Environmental Impacts of Internal Combustion Engine vs Electric Vehicle: Life-Cycle Assessment Review

Electric vehicles (EVs) play a substantial role in future sustainability developments. However, a clear environmental impact comparison between EV types and internal combustion engine vehicles (ICEVs) are required. Therefore, a life-cycle assessment (LCA) could play an important role as an analysis tool in comparing the environmental impacts of different technologies. While several reviews on EV LCA exist, they often lack systematic literature reviews (SLR) using bibliometrics and content analysis.  This article aims to find the research trend of EVs and examine the environmental impacts of different EV types in comparison with ICEVs. The results show the research trend area and provide a sound foundation for a more comprehensive environmental impact of EV by LCA. Our study indicates that locations with a significant reliance on fossil fuel electricity grids face substantial obstacles in reaping the benefits of EVs. Nonetheless, the prospective benefits of EVs are considerable.  Future research has identified critical issues that must be addressed, reflecting the growing complexity of EV adoption challenges.

Environmental impact; Electric Vehicle (EV); Life-Cycle Assessment (LCA); Sustainability; Sustainable transportation

1.1. Background

This study investigated the LCA research on several types of vehicles (ICEVs and EVs) to clarify whether EVs are greener than ICEVs and their environmental impact. The transition from ICEV to EV represents a critical shift in the automotive and energy sectors, driven by goals of reducing GHG emissions, improving air quality, and achieving energy sustainability. Comparing these two vehicle types allows for a detailed evaluation of their technological and environmental impacts. ICEV represents the existing technology, and EV represents future technology that would replace the ICEV, which has combustion as a cause of GHG and air emissions. The comparison highlights both vehicle types, key advantages, and limitations, offering insights into the challenges of EV adoption and areas where ICEV remains competitive. It provides a benchmark for assessing how EVs can meet or exceed the performance and utility of ICEVs. This comparative analysis contributes to the ongoing discussion about decarbonising the transport sector by identifying trade-offs between ICEV and EV. 

Table 1 State-of-the-art for LCA’s article review for vehicles

The literature review used three primary steps. In step 1, bibliometric analysis was conducted by VOSviewer based on selected papers from the Scopus search engine. VOSviewer was recognised as a user-friendly tool known for its simplicity, flexibility, and responsiveness to user needs. VOSviewer was chosen as the bibliometric analysis tool due to its robust capabilities in visualising networks and keyword co-occurrences. Moreover, it offers excellent graphic quality. The software’s ability to generate interactive maps makes it ideal for identifying key research trends and relationships clearly and comprehensibly (Van Eck and Waltman, 2010). Furthermore, VOSviewer is widely recognised for its scalability and suitability for handling large bibliometric datasets, ensuring reliability and accuracy in the analysis. However, it is limited by its reliance on predefined functions and the need to repeat analyses due to its inability to integrate data from multiple sources (Arruda et al., 2022). 

In step 2, a comprehensive literature study was undertaken by gathering papers that contained specific keywords, such as life-cycle analysis, life-cycle assessment, and environmental impact in vehicles (ICEV and EV). In this step, the type of vehicles, particularly EVs, was investigated. The mapping of EV was summarised to indicate its configuration and how it works. Mapping of EV becomes a crucial step to knowing deeply the characteristics of material and energy flow within a vehicle throughout the entire life of a vehicle, especially in the operation of the vehicle. Then, works of literature on LCA were collected, categorised, reviewed, and synthesised. Initially, the basic methodology, approach, and tools of LCA were reviewed as a basic knowledge of the environmental impact of vehicles. The last step involves presenting substantial findings concerning the environmental impact of EVs. 

In the LCA review, the system boundary is one of the crucial aspects of LCA. The system boundary determines processes in the whole life cycle of the relevant systems that require analysis or may be ignored for simplicity. It defines process phases that need to be included within the LCA, and their choice must be consistent with the target of the study. This review's LCA boundary includes raw material extraction, production, manufacturing, and use phase (well-to-wheel). Some of them provide an end-of-life phase (cradle-to-grave). Therefore, the discussion articles of LCA were focused on this boundary.  

1. Bibliometric Analysis

Bibliometric analysis has become increasingly prominent in research (Donthu et al., 2021).  Ninkov et al. (2022) presented several fundamental meanings of bibliometric concepts, including "Evaluative bibliometrics" and "Relational bibliometrics." Evaluative bibliometrics are employed to delineate the attributes of published information. Relational bibliometrics is a research methodology in which researchers examine the occurrences of shared metadata, such as authors, citations, and keywords. Recent advancements in data analysis methodologies and technologies have facilitated the processing of extensive bibliographic datasets, as well as the extraction of valuable insights. This development has substantially augmented the significance of bibliometrics in research evaluation decision-making.

The bibliometric analysis was conducted using data from the Scopus database through a systematic filtering process. Initially, 1,500 articles were identified using the keywords "electric vehicle" and "life cycle assessment." These records were refined in five stages: filtering for articles, narrowing the publication years to 2018–2023, selecting final publications in English, and focusing on studies related to environmental impact assessments. After excluding irrelevant and non-compliant records at each stage, the dataset was reduced to 321 relevant articles, which formed the basis for bibliometric visualization and analysis using VOSviewer.

The results of the bibliometric analysis were shown as a co-occurrence network among keyword and a co-occurrence network for research hotspots in recent years. Figure 1 illustrates a keyword co-occurrence network. Each node represents an entity such as an article, journal, author, institution, country, or keyword. The purpose of this figure is to illustrate the major research themes and their interconnections within the field under study. In Figure 1, the nodes represent keywords. The size of a node indicates how often the keyword occurs. The links between nodes represent the co-occurrence of keywords or when they occur together. The thickness of a link indicates how frequently the co-occurrence happens. Larger nodes indicate a higher frequency of the keyword, while thicker links indicate a higher frequency of co-occurrence between keywords. Each shade corresponds to a thematic cluster, in which the nodes and linkages within that cluster can be utilised to elucidate the coverage of themes and the relationships between the topics that are evident within that particular theme (Donthu et al., 2021).

Figure 1 shows the co-occurrence assessment and link between electric vehicle, life-cycle assessment, environmental impact, and other fields. The authors utilised the keywords from 321 articles to construct this co-occurrence network. A minimum of five keywords is required. Figure 1 illustrates the symbiotic network, which can be categorised into 4 clusters according to their correlation strength. Each cluster is represented by a distinct color. Cluster 1, denoted by the color red, comprises 78 pieces. Cluster 2, denoted by the color green, comprises 73 items. Cluster 3, denoted by the color blue, comprises 71 items. Cluster 4, denoted by the color yellow, comprises 50 items. The cluster consists of 272 items with 15027 links and a total link strength equal to 42026.

Figure 1 Co-occurrence network analysis for “electric vehicle”, “life cycle assessment” and “environmental impact” words

Cluster 1 (red) relates to lithium-ion batteries, battery technologies, recycling, and manufacturing aspects, which are significant contributors to the environmental impact of EVs. The visualisation highlights the major trends in lithium-ion batteries and recycling technologies. It shows rapidly growing research on lithium-ion batteries, including their production, efficiency, environmental footprint, and recycling methods. Larger nodes, such as "life cycle assessment" and "lithium-ion batteries," indicate these are central and frequently discussed topics in the literature. Cluster 2 (green) focuses on environmental impact analysis and LCA in general, including terms like "life cycle assessment", "environmental impact", “sustainability”, and "life cycle analysis". Meanwhile, cluster 3 (blue) covers topics related to broader issues, such as sustainable transportation, mobility, hydrogen, and alternative energy, relevant to emissions reduction.

Specific to the keywords “electric vehicle” and “electric vehicles”, the network shows that the EV is represented by “electric vehicle” (green) and “electric vehicles” (red), as shown in Figure 2. They appeared in 118 and 97 occurrences and have total connection strengths of 1981 and 1596, respectively. Then, followed by the “life cycle” (blue) appeared in 241 occurrences, with a total connection strength of 3842, the “life cycle assessment” (green) appeared in 203 occurrences, with a total connection strength of 3134, and the “environmental impact” (red) appeared 197 occurrences, with a total connection strength of 3105. It can be understood that there are many research contents and strong correlations in the fields of electric vehicle(s), life-cycle, life-cycle assessment, and environmental impact. Furthermore, numerous studies exist concerning global warming, battery technology, gas emissions, sustainable development, and recycling electricity.

Figure 2 Co-occurrence network analysis for (a) “electric vehicle” and (b) “electric vehicles” words

Based on Figure 3, the associated symbiosis network, the analysis examines the current areas of study focus based on the time frame, with brighter colours indicating more recent research in the field. In recent years, various aspects have emerged related to the life cycle of electric vehicle clusters, including battery electric vehicles, recycling, battery recycling, GHG emissions, and electricity grids. Research offers guidance for future academics.

This visualisation shows how research in LCA for EV has evolved from foundational topics (blue/green) to newer, more focused areas (yellow), such as battery technology, recycling, and framework (well-to-wheel). The yellow nodes represent cutting-edge work and provide a glimpse into the future of the field. The bibliometric clusters confirm that key areas of focus in the existing literature align with the study’s objectives, such as comparing the life-cycle environmental impacts of different vehicle types and a comprehensive life-cycle emission framework. 

The emergence of several nodes related to ICEV, such as “internal combustion engine vehicle,” “diesel,” “alternative fuel,” “fuel consumption”, and “diesel engine”, shows that ICEV technology is still competitive with EV, not only in terms of technology performance but also environmental performance. These insights also validate the relevance of the studies comparing EV with ICEV. The clusters also highlight underexplored areas, such as integrating social and economic dimensions with environmental LCA. This reinforces the importance of addressing these gaps in future work. For example, the lack of emphasis on the impacts of economic and social issues in bibliometric clusters directly aligns with the prospective discussion, such as total cost of ownership, costs, cost analysis, human, and fuel economy. It is represented by small nodes that show the lack of discussion. In addition, regarding EV technology, the bibliometric analysis reveals a lack of focus on FCEV and a main focus on BEV. Most studies have assessed the competitiveness of BEV against ICEV in specific commercial fleet applications.

Figure 3 Co-occurrence network analysis for research hotspots in recent years

2. Classification of Electric Vehicle

4.1. Hybrid Electric Vehicle (HEV)

An HEV utilises both an electric motor (EM) and a combustion engine to power the vehicle efficiently. The motor serves as an auxiliary power source under specific operational conditions and does not necessitate recharging at a charging station, with the exception of PHEV. HEV has many application variations, categorised into mild-HEV, full-HEV, and PHEV (Das et al., 2017). In terms of the hybridisation level of HEV, micro-HEV is also classified as HEV. In Das et al. (2017), micro-HEV is categorised as ICEV due to being fully propelled by ICE during cruising. It has a similar type to mild-HEV. However, mild-HEV has a larger electric motor (EM) than micro-HEV and has electric torque assistance. To improve fuel efficiency, most automotive manufacturers have produced full-HEV. Full-HEV uses divided power flow for both the ICE and the motor. Accordingly, it can operate the vehicle independently or in combination. Full-HEV can be classified into four categories: series, parallel, series-parallel, and complex (Tran et al., 2021; Wang et al., 2018).

4.2. Battery Electric Vehicle (BEV)

A BEV is commonly called a "pure EV" of a “full battery EV”. BEV exclusively utilises a singular electric powertrain device. Batteries store energy. This car necessitates charging at dedicated charging stations utilising an electric plug-in with a specialised socket. BEVs are completely devoid of any GHG emissions and noise pollution. consequently, it exerts a beneficial influence on the environment. BEVs are relatively simple to manufacture, operate, and upkeep. However, it may contribute significantly to pollution during electricity production, battery manufacturing, and the disposal of discarded batteries (Li et al., 2017). The BEV’s performance depends entirely on its battery capacity and cooling system management. The battery's temperature is important in determining its performance and lifespan (Lyu et al., 2019). The wheels are driven by a motor powered by a battery (Un-Noor et al., 2017).  

4.3. Plug-in Hybrid Electric Vehicle (PHEV)

A PHEV utilises a combustion engine in conjunction with an electric powertrain. However, it can be recharged at a dedicated charging station. An HEV typically has a lower battery capacity than other vehicles (Dižo et al., 2021). When operating on battery power, PHEVs do not release any pollutants from their exhaust; however, they produce emissions when their electricity is generated at a power plant. The PHEV is predominantly propelled by electricity and is exclusively utilised for short distances. During extended journeys, gasoline is required, but only when the battery power is ultimately used up. Although PHEVs offer a longer driving range than BEVs, they have certain drawbacks. These include a higher upfront cost than BEVs and the fact that they are not environmentally friendly due to their emissions during electricity generation. To tackle these concerns, many studies have been dedicated to analysing methods for enhancing battery performance, such as reducing charging time, analysing cooling systems, conducting tests, and determining optimal ranges. Additionally, efforts have been made to optimise the battery package by reducing its size and weight (Verma et al., 2021). 

4.4. Fuel Cell Electric Vehicle (FCEV)

An FCEV or FCV has garnered significant interest because of its emission-free functioning. FCEVs, similar to BEVs, are propelled by an electric motor (EM). However, FCEVs utilise a fuel cell as their power source instead of a battery. FCEVs utilise specialised tanks to store hydrogen (H₂) for transportation. The fuel cells generate electricity, which is then directed to an EM to operate the vehicle. The car is powered by H2, and the fuel cell turns the chemical energy stored in the H₂ gas into electrical energy to drive the EM. H₂ gas can be generated either by burning fossil fuels like natural gas or by using a process called water electrolysis. FCEVs, similar to ICEVs, possess a brief refilling duration (Un-Noor et al., 2017; Tie and Tan, 2013). Additionally, it can be used in conjunction with batteries and supercapacitors. Without a battery, this type does not affect the power system, as it is not dependent on electric charging from the grid system (Nour et al., 2020). While FCEVs are effective options for reducing GHG emissions, the hydrogen infrastructure still has certain obstacles (Ahmadi et al., 2020; Ahmadi and Kjeang, 2017). FCEVs are widely regarded as the most attractive long-term alternative for passenger vehicles. Numerous obstacles must be overcome, including durability, H2 refueling infrastructure, and cost, before FCEVs can be widely used (de Almeida and Kruczan, 2021). 

1. Life-Cycle Assessment: Environmental Impact on ICEV vs EV

5.1. Basic Methodology of LCA

Life-cycle assessment is an established and evolving technique that investigates the possible environmental impacts of a process, activity, or product (Bjørn et al., 2018; Curran, 2013). Some products are dominated by environmental impact in phase of production (Putri et al., 2023; Bianco et al., 2021), utilisation (Challa et al., 2022), and disposal (Shafique and Luo, 2022). The purpose of LCA is to analyse and quantify the material and energy flows involved in each stage of a product's life cycle and the associated emissions and waste released into the environment. Throughout the whole life cycle of a vehicle, it is essential to include design, extraction of raw materials, production, transportation/distribution, utilisation, disposal, recycling, and resource management. Nevertheless, the assessment may incorporate any combination of these phases. The process of identifying environmental influences along its whole life cycle is one that includes a lot of complexity and sophistication. Therefore, there is a need for a methodical analytical instrument or tool to assess the environmental impact of a vehicle's life cycle.  LCA is an analytical tool as organised in ISO 14040. 

The purpose of the LCA shall be clearly defined, including the study's context and its intended application. The scope shall be thoroughly delineated, explicitly defining the functional unit, the boundaries of the system, the methods for allocation, the categories of effect, the models utilized for LCA, and the standards governing data quality. In this step, assumptions are explicitly stated and supported with justifications. The inventory analysis process involves assessing the flow of materials and energy inside the defined system boundaries and how they interact with the environment. This is done by evaluating the inputs and outputs. Inputs involve water, raw materials, chemicals, and energy. Outputs encompass several components, such as products, co-products, air emissions, solid waste, water discharges, and emissions to land.

During the impact assessment step, the results of the inventory analysis are linked to specific impact categories, including climate change, human toxicity, acidification, ecotoxicity, ionising radiation, eutrophication, ozone depletion, respiratory inorganics, photochemical ozone formation, land use, and resource depletion. Subsequently, a comprehensive impact assessment is conducted, often encompassing three domains of safeguarding: human health, natural resources, and the natural environment (Wolf et al., 2012). Various approaches are introduced to evaluate the many potential impacts on the three domains of protection (Sala et al., 2012). Finally, during the interpretation phase, the results of the LCA are analysed in relation to the intended purpose and scope of the study. This involves assessing the comprehensiveness, sensitivity, and coherence of the outputs (Serenella et al., 2015). Accuracy and uncertainty are also considered.

To perform LCA, commercial software packages are widely used to assist in conducting life-cycle inventory and life-cycle impact assessment, including GREET, SimaPro, GaBi, Umberto, and OpenLCA. Selecting a software package for LCA is extremely vital. Every application possesses distinct characteristics that may differ in terms of database accessibility, user interface, functionality, data quality management, and modeling approaches for constructing product systems  (Silva et al., 2017). The LCA output depends on methods, databases, and impact assessment models that have been developed in software packages to run LCA (Lopes Silva et al., 2019). The two prominent software packages for LCA are GaBi and SimaPro, both of which are commonly used worldwide (Herrmann and Moltesen, 2015).

5.2. LCA Model Approach

The original LCA methodology, often referred to as attributional LCA (A-LCA), is recognised globally and is used to assess the potential environmental burdens linked to a product or service. However, LCA modeling approaches were widely provided in the UNEP 2012 report on “Global Guidance Principles for Life-Cycle Assessment Databases” and are typically categorised into two types: attributional LCA (A-LCA) and consequential LCA (C-LCA) (Schaubroeck et al., 2021; Ekvall, 2019). 

Several terminologies have emerged during the evolution of LCA. Brander et al. (2019) suggested that attributional and consequential elements should be merged within a single analysis. In addition, Guinée et al. (2018) found many more different approaches or modes of LCA, such as backcasting LCA (B-LCA) (Kunttu et al., 2021), decision LCA (D-LCA) (Frischknecht and Stucki, 2010), anticipatory LCA (N-LCA) (Wender et al., 2014), prospective LCA (P-LCA) (Langkau et al., 2023), scenario-based LCA (SLCA) (Rossi et al., 2023) and integrated LCA (I-LCA) (Quest et al., 2022). Environmental, social, and economic integration in LCA becomes a life-cycle sustainability assessment (LCSA) (Larsen et al., 2022). However, in general, most of the mentioned LCA approaches fall into the category of C-LCA due to their focus on change and the impact resulting from future decisions, policies, or scenarios. In contrast, A-LCA focuses more on a specific and detailed assessment of the environmental impact of a particular product or process without considering changes caused by external decisions or policies. 

The selection between A-LCA and C-LCA is contingent upon the objectives of the research and the particular inquiries that require addressing. The A-LCA offers a comprehensive analysis of present environmental stresses, while the C-LCA furnishes insightful perspectives regarding the potential implications of actions or alterations on future environmental conditions stresses. Both entities play a vital role in decreasing the environmental impact and developing sustainable policies. By understanding these differences, researchers, policymakers, and LCA practitioners can choose the approach that best suits their analytical objectives, providing a static picture of environmental impacts (A-LCA) or understanding the dynamic impact of changes in the system (C-LCA). 

Another approach or mode of LCA that was overlooked by Guinée et al. (2018) observation is dynamic LCA, which combines system dynamics (SD) and LCA. Combining SD with LCA needs to be done to overcome the limitations of traditional LCA and produce a more comprehensive and accurate environmental analysis. Traditional LCAs tend to be static and do not consider dynamic changes in the systems being analysed (Zhai et al., 2022; McAvoy et al., 2021). Therefore, they can capture fewer environmental, technological, and policy changes over time. By incorporating SD, a dynamic approach capable of modeling change over time, LCAs can be more realistic in evaluating long-term environmental impacts. In addition, SD enables modeling of complex cause-and-effect relationships and feedback loops that often occur in environmental and industrial systems, thus helping to identify how changes in one part of the system may affect other parts. McAvoy et al. (2021) highlighted that the combination prevents overestimating impacts when technologies change over time and underestimating impacts due to unintended consequences.

5.3. Frameworks of LCA

Due to the complex system, researchers tried to simplify the frameworks of LCA by proposing the main significant parameters. Athanasopoulou et al. (2018) proposed a well-to-wheel (WTW) framework to investigate the emission contribution of ICEV and BEV. The WTW framework encompasses the whole process of the energy flow from extraction to vehicle utilisation. They proposed an approach to evaluate the electricity and fuel consumption for BEV and ICEV, respectively. Almost similar to Zheng and Peng (2021), Athanasopoulou et al. (2018) assessed the life-cycle emission (CO2) of EV and ICEV by considering CO2 emissions from the overall process of energy flow. However, Zheng and Peng (2021) added the cycle of the vehicle body to the analysis. Regarding emissions and energy efficiency during the cycle of vehicle body, the process can be divided into three main phases: manufacture, maintenance, and recycling. 

To comprehensively assess the environmental impact, it is important to analyse the energy consumption pattern of EVs during their operational period. Several factors influence the required energy for driving. Most studies focused on the utilisation factor that affects the LCA. Egede et al. (2015) used LCA for quantitative ecological assessment by proposing the LCA framework for EV by considering internal and external factors. Both internal and external factors were investigated as primary factors to examine the life-cycle. The external factor consists of the user, the surrounding conditions, and the infrastructure. The EV user influences the energy consumption through the driving style and charging behaviour, which leads the environmental impact (Miotti et al., 2021). A more aggressive driving style leads to a higher energy consumption (Szumska and Jurecki, 2020). The climate (ambient temperature, humidity) influences the energy consumption (Al-Wreikat et al., 2022, Skuza and Jurecki, 2022). Topography and type of road are also significant factors for energy consumption which affect speed and traction of the vehicle (Liu et al., 2017). 

Ahmadi (2019) presented a comprehensive work by combining life-cycle emission and cost among EVs (HEV, PHEV, BEV, and FCEV) and compared with ICEV as the base case. The outcome of the works was indicated with the total LCC. The environmental impact is one of the aspects of consideration before it is quantified to LCC. Like other researchers, Ahmadi (2019) used mostly internal influencing factors to analyse life-cycle emission, which differs from Egede et al. (2015). An external influencing factor was assumed to be an urban environment with similar annual mileage and lifetime. In this analysis, the lifetime factor shall be the operational lifetime. EV and ICEV operational lifetimes could span different periods. In addition to that, the lifetime EV and ICEV. Therefore, both of vehicles can be assessed on equal parameters. For example, ICEV has more lifetime than EV, then EV shall be levelized to ICEV lifetime by additional treatment, i.e., refurbishment. 

Similar to Li et al. (2016), Egede et al. (2015) proposed the factors that influence the energy consumption of EVs with more detailed parameters. They categorised it into six main influencing factors. The six influencing factors for investigating EV energy consumption are technology/vehicle, driver, measurement, travel type, artificial environment, and natural environment. However, Li et al. (2016) have measurement factors that affect the energy consumption of EVs. The measurement factor in EV energy consumption appears to focus on how testing and data collection methods impact the analysis of energy usage. The measurement factor emphasises the need for precise, standardised, and comprehensive methods to evaluate EV energy consumption accurately. Each aspect (engine warmup, route planning, test procedures, state of charge (SOC), and variability) plays a role in how consumption data is gathered and interpreted, ultimately influencing how well the results reflect real-world usage.

Regarding the measurement factor, Lyu et al. (2021) reviewed various measurement methods. They reviewed various measuring methods of emissions, including tunnel, laboratory, and on-road measurements. Then, the criteria were summarised by considering the vehicle operating features and road characteristics, including assessing automobile emissions at different road segments and intersections.

5.4. Significant Findings of Environmental Impacts

The environmental impact of various vehicle technologies has been extensively studied, with notable differences in outcomes based on factors such as electricity grids, manufacturing processes, and regional contexts. Girardi et al. (2015) conducted an LCA comparing EV and ICEV in Italy. Their findings revealed that the life-cycle impacts of EV were lower than ICEVs. The study highlighted the importance of a high-efficiency electricity grid in reducing the life-cycle impacts of EVs. Like previous studies, Tagliaferri et al. (2016) focused on GHG emissions during the life-cycle of BEV compared to ICEV. Their study emphasised the environmental challenges of the BEV manufacturing phase, particularly the high impact caused by the extensive use of metals in battery packs. Despite this, the life-cycle GHG emissions of BEVs were found to be lower than ICEVs, reinforcing the environmental advantages of BEVs when operational emissions are considered.

The influence of energy grids on vehicle LCA was also further explored by Athanasopoulou et al. (2018) in a European context. They found that the life-cycle CO₂ emissions of BEV were lower than those of ICEV when the electricity grid relies heavily on renewable energy and nuclear power. However, this trend reverses when the grid depends more on oil and coal, underscoring the regional variability in EV sustainability. This finding emphasises the need for cleaner energy sources to maximise the environmental benefits of BEV.

In the Czech Republic and Poland, Burchart-Korol et al. (2018) provided a comparative analysis of EV and ICEV, focusing on several environmental impact categories. Their results showed that EV generally have greater life-cycle GHG emissions than ICEVs, primarily due to the carbon-intensive electricity grids. However, they also noted that renewable electricity significantly reduces the environmental impacts of EV, highlighting the role of energy grid decarbonisation in improving their sustainability. Pipitone et al. (2021) examined environmental impacts in Europe, identifying significant challenges for BEVs. While BEVs exhibit a ~60% reduction in global warming potential compared to ICEVs, they show doubled impacts in acidification and particulate matter (PM) formation.

China is the most significant growth of EV. Yu et al. (2018) conducted a detailed LCA in South China comparing EV and ICEV, revealing that EV have greater impacts. However, their study also emphasises the potential for optimising the electricity grid and increasing battery energy density to reduce emissions significantly. Qiao et al. (2019) focused on GHG emissions in China, reporting that the life-cycle GHG emissions of EVs are 18% lower than ICEVs. Despite rapid reductions in the WTW emissions phase of EVs, challenges remain in addressing the cradle-to-gate emissions phase, which may impede EVs from fully realizing their environmental potential. Then, Qiao et al. (2020) expanded on their earlier findings by examining LCC and GHG emissions. They found that EVs have an LCC higher than ICEVs, while their life-cycle GHG emissions are lower than ICEVs. The study highlighted the potential of battery recycling and pilot projects to reduce LCC and emissions, though further advancements are needed to make these solutions viable at scale. Zheng and Peng (2021) explored the influence of energy sources on CO₂ emissions in China. Their findings indicate that BEVs exhibit higher CO₂ emissions than ICEVs in regions dominated by coal-fired power plants. However, when the carbon intensity of electricity drops below ~320 g/kWh, BEV become more environmentally favorable than ICEV. Shang et al. (2024) explored GHG emissions and air pollution in China for EV and ICEV. They found that EVs contribute to lower emissions of CO₂, volatile organic compounds (VOCs), and nitrogen oxides (NOx) compared to ICEVs. However, they also noted that EVs result in higher emissions of PM2.5 and SO₂, which highlights the need for advancements in grid sustainability and pollution control in EV production and operation.

In America region, Bicer and Dincer (2018) extended the comparison of ICEV to alternative fuel vehicles, including hydrogen-powered ICEV (H₂). Their study in Canada highlighted that BEV and HEV have higher human toxicity and acidification values due to manufacturing and maintenance processes. Interestingly, hydrogen-powered ICEV emerged as the most eco-friendly option, owing to their high energy density and minimal fuel consumption. Bauer et al. (2018) examined the energy and GHG emissions of BEV, HEV, and ICEV in New York, demonstrating that EV reduced GHG emissions by an impressive 73% compared to ICEV. This study underscores the importance of cleaner energy grids in amplifying the environmental advantages of EV. Ahmadi (2019) compared various vehicle technologies in the USA, including ICEV, HEV, FCEV, BEV, and PHEV, focusing on emissions and cost. Their findings revealed that FCEVs and BEVs have the most significant improvements in air quality. However, HEV demonstrated the lowest LCC, making them an economical choice. The study also highlighted increased carbon damage costs associated with the greater penetration of HEV and BEV, emphasising the need for further policy interventions. Challa et al. (2022) focused on the GHG emissions of EVs and ICEVs in the United States. They found that EVs have lower life-cycle GHG emissions than ICEVs under most conditions. The decline was attributed to the substitution of nuclear power with natural gas plants and ongoing initiatives aimed at grid decarbonisation. 

Several studies explored for multiple regions. Peng et al. (2018) expanded their analysis across multiple regions, including China, the USA, Japan, Canada, and the EU, focusing on the geographic variability of GHG emissions. Their results confirm that BEVs consistently have lower GHG emissions than ICEVs, but the degree of reduction depends heavily on the carbon intensity of the regional electricity grid. The study also notes that advancements in low-carbon electricity further enhance the environmental benefits of EVs. Kawamoto et al. (2019) conducted a multi-regional analysis in Australia, China, the EU, Japan, and the USA, comparing the life-cycle CO₂ emissions of ICEV and BEV. They found that BEVs have lower life-cycle CO₂ emissions than ICEVs when powered by renewable energy or low-carbon power plants. However, when accounting for emissions from battery production, BEVs can exhibit higher life-cycle CO₂ emissions, especially when driving distances exceed 160,000 km. Franzò and Nasca (2021) analysed the life-cycle CO₂ emissions of EVs compared to ICEVs. Their study concluded that the life-cycle CO₂ of EVs are consistently lower than that of ICEVs across all scenarios. They emphasised that geographical location significantly influences CO₂ emissions due to differences in energy generation methods. Furthermore, the use phase of EVs has the most considerable impact on their CO₂ emissions, underlining the importance of sustainable energy sources for charging EVs.

In the Southeast Asia region, Veza et al. (2023) conducted their research in Indonesia, comparing the environmental impacts of BEV, HEV, PHEV, and ICEV. Their findings highlighted that EVs exhibit the lowest CO and CO₂ emissions among the studied vehicles. However, EVs were also shown to produce higher levels of NO_x, N₂O, SO₂, and PM₁₀ due to the reliance on fossil fuels for electricity generation. This suggests that the environmental benefits of EVs can be amplified with cleaner electricity sources. The following tables summarise the LCA's significant findings of environmental impact. Error! Not a valid bookmark self-reference. shows the comparison of the LCA of ICEV and EV in various regions.

6.1. Conclusions

The bibliometric analysis highlighted the trends of LCA research for EVs, transitioning from foundational studies on environmental impact to recent advancements in battery technology, materials, and recycling. However, ICEV is still an interesting discussion in the world of vehicles. These trends underscore the need for future research to prioritise scalable recycling technologies, resource-efficient battery designs, and context-specific LCA frameworks to support the sustainable growth of EVs. Therefore, potential topics such as social and economic aspects indicate a growing interdisciplinary focus aimed at aligning LCA research with global sustainability goals. This literature review shows that EVs have lower environmental impacts than ICEVs, particularly in terms of GHG emissions during the operational phase. However, the environmental benefits of EVs are highly dependent on the electricity grid; reliance on fossil fuel-based electricity can diminish these advantages. Conversely, the production phase of EV, particularly concerning batteries, demonstrates greater environmental impacts in comparison to ICEV owing to the utilization of rare materials and energy-intensive manufacturing processes. Nevertheless, the operational phase benefits of EVs often offset their production-related impacts. To maximise the environmental advantages of EVs, a transition to renewable energy sources or clean power plants and advancements in battery recycling technologies are critical. The shift from ICEV to EV has the potential to deliver significant environmental benefits. However, it requires a holistic approach that considers the entire life-cycle of the vehicles and the development of sustainable supporting infrastructure toward a low-emission transportation system.

6.2. Future Works

Attention must be paid to the fact that LCA for EV is a tool to evaluate a product only from one side of the viewpoint (environmental). To promote and expand the ecosystem of EV, it shall be evaluated in multiple aspects, such as economic, social, and technological. Therefore, integrating socio-economic factors, such as public health benefits and economic implications, and advancing recycling innovations will ensure a holistic understanding of EV sustainability. In addition, future research should focus on developing a comprehensive LCA framework that incorporates cradle-to-cradle methodologies to promote material circularity and resource recovery for all vehicle types. Regarding EV technology, recent studies have not widely discussed LCA for FCEV. It allows for investigation and comparison with other EVs. There is no single method for performing LCA, particularly for facing the dynamic of vehicle technology with high complexity due to multiple aspects considered. Hence, by integrating system dynamics, LCA can better account for technological advancements, shifts in energy policies, and evolving market behaviors, providing a more realistic and forward-looking assessment. This dynamic approach enables researchers and policymakers to identify long-term trends, evaluate the effectiveness of mitigation strategies, design adaptive solutions for sustainable development, and enabling actionable policy recommendations for a global transition to cleaner transportation.

We would like to express our profound appreciation to the PLN for their invaluable support in facilitating this research. Their substantial donations have facilitated the execution of this study and greatly contributed to its successful completion.

Author Contributions

Muhammad Idris: Conceptualization, Project administration, Writing – review and editing; Iwa Garniwa: Methodology, Supervision; Tri Edhi Budhi Soesilo: Methodology, Supervision; Suyud Warno Utomo: Supervision, Writing – review and editing; Muhammad Zacky Asyari: Validation, Project administration.

Conflict of Interest

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