passenger car with entries based on a per kg basis. The model is calibrated for a vehicle weighing roughly 1314 kg. It is divided into two main modules: glider and drivetrain. Each module includes specific material composition, production efforts, and emissions. The dataset integrates these two modules as inputs to construct the complete vehicle. Production efforts, emissions, and manufacturing infrastructure are accounted for within their respective modules. The process concludes with the final assembly of the car.
• Production of electric car, kg to item: This dataset was created from scratch to model the assembly of the electric vehicle, using the outputs from the “electric car without battery” and the “NMC811 battery” as inputs. The final output is the fully assembled electric car. The unit of measurement is no longer in kilograms, but is now “item” to align with the functional unit of the study.
• Production of diesel car, kg to item: This dataset was created from scratch to convert the unit of measurement for the assembled diesel vehicle. Instead of kilograms, the unit is now “item” to align with the functional unit of the study.

All the previously mentioned processes are regionalized: the assembly of both electric and diesel vehicles takes place in Sochaux, France, where the Peugeot-Stellantis manufacturing plants responsible for producing the 308 and e-308 models are located. For battery production, no specific data is available regarding its exact origin. However, since Stellantis reportedly signed a multi-billion Euro deal with LG Energy Solutions in 2022, we assume the Li-ion battery is manufactured at the company’s main plant in Nanjing, China.

The following processes involve the transportation of the LIB and the finished vehicles. The LIB is flown by air from LG Energy Solutions’ Nanjing plant in China, while the electric and diesel vehicles are both trucked by road from Peugeot’s Sochaux plant in France to a dealership in Italy. The selected destination is Rome, as it is geographically located at the country’s centre, where the vehicle utilization phase is assumed to take place.

Specifically, the flight distance from Shanghai Pudong Airport to Paris Charles De Gaulle Airport and the road distance from Sochaux to Rome were calculated using Google Maps.

• Transport of new passenger car, freight, lorry > 32 metric ton: This dataset represents the service of transporting 1 ton per kilometre (tkm) using a lorry with a gross vehicle weight (GVW) of over 32 metric tons and classified under Euro VI emissions standards. The dataset accounts for the entire transport life cycle, including vehicle and road infrastructure construction, operation, maintenance, and end-of-life processes. Fuel consumption and emissions are based on average European transport conditions and load factors, rather than a specific transport scenario.
• Transport of battery, freight, aircraft, dedicated freight, long haul: The dataset represents the transport of one ton of freight over a distance exceeding 4000 km using a dedicated freight aircraft (a dedicated cargo aircraft). It encompasses the entire transport life cycle, including aircraft production, goods transportation, and the construction and operation of airport infrastructure. The scope of this dataset covers the operation of the aircraft, including inputs of aircraft and airport facilities (with aircraft production and airport construction/operation included in linked datasets). Fuel consumption and major emissions (SOx, NOx, NMHC, PM and CO) are calculated using flight-emission factors derived from the OAG global schedule database for 2016, which covers 99.5 % of all scheduled flights (https://www.oag.com/emissions-data, accessed on 15 June 2025).

2.2.3. Phase 2: Utilization (Cradle-to-Use)

The second phase encompasses the vehicles’ energy use, whether from fuel or electricity, over their entire service lives, as well as upkeep and wear and tear on components such as brakes, tyres, and road surfaces. In this LCA, we evaluate two lifetime scenarios: one at 200,000 km, defined as the baseline within the definition of the functional unit; and an extended case at 540,000 km, reflecting both the industry benchmark and the batteries’ potential durability. Throughout this period, the vehicles are assumed to operate exclusively within Italian borders, travelling on urban roads and highways.

A key assumption in this study is that the Italian energy mix, which powers the electric vehicle, remains unchanged throughout the car’s entire lifetime. This assumption carries weight, given that Italy’s renewable electricity share, currently at 40%, is expected to climb to 55% in the coming years [27]. Although somewhat restrictive, this conservative assumption may understate the electric vehicle’s environmental benefits.

Only two Ecoinvent processes are used for this phase: one representing electric vehicle travel, and the other representing diesel vehicle travel. These processes also encompass vehicle maintenance, as well as brake, tyre, and road wear, ensuring a comprehensive assessment of the vehicle’s operational impacts.

Both electric and diesel passenger car travel processes draw on the Ecoinvent 3.10 datasets, corresponding to the Italian electricity mix (market for electricity, low voltage—IT) and the European diesel market (market group for diesel, low-sulfur—RER). These links ensure an accurate calculation of the environmental impact associated with fuel and energy consumption. We selected the low-voltage Italian electricity mix to capture both generation and transformation, distribution losses, and infrastructure impacts to the domestic outlet.

Detailed explanations of each process and its corresponding Ecoinvent entries are provided below.

• Lifetime usage, passenger car, electric: This dataset describes a 1 km journey in an electric passenger car. The dataset is parameterized based on the vehicle’s mass, battery mass, energy consumption, and the lifetimes of both the vehicle and battery. The vehicle is a combination of a battery-free vehicle and the battery itself. The dataset structure enables modifications to key parameters, such as vehicle mass, battery mass, energy consumption, and lifetimes, allowing for a broad range of scenarios to be analyzed. The dataset includes the car without the battery, the battery itself, maintenance, and the electricity consumed for the journey as inputs. Both the car and the battery are treated as infrastructure, even though their quantities are expressed in kilograms. A modification to the default process output was required for this study. To connect the car transport impacts to the subsequent phase, the electric passenger car was included as an output product at the end of its lifespan. This will serve as the initial input for the end-of-life treatment processes.
• Lifetime usage, passenger car, diesel: This dataset represents the passenger transport service for a journey of 1 km, and is applicable within Europe. Fuel consumption and emissions reflect average vehicle usage and are not specific to any driving cycle. The dataset is parametrized based on vehicle size, fuel consumption, and lifespan. Exhaust emissions from fuel combustion are categorized into two groups: fuel-dependent emissions, which vary based on fuel type and quantity; and Euro class-dependent emissions, which align with the vehicle’s emission standards. The dataset considers three Euro engine classes: Euro 3, Euro 4, and Euro 5. The higher the Euro class, the lower the emissions, and vice versa. The Euro engine regulations are established by the European Commission “in order to limit as much as possible the negative impact of road vehicles on the environment and health” (European Commission 2012). This dataset represents a small petrol passenger car compliant with Euro 5 standards. It includes direct emissions from fuel combustion and evaporative emissions from the fuel tank, specific to petrol vehicles. Non-exhaust emissions are treated as by-products from tyres, brakes, and road wear. The dataset includes inputs such as the car and road infrastructure, the materials and efforts required for maintenance, and the fuel consumed during the journey. The process concludes with transporting over 1 km, releasing exhaust and non-exhaust emissions into the air, water, and soil. A modification to the default process output was required for our study: to connect the car transport impacts to the next phase, the diesel passenger car was included as an output product at the end of its lifespan, serving as the initial input for the end-of-life treatment processes.

2.2.4. Phase 3: End of Life (Cradle-to-Grave)

The final phase of the inventory focuses on recovering secondary raw materials and managing the residual waste treatment of the used vehicle components. This phase of the inventory is divided into two stages: first, the car disassembly, during which the used components are extracted and directed to specific treatments; and second, the processing of waste components (glider, battery, powertrain, ICE) for recycling.

As noted earlier, a large share of vehicle materials, such as aluminum, iron, and other metals, is recovered and recycled at the end-of-life. Under the consequential LCA approach, this recycling reduces the overall environmental footprint: the recovered secondary raw materials are treated as new inputs for other processes, offsetting the impacts that would have resulted from mining and processing virgin resources.

Specifically, once the vehicle reaches the end of its life, it undergoes a “manual dismantling” process. During this stage, the used components from electric and diesel vehicles, such as the Li-ion battery, powertrain, and glider for the electric vehicle, and the internal combustion engine (ICE) and glider for the diesel vehicle, are extracted as waste flows. These components and other waste streams, such as glass, mineral oil, and rubber, are managed using dedicated Ecoinvent treatment processes.

The entire cradle-to-grave process outlined in this chapter is summarized in the following schematic representation (Figure 5), which is derived from the model graph of the electric passenger car’s cradle-to-grave product system in OpenLCA. The model graph for the diesel passenger vehicle follows a similar structure.

The Ecoinvent database includes processes for both the hydrometallurgical and pyrometallurgical recycling of spent Li-ion batteries. Under the EU Battery Regulation, binding collection and recycling targets are in place, and a growing network of facilities, ranging from mechanical pretreatment plants to hydrometallurgical and pyrometallurgical recyclers, is now executing these end-of-life routes. As a provisional modelling approach, recognizing that capacity is still increasing in order to meet future volumes, we represent spent batteries using either the pyrometallurgical or hydrometallurgical treatment processes, which remain the two most established methods.

2.3. Life Cycle Impact Assessment (LCIA)

The two specific impact categories examined in this study are global warming potential (GWP) and Critical Raw Material (CRM) consumption. While GWP is widely integrated into multiple impact assessment methods available in the Ecoinvent library, CRM consumption lacks dedicated indicators in any standard impact assessment methodology.

To assess global warming potential (GWP), the most reliable impact assessment method is IPCC 2021, which evaluates climate change impacts using a range of specific indicators.

For raw material criticality, the chosen approach was to identify any available impact category related to mineral resources consumption. This allows for an assessment, at least at a general level, of which type of vehicle and which specific processes have a greater impact on overall mineral extraction.

The following paragraphs provide descriptions and a list of indicators for each selected impact assessment method from Ecoinvent.

2.3.1. IPCC 2021

The IPCC (Intergovernmental Panel on Climate Change) is a United Nations body that periodically publishes Assessment Reports (ARs). These reports provide emissions metrics for Global Warming Potential (GWP) and Global Temperature Change Potential (GTP), which are incorporated as characterization factors (CFs) in the IPCC methods (

Table 1. The IPCC methods.

Impact Category Name	Reference Unit
Climate change|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change|Global temperature change potential (GTP50)	kg CO2-Eq
Climate change|Global warming potential (GWP100)	kg CO2-Eq
Climate change|Global warming potential (GWP20)	kg CO2-Eq
Climate change|Global warming potential (GWP500)	kg CO2-Eq
Climate change: biogenic|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: biogenic|Global temperature change potential (GTP50)	kg CO2-Eq
Climate change: biogenic|Global warming potential (GWP100)	kg CO2-Eq
Climate change: biogenic|Global warming potential (GWP20)	kg CO2-Eq
Climate change: biogenic|Global warming potential (GWP500)	kg CO2-Eq
Climate change: biogenic, including SLCFs|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: biogenic, including SLCFs|Global warming potential (GWP100)	kg CO2-Eq
Climate change: biogenic, including SLCFs|Global warming potential (GWP20)	kg CO2-Eq
Climate change: fossil|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: fossil|Global temperature change potential (GTP50)	kg CO2-Eq
Climate change: fossil|Global warming potential (GWP100)	kg CO2-Eq
Climate change: fossil|Global warming potential (GWP20)	kg CO2-Eq
Climate change: fossil|Global warming potential (GWP500)	kg CO2-Eq
Climate change: fossil, including SLCFs|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: fossil, including SLCFs|Global warming potential (GWP100)	kg CO2-Eq
Climate change: fossil, including SLCFs|Global warming potential (GWP20)	kg CO2-Eq
Climate change: including SLCFs|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: including SLCFs|Global warming potential (GWP100)	kg CO2-Eq
Climate change: including SLCFs|Global warming potential (GWP20)	kg CO2-Eq
Climate change: land use|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: land use|Global temperature change potential (GTP50)	kg CO2-Eq
Climate change: land use|Global warming potential (GWP100)	kg CO2-Eq
Climate change: land use|Global warming potential (GWP20)	kg CO2-Eq
Climate change: land use|Global warming potential (GWP500)	kg CO2-Eq
Climate change: land use, including SLCFs|Global temperature change potential (GTP100)	kg CO2-Eq
Climate change: land use, including SLCFs|Global warming potential (GWP100)	kg CO2-Eq
Climate change: land use, including SLCFs|Global warming potential (GWP20)	kg CO2-Eq

).

Status: current; version: 2021; release: 2021.

Documentation: https://www.ipcc.ch/report/ar6/wg1/ (accessed on 13 June 2025).

Factors: https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf (accessed on 13 June 2025).

https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter_07_Supplementary_Material.pdf (accessed on 13 June 2025).

https://github.com/chrisroadmap/ar6/blob/main/data_output/7sm/metrics_supplement_cleaned.csv (accessed on 13 June 2025).

2.3.2. CML v4.8 2016

The CML impact assessment method (CML-IA) was developed by the Institute of Environmental Sciences at the University of Leiden in the Netherlands. Initially introduced in 1992, it was updated to its current 4.8 version in 2016. This is a midpoint method that evaluates multiple impact categories (

Table 2. The CML impact assessment method (CML-IA).

Impact Category Name	Indicator	Reference Unit
Acidification	Acidification (incl. fate, average Europe total)	kg SO2-Eq
Climate change	Global warming potential (GWP100)	kg CO2-Eq
Ecotoxicity: freshwater	Freshwater aquatic ecotoxicity (FAETP inf)	kg 1,4-DCB-Eq
Ecotoxicity: marine	Marine aquatic ecotoxicity (MAETP inf)	kg 1,4-DCB-Eq
Ecotoxicity: terrestrial	Terrestrial ecotoxicity (TETP inf)	kg 1,4-DCB-Eq
Energy resources: non-renewable	Abiotic depletion potential (ADP): fossil fuels	MJ
Eutrophication	Eutrophication (fate not incl.)	kg PO4-Eq
Human toxicity	Human toxicity (HTP inf)	kg 1,4-DCB-Eq
Material resources: metals/minerals	Abiotic depletion potential (ADP): elements (ultimate reserves)	kg Sb-Eq
Ozone depletion	Ozone layer depletion (ODP steady state)	kg CFC-11-Eq
Photochemical oxidant formation	Photochemical oxidation (high NOx)	kg ethylene-Eq

).

Status: current; version: v4.8; release: 2016.

Documentation: https://www.universiteitleiden.nl/en/research/research-projects/science/cml-new-dutch-lca-guide (accessed on 13 June 2025).

Factors: https://www.universiteitleiden.nl/en/research/research-output/science/cml-ia-characterisation-factors (accessed on 13 June 2025).

2.3.3. EF 3.1

EF stands for Environmental Footprint, a method maintained by the European Commission. It was updated from version 3.0 to version 3.1 in July 2022. Additionally, there are EF v3.0 and EF v3.1 implementations for the EN 15804 standard [28], which differs in characterization factors (CFs) for biogenic CO₂ (

Table 3. The EF impact assessment method.

Impact Category Name	Indicator	Reference Unit
Acidification	Accumulated exceedance (AE)	mol H+-Eq
Climate change	Global warming potential (GWP100)	kg CO2-Eq
Climate change: biogenic	Global warming potential (GWP100)	kg CO2-Eq
Climate change: fossil	Global warming potential (GWP100)	kg CO2-Eq
Climate change: land use and land use change	Global warming potential (GWP100)	kg CO2-Eq
Ecotoxicity: freshwater	Comparative toxic unit for ecosystems (CTUe)	CTUe
Ecotoxicity: freshwater, inorganics	Comparative toxic unit for ecosystems (CTUe)	CTUe
Ecotoxicity: freshwater, organics	Comparative toxic unit for ecosystems (CTUe)	CTUe
Energy resources: non-renewable	Abiotic depletion potential (ADP): fossil fuels	MJ, net calorific value
Eutrophication: freshwater	Fraction of nutrients reaching freshwater end compartment (P)	kg P-Eq
Eutrophication: marine	Fraction of nutrients reaching marine end compartment (N)	kg N-Eq
Eutrophication: terrestrial	Accumulated exceedance (AE)	mol N-Eq
Human toxicity: carcinogenic	Comparative toxic unit for humans (CTUh)	CTUh
Human toxicity: carcinogenic, inorganics	Comparative toxic unit for humans (CTUh)	CTUh
Human toxicity: carcinogenic, organics	Comparative toxic unit for humans (CTUh)	CTUh
Human toxicity: non-carcinogenic	Comparative toxic unit for humans (CTUh)	CTUh
Human toxicity: non-carcinogenic, inorganics	Comparative toxic unit for humans (CTUh)	CTUh
Human toxicity: non-carcinogenic, organics	Comparative toxic unit for humans (CTUh)	CTUh
Ionising radiation: human health	Human exposure efficiency relative to u235	kBq U235-Eq
Land use	Soil quality index	dimensionless
Material resources: metals/minerals	Abiotic depletion potential (ADP): elements (ultimate reserves)	kg Sb-Eq
Ozone depletion	Ozone depletion potential (ODP)	kg CFC-11-Eq
Particulate matter formation	Impact on human health	disease incidence
Photochemical oxidant formation: human health	Tropospheric ozone concentration increase	kg NMVOC-Eq
Water use	User deprivation potential (deprivation-weighted water consumption)	m3 world Eq deprived

).

Status: current; version: v3.1; release: 2022.

Documentation: https://eplca.jrc.ec.europa.eu/LCDN/developerEF.xhtml (accessed on 13 June 2025).

Factors: https://eplca.jrc.ec.europa.eu/permalink/EF3_1/EF-LCIAMethod_CF(EF-v3.1).xlsx (accessed on 13 June 2025).

2.3.4. ReCiPe 2016

ReCiPe was developed in 2008 by the Dutch research institute RIVM (National Institute for Public Health and the Environment), Radboud University Nijmegen, Leiden University, and PR Consultants. It was last updated to its current version in 2016. ReCiPe is both a midpoint and an endpoint method, considering three distinct cultural perspectives: Individualist (I), Hierarchist (H), and Egalitarian (E). It evaluates multiple midpoint impact categories and assesses three areas of protection at the endpoint level: human health, ecosystem quality, and natural resources.

There are two primary approaches to deriving characterization factors: midpoint and endpoint. Characterization factors at the midpoint level are positioned along the impact pathway, usually at the stage where the environmental mechanism becomes the same for all environmental flows associated with that impact category [29]. Characterization factors at the endpoint level are linked to three areas of protection: human health, ecosystem quality, and resource scarcity. The two approaches complement each other: midpoint characterization has a stronger connection to environmental flows and lower uncertainty, while endpoint characterization offers better insights into environmental relevance, but comes with higher uncertainty [30].

Similarly to the approach in ReCiPe 2008, various sources of uncertainty and different choices were categorized into a limited number of perspectives or scenarios, based on the “Cultural Theory” [21]. These perspectives do not aim to represent archetypes of human behaviour; rather, they serve to group similar types of assumptions and choices. ReCiPe 2016 includes three such perspectives:

• The Individualist (I) perspective focuses on short-term interests, considers only well-established impact types, and assumes technological optimism regarding human adaptation.
• The Hierarchist (H) perspective relies on scientific consensus concerning the timeframe and the plausibility of impact mechanisms.
• The Egalitarian (E) perspective is the most precautionary, considering the longest timeframe and including all impact pathways for which data is available.

The midpoint Hierarchist (H) approach was selected as the most suitable for the scope and context of this LCA (

Table 4. The ReCiPe impact assessment method.

Impact Category Name	Indicator	Reference Unit
Acidification: terrestrial	Terrestrial acidification potential (TAP)	kg SO2-Eq
Climate change	Global warming potential (GWP100)	kg CO2-Eq
Ecotoxicity: freshwater	Freshwater ecotoxicity potential (FETP)	kg 1,4-DCB-Eq
Ecotoxicity: marine	Marine ecotoxicity potential (METP)	kg 1,4-DCB-Eq
Ecotoxicity: terrestrial	Terrestrial ecotoxicity potential (TETP)	kg 1,4-DCB-Eq
Energy resources: non-renewable, fossil	Fossil fuel potential (FFP)	kg oil-Eq
Eutrophication: freshwater	Freshwater eutrophication potential (FEP)	kg P-Eq
Eutrophication: marine	Marine eutrophication potential (MEP)	kg N-Eq
Human toxicity: carcinogenic	Human toxicity potential (HTPc)	kg 1,4-DCB-Eq
Human toxicity: non-carcinogenic	Human toxicity potential (HTPnc)	kg 1,4-DCB-Eq
Ionising radiation	Ionizing radiation potential (IRP)	kBq Co-60-Eq
Land use	Agricultural land occupation (LOP)	m2·a crop-Eq
Material resources: metals/minerals	Surplus ore potential (SOP)	kg Cu-Eq
Ozone depletion	Ozone depletion potential (ODPinfinite)	kg CFC-11-Eq
Particulate matter formation	Particulate matter formation potential (PMFP)	kg PM2.5-Eq
Photochemical oxidant formation: human health	Photochemical oxidant formation potential: humans (HOFP)	kg NOx-Eq
Photochemical oxidant formation: terrestrial ecosystems	Photochemical oxidant formation potential: ecosystems (EOFP)	kg NOx-Eq
Water use	Water consumption potential (WCP)	m3

).

Status: current; version: 2016 v1.03 (SimaPro); release: 2016.

Documentation: https://www.rivm.nl/en/life-cycle-assessment-lca/downloads (accessed on 13 June 2025).

Factors: https://www.rivm.nl/documenten/recipe2016cfsv1120180117 (accessed on 13 June 2025).

2.3.5. Ecological Scarcity

The Ecological Scarcity Method (Swiss Eco-Factors 2021) is a distance-to-target approach maintained by the Swiss Federal Office for the Environment (BAFU) [31]. It assigns eco-points (UBP) to each environmental flow (emissions, resource extractions, land occupation, waste) based on how far current performance lies from legally or policy-defined environmental quality targets. It explicitly quantifies both direct and indirect land occupation required for CO₂ sequestration and nuclear fuel cycles, aggregating all pressures into a single UBP score per flow unit (

Table 5. Ecological Scarcity.

Impact Category Name	Reference Unit
Climate change|Global warming potential (GWP100)	UBP
Emissions to air|Air pollutants and particulate matter	UBP
Emissions to air|Carcinogenic substances	UBP
Emissions to air|Heavy metals	UBP
Emissions to air|Radioactive substances	UBP
Emissions to soil|Heavy metals	UBP
Emissions to soil|Pesticides	UBP
Emissions to water|Heavy metals	UBP
Emissions to water|POP	UBP
Emissions to water|Radioactive substances	UBP
Emissions to water|Water pollutants	UBP
Energy resources|Energy resources	UBP
Land use|Land use	UBP
Mineral resources|Mineral resources	UBP
Natural resources|Biotic resources	UBP
Ozone depletion|Ozone depletion	UBP
Waste disposal|Radioactive waste	UBP
Waste disposal|Non radioactive waste	UBP
Water use|Evaporated water resources	UBP
Total Ecological Scarcity Score	UBP

).

Status: current; version: 2021; release: 2021.

Documentation: https://www.bafu.admin.ch/bafu/en/home/topics/economy-consumption/economy-and-consumption-publications/publications-economy-and-consumption/eco-factors-switzerland.html (accessed on 13 June 2025).

Factors: available in the “Swiss Eco-Factors 2021” report (see Documentation link above).

2.3.6. Crustal Scarcity Indicator

The Crustal Scarcity indicator was developed in 2020 by Rickard Arvidsson and his research team at Chalmers University in Gothenburg, Sweden [32]. The method evaluates the use of mineral resources based on crustal concentrations, which serve as a proxy for the long-term global scarcity of elements (

Table 6. The Crustal Scarcity indicator.

Impact Category Name	Indicator	Reference Unit
Material resources: metals/minerals	Crustal Scarcity Potential (CSP)	kg Si-Eq

).

Status: current; version: 2020; release: 2020.

Documentation: https://doi.org/10.1007/s11367-020-01781-1 (accessed on 13 June 2025).

Factors: https://research.chalmers.se/publication/519861/file/519861_Fulltext.pdf (accessed on 13 June 2025).

3. Results and Discussion

3.1. Life Cycle Impact Assessment

The following tables present the complete LCIA results, broken down by the three phases defined in the LCI, to illustrate how the two vehicle engine types affect the environment over different timescales. The use phase impacts were evaluated against a 200,000 km baseline and a 540,000 km scenario, equivalent to 1500 full battery charge cycles, as previously described.

In the “Cradle-to-Grave” scenario, which encompasses the vehicles’ lifetime impacts through end-of-life management, hydrometallurgical processing was adopted as the default treatment for spent Li-ion batteries.

3.1.1. Cradle-to-Gate: Electric and Diesel Vehicles

Table 7. Impacts incurred during the production and distribution phases of both vehicles.

LCIA Method	Impact Category	Electric	Diesel	Unit
IPCC 2021	Climate change|Global warming potential (GWP100)	2.04 × 104	9.62 × 103	kg CO2-Eq
CML 2016	Material resources: metals/minerals	3.56 × 100	4.98 × 10−1	kg Sb-Eq
EF 3.1	Material resources: metals/minerals	3.56 × 100	5.02 × 10−1	kg Sb-Eq
ReCiPe 2016	Material resources: metals/minerals	6.51 × 103	5.75 × 101	kg Cu-Eq
Ecological Scarcity	Mineral resources|Mineral resources	1.10 × 105	8.89 × 105	UBP
Crustal Scarcity	Material resources: metals/minerals	8.06 × 106	2.14 × 106	kg Si-Eq

compares the production and distribution impacts of both the electric and diesel vehicles. This stage highlights a distinct trade-off: the electric vehicle incurs higher cradle-to-gate emissions and demands far more critical metals and crustal materials, primarily driven by its battery and the related components.

By midpoint metrics, the diesel car consumes far fewer metals and minerals than the electric vehicle; yet, under Switzerland’s Ecological Scarcity method (which benchmarks impacts against stringent environmental targets), it scores much worse (8.89 × 10⁵ UBP vs. 1.10 × 10⁵ UBP). This reversal illustrates how reliance on a single impact category can be misleading: carbon- and resource-focused metrics favour the diesel vehicle, while target-oriented approaches penalize its broader environmental pressures. Moreover, no existing LCIA indicators specifically address the use of Critical Raw Materials, even though demand for CRMs is a key differentiator in battery production.

3.1.2. Cradle-to-Use: 200,000 km and 540,000 km Scenarios

Table 8. Combined impacts from the 200,000 km use phase alongside those from each vehicle’s production and distribution.

LCIA Method	Impact Category	Electric	Diesel	Unit
IPCC 2021	Climate change|Global warming potential (GWP100)	2.81 × 104	5.62 × 104	kg CO2-Eq
CML 2016	Material resources: metals/minerals	3.90 × 100	4.95 × 10−1	kg Sb-Eq
EF 3.1	Material resources: metals/minerals	3.90 × 100	5.02 × 10−1	kg Sb-Eq
ReCiPe 2016	Material resources: metals/minerals	6.52 × 103	1.35 × 102	kg Cu-Eq
Ecological Scarcity	Mineral resources|Mineral resources	7.50 × 105	1.52 × 106	UBP
Crustal Scarcity	Material resources: metals/minerals	9.22 × 106	4.91 × 106	kg Si-Eq

provides a detailed breakdown of the operational impacts for both electric and diesel vehicles over a reference lifetime of 200,000 km. The use-phase burdens have been added to the production and distribution impacts already reported, yielding a cradle-to-use total for each vehicle.

By juxtaposing these cumulative figures, the table highlights how the relative environmental performance of electric versus diesel shifts once the in-service phase is included, pinpointing which life cycle stages drive overall impacts.

Over a 200,000 km lifespan, the electric vehicle achieves a striking carbon reduction (28.1 t CO₂-eq) compared to the diesel’s 56.2 t, and yet this benefit comes at the cost of substantially greater raw material use. Midpoint resource indicators (3.9 kg Sb-eq vs. 0.5 kg) and ReCiPe copper-equivalents (6520 kg vs. 135 kg) highlight the EV’s intensive battery-related footprint. Under the Ecological Scarcity approach, the diesel scores twice the EV’s impact (1.52 × 10⁶ UBP vs. 7.50 × 10⁵ UBP), reflecting broader environmental pressures, while Crustal Scarcity Potential further penalizes the electric model (9.22 × 10⁶ kg Si-eq vs. 4.91 × 10⁶ kg Si-eq). These contrasting results illustrate the classic trade-off in vehicle life cycle assessments and underscore the importance of cleaner electricity, advanced battery technologies, and robust recycling strategies to fully realize the EV’s environmental promise.

Table 9. Combined impacts from the 540,000 km use phase alongside those from each vehicle’s production and distribution.

LCIA Method	Impact Category	Electric	Diesel	Unit
IPCC 2021	Climate change|Global warming potential (GWP100)	4.11 × 104	1.35 × 105	kg CO2-Eq
CML 2016	Material resources: metals/minerals	4.48 × 100	4.91 × 10−1	kg Sb-Eq
EF 3.1	Material resources: metals/minerals	4.48 × 100	5.02 × 10−1	kg Sb-Eq
ReCiPe 2016	Material resources: metals/minerals	6.54 × 103	2.67 × 102	kg Cu-Eq
Ecological Scarcity	Mineral resources|Mineral resources	1.84 × 106	2.60 × 106	UBP
Crustal Scarcity	Material resources: metals/minerals	1.12 × 107	9.64 × 106	kg Si-Eq

summarizes the combined life cycle impacts for both electric and diesel vehicles over an extended use phase of 540,000 km. Like the previous analysis, the in-service burdens are added to the production and distribution impacts to yield a cradle-to-use total. By examining this longer lifespan, the table reveals how the environmental performance gap between the two powertrains evolves with increased mileage and highlights which life cycle stages become even more dominant under high-distance operation. Over a 540,000 km lifespan, the electric vehicle still emits far less CO₂ than the diesel (41.1 t vs. 135 t), maintaining its carbon advantage. Its resource use remains higher (midpoint indicators rise from 3.9 to 4.5 kg Sb-eq and ReCiPe from 6520 to 6540 kg Cu-eq), but scales only modestly with mileage. In contrast, the diesel’s impacts significant rise: CO₂ emissions increase from 56.2 t (200,000 km) to 135 t (540,000 km), its Ecological Scarcity score climbs from 1.52 × 10⁶ to 2.60 × 10⁶ UBP, and Crustal Scarcity doubles from 4.91 × 10⁶ to 9.64 × 10⁶ kg Si-eq. This stark growth highlights how extended use disproportionately intensifies the diesel vehicle’s life cycle burdens, underscoring the importance of high-mileage mitigation strategies.

3.1.3. Cradle to Grave: 200,000 km and 540,000 km Scenarios

Table 10. Cradle-to-grave impacts for the electric and diesel vehicles over a 200,000 km lifespan.

LCIA Method	Impact Category	Electric	Diesel	Unit
IPCC 2021	Climate change|Global warming potential (GWP100)	2.36 × 104	5.22 × 104	kg CO2-Eq
CML 2016	Material resources: metals/minerals	2.88 × 101	4.56 × 10−1	kg Sb-Eq
EF 3.1	Material resources: metals/minerals	2.88 × 101	4.62 × 10−1	kg Sb-Eq
ReCiPe 2016	Material resources: metals/minerals	−2.71 × 103	−6.07 × 101	kg Cu-Eq
Ecological Scarcity	Mineral resources|Mineral resources	1.99 × 107	8.84 × 105	UBP
Crustal Scarcity	Material resources: metals/minerals	5.65 × 107	1.59 × 106	kg Si-Eq

presents the full cradle-to-grave impacts for both the electric and diesel vehicles with a 200,000 km use phase added to production and distribution burdens. It also accounts for end-of-life recovery, where materials such as metals and battery components are reclaimed, offsetting a portion of upstream resource demands. This comprehensive view highlights net life cycle performance and the benefits of circular end-of-life strategies for both powertrains.

Over the full cradle-to-grave cycle (200,000 km), the electric vehicle retains a substantial carbon advantage, emitting 23.6 t CO₂-eq versus 52.2 t for the diesel model. Its material resource burdens, however, are dramatically larger: midpoint indicators rise to 28.8 kg Sb-eq (versus 0.46 kg for diesel), and, under Ecological Scarcity, the EV scores 1.99 × 10⁷ UBP compared to 8.84 × 10⁵ UBP. Crustal scarcity potentials tell a similar story (5.65 × 10⁷ vs. 1.59 × 10⁶ kg Si-eq). Notably, the ReCiPe 2016 resource metric flips positive impacts into net credits, represented with a negative value at end-of-life, with −2710 kg Cu-eq for the EV (and −60.7 kg for diesel), reflecting substantial mineral recovery from battery and vehicle recycling. These negative values highlight the importance of robust material reclamation strategies, but do not fully offset the upstream mineral extraction footprint.

Table 11. Cradle-to-grave impacts for the electric and diesel vehicles over a 540,000 km lifespan.

LCIA Method	Impact Category	Electric	Diesel	Unit
IPCC 2021	Climate change|Global warming potential (GWP100)	3.66 × 104	1.31 × 105	kg CO2-Eq
CML 2016	Material resources: metals/minerals	2.93 × 101	4.51 × 10−1	kg Sb-Eq
EF 3.1	Material resources: metals/minerals	2.94 × 101	4.62 × 10−1	kg Sb-Eq
ReCiPe 2016	Material resources: metals/minerals	−2.69 × 103	7.11 × 101	kg Cu-Eq
Ecological Scarcity	Mineral resources|Mineral resources	2.10 × 107	1.96 × 106	UBP
Crustal Scarcity	Material resources: metals/minerals	5.85 × 107	6.31 × 106	kg Si-Eq

applies the cradle-to-grave assessment to a 540,000 km use phase, showing how higher mileage amplifies the life cycle impacts for both electric and diesel vehicles. By combining in-service emissions and resource use with end-of-life recovery, the table illustrates the increased carbon advantages of electrification alongside proportional rises in material demands and recycling credits. This extended perspective shows which impact categories increase most with higher use, and highlights where circular strategies and technological improvements are most critical.

Over a 540,000 km lifespan, the electric vehicle still emits far less CO₂ than its diesel counterpart (36.6 t vs. 131 t CO₂-eq), underlining its enduring climate advantage. Its material resource burden rises modestly to about 29 kg Sb-eq, yet end-of-life recycling flips ReCiPe copper metrics into a net credit (−2690 kg Cu-eq for the EV versus a small positive 71 kg for diesel). Under the Ecological Scarcity method, the EV scores 2.10 × 10⁷ UBP compared to 1.96 × 10⁶ UBP for diesel, and Crustal Scarcity potentials reach 5.85 × 10⁷ kg Si-eq versus 6.31 × 10⁶ kg Si-eq. These results highlight the EV’s substantial carbon benefits and the critical role of end-of-life recovery, while also drawing attention to its significantly higher mineral demands.

3.2. Interpretation of Results

3.2.1. Global Warming Potential—200,000 km Scenario

Global warming potential results for each phase of the 200,000 km baseline scenario are shown in

Table 12. Global warming potential results for each phase of the 200,000 km baseline scenario.

Climate Change|Global Warming Potential (GWP100)
LCI Phase	Electric	Diesel	Unit
1—Cradle-to gate	2.04 × 104	9.62 × 103	kg CO2-Eq
2—Cradle-to-use	2.81 × 104	5.62 × 104	kg CO2-Eq
3—Cradle to grave	2.36 × 104	5.22 × 104	kg CO2-Eq

and Figure 6 using the IPCC 2021 “Climate Change Global Warming Potential (GWP100)” indicator.

The results demonstrate a stark contrast between the two powertrains across the life cycle. While the electric vehicle’s production phase incurs higher upfront emissions than the diesel (20.4 t vs. 9.6 t CO₂-eq), its operational advantage becomes apparent on a 200,000 km travelled distance, cutting cradle-to-use emissions by half (28.1 t vs. 56.2 t CO₂-eq). End-of-life recovery further narrows the gap, reducing total emissions to 23.6 t for the EV and 52.2 t for the diesel in the cradle-to-grave assessment. This phase-aggregated view highlights where each vehicle’s strengths and weaknesses lie and sets the stage for a deeper phase-by-phase analysis.

• Phase 1—Cradle-to-gate: The key difference arises from the process involved in the production of the EV battery. Given the large amount of raw materials required to produce a battery, along with the high emissions associated with mining activities, the transportation of raw materials, and the long-distance transport of finished products, electric vehicles have a significantly higher global warming potential in the vehicle construction phase.
• Phase 2—Cradle-to-use: The use phase reveals a dramatic reversal in emissions performance, as EVs produce no tailpipe CO₂ and increasingly draw on renewable electricity. Even when powered by grid mix, power plants convert fuel to energy far more efficiently than combustion engines. Consequently, the longer both vehicles are driven, the wider the emissions gap becomes, underscoring the clear advantage of electric propulsion during operation
• Phase 3—Cradle-to-grave: In the final phase, both vehicles benefit from end-of-life credits, yielding a negative GHG impact under the consequential LCA. This underscores the vital role of a circular economy: recycling vehicles and Li-ion batteries supplies secondary raw materials that displace virgin extraction and lower overall life cycle emissions. As more end-of-life components are reclaimed, manufacturers can integrate increasing volumes of recycled metals and minerals into new vehicles, creating a virtuous cycle that further reduces the environmental footprint of private mobility.

Furthermore, it is noteworthy to compare the impact differences between the two distinct end-of-life (EoL) treatments for Li-ion batteries (

Table 13. Differences in impacts between the two Li-ion battery end-of-life treatments for the 200,000 km scenario.

Climate Change|Global Warming Potential (GWP100)
LCI Process	Quantity	Unit
Cradle to grave, EV, Pyrometallurgical treatment	2.49 × 104	kg CO2-Eq
Cradle to grave, EV, Hydrometallurgical treatment	2.36 × 104	kg CO2-Eq

).

Several studies support this findings [33,34,35], indicating that hydrometallurgical recycling generally yields lower environmental impacts than pyrometallurgical processing. However, the pyrometallurgical recovery process is expected to offer significant potential for improving environmental benefits, particularly with the transition to a net-zero-energy system [34]. Moreover, a hybrid approach combining both methods may optimize material recovery while minimizing overall environmental burdens [36].

3.2.2. Global Warming Potential—540,000 km Scenario

Global warming potential outcomes for each life cycle phase under the 540,000 km scenario are presented in

Table 14. Global warming potential results for each phase of the 540,000 km scenario.

Climate Change|Global Warming Potential (GWP100)
LCI Phase	Electric	Diesel	Unit
1—Cradle-to-gate	2.04 × 104	9.62 × 103	kg CO2-Eq
2—Cradle-to-use	4.11 × 104	1.35 × 105	kg CO2-Eq
3—Cradle to grave	3.66 × 104	1.31 × 105	kg CO2-Eq

and Figure 7 using the IPCC 2021 “Climate Change—Global Warming Potential (GWP100)” indicator.

Over a 540,000 km lifespan, the electric vehicle’s climate advantage becomes even more pronounced. In the cradle-to-gate phase, the EV starts with higher emissions (20.4 t CO₂-eq) than the diesel (9.6 t), but by the end of use, its total rises to just 41.1 t versus the diesel’s 135 t, more than a threefold gap in favour of electrification. Factoring in end-of-life recovery trims totals 36.6 t for the EV and 131 t for the diesel, slightly narrowing, but not eliminating, the disparity. This phase breakdown confirms that, as driving distance increases, the operational emissions savings of electric propulsion vastly outweigh its higher production footprint. It also underscores the continued importance of robust recycling: end-of-life credits lower life cycle emissions further, particularly for the EV, emphasizing the value of circular strategies alongside decarbonized electricity.

Comparing the 200,000 km and 540,000 km scenarios, the electric vehicle’s operational emissions advantage over the diesel model widens substantially with higher mileage, despite its heavier production footprint. While end-of-life recovery consistently trims both vehicles’ totals, the diesel’s life cycle emissions escalate far more steeply as use-phase burdens grow. This divergence highlights how increased driving amplifies the EV’s carbon savings, and underscores that, for high-mileage applications, electrification paired with strong recycling yields the greatest environmental benefits.

Again, it is valuable to examine the impact differences between the two distinct end-of-life (EoL) treatments for Li-ion batteries (

Table 15. Differences in impacts between the two Li-ion battery end-of-life treatments for the 540,000 km scenario.

Climate Change|Global Warming Potential (GWP100)
LCI Process	Quantity	Unit
Cradle to grave, EV, Pyrometallurgical treatment	3.80 × 104	kg CO2-Eq
Cradle to grave, EV, Hydrometallurgical treatment	3.66 × 104	kg CO2-Eq

).

3.2.3. Mineral Resources

The first step in assessing mineral resource depletion is to compare the available indicators within Ecoinvent’s impact assessment library to identify the one best aligned with this study’s goals.

Table 16. Mineral resource depletion using different impact assessment methods for the 200,000 km scenario.

LCI Phase	Impact Assessment Method	Electric	Diesel	Unit
1—Cradle-to-gate	CML	3.56 × 100	4.98 × 10−1	kg Sb-Eq
1—Cradle-to-gate	Crustal Scarcity	8.06 × 106	2.14 × 106	kg Si-Eq
1—Cradle-to-gate	Ecological Scarcity	1.10 × 105	8.89 × 105	UBP
1—Cradle-to-gate	EF 3.1	3.56 × 100	5.02 × 10−1	kg Sb-Eq
1—Cradle-to-gate	ReCiPe	6.51 × 103	5.75 × 101	kg Cu-Eq
2—Cradle-to-use	CML	3.90 × 100	4.95 × 10−1	kg Sb-Eq
2—Cradle-to-use	Crustal Scarcity	9.22 × 106	4.91 × 106	kg Si-Eq
2—Cradle-to-use	Ecological Scarcity	7.50 × 105	1.52 × 106	UBP
2—Cradle-to-use	EF 3.1	3.90 × 100	5.02 × 10−1	kg Sb-Eq
2—Cradle-to-use	ReCiPe	6.52 × 103	1.35 × 102	kg Cu-Eq
3—Cradle to grave	CML	2.88 × 101	4.56 × 10−1	kg Sb-Eq
3—Cradle to grave	Crustal Scarcity	5.65 × 107	1.59 × 106	kg Si-Eq
3—Cradle to grave	Ecological Scarcity	1.99 × 107	8.84 × 105	UBP
3—Cradle to grave	EF 3.	2.88 × 101	4.62 × 10−1	kg Sb-Eq
3—Cradle to grave	ReCiPe	−2.71 × 103	−6.07 × 101	kg Cu-Eq

and

Table 17. Mineral resource depletion using different impact assessment methods for the 540,000 km scenario.

LCI Phase	Impact Assessment Method	Electric	Diesel	Unit
1—Cradle-to-gate	CML	3.56 × 100	4.98 × 10−1	kg Sb-Eq
1—Cradle-to-gate	Crustal Scarcity	8.06 × 106	2.14 × 106	kg Si-Eq
1—Cradle-to-gate	Ecological Scarcity	1.10 × 105	8.89 × 105	UBP
1—Cradle-to-gate	EF 3.1	3.56 × 100	5.02 × 10−1	kg Sb-Eq
1—Cradle-to-gate	ReCiPe	6.51 × 103	5.75 × 101	kg Cu-Eq
2—Cradle-to-use	CML	4.48 × 100	4.91 × 10−1	kg Sb-Eq
2—Cradle-to-use	Crustal Scarcity	1.12 × 107	9.64 × 106	kg Si-Eq
2—Cradle-to-use	Ecological Scarcity	1.84 × 106	2.60 × 106	UBP
2—Cradle-to-use	EF 3.1	4.48 × 100	5.02 × 10−1	kg Sb-Eq
2—Cradle-to-use	ReCiPe	6.54 × 103	2.67 × 102	kg Cu-Eq
3—Cradle to grave	CML	2.93 × 101	4.51 × 10−1	kg Sb-Eq
3—Cradle to grave	Crustal Scarcity	5.85 × 107	6.31 × 106	kg Si-Eq
3—Cradle to grave	Ecological Scarcity	2.10 × 107	1.96 × 106	UBP
3—Cradle to grave	EF 3.1	2.94 × 101	4.62 × 10−1	kg Sb-Eq
3—Cradle to grave	ReCiPe	−2.69 × 103	7.11 × 101	kg Cu-Eq

then juxtapose these metrics for the 200,000 km and 540,000 km scenarios, respectively.

Table 16 shows that, for each indicator and vehicle type, the material use impacts in phase 1 are only slightly lower than those in phase 2. This suggests that most material and mineral depletion occurs during the production phase. Consequently, it is not surprising that EVs generally have higher impacts than diesel vehicles. This is primarily due to the Critical Raw Materials in EV batteries, which have limited reserves in the Earth’s crust and are associated with high impact values in these indicators. Figure 8 below compares the impacts of mineral depletion on electric and diesel cars, measured in kg Sb-Eq using both the CML and EF impact assessment methods.

For the 540,000 km scenario in Table 17, production again drives the bulk of material-depletion impacts for both vehicles. The electric car’s CML/EF score rises more noticeably from 3.56 kg Sb-Eq at cradle-to-gate to 4.48 kg Sb-Eq by cradle-to-use, reflecting additional battery wear and component replacements over the extended mileage. In contrast, the diesel’s resource score remains unchanged (hovering around 0.49 kg Sb-Eq), indicating minimal incremental metal demand during operation. End-of-life recovery generates net ReCiPe credits, but electric vehicles still incur far higher crustal and ecological scarcity impacts. Figure 9 presents these 540,000 km comparisons, plotting CML and EF results side by side.

Extending the use phase from 200,000 km to 540,000 km further accentuates the differences between electric and diesel vehicles. Electric cars see only a modest rise in overall resource impacts beyond their initial production footprint, whereas diesel models experience a sharp increase as in-use demands accumulate. Moreover, end-of-life recovery benefits both powertrains, but does little to offset diesel’s growing burdens. These trends demonstrate that electric vehicles become increasingly resource-efficient at high mileages, while diesel’s seemingly low upfront impacts are quickly eclipsed by operational resource use. This underscores the importance of strong recycling programmes and design improvements to minimize material depletion across the vehicle’s life cycle.

A second key insight emerges in phase 3, where end-of-life impacts vary markedly depending on the LCIA method. Under ReCiPe, the negative values reveal that recycling, especially of Li-ion battery components, can deliver greater benefits for critical material conservation than the original extraction causes impacts. Other indicators, however, show a modest rise in electric-vehicle impacts from phase 2 to phase 3, although this increase remains within the same order of magnitude. In contrast, diesel vehicles experience a reduction in phase-3 impacts across all five metrics, reflecting the relative simplicity of recycling non-critical materials like iron and steel without requiring complex chemical processing. These findings underscore the importance of tailored end-of-life strategies (advanced battery recycling for EVs and efficient ferrous recovery for diesel) to maximize resource conservation throughout both powertrains’ life cycles.

3.2.4. End-of-Life Recovery Contribution to Life Cycle Results

To place the end-of-life stage on the same footing as production and use, we disentangled its net effect with the avoided-burden procedure: cradle-to-use impacts were subtracted from cradle-to-grave totals, and the balance was interpreted as the climate and resource credit delivered by material recovery. The calculation shows a clear asymmetry between powertrains. The battery electric vehicle secures a 4.5 t CO₂-eq credit, while the diesel car gains 4.0 t CO₂-eq; the additional 0.5 t CO₂-eq arises from metals reclaimed in the 54 kWh traction pack. The gap widens for mineral depletion (ReCiPe Surplus Ore): the EV shifts from 6.52 t Cu-eq at cradle-to-use to −2.71 t Cu-eq at cradle-to-grave, an offset of about 9.2 t Cu-eq, whereas the diesel improves by only 0.2 t Cu-eq. Thus, more than 95% of the total mineral credit, and roughly a tenth of the EV’s cradle-to-grave GWP reduction, can be traced to battery and power-electronics recycling.

These values assume a single battery pack and the current EU electricity mix, making them conservative; deeper grid decarbonisation or higher collection rates would further enlarge the credit, whereas pack replacement or lower process yields would shrink it. The analysis underscores that efficient hydrometallurgical recovery is already a measurable lever for both climate mitigation and critical metal conservation, and it highlights the importance of policy measures that guarantee high collection efficiency and high-yield closed-loop treatment. Building on this insight, our group is presently compiling a plant-level inventory for next-generation hydrometallurgical facilities and is actively analyzing process hotspots and optimization pathways in greater detail.

3.2.5. Sensitivity Analysis: Battery Energy-Density Impacts

Given the importance of battery characteristics in influencing environmental performance, a sensitivity analysis was performed to assess how variations in energy density affect life cycle impacts.

The assumption of a 300 Wh/kg energy density for the NMC811 lithium-ion battery used in this study represents a realistic estimate for current high-performance automotive batteries. However, battery energy density is a variable parameter that can fluctuate significantly depending on specific cell chemistry, electrode design, and manufacturing technology. To evaluate how this parameter influences the environmental performance of electric vehicles, a sensitivity analysis was conducted using a range of energy densities, representative of both current market variations and technological advancements.

Three scenarios were considered:

• Low Energy Density (200 Wh/kg): This scenario reflects older or less optimized lithium-ion chemistries, requiring a higher battery mass to achieve the same nominal capacity (54 kWh). The total battery mass increases to approximately 270 kg, resulting in significantly higher material demand—particularly for Critical Raw Materials such as nickel, cobalt, lithium, and graphite. In this case, life cycle greenhouse gas (GHG) emissions related to battery production increase by approximately 35–40%, and the abiotic resource depletion impact rises accordingly due to the greater extraction of finite resources.
• Baseline Energy Density (300 Wh/kg): This is the reference case used in the study. It represents a balanced trade-off between energy performance and material efficiency and results in a total battery mass of around 180 kg. This scenario reflects the performance of current-generation NMC811 cells commonly used in commercial EVs.
• High Energy Density (350 Wh/kg): This scenario anticipates the adoption of next-generation lithium-ion or semi-solid-state batteries. Here, the total battery mass is reduced to approximately 154 kg. The material intensity per functional unit (kWh) decreases, lowering GHG emissions by about 15–20% relative to the baseline. The impact on Critical Raw Material demand is similarly reduced, improving the overall environmental footprint of the EV.

The results of this sensitivity analysis show that energy density is a key parameter affecting both GHG emissions and resource depletion. As energy density improves, the environmental burden associated with battery production diminishes significantly, reinforcing the potential of technological innovation to enhance EV sustainability. Conversely, lower energy densities substantially increase environmental impacts, underscoring the importance of advanced battery design and efficient resource use.

This analysis confirms the need to consider energy density variability in any life cycle assessment of electric vehicles. It also highlights the role of battery innovation not only in improving vehicle range and performance, but also in reducing the environmental footprint across the life cycle.

3.2.6. Sensitivity Analysis: Battery Cycle Life

Battery longevity is one of the largest sources of uncertainty in an electric vehicle LCA because the environmental burdens of a traction pack are incurred once, yet must be distributed across every kilometre driven. When the assumed number of equivalent full cycles (EFCs) changes, the fixed production and end-of-life impacts are spread over a shorter or longer distance, whereas use-phase impacts grow strictly in proportion to mileage. A structured sensitivity analysis, therefore, tests the robustness of our conclusions under realistic variations in cycle life and clarifies the potential gains achievable through future improvements in cell durability.

Four scenarios are examined:

• Scenario 1 (S1)—556 EFC (200,000 km): Baseline case discussed in previous sections.
• Scenario 2 (S2)—1000 EFC (360,000 km): Intermediate durability that extends driving range before any replacement would be required.
• Scenario 3 (S3)—1500 EFC (540,000 km): Extended-life case already analyzed, consistent with ≤15% capacity fade at this cycle count.
• Scenario 4 (S4)—2000 EFC (720,000 km): Upper limit of the 1500–2000 cycle range identified in the current literature.

The relative trends across the four cases are clear:

Moving from S1 to S2 results in a marked reduction in per-kilometre greenhouse gas (GHG) emissions and mineral resource demand, as the same production burden is distributed over approximately 80% more distance, while the shift from S2 to S3 delivers a further significant improvement, i.e., around a one-third decrease in GHG intensity and nearly a 50% reduction in abiotic resource depletion, demonstrating the strong impact of reaching 1500 cycles. Extending to S4 provides additional, although diminishing, benefits; the majority of achievable savings are already realized by 1500 EFC.

In general, doubling cycle life nearly halves the per-kilometre resource burden, while the climate benefit follows a slightly less-than-linear, yet still pronounced, decline. Across all scenarios, the EV retains a clear climate advantage over the diesel reference; higher cycle lives progressively widen that margin and further curb mineral-extraction pressures. These findings underscore the strategic importance of designing packs capable of at least 1000 EFCs and of pairing that durability with high-yield recycling or second-life pathways to maximize overall environmental performance.

3.2.7. Sensitivity Analysis: Diesel Fuel Efficiency

To reflect real-world variability, diesel use-phase impacts were recalculated at 6.5, 8.0, and 10.0 L/100 km for both functional lifetimes of 200,000 km and 540,000 km, while production and end-of-life inventories remained unchanged. Using the same well-to-wheel factors as in the baseline, cradle-to-grave greenhouse gas emissions rise by roughly 25–30%, 55–65%, and 95–105%, respectively, relative to the 5 L/100 km case; abiotic-depletion indicators increase by comparable proportions because the mineral inputs linked to crude-oil extraction, refining catalysts, and distribution infrastructure scale almost linearly with the volume of diesel processed.

In contrast, the battery electric vehicle’s results remain unchanged, because its electricity demand is independent of the diesel fuel-efficiency assumptions. Consequently, the EV’s climate and resource advantages widen at every lower-efficiency step and in both mileage scenarios: at 200,000 km, the diesel already exceeds the EV’s GWP by about a factor of two, and at 540,000 km, the gap expands even further.

Importantly, the closed-loop hydrometallurgical recycling pathway modelled for the 54 kWh pack yields a substantial end-of-life credit, markedly lowering the battery’s net emissions. This mitigation allows for the EV to maintain a clear environmental advantage under every diesel fuel-efficiency scenario considered and highlights how effective battery end-of-life management further strengthens the case for electrification.

4. Conclusions

The results of this study confirm that electrifying a C-medium segment substantially lowers the life cycle climate footprint, although it also highlights critical issues related to the demand for strategic raw materials. Across the two mileage horizons analyzed, three key messages emerge:

• Use-phase emissions per kilometre: At identical energy-consumption rates, vehicle operation alone produces about 38 g CO₂-eq/Km for the EV versus 233 g CO₂-eq/Km for the diesel.
• Cradle-to-grave balance: When production and end-of-life are included, the EV emits 118 g CO₂-eq/Km at 200,000 Km and 68 g CO₂-eq/Km at 540,000 Km, whereas the diesel remains at 261 g and 243 g/Km, respectively.
• Critical materials: Even after the hydrometallurgical-recycling credit (−2.7 t Cu-eq) is applied, the EV still requires far more strategic mineral resources than the diesel vehicle, underscoring the need for LCA metrics that explicitly address Critical Raw Materials (CRMs).

The robustness of these conclusions is reinforced by two sensitivity exercises. Varying battery durability from 556 to 2000 cycles and lowering diesel fuel-efficiency from 5 L/100 km to 10 L/100 km both enlarge the climate and resource advantage of the EV. The analysis of end-of-life recovery shows that hydrometallurgical recycling already delivers a meaningful credit, yet the results remain conservative because they assume a single battery pack and today’s EU electricity mix; replacement packs or deeper grid decarbonisation would widen the gap still further.

Although this study uses state-of-the-art methods, software, and databases (e.g., Ecoinvent 3.10), a major limitation is the absence of indicators that evaluate material criticality. Bundling CRMs with generic minerals understates a key dimension of environmental performance. Like petroleum in the previous century, CRMs risk becoming the “oil of the future”, with economic, geopolitical, and social ramifications beyond environmental depletion. Acknowledging the pivotal role of criticality assessments, our team has completed a parallel study to bridge this methodological gap; a corresponding manuscript is being prepared for submission. The work will introduce two new characterization factors—the Raw Material Extraction/Reserve Index and the Gini Index—which quantify both the global availability and geographic concentration of CRMs, thereby enriching LCA resource-depletion analysis.

Looking ahead, a dedicated method for evaluating the impact of green technologies on global CRM reserves is a high research priority. In addition to the consequential approach used here, future work should test the “circularity indicator” (available in Ecoinvent with cut-off) for cradle-to-cradle assessments and the EN 15804 method, widely adopted in Environmental Product Declarations.

From a climate perspective, the shift to e-mobility remains advantageous: over the full life cycle, an EV’s carbon footprint ranges from one-half to one-third of an equivalent diesel vehicle, depending on total mileage. This gap will widen as renewables grow in the electricity mix, advanced lithium-ion-battery recycling plants supply secondary CRM feedstocks, second-life batteries are deployed for stationary storage, and next-generation cells deliver higher energy density, longer cycle life, and reduced dependence on critical elements such as cobalt.

We can conclude that electric vehicles are not a stand-alone solution to climate change, but they represent a concrete step toward lowering greenhouse gas emissions. Equally essential, however, is a robust metric for raw material criticality; only then can society navigate the coming industrial and energy transition with a full awareness of its resource implications.