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Article

Assessing the Deployment of Electric Aircraft from Energy, Environmental, and Economic Perspectives

by
Ye Liang
1,
Wei Zhang
1 and
Chengjiang Li
2,3,*
1
East China Regional Air Traffic Management Bureau of Civil Aviation Administration of China, Shanghai 200335, China
2
School of Management, Guizhou University, Guiyang 550025, China
3
School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5453; https://doi.org/10.3390/su17125453
Submission received: 22 April 2025 / Revised: 4 June 2025 / Accepted: 10 June 2025 / Published: 13 June 2025
(This article belongs to the Special Issue Energy Saving and Emission Reduction from Green Transportation)
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Abstract

:
Electric aircraft represent a promising pathway for decarbonizing the aviation sector, offering significant potential for sustainable transformation in air transportation. This study develops a life cycle assessment–multi-criteria decision-making analytical framework to evaluate the developmental prospects of electric aircraft. This study employs life cycle assessment (LCA) to evaluate electric aircraft development and integrates multi-criteria decision making (MCDM) to assess their potential. First, LCA and life cycle cost (LCC) are applied to compare the energy consumption, environmental impact, and economic costs of electric versus conventional aircraft. These results then inform MCDM, with the system boundary guiding indicator selection. The results show that electric aircraft consume slightly more energy than conventional aircraft, and the pollutant emissions are only 50% of that of conventional aircraft, thereby significantly reducing life cycle pollutant emissions and exhibiting high development potential. The cost of conventional aircraft significantly exceeds that of electric aircraft. Total energy consumption, global warming potential, and fuel usage cost are essential for electric aircraft development. This study provides valuable insights for stakeholders seeking to advance sustainable aviation solutions while addressing complex technical and economic considerations.

1. Introduction

Amid accelerating economic globalization, soaring air travel demand has triggered a sharp rise in greenhouse gas (GHG) emissions in aviation. Aviation now ranks third among transportation, surpassed only by the road and maritime sectors [1,2]. The primary contributor to these emissions is carbon dioxide (CO2), which results from the combustion of fossil-based jet fuel. CO2 emissions from fossil-derived jet fuel combustion dominate aviation’s climate impact, constituting nearly 87% of sectoral GHGs [3,4]. Although carbon dioxide contributes to long-term climate warming, engine operation also emits nitrogen oxides (NOx), particulate matter, and other compounds that increase atmospheric warming and public health risks [5,6]. According to data from the International Energy Agency (IEA), aviation accounted for about 2.5% of global CO2 in 2023, highlighting the sector’s growing role in the climate challenge [7]. Projections indicate that global air traffic will increase by 3.6% every year, doubling every 16 years if current trends persist [8,9]. Although advancements in aircraft technology have improved fuel efficiency by approximately 25% with each new generation of planes, these gains are insufficient to offset the rapid growth in air travel demand [10]. Consequently, aviation-related CO2 and NOx emissions could triple by 2050 without substantial intervention, positioning the industry as one of the most significant long-term contributors to global emissions [11]. Statistical data further highlights the urgency of this issue. Between 2010 and 2022, the sector’s CO2 emissions increased by 3.6% every year, reflecting increased passenger numbers and cargo transport [12]. In absolute terms, global aviation emissions reached a staggering 1.027 billion metric tons in recent years, marking a 52% increase compared to the 674 million metric tons recorded in 2000 [13,14]. This upward trajectory is exacerbated by the rise of low-cost carriers, expanding middle-class populations in emerging economies, and the growing reliance on air freight for global trade. Given these trends, the need for a low-carbon transformation in the aviation industry is more pressing than ever. While incremental improvements in fuel efficiency and operational optimizations provide some relief, they are inadequate to meet international climate targets. Therefore, exploring and implementing effective carbon reduction strategies has become a critical priority for policymakers, industry leaders, and researchers alike. To address the environmental needs of the aviation industry, integrated solutions through the coordinated implementation of advanced technologies, policy reforms, and changing user habits are needed to achieve environmentally viable industry development.
To address the growing challenge of aviation-related carbon emissions, nations and international organizations have intensified their efforts to develop comprehensive decarbonization strategies, implementing a range of policy measures and industry initiatives to accelerate the sector’s low-carbon transition [15,16,17] (see Figure 1). The European Union’s ‘Fit 55’ for 2021 sets binding climate targets to reduce GHGs by 55% by 2030. Specific measures for aviation include phasing out free carbon credits, which projections suggest could meet 63% of the industry’s energy needs by 2050 [18]. Building on this momentum, the UK government unveiled its ‘Jet Zero’ in 2022, mapping out a route to achieve net zero emissions for aviation through a combination of cutting-edge technological developments, substantial increases in SAF production and adoption, and comprehensive operational efficiency enhancements across all aspects of air transport [19]. The International Air Transport Association further reinforced these commitments in 2021 by announcing an industry-wide pledge to achieve decarbonization before 2050, with a strategic focus on four key pillars: the accelerated development and deployment of next-generation aircraft technologies, the rapid scaling-up of SAF production and utilization to account for an estimated 65% of total emission reductions, the implementation of robust carbon offset and removal mechanisms, and systematic improvements in operational efficiency through optimized air traffic management and flight procedures [20]. These governmental and industry-led efforts are being complemented by innovative, collaborative programs such as the Clean Sky initiative, a public–private partnership that fosters cross-sector cooperation in developing breakthrough technologies for cleaner aircraft. The Carbon Offsetting and Mitigation Framework for International Aviation has implemented an internationally coordinated economic approach aimed at maintaining record levels of GHGs in 2019. While these multifaceted approaches demonstrate significant progress, the aviation sector continues to face substantial challenges in its decarbonization journey, including the current high costs and limited availability of sustainable aviation fuels, the need for massive infrastructure investments to support new propulsion technologies, and the complexity of aligning diverse national and regional regulatory frameworks, all of which underscore the critical importance of sustained international cooperation, substantial investments, and coordinated policy support to ensure the successful transition to a sustainable aviation future [21,22,23,24].
With the continued rapid development of the aviation industry and significant advances in emerging technologies, the shift from traditional fossil fuels to cleaner energy sources is particularly urgent [25,26]. In this context, electrification is a potential path for the aviation industry [27,28,29] that could contribute to reducing greenhouse gas emissions [20,30,31]. Electric aircraft, replacing conventional aircraft engines, are a promising path to curb the impact of climate change in aviation [32,33]. Electric aircraft do not directly produce carbon emissions during flight, significantly reducing the CO2 emissions during their life cycle [34,35]. Electric aircraft have zero-emission advantages, low cost, and low noise, but their application scale is still less than 1% [13], and the difference between them and conventional aircraft needs to be explored. Therefore, to further promote electric aircraft in a broader range, it is necessary to develop a holistic analysis framework to explore the development potential of electric aircraft and the factors that influence their development, thereby providing better policy solutions and maximizing the carbon emission reduction potential.

2. Literature Review

Most previous studies that have comprehensively analyzed the land and maritime domains have predominantly employed life cycle assessment (LCA) to quantify the energy consumption and pollutant emissions of the research objects [6,14]. These studies are typically conducted from the environmental perspective or the economic perspective, with some focusing on a single dimension, while others incorporate multiple dimensions [36,37,38]. Alternative fuel vehicles are generally assessed from the energy and environment perspectives [39]. The life cycle costs of alternative fuel ships are evaluated from the economic perspective [40]. LCA is a structured analytical framework designed to quantify the ecological footprint of products across the value chain. This approach involves a cradle-to-grave assessment, beginning with resource procurement, moving through the manufacturing and utilization phases, and ending with end-of-life management [41,42]. LCA is a mature method that is applied to relevant research in transportation, such as vehicles and ships [43,44,45]. Life cycle assessment often involves multiple dimensions, such as energy, environment, economy, etc. According to the energy dimension, the energy consumption of the research objects from cradle to grave was assessed [46,47]. For example, by comparing electric vehicles with conventional vehicles, it has been found that the energy consumption of electric vehicles is 60–80% lower than that of conventional vehicles [48]. In the environmental dimension, relevant software and tools have been used to calculate pollutant emissions in the life cycle and convert them into potential environmental impact indicators to compare the environmental impact differences among different research objects [49]. Almost zero carbon emissions are generated by electric aircraft. Still, carbon emissions in other stages (such as battery production, etc.) also produce significant CO2 emissions [12]. In addition, the negative impact of battery scrapping and recycling on the environment is higher than that of conventional aircraft [11], but there are few studies on its quantitative analysis. The life cycle cost (LCC) method [50] is usually adopted to calculate the cost of various aspects of the research object.
In comprehensive analyses of aircraft, multi-criteria decision making (MCDM) has emerged as a predominant research methodology, enabling qualitative or quantitative policy analysis from a macro perspective [51,52,53]. This approach addresses complex decision-making challenges, such as planning selection, resource allocation optimization, and policy assessment, with evaluation dimensions extending from single-objective paradigms (economic benefit maximization or cost minimization) to multi-dimensional synergistic optimization, where typical metrics include economic costs and environmental impacts [53,54,55]. The current MCDM technical framework encompasses the weighted sum model, the analytic hierarchy process (AHP) and fuzzy AHP, the technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS, the preference ranking organization method, multi-objective optimization models, and so on [28,56,57,58]. Although these methods exhibit significant differences in application scenarios and algorithmic characteristics, no unified methodological paradigm has been established in academia [58]. Consequently, cross-validation through multiple methods is often adopted to enhance conclusion robustness [59]. The construction of evaluation indicator systems directly determines the validity of MCDM output rankings [60]. However, existing studies frequently suffer from subjective indicator selection and insufficient systematization. This leads to divergent conclusions due to varying metric choices, a phenomenon that is observed even within identical national or regional contexts.
Above all, this study advances theoretical understanding by proposing a novel analytical framework to evaluate the development potential of electric aircraft through an integrated energy–environment–economy lens. Building upon systems theory, this study developed an integrated evaluation architecture by combining the life cycle assessment with multi-criteria decision analysis (LCA-MCDM) through a novel synthesis and promoted the structured analysis of the operational indicators of electric aviation. The developed framework facilitates the identification of critical success factors in electric aviation advancement and establishes a methodological foundation for assessing emerging aircraft technologies. Through the rigorous analysis of key development drivers, this study provides actionable strategic guidance for industry stakeholders to prioritize innovation pathways and allocate research resources effectively. Furthermore, the research outcomes offer policymakers a structured decision-support tool to formulate targeted interventions that accelerate the transition toward sustainable aviation technologies.

3. Methodology

To comprehensively analyze and compare electric aircraft with conventional aircraft in transportation before developing strategies, this study uses system theory and novel integrated life cycle assessment and multi-criteria decision making (LCA-MCDM) methods to quantify the development potential and influencing factors of electric aircraft. The proposed method ensures a comprehensive and systematic evaluation of electric and conventional aircraft, identifies the development potential of electric aircraft and the disadvantages that need to be overcome, and provides decarbonization development suggestions for the world’s aviation. Figure 2 shows the detailed framework of this study.

3.1. Life Cycle Assessment

Life cycle assessment (LCA) adheres to the ISO 14040 [61] series of international standards, which defines four main steps (Figure 1). First, it clarifies the reasons and intentions for conducting an LCA, and scope definition describes the functional units, system boundaries, etc., of the electric and conventional aircraft. Second, it quantifies resource and energy consumption and emissions to the environment throughout the life cycle stages of the aircraft. Third, it comprehensively assesses the potential environmental impacts of the research object on the environment throughout its life cycle. Finally, there is the interpretation of the life cycle assessment results. According to the objectives and scope of LCA, the results obtained in the previous stages are comprehensively assessed to identify and understand the environmental impacts of the product or service throughout its life cycle, and the corresponding conclusions and recommendations are presented.

3.1.1. Objective and Scope Definition

Drawing on the relevant domestic and international literature and considering the completeness and accuracy of aircraft data, this study selects the existing electric and conventional aircraft in the domestic and international markets as research objects. Specifically, the Alice all-electric aircraft from Israel was chosen as the electric aircraft, and the Airbus A320 was selected as the conventional aircraft (Detailed in Table 1). Due to the differences between the two models, the unified passenger capacity in this study was 180 people for comparison to make the results more comparative. The relevant data in this study are derived from the IEA, the database of GaBi software, aircraft manufacturer websites, and statistical data, with incorporated primary data from industry partners.
The life cycle of aircraft includes the fuel cycle and the aircraft cycle [43,44,45]. Raw material acquisition, raw material transportation, fuel/electricity production, fuel/electricity transportation, and fuel/electricity use are described as fuel cycle. The aircraft cycle includes infrastructure, raw material acquisition, aircraft manufacturing, aircraft operation (fuel usage), and aircraft scrape and recycling. Energy consumption, emissions, and cost are calculated based on the division of fuel cycles and vehicle cycles. In this study, according to the data from International Civil Aviation Organization, the life span of an aircraft is 25 years, which is about 50 million kilometers. In this study, the technical routes of electricity production and jet fuel production are shown in Figure 3.

3.1.2. Life Cycle Energy Consumption and Emissions Assessment

The energy and environmental assessment of the electric and conventional aircraft are comprehensively assessed based on GaBi 9.2.1 software (Detained steps are shown in Figure 4). Fuel and aircraft cycles are included to accurately quantify the energy consumption and pollution emissions of each aircraft. The Gabi software was originally developed by the German company PE International (later renamed Thinkstep) and is now affiliated with Sphera Solutions. It is a professional tool dedicated to life cycle assessment that is widely used in the product environmental impact assessment, sustainability management, and low-carbon economy fields. It also supports users in building a full life cycle model of products or services, covering all links from raw material extraction, production, transportation, and use to waste recycling.
The consumption and emissions of aircraft were evaluated by utilizing Gabi software, which involved inputting life cycle inventory data for analysis. The pollutant emissions include carbon dioxide (CO2), volatile organic compounds (VOC), nitrogen oxides (NOx), sulfur oxides (SOx), particulate matter (PM), and methane (CH4). These pollutants have significant environmental impacts, ranging from contributing to global warming and air pollution to affecting ecosystems.

3.1.3. Life Cycle Impact Assessment

Life cycle impact assessment (LCIA) quantitatively and comprehensively evaluates and describes environmental impact indicators in inventory analysis. SETAC, ISO, and the UK EPA classify environmental impacts in three steps: classification and characterization, normalization, and quantification. In this study, based on the localized data situation, combined with China’s national conditions, the CML2001 method, which is recognized as one of the most complete and widely used mid-point methods around the globe, is used for environmental impact assessment. This study selects eight potential environmental impact indicators, including the acidification potential (AP), eutrophication potential (EP), freshwater aquatic ecotoxicity potential (FAETP), global warming potential (GWP), human toxic potential (HTP), marine aquatic ecotoxicity potential (MAETP), photochemical ozone creation potential (POCP), and terrestric ecotoxicity potential (TETP) (detailed in Figure 5). The selected potential environmental impact indicators could comprehensively quantify the effects of the electric aircraft and conventional aircraft on human health, the atmosphere, soil, and water.

3.2. Life Cycle Cost Analysis

Life Cycle Cost Analysis (LCCA) is a methodology for estimating all relevant life-cycle costs. LCCA is widely used in engineering and economic analyses, including the assessment of aircraft. This study considers the cost of infrastructure, manufacturing, fuel usage, maintenance, and the scrape and recycling stages of electric aircraft and conventional aircraft (shown in Figure 6).

3.3. Multi-Criteria Decision-Making

As a systematic analytical framework, multi-criteria decision analysis (MCDM) delivers strategic guidance for different interest groups involved in complex choice scenarios. This strategy divides complex tasks into practical sub-issues, providing policymakers with a systematic approach to prioritizing competing options. This study aims to identify the influencing factors in electric aircraft development, quantify their relative importance, and provide actionable insights for decision-makers in deploying electric aircraft technologies. By integrating LCA, LCIA, and LCCA, an LCA-MCDM comprehensive analysis model is established. In the proposed model, the CRITIC calculates the weight values for evaluation indicators and ranks the influencing factors accordingly.
The Analytical Hierarchy Process (AHP) is a universal and applicable tool for decision-making based on multiple criteria. It translates qualitative problems into quantifiable analyses by subdividing complex issues into structured, interrelated multilevel frameworks. This systematic approach helps policymakers to effectively assess and prioritize options. Building upon the factors derived from LCA, LCIA, and LCCA, this study establishes an evaluation model that primarily considers energy, environmental, and economic as its hierarchy (Figure 7).
In Figure 7, the objective is presented in the top box (goal). At the same time, the evaluation criteria are displayed in the target layer and hierarchy. Considering energy, select the energy consumption of the fuel cycle (A1), the energy consumption of the aircraft cycle (A2), the total energy consumption (A3), and the proportion of energy consumption (A4). Considering the environment, select the human toxic potential (B1), global warming potential (B2), acidification potential (B3), and marine aquatic ecotoxicity potential (B4). Considering economics, select the infrastructure cost (C1), manuscript cost (C2), fuel usage cost (C3), and scrap and recycling (C4).
The hierarchical element relationship is quantified by using paired evaluation to determine the metric weight. CRITIC uses variability metrics for data-driven weight assignment. Its fundamental concept involves two key metrics: variability (contrast intensity) and conflict. Comparative strength quantification uses a statistical dispersion measure, where an increase in the dispersion value corresponds to an increase in data divergence, thus increasing the importance of the parameter. Inter-indicator covariant analysis evaluates inverse adjustment weights by correlation amplitude, thereby reducing statistical interdependence to produce amplified influence coefficients. The contrast intensity and conflict metrics are multiplied during weight calculation, followed by normalization to derive the final weights. The detailed computational procedure is shown in Appendix B.

4. Results and Discussion

The data collection process follows the principle of collecting primary data, and if the primary statement is inaccurate or cannot be collected, secondary data from the literature are cited. Secondary data include government statistical releases, national accounts matrices, legislative frameworks, and official policy documents.

4.1. Life Cycle Assessment Results

4.1.1. Life Cycle Energy Consumption and Emissions

According to LCA, the energy consumption of electric aircraft and conventional aircraft includes both the fuel and the aircraft cycle. The material consumption inventory is shown in detail in Appendix A, which contains a list of input and output data involved in all phases. The energy consumption and pollutant emissions of the aircraft could be calculated by importing all the input and output data in the attached table into the GaBi 9.2.1 software for modeling. Figure 8 shows the results for the fuel cycle, aircraft cycle, and total energy consumption, showing the percentage of energy consumed at different stages.
As shown in Figure 8, considering the fuel cycle, the energy consumption of conventional aircraft is much higher than that of electric aircraft, measuring 5.6 times higher. Therefore, the energy savings potential from fuel production for conventional aircraft is limited. In contrast, the fuel cycle energy consumption of electric aircraft is lower, and the energy consumption of the electric aircraft’s fuel cycle is reduced by 82% compared to that of conventional aircraft, reflecting its energy-saving advantage. In terms of the aircraft cycle, there is little difference between the body fabrication of an electric aircraft and a conventional aircraft. The aircraft cycle energy consumption of electric aircraft demonstrates a slightly greater advantage than that of conventional aircraft. The body manufacturing process of electric aircraft involves the manufacturing of batteries, etc., which consume a higher amount of energy, making its energy consumption higher than that of conventional aircraft. The percentage of aircraft cycle energy consumption for electric and conventional aircraft is 53.3% and 46.7%, respectively. From the perspective of the aircraft cycle, the fuselage manufacturing segment of the electric aircraft does not have an energy-saving advantage. According to the total energy consumption, conventional aircraft have lower total energy consumption than electric aircraft. The energy consumption of the fuel cycle is much lower than the airplane cycle for both electric and conventional aircraft. This leads to the finding that although the fuel cycle energy consumption of an electric airplane has a greater advantage, it does not affect the overall energy consumption results. Therefore, in order to optimize the energy performance benefits of electrified aviation, targeted improvements are required in propulsion cycle efficiency metrics, particularly during energy consumption during the aircraft cycle.

4.1.2. Life Cycle Environment Impact

After modeling in GaBi software, the life cycle emissions of different aircraft with different emissions are calculated. Regarding life cycle impact assessment, this study considers six emissions, including CO2, VOC, NOX, SOX, PM, and CH4 (Figure 9). In terms of the emissions of the main pollutant, CO2, the emissions of electric aircraft are significantly lower than those of conventional aircraft, and electric aircraft are the best choice when only emissions are considered. The CO2 emissions of electric aircraft are 55% lower than those of conventional aircraft, reflecting a higher level of emission reduction. From an emissions perspective, CO2 emissions are much higher than other emissions. Among VOC, NOX, SOX, PM, and CH4, PM emissions are the smallest. Of these five emissions, electric aircraft reduce them by about 20% relative to conventional aircraft.
The life cycle environmental impacts of different aircraft were calculated after modeling according to GaBi software. For normalization and weighted assessment, the normalized values of CML2001-Jan.2016 in GaBi software were selected. Figure 10 shows the environmental impact results.
Electric aircraft have significantly lower environmental impacts than conventional aircraft, making them highly environmentally friendly. Among the eight potential environmental impacts assessed, the value of MAETP is much higher than that of the others. For electric vehicles compared to conventional vehicles, MAETP is reduced by about 60%. This substantial reduction highlights the lower toxicity risks associated with electric aircraft operations. Next are GWP and HTP, with reductions of 50–60%. These reductions indicate that electric aircraft contribute less to climate change and pose lower risks to human health compared to their conventional aircraft. Following these are FAETP, AP, and TETP. For FAETP, electric vehicles show a reduction of about 70% compared to conventional vehicles, suggesting a significantly lower impact on freshwater ecosystems. In terms of AP and TETP, electric vehicles reduce these impacts by about 50%, indicating lesser contributions to acid rain and terrestrial toxicity. Lastly, EP and POCP are the lowest among the assessed impact potentials. These lower values imply that electric aircraft have a minimal impact on water quality degradation, which is a key component of smog. Overall, conventional aircraft have a larger negative environmental impact, while electric aircraft can reduce the overall environmental impact by approximately 50%. This comprehensive reduction underscores the significant environmental benefits of transitioning to electric aircraft, making them a promising solution for sustainable aviation.

4.2. Life Cycle Cost Analysis Results

Based on the LCC, an assessment of the economics of an aircraft should include costs for all phases of its life cycle, including infrastructure, manufacturing, fuel usage, maintenance, and scrap and recycling costs for the selected aircraft (Table 2 and Figure 11).
As can be seen in Figure 10, electric aircraft are far more economical than conventional aircraft. In terms of infrastructure costs, the infrastructure construction cost of conventional aircraft is 10 times that of electric aircraft. In terms of aircraft manufacturing, due to electric aircraft involving the manufacture or purchase of batteries, the manufacturing cost of electric aircraft is three times that of conventional aircraft. In terms of fuel usage, at the same driving distance, the fuel use cost of electric aircraft and conventional aircraft is not much different. Conventional aircraft cost slightly more to operate than electric ones. The life cycle cost of electric aircraft is 13% lower than that of conventional aircraft. In terms of maintenance, the amount of maintenance is lower for conventional aircraft, but the price of a single maintenance session is higher. As a result, the maintenance cost of conventional aircraft is higher than that of electric aircraft. In terms of scrap and recycling, compared with conventional aircraft, electric vehicles undergo more battery scrap recycling, and the income related to recycling is slightly higher than that of conventional aircraft.
Based on the life cycle energy, environmental, and economic (3E) assessment, the energy consumption of electric aircraft is moderately higher than that of conventional aircraft. Still, the negative environmental impact of electric aircraft is much lower than that of conventional aircraft. From an economic perspective, electric aircraft exhibit significantly lower costs than conventional aircraft, demonstrating superior cost-effectiveness. Therefore, electric aircraft are a potential path for the decarbonization development of aviation.

4.3. Multi-Criteria Decision-Making Results

By applying the weighting criteria derived from the integrated assessment framework, this study systematically ranked the indicators to reflect their relative importance and contribution to the system, thus establishing a prioritized hierarchy of key factors influencing the decision-making process. The ranking results are shown in Figure 12.
The comprehensive analysis reveals significant variations in the relative importance of factors across different dimensions. From an energy perspective, A3 (total energy consumption) is the most substantial contributing factor. In contrast, A2 (aircraft cycle energy consumption) demonstrates the least influence, indicating that total energy consumption exerts a more pronounced systemic impact compared to other parameters, with variations in aircraft fuselage characteristics showing relatively marginal effects when contrasted against other operational phases. According to the environmental perspective, B2 (global warming potential) dominates as the primary determinant, whereas B3 (acidification potential) exhibits minimal influence, highlighting that carbon footprint considerations constitute the most critical environmental driver for development, particularly given the technology’s inherent emission reduction capabilities, which position it favorably within current regulatory landscapes focused on minimizing lifecycle carbon emissions. From an economic perspective, the analysis identifies C3 (fuel usage cost) as carrying the most significant weight among economic indicators, with C4 (scrap and recycling) showing the least significance, underscoring how operational cost dynamics at the fuel utilization stage affect overall economic viability and consequently become prioritized in decision-making processes, while other cost components demonstrate comparatively reduced influence on the comprehensive evaluation outcomes.

4.4. Sensitivity Analysis

The rapid development of technology and the emergence of new technologies significantly change the feasibility and competitiveness of electric aviation. This study explores the differentiated impact of the technological development path on the competitiveness of electric aviation by constructing a multi-level scenario system. Three scenarios were set up to study the priority ranking of electric and conventional aircraft development in different scenarios (Detailed in Table 3).
The first-level scenario is the optimistic scenario and the pessimistic scenario. The optimistic scenario is that the proportion of renewable energy is relatively high. The pessimistic scenario is that the proportion of renewable energy is relatively low. The reason for setting it up is that the proportion of renewable energy has continuously increased in recent years. The process of energy structure transformation directly determines the technical route choice for decarbonization in the aviation industry. Fluctuations in the penetration rate of renewable energy reshape the life cycle emission benefits of different power systems. The second-level scenario is the basic scenario and emerging technologies scenario. The reason for the setting is that battery technology is still evolving. As a key technical bottleneck for electric aviation, the breakthrough speed of energy density of power batteries and the industrialization process of new energy storage technologies (such as solid-state batteries and hydrogen fuel cells) are significantly uncertain. For this purpose, basic and emerging technology breakthrough scenarios are constructed to compare the differentiated development paths of incremental improvement and disruptive innovation. The third-level scenarios are short-haul flight scenarios and long-haul flight scenarios. Considering the battery’s capacity, the distance’s length affects the usage result. The range limitation causes the economy of electric aviation to show a significant divergence in different transportation distance scenarios. The short-haul and long-haul flight scenarios are distinguished to reveal the gradient characteristics of market penetration. The results are shown in Figure 13.
When emerging technologies develop, the priority of electric aircraft is higher than that of conventional aircraft, such as in scenarios I3, I4, and D3. However, renewable energy also affects the power supply. Therefore, in scenario D3 (where the proportion of renewable energy is low, but the technology is well developed), during long-haul flights, the priority of conventional aircraft is higher than that of electric aircraft.
In basic scenarios, electric aircraft are still disadvantaged in long-haul flights. When the proportion of renewable energy is high, electric aircraft have a greater advantage in short-haul flights than traditional aircraft, such as in scenarios I1 and D3. This is because when the proportion of renewable energy is higher, the environmental impact and electricity consumption costs are smaller than those of conventional aircraft.
Above all, the application priority of electric aircraft is dynamically influenced by three factors: technological maturity, energy structure, and flight distance, and presents differentiated characteristics in different scenarios. Electric aircraft have more advantages regarding a high proportion of renewable energy, good technological development, and short-haul flight. In the future, to overcome deficiencies such as battery capacity, future technological breakthroughs need to focus on multi-dimensional innovation.

5. Conclusions and Policy Implications

This study established a comprehensive analysis framework using system theory to explore the development potential of electric aircraft and factors influencing their development. To comprehensively analyze and compare electric aircraft with conventional aircraft in transportation before developing strategies, this study proposed novel LCA-MCDM methods to quantify the development potential and influencing factors of electric aircraft. Energy consumption is calculated in the energy dimension. In the environmental dimension, pollutant emissions, including CO2, VOC, NOX, SOX, PM, and CH4, are calculated and converted into eight kinds of ecological impact potential for comparative analysis. The life cycle costs covering the infrastructure, manufacturing, fuel usage, maintenance, and scrap and recycling stages are calculated in the economic cost dimension. Finally, MCDM is applied to establish an impact factor system with LCA results. The impact factor is ranked to reveal the mechanism of impact of development factors. Electric aircraft have great potential for development on a larger scale and are faster than conventional aircraft under appropriate technological investment and fuel subsidy conditions. The specific conclusions are as follows:
  • In terms of energy consumption, electric aircraft consume slightly more energy than conventional aircraft. The aircraft cycle generation mainly caused by electric aircraft accounts for the majority of energy consumption, offsetting the advantage of the low energy consumption of the fuel cycle.
  • In terms of environmental impact, the pollutant emissions of electric aircraft are only 50% of that of conventional aircraft on average, which significantly reduces life cycle pollutant emissions and has high development potential. Electric aircraft have a much lower negative impact on the environment than conventional aircraft.
  • In terms of economic cost, the life cycle cost of conventional aircraft is significantly higher than that of electric aircraft. The infrastructure construction cost of conventional aircraft requires larger venues and other facilities than electric aircraft, resulting in relatively high costs. However, in the fuselage manufacturing stage, electric aircraft cost more than conventional aircraft because electric aircraft require a large number of batteries, resulting in higher costs.
  • The ranking results of factors reveal that total energy consumption emerges as the predominant determinant in shaping electric aircraft development from an energy standpoint. At the same time, global warming potential constitutes the most critical environmental consideration, and fuel usage cost represents the paramount economic factor that influences decision-making processes.
According to the results of the proposed LCA-MCDM model, the policy implications for electric aircraft in China are as follows:
  • Enhance scientific and technological innovation to reduce energy consumption and economic costs. Strengthen the breakthrough innovation of related technologies, including whole-machine research and development, the main control chip, three-power system, airborne transmission, etc., to speed up technical research. Encourage relevant enterprises, universities, and scientific research institutions to set up key laboratories, technological innovation centers, and other innovative research institutions and build an innovation ecological chain throughout the whole process of ‘basic research + technological research + industrialization of achievements’.
  • Speed up infrastructure construction. Focus on improving the general aviation airport network system, thereby increasing the construction density of general aviation airports in the central and western regions and upgrading the supporting systems, such as the road network power supply and low-altitude meteorological monitoring, simultaneously, as well as strengthening the function of peacetime and wartime conversion. An example of this goal is the second batch of demonstration projects of the ‘National Comprehensive Three-Dimensional Transport Network Plan’. Improve the take-off and landing infrastructure network system and increase the construction of general airports. Infrastructure, such as road networks and electric power, could be enhanced, low-altitude aviation meteorological monitoring facilities could be added, and peacetime and wartime conversion functions, such as general airports and landing sites, will be strengthened.
  • Make full use of cloud computing, artificial intelligence, and other technologies to lay out the low-altitude intelligence network of communication perception integration. Promote the construction of digital infrastructure; integrate satellite navigation, the Internet of Things, AI algorithms, and live-action three-digit digital twin technology;, and improve the corresponding speed of flight service systems and the processing capacity of various flight data.
  • Accelerate the standardization of electric aircraft. Improve the collaborative management mechanism, actively promote the optimization of the application and approval process of airline flight plans, and form a whole-process, traceable safety supervision system. Encourage domestic universities, research institutions, and enterprises to cooperate and participate in the research and development of domestic standards.
  • The noise of electric aircraft is reduced at the source through electric drive systems, new propulsion technologies, and intelligent control, which not only lowers noise but also reduces carbon emissions, which is in line with the global carbon neutrality goal. Promote upgrading regulations and standards, accelerate the green transformation of the aviation industry, and form replicable governance models.

Author Contributions

Y.L.: conceptualization, methodology, and writing—original draft; W.Z.: writing—original draft, investigation, and visualization; C.L.: writing—review and editing, visualization, formal analysis, and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Guizhou Office of Philosophy and Social Science Planning, China [grant number: 22GZQN20].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on request. Further inquiries can be directed to the corresponding author (chengjiang.li@utas.edu.au).

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.

Abbreviations

The following abbreviations are used in this manuscript:
A1Energy Consumption of Fuel Cycle
A2Energy Consumption of Aircraft Cycle
A3Total Energy Consumption
A4Proportion of Energy Consumption
AHPAnalytic Hierarchy Process
APAcidification Potential
B1Human Toxic Potential
B2Global Warming Potential
B3Acidification Potential
B4Marine Aquatic Ecotoxicity Potential
C1Infrastructure Cost
C2Manuscript Cost
C3Fuel Usage Cost
C4Scrap and Recycling
CH4Methane
CO2Carbon Dioxide
EPEutrophication Potential
FAETPFreshwater Aquatic Ecotoxicity Potential
GHGGreenhouse Gas
GWPGlobal Warming Potential
HTPHuman Toxicity Potential
IEAInternational Energy Agency
LCALife Cycle Assessment
LCIALife Cycle Impact Assessment
LCCLife Cycle Cost
MAETPMarine Aquatic Ecotoxicity Potential
MCDMMulti-Criteria Decision-Making
NOxNitrogen Oxides
POCPPhotochemical Ozone Creation Potential
PMParticulate Matter
SOxSulfur Oxides
TETPTerrestric Ecotoxicity Potential
TOPSISTechnique for Order Preference by Similarity to Ideal Solution

Appendix A. Material Consumption Inventory

Table A1. Analysis of the quality flow inventory of 1 kWh in China.
Table A1. Analysis of the quality flow inventory of 1 kWh in China.
Input/OutputItemValue
InputCrude Oil0.00348 kg
Coal0.306 kg
Natural Gas0.00326 kg
Uranium7.45 × 10−7 kg
Water619 kg
Other inorganic substances4 kg
OutputGasoline1 kWh
Waste0.736 kg
Waste liquid612 kg
Exhaust gas10.5 kg
Source: International Energy Agency (https://www.iea.org/); International Renewable Energy Agency (https://www.irena.org/).
Table A2. Analysis of the quality flow inventory of 1 kg of jet fuel in China.
Table A2. Analysis of the quality flow inventory of 1 kg of jet fuel in China.
Input/OutputItemValue
InputCrude Oil1.3 kg
H20.02 kg
Electricity0.8 kWh
Heat15 MJ
Natural Gas0.15 kg
Water75 kg
Catalyst0.0001 kg
Other inorganic substances0.0002 kg
OutputJet Fuel1 kg
Diesel0.25 kg
Quebrith0.001 kg
Source: International Energy Agency (https://www.iea.org/); International Renewable Energy Agency (https://www.irena.org/).

Appendix B. Specific Calculation Process of Index Weighting

Step 1: Dimensionless processing
To eliminate the influence of the evaluation results due to the difference in scale, it is necessary to carry out the dimensionless processing of the indicators. The CRITIC method generally uses forward or reverse processing. It is not recommended to use standardized treatment because if standardized treatment is used, the standard deviation of all indicators will become the number 1, and the standard deviation of all indicators will be exactly the same, which will lead to the meaninglessness of the volatility indicator.
If the value of the indicator used is larger, the result is better (positive indicator), as follows:
X i j ′ = x j − x m i n x m a x − x m i n
If the value of the indicator used is as small as possible (reverse indicator),
X i j ′ = x m a x − x j x m a x − x m i n
Step 2: Indicator variability
Indicator variability is expressed in the form of standard deviation, as follows:
x ¯ j = 1 n ∑ i = 1 n x i j S j = ∑ i = 1 n ( x i j − x ¯ j ) 2 n − 1
S j represents the standard deviation of the j indicator.
In the CRITIC method, the standard deviation is used to indicate the fluctuation of the difference in the value of each indicator. The larger the standard deviation, the more significant the difference in the indicator’s value is, and the more information can be projected. The strength of the evaluation of the indicator itself is also more substantial, and more weight should be assigned to the indicator.
Step 3: Conflicting indicators
Conflicting indicators are expressed using the correlation coefficient as follows:
R j = ∑ i = 1 p ( 1 − r i j )
r i j represents the correlation coefficient between evaluation indicators i and j .
The correlation coefficient is used to indicate the correlation between the indicators. The stronger the correlation with other indicators, the less conflict the indicator has with other indicators, the more the same information is reflected, and the more repetitive the evaluation content is. To a certain extent, this also weakens the evaluation strength of the indicator, and the weight assigned to the indicator should be reduced.
Step 4: The amount of information is calculated as follows:
C j = S j ∑ i = 1 p ( 1 − r i j ) = S j × R j
The larger C j is, the greater the role of j and the more weight should be assigned to it.
Step 5: Objective weights
The objective weight of the j indicator is
W j = C j ∑ j = 1 p C j

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Figure 1. Policy development history.
Figure 1. Policy development history.
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Figure 2. Framework of this study.
Figure 2. Framework of this study.
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Figure 3. System boundary of the electric and conventional aircraft.
Figure 3. System boundary of the electric and conventional aircraft.
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Figure 4. Flowchart of Gabi.
Figure 4. Flowchart of Gabi.
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Figure 5. Definition of potential environmental impacts.
Figure 5. Definition of potential environmental impacts.
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Figure 6. Life cycle cost stages of aircraft.
Figure 6. Life cycle cost stages of aircraft.
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Figure 7. Multi-criteria decision-making of the research objects.
Figure 7. Multi-criteria decision-making of the research objects.
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Figure 8. Energy consumption and proportion of the fuel cycle and aircraft cycle.
Figure 8. Energy consumption and proportion of the fuel cycle and aircraft cycle.
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Figure 9. Life cycle emissions of conventional aircraft and electric aircraft.
Figure 9. Life cycle emissions of conventional aircraft and electric aircraft.
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Figure 10. Environmental impact results.
Figure 10. Environmental impact results.
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Figure 11. Life cycle cost of electric aircraft and conventional aircraft.
Figure 11. Life cycle cost of electric aircraft and conventional aircraft.
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Figure 12. Indicator priority changes.
Figure 12. Indicator priority changes.
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Figure 13. Priority in different scenarios.
Figure 13. Priority in different scenarios.
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Table 1. Main parameters of the selected objects.
Table 1. Main parameters of the selected objects.
TypeFlight DistanceMaximum Takeoff WeightPassenger Capacity
Conventional aircraftAirbus A320 (Europe)5000 km73,500 kg180
Electric aircraftAlice (Israel)815 km6668 kg11
Table 2. Life cycle cost of research objectives.
Table 2. Life cycle cost of research objectives.
CostElectric AircraftConventional Aircraft
InfrastructureUni cost: 5 × 107 USDUnit cost: 5 × 109 USD
ManufacturingBattery: 3 × 106 USD/
Fuel usage0.15 USD/kWh930 USD/ton
MaintenanceReplace the battery: 5 × 106 USDMaintenance: 5 × 107 USD
Maintenance facilities: 2 × 106 USD
Scrap and recyclingBattery recycling: 5 × 106 USD/
Table 3. Scenario setting.
Table 3. Scenario setting.
First-Level ScenarioSecond-Level ScenarioThird-Level Scenario
Optimistic Scenario:
The proportion of renewable energy is relatively high		BasicI1: Short-haul flight
I2: Long-haul flight
Emerging technologiesI3: Short-haul flight
I4: Long-haul flight
Pessimistic Scenario:
The proportion of renewable energy is relatively low		BasicD1: Short-haul flight
D2: Long-haul flight
Emerging technologies emergeD3: Short-haul flight
D4: Long-haul flight
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Liang, Y.; Zhang, W.; Li, C. Assessing the Deployment of Electric Aircraft from Energy, Environmental, and Economic Perspectives. Sustainability 2025, 17, 5453. https://doi.org/10.3390/su17125453

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Liang Y, Zhang W, Li C. Assessing the Deployment of Electric Aircraft from Energy, Environmental, and Economic Perspectives. Sustainability. 2025; 17(12):5453. https://doi.org/10.3390/su17125453

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Liang, Ye, Wei Zhang, and Chengjiang Li. 2025. "Assessing the Deployment of Electric Aircraft from Energy, Environmental, and Economic Perspectives" Sustainability 17, no. 12: 5453. https://doi.org/10.3390/su17125453

APA Style

Liang, Y., Zhang, W., & Li, C. (2025). Assessing the Deployment of Electric Aircraft from Energy, Environmental, and Economic Perspectives. Sustainability, 17(12), 5453. https://doi.org/10.3390/su17125453

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