Comparison of Battery Electrical Vehicles and Internal Combustion Engine Vehicles–Greenhouse Gas Emission Life Cycle Assessment

Abstract

Battery electrical vehicle (BEV) ownership has increased in recent years. There is a general concern over the life cycle of the batteries used in such vehicles. This study provides a comprehensive overview of electric vehicles, encompassing their technical evolution, autonomy, and ownership. The analysis delved into the various types of batteries utilized in these vehicles, examining the composition of their constituent materials and the mechanisms underlying their operation. Additionally, it assessed their performance in terms of energy density storage, recharge capabilities, autonomy, and prospects. A critical evaluation of electric vehicles and their internal combustion engine vehicle (ICEV) counterparts, considering the Life Cycle Assessment (LCA) criterion, was conducted. The LCA criterion encompasses emissions during the entire lifecycle, from the “cradle” to the “tank” (WTT) and the “tank” until the end of its cycle (TTW). The findings of this study indicate that BEVs consistently outperformed ICEVs in terms of greenhouse gas (GHG) emissions in all the sizes of vehicles studied.

1. Introduction

1.1. Autonomy

Electric vehicles are a pioneering technology for combustion engines, which date back to 1876 when Nicolau Otto built the first internal combustion engine [1]. While ICEs obtain their energy from the combustion of petrol or diesel, BEVs obtain energy directly from a large set of batteries. Figure 1 shows the evolution of the autonomy of BEVs from 2011 to the present; due to technical improvements, the autonomy of BEVs has noticeably increased [2].

Despite technological advances, most contemporary battery systems have significantly lower specific energies than liquid fuels, which often limits the maximum autonomy of these electric vehicles. Lithium-ion and lithium-polymer batteries are the most common types used in modern electric vehicles due to their favorable energy density-to-weight ratio [3].

1.2. Batteries

The batteries in BEVs differ from starting, lighting, and ignition batteries in that they are designed as deep cycle batteries intended to provide power for extended periods. These batteries are characterized by their relatively high power/weight ratio, specific energy, and energy density.

Table 1. Comparison of different types of batteries (adapted from [4,5,6,7,8]).

Parameter	Pb-PbO2	Ni-Cd	Ni-MH	ZnBr2	Na-NiCl2	Na-S	Li-Ion
Temperature of operation [°C]	−20–50	0–50	0–50	10–40	300–350	300–350	−20–60
Energy density [Wh/kg]	30–40	40–60	60–120	60–80	120–150	150–240	150–260
Voltage of each cell [V]	2.0	1.2	1.2	1.79	2.58	2.08	3.6
Max. No. of charging cycles	200–300	500–1000	500–1000	>2000	1500–2000	2500–4500	400–3000
Emissions [gCO2e/kWh]	200–300	250–350	300–400	400–500	500–600	600–700	150–200
Cost [EUR/Wh]	5	5	4	2	1.3–1.7	0.7–0.8	2

presents a comparative analysis of the main characteristics of different batteries based on data from several authors [4,5,6]. The battery emissions referred to in this table include emissions from the extraction of the materials that make up the battery up to its assembly in the vehicle.

Current electric vehicle technologies are predominantly based on lithium-ion batteries due to their superior performance. Such batteries account for 85.6% of the energy storage systems used in the global market in 2015; despite being the most expensive, they have the lowest cost per cycle [9]. Electric vehicles require a battery system that is made up of hundreds or thousands of individual cells grouped to provide the necessary voltage, power, and energy.

Although lithium-ion batteries have a greater environmental impact during production due to material extraction and processing, ongoing advancements and recycling efforts are mitigating their global carbon footprint, and solid-state batteries have the potential to further reduce emissions, although they are not yet commercially viable [10,11].

In 2020, the highest gravimetric energy density for lithium-ion cells was 304 Wh/kg using NMC 811 cathodes [12]. Recent developments include 400 Wh/kg cells with nickel-rich cathodes and 720 Wh/kg cells with LiCoMnO₄ cathodes [13]. The EU and Asia aim to produce batteries with a gravimetric energy density of more than 400 Wh/kg by 2030 [12,13]. Innovations in battery packaging, such as CTP designs, can also improve performance.

The prices of lithium-ion batteries have been reduced from USD 1200 to 132 per kWh of battery. This reduction is mainly due to advances in battery technology and manufacturing efficiency.

Table 2. Comparison of CO₂e emissions in the production of batteries, actual values in 2023, and forecasted values for 2030 (adapted from [14]).

Stage in Production of Batteries	2023 [kgCO2e/kWh]	Forecast for 2030 [kgCO2e/kWh]
Extraction and refining	26	10
Production of active materials	32	10
Logistics	14	4
Manufacturing of cells	25	0
Total	97	24

and

Table 3. CO₂e emissions from producing batteries in several countries (adapted from [14]).

Country of Production	Intensity of Emissions in [kgCO2e/kWh] 1
Sweden	45
South Corea	72
USA	74
China 2	79
China	108

¹ Valid for small, medium, and large BEVs, with 45, 60, and 75 kWh, respectively. ² Based on a battery without Ni; all the other values are for batteries rich in Ni.

show the amount of emissions for each kWh battery produced in different countries, the actual values in 2023, and forecasts for 2030. Sweden, with its low emissions, serves as a model for sustainable production.

The total CO₂e emission of 97 kgCO₂e/kWh shown in Table 2 is a weighted average that was calculated considering the battery-manufacturing countries. The weight given to each country for the calculation of that average is directly related to the number of batteries produced in that country. Therefore, the value shown is closer to the Chinese value than to the Swedish value.

1.3. Electric Car Ownership

BEVs are an important part of achieving global climate change goals. Figure 2 shows the market share of new vehicle registrations in the 27 EU countries, by type of energy, in 2023.

Achieving the European Union’s target of around 35% of sales of new zero- or low-emission tourism vehicles by 2030 will have a significant impact on the entire EV value chain and ecosystem, considering that in 2023 such sales were around 23% (including BEVs and plug-in hybrids). Of note is Norway, where an EV market share of up to 95% is projected to be within reach by 2024. According to statistics from the Norwegian EV Association [16], Norway will experience another year of record electric vehicle sales in 2023, with a market share of 82.4%.

Figure 3 shows the evolution of electric vehicle ownership in some regions of the world from 2013 to 2023.

1.4. EU Climate Goals

Table 4. GHG emission objectives in the EU from 1990 to 2050 [18,19,20].

Year	The Target for GHG Emissions in the EU	Results and Forecasts (Reference Year: 1990)
1990	Reference year for most EU climate objectives.	------------------------
2000	No formal, binding EU-level target for this year.	------------------------
2012	Kyoto Protocol of 1997 to reduce GHG emissions by 8% (compared to 1990 levels) between 2008 and 2012.	Reduction of 12.2%
2020	A 20% reduction compared to 1990 levels in 2007 under the EU 2020 “Climate and Energy” package.	Reduction of 24%
2030	A 55% reduction compared to 1990 levels, in line with the “Fit for 55” objective achieved in 2021.	Reduction of 51%
2050	Net-zero emissions target.	Reduction of 65%

lists the GHG emission objectives of the EU. The adoption of EVs is due to a combination of political, economic, environmental, and technological factors. Politically, stringent environmental objectives such as the Kyoto Protocol, the EU’s 2020 Climate and Energy Package, the Paris Agreement, the European Green Deal, and the EU’s “Fit for 55” Package, all include reductions in GHG emissions, promoting EV adoption [18,19,20]. Financial subsidies and tax breaks also make EVs more affordable, stimulating market growth in countries such as Norway, Germany, and China.

The EU’s strategic plan includes achieving a 100% zero-emission vehicle fleet in urban areas by 2050 [5].

Beyond 2030, the disparity between the objectives and the expected impact of current and planned measures is pronounced. Considering the measures that have been adopted and those that are currently planned, net emissions are expected to be reduced by 60% compared to 1990 levels by 2040 and reduced by 64% by 2050 [21].

Figure 4 shows the carbon intensity of electricity production in some countries from 1990 to 2030. Sweden has emerged as a hub for future big factories, with the intention of increasing the European battery production capacity and reducing the dependence on non-EU sources [22].

Of the EU countries, as shown in Figure 4, Portugal is the only one that, in 2023, was already below the target to be achieved by 2030, corresponding to a 55% (see Table 4) reduction in the 1990 emission level. So, in this case, it makes sense to review, for Portugal, the targets proposed for GHG emissions presented in Table 4. It must be noted that the low GHG emissions in France are due to nuclear energy, due to renewable energy, namely hydraulic energy, in Norway, while in Sweden, they are due to both nuclear energy and renewable energy.

2. Method for Comparing ICEVs and BEVs—Life Cycle Assessments (LCAs)

Understanding the lifecycle emissions of different vehicle types is essential for assessing their environmental impact and guiding consumer policy and choice. Life Cycle Assessments (LCAs) provide a comprehensive assessment covering raw material extraction, vehicle manufacturing, energy production, and end-of-life processes, including recycling and reuse. It provides a comprehensive comparison of the environmental impacts of BEVs and ICEVs and identifies critical areas for environmental improvement throughout the vehicle life cycle. This comprehensive approach uses metric tonnes of CO₂e as the standardized unit, considering several greenhouse gases and their global warming potential [24].

The BEV production phase is around 40% more intensive in terms of emissions than that of hybrid and ICE vehicles [25]. This disparity is mainly due to the extraction and refining of raw materials such as lithium, cobalt, and nickel, which are essential for battery production, and the energy-intensive manufacturing processes involved [25]. These factors significantly contribute to the higher production emissions associated with BEVs [26]. However, by 2050, these BEV production emissions are expected to be only marginally higher than those of conventional vehicles. Batteries are responsible for around 60% of the GHG emissions from the production of electric vehicles [14].

This paper presents a Life Cycle Assessment (LCA) methodology, along with the framework, boundaries, limitations, assumptions, and rationale behind it. It provides key findings from each stage of the life cycle phase, highlights the method’s sensitivity to assumptions, and concludes with recommendations. The study examined a range of vehicle configurations, including traditional internal combustion engines and electrified powertrains, assessing their environmental performance under current and future market scenarios. The study also assessed the environmental footprint of BEVs and ICE vehicles over their entire life cycle, from raw material extraction to end-of-life processes. It identified the critical factors influencing emissions and the conditions under which BEVs become environmentally advantageous. The key objectives included the following:

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Comparing the total life cycle emissions for BEVs and ICE vehicles;

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Evaluating the impact of energy sources and battery production on GHG emissions;

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Assessing the role of recycling and maintenance in emission reductions.

The scope focused on light-duty passenger vehicles of various sizes (small, medium, and large) and using different energy sources (petrol, diesel, and electricity), considering regional variations in electricity production and battery manufacturing. This research followed the ISO 14040 standard’s four-step process, as shown in Figure 5.

Step 1. Goal and Scope Definition.

• Establish clear objectives for comparing BEVs and ICE vehicles.
• Define boundaries to include vehicle production, energy production, use phase, maintenance, and end-of-life processes.
• Document assumptions, such as energy grid compositions and vehicle lifespans, ensuring that they are transparent and aligned with the study’s objectives.

Step 2. Life Cycle Inventory (LCI). Compile raw material, energy input, emission, and by-product data across all phases.

• Production Phase: includes emissions from vehicle manufacturing (excluding and including battery production) and battery production (varying by country and battery chemistry).
• Use Phase: combines Well-to-Wheel (WTW) emissions including WTT (Well-to-Tank; emissions from fuel extraction, refining, and transportation) and TTW (Tank-to-Wheel; combustion or electricity use during operation).
• End-of-Life Phase: covers emissions from recycling, disposal, and remanufacturing.

Step 3. Impact Assessment. Quantify GHG emissions using CO₂ equivalents (tCO₂e) for the key phases:

• Production, differentiating BEVs and ICE vehicles by material intensity.
• Operation, analyzing energy losses in electricity generation, distribution, and use.
• End of life, evaluating emissions savings from advanced recycling technologies.
• Region-specific energy mix emission data were applied to refine the calculations.

Step 4. Interpretation and Sensitivity Analysis.

• Analyze results to identify the critical phases and factors influencing emissions.
• Evaluate scenarios under varying assumptions, such as grid decarbonization or advances in battery production and recycling.

WTT refers to the processes required to convert crude oil from the wells at the fuel depot into usable petrol or diesel fuel, and TTW refers to combustion in the engine, as shown in Figure 6. This terminology has also been adopted for BEVs, with WTT referring to all impacts of electricity generation that occur prior to vehicle charging, and TTW referring to the direct impacts of driving the vehicle [5].

The transition to electric vehicles has spurred interest in understanding their environmental impacts compared to conventional ICE vehicles. This study employed a Python 3.10.14-based model to conduct a comprehensive LCA of GHG emissions for BEVs and ICE vehicles (see Appendix A). The model’s results provided insights into the relative emissions performance of these vehicles under various scenarios, serving as a decision-making tool for policymakers and researchers.

The input data were (i) emissions due to vehicle production, excluding the battery, from Table 5; (ii) emissions associated with the maintenance of either ICEs or BEVs per km, with values of 8.34 × 10⁻⁶ and 4.17 × 10⁻⁶ tCO_2e/km, respectively; (iii) direct CO₂e emissions per km resulting from the combustion of petrol or diesel in ICEVs, using the average values in Table 7; (iv) emissions associated with the extraction/refining/transportation of fuel from Table 7; and (v) CO2e emissions per kWh of electricity generated, varying by country, from Table 8; (vi) emission intensity during the manufacture of batteries from Table 3; (vii) end-of-life emissions from Table 9. Additional parameters such as vehicle size, fuel type, and battery manufacturing location further defined the scenarios analyzed.

The outputs after comparing the emissions for each scenario are displayed, and the code prints the scenarios where BEVs become more environmentally friendly than ICE vehicles. The results include the vehicle size, fuel type, country, battery production location, the distance at which BEVs become greener, and the emissions for both vehicles at that distance. Finally, the code also calculates and shows the total emissions after 240,000 km (this mileage was used in [24,28,29]) for each scenario.

The initial emissions are a significant factor in the overall Life Cycle Assessment, highlighting the initial environmental cost of BEVs compared to ICEVs (see Table 5).

Table 5. Emissions from vehicle production, excluding battery production and depending on vehicle dimensions (adapted from [25]).

	BEVs			ICEVs-Petrol			ICEVs-Diesel
	Small	Medium	Large	Small	Medium	Large	Small	Medium	Large
tCO2e/vehicle	5.0	6.0	7.0	5.5	6.7	7.8	5.8	7.0	8.2

Table 5. Emissions from vehicle production, excluding battery production and depending on vehicle dimensions (adapted from [25]).

	BEVs			ICEVs-Petrol			ICEVs-Diesel
	Small	Medium	Large	Small	Medium	Large	Small	Medium	Large
tCO2e/vehicle	5.0	6.0	7.0	5.5	6.7	7.8	5.8	7.0	8.2

The intensity of emissions from battery production depends on the country where this manufacturing takes place and the type of battery used (see Table 3). For example, battery production in Sweden is associated with relatively low emissions of 45 kg CO₂e/kWh, while in countries such as China, the emissions range between 79 and 108 kg CO₂e/kWh, depending on the specific battery chemistry. Notably, the higher values are based on battery types that do not contain nickel, while others with higher nickel content generally have lower emissions. As mentioned above, the average global emissions amount to 97 kg CO₂e/kWh, highlighting variability based on geography and material composition.

The average vehicle’s fuel and energy consumption values vary according to the size of the vehicle (small, medium, or large), influencing the operational emissions throughout the vehicle’s useful life (see Table 6).

Table 6. The average vehicle’s fuel and energy consumption values (adapted from [30]).

Size of Vehicle	ICEVs-Petrol (L/100 km)	ICEVs-Diesel (L/100 km)	BEVs (kWh/100 km)	Battery Capacity of BEVs (kWh)
Small	6.5	5.4	16.0	45.0
Medium	7.5	6.2	17.5	60.0
Large	8.8	6.7	19.0	75.0

Table 6. The average vehicle’s fuel and energy consumption values (adapted from [30]).

Size of Vehicle	ICEVs-Petrol (L/100 km)	ICEVs-Diesel (L/100 km)	BEVs (kWh/100 km)	Battery Capacity of BEVs (kWh)
Small	6.5	5.4	16.0	45.0
Medium	7.5	6.2	17.5	60.0
Large	8.8	6.7	19.0	75.0

The ICE operational emissions were derived from both the production and combustion of the fuel. Together, the WTT and TTW emissions form the WTW emissions. Table 7 provides these values for Europe.

Table 7. WTT and TTW GHG emissions in Europe from petrol and diesel (adapted from [30]).

Fuel	WTT (gCO2e/L)	TTW (gCO2e/L)	WTW (gCO2e/L)
Petrol	680	2240	2920
Diesel	980	2440	3420

Table 7. WTT and TTW GHG emissions in Europe from petrol and diesel (adapted from [30]).

Fuel	WTT (gCO2e/L)	TTW (gCO2e/L)	WTW (gCO2e/L)
Petrol	680	2240	2920
Diesel	980	2440	3420

The emissions per kWh from electricity production vary significantly from country to country, reflecting differences in the energy sources used in the electricity mix (see Table 8). Countries with a greater dependence on fossil fuels for electricity production will have higher emissions per kWh, while those using more renewable energy will have lower emissions.

Table 8. Emissions in grams of CO₂eq for every kWh of electricity produced in different countries in 2023 (adapted from [23]).

Emissions [gCO2e/kWh]	Portugal	USA	China	Norway	Poland	Sweden	EU 27	France
	166	369	582	30	662	41	229	56

Table 8. Emissions in grams of CO₂eq for every kWh of electricity produced in different countries in 2023 (adapted from [23]).

Emissions [gCO2e/kWh]	Portugal	USA	China	Norway	Poland	Sweden	EU 27	France
	166	369	582	30	662	41	229	56

The emissions from electricity consumption during vehicle operation were adjusted for charging inefficiencies. To reflect real-world conditions, the energy drawn from the grid was adjusted by a factor of 1/0.857 (approximately 1.167) because only 85.7% of the grid electricity is efficiently stored in the vehicle’s battery. This adjustment ensures that the total emission calculation reflects the grid electricity consumed, not just the net battery energy. In fact, during the process of charging a BEV, in 95% of the energy inflow at home (95%, not 100% due to WTT losses), there is a loss of 5% of the 95%, resulting in 90.25%, and the AC/DC inversion causes a loss of an addition 5% of the 90.25%; thus, the battery charging efficiency is 85.7% [30].

Maintenance emissions represent the GHG emissions produced during vehicle maintenance throughout its lifetime. These values reflect the lower maintenance requirements of BEVs, around 4.17 g of CO₂ equivalent per km, as they have fewer moving parts and typically require less frequent maintenance compared to ICE-powered vehicles (around 8.34 g of CO₂ equivalent per km) [24].

Recycling a BEV at the end of its useful life can reduce its total carbon footprint. The end-of-life emissions, expressed in tonnes of CO₂e, are shown in Table 9.

Table 9. End-of-life emissions depending on vehicle dimensions (adapted from [24]).

Type of Vehicle	BEVs			ICEVs-Petrol			ICEVs-Diesel
	Small	Medium	Large	Small	Medium	Large	Small	Medium	Large
tCO2e/vehicle	1.6	2.0	2.4	1.1	1.3	1.6	1.1	1.3	1.6

Table 9. End-of-life emissions depending on vehicle dimensions (adapted from [24]).

Type of Vehicle	BEVs			ICEVs-Petrol			ICEVs-Diesel
	Small	Medium	Large	Small	Medium	Large	Small	Medium	Large
tCO2e/vehicle	1.6	2.0	2.4	1.1	1.3	1.6	1.1	1.3	1.6

The code used in the current study was designed to compare the life cycle CO₂e emissions of BEVs and ICEVs in various scenarios. It calculates the total emissions for both types of vehicles over a given distance, allowing us to identify when BEVs become more environmentally friendly than ICE-powered vehicles using various constants and data sets.

In the calculations performed, the Python code, shown in Appendix A, implements a modular framework to compute the total emissions for BEVs and ICE vehicles using Equation (1):

$$E_{total}=E_{manufacturing}+E_{battery production}+E_{operation}+E_{maintenance}+E_{end-of-life}$$

(1)

For BEVs, $E_{battery production}$ accounts for the battery’s emissions based on its production location and capacity. $E_{operation}$ incorporates electricity emissions adjusted for charging inefficiencies, calculated as

$$E_{electricity}=consumption\times distance\times 1.167\times E_{electricity mix emissions}$$

(2)

For ICE vehicles, $E_{operation}$ is derived from tailpipe and WTT emissions based on the fuel type and distance traveled.

The Python model evaluates emissions across a wide array of parameters, such as vehicle size, fuel type (for ICE vehicles), electricity mix (for BEVs), and battery production location (for BEVs).

Using Python’s iterative calculation capabilities, the model determines the distance at which the cumulative BEV emissions fall below those of ICE vehicles for each scenario. This iterative approach ensures accurate cross-over distances to account for real-world variability in conditions.

3. Results

Several simulations were carried out to obtain the best scenario regarding GHG emissions.

BEVs outperformed diesel and petrol vehicles in all possible scenarios for 240,000 km of mileage, as can be seen in Figure 7. Higher GHG emissions were indeed produced in the supply of raw materials and the manufacture of BEVs compared to ICEVs. The net life cycle impacts depended on the operation of the vehicle and on the electricity mix used to recharge the BEVs.

It can be seen from Figure 7 that, on average, an electric vehicle in the EU emitted almost 3 times less CO₂e than a conventional, either diesel or petrol, vehicle in 2023.

In the best-case scenario where a BEV is powered by clean, renewable electricity from Norway, its GHG impact dropped to 9.2 tCO₂e, which is 6.37 and 6.52 times lower than that of equivalent vehicles powered by diesel and petrol, respectively. In the worst-case scenario where the battery was produced in China and the electric vehicle was operating in one of the most carbon-intensive grids in the EU (Poland), the lifetime emission increased to 43.9 tCO₂e but electric vehicles were still 16% cleaner than diesel vehicles and 18% cleaner than the equivalent petrol vehicles.

The carbon intensity of the electricity used to charge a BEV has a significant impact on its overall CO₂e emissions. In the current study, it was assumed that the battery was produced using the average global value, around 97 kg CO₂e for each kWh of battery [9] (see Figure 8).

In Poland, which has one of the most carbon-intensive grid energy mixes in Europe (mostly due to coal), a BEV was still 26% and 28% cleaner than a diesel-powered or petrol-powered ICEV, respectively, as can be ascertained from the values in Figure 7 and Figure 8.

Figure 9 shows that after production, BEVs quickly offset their carbon debt compared to ICE cars. Considering an annual mileage of 12,000 km (according to the median mileage in the USA, similar to the European value), it takes around two to three years of driving for a BEV to reach parity with diesel and petrol ICEVs [31]. Figure 9 also shows the CO₂e emissions per kilometer accumulated for the distance travelled.

BEVs are initially more polluting than ICEVs since the BEV production phase is around 40% more intensive in terms of emissions than that of ICEVs, mainly owing to the emissions released during battery production.

In the best-case scenario, for BEVs with a battery produced with clean electricity in Sweden and running on clean electricity in Norway, the excess carbon debt would be repaid after around 5990 and 6189 km for an ICEV using petrol and -diesel, respectively. In the worst case, for a BEV with a battery produced in China and powered with the most polluting electricity in the EU (from Poland), it will be necessary to travel around 62,694 km compared to an ICEV using diesel, and for an ICEV using petrol, it will be necessary to reach 50,234 km.

The energy consumption varied for different BEV weights between 16 and 19 kWh/100 km for small and large cars, respectively [30]. For petrol and diesel ICEVs, the energy consumption varied between 6.5 and 8.8 L/100 km and 5.4 and 6.7 L/100 km, respectively. The equivalent CO₂e emissions for each kWh of electricity produced in mainland Portugal in 2023 is 166 gCO₂e/kWh, and emission values to produce the battery, according to the global average, is around 97 kg CO_2e for each kWh of battery. Figure 10 shows the Portuguese CO₂ equivalent emission results.

Figure 10 shows that BEVs are approximately 70% less polluting when compared to ICE vehicles with the same dimensions, and even when comparing a large BEV with a small ICEV, the large BEV is approximately 60% less polluting. Considering only exhaust pipe emissions for ICEVs and total emissions for BEVs, it appears that BEVs are 2.3 and 2.1 times less polluting than petrol and diesel ICEVs, respectively. This calculation assumed the best-case scenario for battery manufacturing (see Table 3).

In Figure 11, carrying out the same calculations but for BEVs in Portugal in the best- and worst-case scenarios and considering the segment and location of battery production. The analysis considered the production of BEV batteries. In the best-case scenario, the batteries produced in Sweden had a carbon intensity of 45 kg CO₂e/kWh, while in the worst-case scenario, they were manufactured in China and had a density of 108 kg CO₂e/kWh.

Table 10. Mileage of a BEV up to parity in Portugal, considering the best- and worst-case scenarios for each segment, compared to diesel and petrol ICEVs.

Scenario	ICEVs-Diesel (km)	ICEVs-Petrol (km)
Best scenario (small BEVs)	5535	5334
Worst scenario (small BEVs)	20,841	20,086
Best scenario (medium BEVs)	7131	6864
Worst scenario (medium BEVs)	27,865	26,840
Best scenario (large BEVs)	9894	9525
Worst scenario (large BEVs)	36,231	34,878

shows the mileage required for BEVs to reach environmental parity with diesel and petrol ICEVs in Portugal in the various scenarios in Figure 9.

From another point of view, the greater carbon intensity of battery production leads to a considerable increase in the initial emissions associated with BEVs, which may require wider use to offset the environmental impact.

4. Conclusions

The objective of this study was to compare BEVs with ICEVs using LCAs.

The study showed that the relevance of BEVs is increasing as the number of vehicles in circulation has increased. In addition, the operation of BEVs is becoming easier as the autonomy of these vehicles has increased significantly.

BEVs showed significant environmental benefits compared to ICE vehicles, even in scenarios where the electrical grid is carbon intensive, such as in Poland. In several scenarios, BEVs consistently outperformed diesel and petrol vehicles in terms of GHG emissions.

In the best-case scenario, in which BEVs are recharged using clean electricity and equipped with batteries produced using renewable energy, the environmental impact was reduced by up to six times compared to conventional ICE vehicles.

It is important to highlight that BEVs can offset the initial “carbon debt” incurred during battery production in just over a year of operation on average, and in the best-case scenario, after half a year, it will have already paid off that “carbon debt”. Over their entire life cycle, BEVs are expected to save between 20 and 50 tCO₂e for the worst-case and best-case scenarios, respectively, compared to their ICEV counterparts, highlighting their critical role in mitigating climate change. It was also observed that pollutant emissions from these vehicles were lower for smaller vehicle sizes/weights.

In short, from both environmental and financial perspectives, BEVs offer superior performance and long-term benefits, making them a critical element in the transition to sustainable transportation.

These analyses and results are critical to guiding policymakers and original equipment manufacturers (OEMs) in designing policies and strategic decisions based on long-term goals for countries, states, and cities.

To build on this work, it seems appropriate, as a next step, to compare BEVs and ICEVs using other criteria, such as total cost of ownership.