Integrated life cycle assessment and multicriteria decision making framework for evaluating renewable energy technologies at Istanbul Airport

Abstract

This study presents an integrated decision-support framework for the multidimensional assessment of renewable energy technologies in large-scale airport terminals. We propose a hybrid approach that integrates quantitative Life Cycle Assessment (LCA) results with Multi-Criteria Decision-Making (MCDM) to support strategic energy planning. Given their high energy demand and operational complexity, airports are critical hubs for sustainability research. Our framework combines LCA, the CRITIC method, and expert-based AHP to evaluate seven energy technologies across environmental, economic, social, and technical dimensions. Environmental indicators from the LCA were objectively weighted via CRITIC, while other criteria were assessed through AHP. These insights were then integrated using the MULTIMOORA model to determine global sustainability performance. Using Istanbul Airport as a realistic case study, our findings reveal that technologies focused on energy management and electrification offer superior environmental performance due to operational efficiencies. While geothermal systems excel in heating and cooling, PV, wind, and biomass technologies show more pronounced impacts within their supply chains. Ultimately, this research provides a scalable and methodologically robust framework for assessing renewable energy applications at the airport scale.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-40657-6.

Introduction

Airports are among the most complex and energy-intensive components of modern transportation networks. These large-scale infrastructures, which include numerous elements such as terminal buildings, apron operations, lighting systems, heating, ventilation, and air conditioning (HVAC) loads, and ground support equipment, consume significant amounts of electricity and fuel due to the need for continuous, uninterrupted service. Numerous studies have shown that terminal energy use intensity (EUI) is significantly higher than that of other commercial buildings, even surpassing that of hospitals and shopping centers of the same size in some regions^1,2. This makes airports critical points, accounting for a significant share of global greenhouse gas emissions. HVAC systems, terminal lighting, and fossil-fuel-based vehicles used in apron operations account for the largest share of the total carbon footprint.

Increasing passenger volumes, expanding terminal capacities, and the requirement for 24/7 operations indicate that airports’ energy demand will continue to rise in the coming years IATA³. This trend is steering airport operators toward lower-carbon solutions in line with global sustainability goals and international carbon reduction policies. In recent years, renewable energy technologies have begun to be seen in the aviation sector not only as an environmental approach but also as a strategic requirement that increases operational efficiency. In this context, the shift of airport energy supply towards renewable sources has become an inevitable transformation. Options such as photovoltaic systems, biomass energy conversion solutions, geothermal systems, heat pump applications, and sustainable aviation fuels are increasingly used to meet terminal operations’ energy needs and electrify apron processes⁴.

In addition, the use of electric ground support equipment (e-GSE) reduces fossil fuel dependency in apron operations and significantly lowers PM/CO₂ emissions⁵. Renewable energy-supported microgrids and smart energy management systems increase energy security in airport infrastructure while also reducing carbon intensity. Recent studies emphasize that hybrid robust optimization models⁶ and virtual power plant aggregation mechanisms⁷ are critical for managing uncertainties in such multi-energy systems. These developments demonstrate that renewable energy solutions are no longer merely complementary options in aviation but have become a fundamental component of sustainable airport management.

Airports are among the most complex structures in modern infrastructure systems due to their high energy requirements, intensive greenhouse gas emissions, and extensive environmental impacts. Consequently, in recent years, policies aimed at energy efficiency, renewable energy use, and carbon reduction have become central to airport sustainability research. Early studies primarily focused on the relationship between energy consumption and emissions. For example, Kılkıs (2014), in a study examining Istanbul New Airport, analysed the terminal buildings in terms of energy and exergy; he detailed the effects of afforestation strategies and building material choices on carbon emissions. Similarly, Falvo et al.⁸, remodelled Terminal 3 at Rome Fiumicino Airport using the Nearly Zero Energy Building (NZEB) approach, demonstrating that cogeneration systems, LED lighting, and photovoltaic solutions significantly improved energy performance. The multidimensional assessment of energy technologies is becoming widespread in the literature, and multi-criteria assessment frameworks such as 7E have been applied to airport energy systems².

Research into the sub-components that constitute energy consumption has also diversified during this period. Lipan⁹, examined the lighting infrastructure at an international airport and reported that energy losses could be significantly reduced by eliminating harmonic distortions. In the same year, Uysal and Söğüt¹⁰ developed an energy management model integrated into the architecture, using Istanbul Airport as an example, and presented an innovative framework that integrates energy management with information systems.

In recent years, sustainable architecture and photovoltaic applications have begun to be highlighted in the literature. Azami et al.¹¹, modelled Building Integrated Photovoltaic (BIPV) systems for Tabriz Airport using a climate-sensitive approach; Baxter et al.¹² assessed the carbon reduction potential of LED conversions and heat pump applications at Copenhagen Airport. Shafei et al.¹³, demonstrated that smart control systems and PV integration reduced energy consumption by up to 25% at Sharm El-Sheikh Airport through their simulation-based analysis.

Greer et al.¹⁴, systematically reviewed 108 studies. They categorised the methods used to assess airport sustainability, emphasising the importance of considering life-cycle assessment (LCA), carbon footprint calculations, energy management, and multidimensional sustainability indicators.

In the post-2021 period, regional comparisons began to attract more attention. Sreenath et al.^2,15, evaluated airports in ASEAN countries across environmental, social, and economic dimensions; Mancinelli et al.¹⁶, compared the energy and water management performance of small and medium-sized airports in the Adriatic region.

In recent years, the integration of energy efficiency measurements, life-cycle analyses, and Multi-Criteria Decision Making (MCDM) methods has become increasingly prominent. Huertas¹⁷, conducted an energy audit at two medium-sized airports in Mexico to determine GHG-reduction strategies and to calculate the ROI, NPV, and IRR of HVAC renovations and photovoltaic investment options. Akyüz et al.¹⁸, identified an approximate 22% energy saving potential according to the TS 825 standard by jointly considering the thermal performance, indoor air quality, and carbon footprint of terminal buildings. Ugural et al.¹⁹, compared concrete, steel, and wood construction materials using the LCA method and found that wood is the most advantageous option in terms of global warming potential. Efforts to improve energy efficiency in terminal buildings generally focus on HVAC optimization, with control strategies recommended that yield energy savings of 10–30%²⁰.

In the same year, Mızrak et al.²¹ analysed the Istanbul Airport sustainability plan using Entropy and 2-Tuple Linguistic T-Spherical Fuzzy MCDM methods, calculating the priority levels of energy, water, and waste management criteria. Working from a macro perspective, Guzman et al.²², evaluated pre- and post-COVID-19 energy intensity trends at airports in Saudi Arabia, demonstrating that operational improvements increased energy efficiency after the pandemic. Mızrak and Şahin²³ evaluated renewable energy investments using the Spherical Fuzzy MCDM method and proposed a multi-criteria decision framework integrating technical, environmental, and economic dimensions. A comprehensive review by Sleiman and Ouf²⁴ emphasised the need to jointly evaluate the relationship between energy applications and indoor environmental quality (IEQ).

A review of the existing literature reveals that the majority of studies on airport energy management focus on specific sub-areas such as energy consumption in terminal buildings, optimisation of HVAC systems, lighting arrangements, and the efficiency of photovoltaic applications. Various studies have separately addressed the carbon footprint, life-cycle impacts, emission-reduction potential, and environmental benefits of renewable energy applications in airports; some have also evaluated the relationship between the circular economy, waste management processes, and terminal operations. However, a significant portion of these studies focus on a single energy technology, a specific environmental indicator, or only the technical efficiency perspective, thereby failing to provide a comprehensive assessment.

Most research on renewable energy technologies has focused on specific topics, such as the integration of photovoltaic systems into terminal roofs, heat pump applications, biomass processes, and the environmental impacts of sustainable aviation fuels. However, comprehensive studies evaluating these technologies at airport scale, in conjunction with multidimensional indicators such as energy, emissions, water/waste management, social impact, economic feasibility, and technical applicability, remain limited. In particular, there is a notable lack of methods that integrate the LCA impacts of different energy technologies with social and technical criteria based on expert opinion.

Furthermore, there are very few analyses in the literature based on large-scale international airports. A significant portion of existing studies has been conducted on medium-sized terminals or individual system components; comprehensive models comparing renewable energy technologies using an integrated LCA-MCDM framework have not been developed. Considering the high energy intensity and sensitivity to climatic conditions of terminal operations, the development of such models for large-scale airports constitutes a critical research need.

In this context, the present study aims to fill this gap in the literature. In this study, LCA-based environmental indicators for Istanbul Airport were combined with expert assessments of the economic, social, and technical dimensions, and different renewable energy technologies were compared holistically and multidimensionally. This approach aims to make a unique contribution to the literature by combining realistic operational-scale data with integrated consideration of sustainability criteria.

The study developed in this context proposes a multi-layered, integrated evaluation model to address this gap in the literature. In the environmental dimension, a quantitative assessment was conducted using LCA indicators for Istanbul Airport operations, which were objectively weighted using the CRITIC method. Economic, social, and technical criteria were analysed using the AHP approach based on expert opinions, and the relative importance of each dimension was systematically determined. In the final stage, both CRITIC and AHP outputs were converted to a standard scale to obtain global criterion weights; this integrated structure was used to compare the multidimensional performance of different renewable energy technologies using the MULTIMOORA method. Thus, the study presents a unique and comprehensive decision-making framework that combines LCA-based quantitative analyses with qualitative expert judgements for a large-scale airport.

This study does not aim to provide a comprehensive Life Cycle Sustainability Assessment (LCSA) that addresses LCA, life cycle costing (LCC), and social life cycle assessment (SLCA) together. Instead, a hybrid decision support framework has been developed to support strategic energy planning in large-scale airports, integrating quantitative LCA indicators focused on the operational phase with expert-based assessments in economic, social, and technical dimensions. This approach offers a suitable method, particularly for early-stage decision-making problems where detailed life cycle cost and social data sets are limited, inaccessible, or confidential.

In this regard, the subsequent sections of the study are structured as follows. The second section presents the research materials and methods; it details how LCA data for Istanbul Airport were processed, the process of obtaining environmental indicators, the application of the CRITIC and AHP weighting methods, and the MULTIMOORA-based evaluation steps. The third section presents the analysis results and findings related to alternative renewable energy technologies. The fourth section discusses the findings in light of the literature and evaluates the significance of the results obtained in the context of airport sustainability. The final section summarises the study’s overall contributions, limitations, and future research recommendations.

Materials and methods

Research methodology

This study proposes a multi-stage decision-making approach that aims to evaluate renewable energy technologies from a holistic perspective in terms of sustainability. The method used has a hybrid structure that combines both numerical data and qualitative information obtained from expert opinions within a single framework. The research process consists of four main stages: (i) determining the evaluation criteria, (ii) weighting the criteria (LCA–CRITIC and AHP), (iii) establishing global criterion weights, and (iv) analyzing the alternatives using the MULTIMOORA method. Within the scope of the AHP method, the opinions of three experts were sought to determine the relative importance levels of sustainability criteria. The panel was carefully selected to represent academic, regulatory, and operational perspectives:

1. Academic Expert: Holds a PhD in environmental engineering and is recognized for his academic work on sustainable infrastructure and life cycle analyses.

2. Consultant/Policy Expert: A senior energy consultant with experience in both the public and private sectors. This expert specializes in energy regulations, carbon reduction policies, and compliance with international aviation standards, ensuring the regulatory feasibility of the selected technologies.

3. Operational/Passenger-Centric Expert: A field practitioner working in facility management at an international airport. This expert is directly responsible for indoor environmental quality (IEQ) and passenger comfort standards, providing critical insights into the passenger experience and social impact dimensions.

The general methodological progression of the study is presented in Fig. 1.

Fig. 1.

General methodological progress of the study.

In the first stage, alternatives to be used in the assessment of renewable energy technologies in terms of sustainability were determined. A comprehensive set of criteria was developed to encompass the environmental, economic, social, and technical dimensions. These criteria were formulated on the basis of current research in the relevant literature, sectoral reports, and opinions from field experts^22–24. These criteria were then divided into two groups: those that are measurable and those that are evaluated based on expert judgment. LCA was used for measurable indicators related to the environmental dimension, while economic and social criteria were defined qualitatively based on expert assessments. The criteria were organised within a hierarchical decision model to establish a holistic structure.

LCA results were used as a basis for analyzing environmental performance, and objective criterion weights were calculated using the CRITIC method based on this data.The CRITIC method is a statistical technique that quantifies the information-carrying capacity of each criterion. It does so by considering the variances of the criteria and the correlations between them, thereby ensuring an unbiased weight set for environmental indicators²⁵. The weights obtained at this stage were then normalized for use in global weighting.

For criteria related to economic and social dimensions, expert opinions were used as a basis, and weighting was performed using the AHP method. An AHP matrix was created based on the pairwise comparisons obtained from the experts, ensuring that the consistency ratio was within acceptable limits and calculating the relative importance levels of the criteria. The results were then subjected to a process of normalisation and preparation for the subsequent global weighting procedure. The primary function of AHP at this stage is the systematic and consistent conversion of qualitative expert judgments into numerical values.

One of the most important steps in the hybrid structure of the study is the integration of CRITIC (quantitative–environmental) and AHP (qualitative–economic–social) results under a unified framework. The weights obtained from both methods were subjected to appropriate normalization processes, resulting in an integrated global weight set covering all criteria. This integration ensures that both the data-driven and expert-based aspects of the sustainability assessment are represented in a balanced manner on the same scale. The main reason for the qualitative assessment of economic criteria is that LCA analysis applied to the environmental dimension and there is no comparable quantitative life cycle data for economic indicators. Therefore, the economic dimension was systematically assessed using the AHP approach based on expert opinions, as widely accepted in the literature.

After this weighting set was created, the performance of each renewable energy alternative in terms of each criterion was transferred to the decision matrix, taking into account numerical data and expert evaluations. Subsequently, the global criterion weights were integrated into the matrix, yielding a weighted decision matrix. This structure, which combines both the objective weights derived from the LCA–CRITIC approach and the subjective weights obtained through AHP, enables a multidimensional evaluation of the alternatives. In the final stage, the MULTIMOORA method was employed to evaluate the sustainability performance of the energy technologies and identify the most advantageous alternative. Thus, a robust and multifaceted evaluation framework that comprehensively addresses environmental, economic, social, and technical dimensions has been established.

Determining selection criteria and alternatives

The evaluation criteria used in this study were determined through a literature review, in line with international sustainability standards, in order to provide comprehensive coverage of the environmental, economic, social and technical dimensions of airport sustainability^{2–4,9,10,16}. Energy, emissions, water and waste performance indicators were selected so that only measurable and comparable indicators could be used for the environmental dimension. Economic criteria are based on cost-focused indicators, while social and technical criteria are based on qualitative elements that directly influence the decision-making process, such as passenger experience, ease of implementation, employee safety and stakeholder acceptance. The criteria determined in this regard have been grouped under the following sustainability dimensions:

Environmental criteria (C1–C7)

Environmental criteria are composed of indicator-based sub-criteria that enable the direct measurement of LCA outputs.

Energy Consumption (C1), This indicator determines the EUI per unit area or per passenger of airport terminals. Within the framework of LCA, the energy inputs and outputs across the system are analysed with the aim of minimising long-term energy demand^12,16.

Use of Renewable Energy (C2), This sub-criterion measures the proportion of renewable sources (e.g. photovoltaic, wind, geothermal) within total energy consumption. LCA analyses contribute to reducing fossil fuel dependency and validating the transition to sustainable energy by assessing the impact of renewable energy use on Cumulative Energy Demand (CED)^12,16.

Global Warming Potential (C3), this indicator expresses the warming potential of greenhouse gases (CO₂, CH₄, etc.) in terms of carbon equivalent. LCA analyses enable the identification of emission-intensive processes and the prioritisation of low-carbon technologies^14,19.

Acidification Potential (C4), it measures the environmental impact of emissions that cause acidification, such as SO₂ and NO_x. Assessments conducted within the scope of LCA contribute to the development of sustainable emission control strategies by determining the acidification impact of fuel consumption and industrial processes^2,19.

Particulate Matter Emission (C5), PM10 concentration is an important indicator for indoor and outdoor air quality. LCA analyses evaluate particulate matter emissions from vehicle emissions and construction activities, revealing the impact of filtration systems and renewable transport solutions^14,19.

Water Usage (C6), It measures water consumption per functional unit and covers processes such as drinking water, irrigation, and wastewater treatment. LCA-based studies show that greywater recovery and rainwater harvesting systems reduce the water footprint^14[,16.

Amount of Solid Waste (C7), This indicator measures the amount of waste generated per functional unit and assesses the efficiency of recycling and waste-to-energy conversion processes. LCA results show that the effective management of operational waste streams contributes to zero waste targets^2,21.

The primary reason for selecting these environmental indicators is that they are suitable for LCA-based calculations and comprehensively represent the environmental burden of airport operations in the energy–emissions–water–waste quadrangle.

Economic criteria (C8-C9)

Economic criteria have been established to assess the financial viability of renewable energy and smart airport technologies.

Investment and Operating Costs (C8), Technological solutions such as renewable energy systems, smart control equipment or infrastructure conversions require significant capital investment. Therefore, the initial investment cost is one of the most critical components of the decision-making process for airport operators. The literature emphasises that the initial cost is decisive in the acceptability of investment in sustainable technology choices^2,15. The literature indicates that investment and operating costs are one of the most critical assessment dimensions in sustainable infrastructure^16,18.Therefore, this criterion is one of the sub-decision criteria that directly reflects the economic feasibility of technologies.

Economic Contribution (C9), aims to assess the direct and indirect economic benefits that the investment will provide to the airport operator. The economic contribution criterion covers the direct and indirect economic impacts that sustainable technologies have on airport operations and the regional/national economy. This criterion offers a broader economic perspective, taking into account not only cost reduction but also the aviation sector’s capacity to generate economic value. The role of airports in economic development is discussed in the literature in terms of direct, indirect and catalytic effects, and it is emphasised that sustainable investments strengthen these areas of impact^4,14.

Although investment costs are typically quantitative indicators, obtaining consistent real-market cost data for seven different technologies at the airport scale is challenging due to commercial confidentiality and project-specific variability. Therefore, in this study, the cost dimension was evaluated comparatively based on expert judgment rather than a detailed Life Cycle Costing (LCC) analysis.

Social and technical criteria (C10–C12)

Social criteria have been established to evaluate the people-centred aspects of sustainable airport management, such as passenger welfare, employee safety and environmental and social impact. Due to the operational nature of aviation facilities, indoor environmental quality, service experience and social impact are considered critical social indicators in sustainability analyses. The literature emphasises that the dimension of social sustainability contributes to the long-term success of airports through passenger satisfaction, employee welfare and the strengthening of relationships with communities^2,12,14,15. These criteria are supported by expert opinions and ensure that the wider stakeholder community around the airport is involved in the assessment process.

Indoor Environmental Quality (IEQ) (C10), IEQ is an indicator that directly impacts user health, comfort and satisfaction in airport terminals. The reason for including IEQ in the sustainability assessment is that creating a safe, comfortable and efficient indoor environment for both passengers and employees is directly related to operational performance^4,24.

Passenger Experience (C11), assesses service quality, level of digitalisation and the smoothness of operational processes. Technologies such as self check-in, digital signage and IoT-based services enhance the user experience; reducing waiting times increases comfort and satisfaction levels. Passenger experience is considered a critical area of assessment in terms of social sustainability, as it has a strategic impact on airport competitiveness and brand perception^2,14,15.

Social Impact (C12), It considers the airport’s wide-ranging social impact on local communities and its employees. Effective noise management is crucial for community quality of life and regional peace. Employee development and occupational safety are also fundamental to the successful execution of sustainable operations. Furthermore, community investments and corporate social responsibility initiatives undertaken by airports play a significant role in building long-term social support and ensuring social legitimacy^2,13.

The set of criteria enables a comprehensive assessment of sustainability performance across a wide range of areas, including airports’ energy and emissions performance, cost structure, passenger comfort, and social interaction. All criteria used in this study, along with brief descriptions, relevant sub-categories and key literature references, are summarised in Table 1.

Table 1.

Sustainability assessment criteria, descriptions and key sources.

Criteria code	Criteria	Description	Key source
C1	Energy consumption	Energy use intensity per terminal area or passenger.	10,14
C2	Use of renewable energy	Share of renewable sources in total energy consumption.	10,14
C3	Global warming potential	Carbon-equivalent impact of greenhouse gas emissions.	12,17
C4	Acidification potential	Environmental impact of acidifying emissions (SO₂, NOₓ).	13,17
C5	Particulate matter emission	Impact of PM10 emissions on indoor/outdoor air quality.	12,17
C6	Water usage	Water consumption per functional unit.	12,14
C7	Amount of solid waste	Amount of waste generated and recycling efficiency.	13,19
C8	Investment and operating costs	Initial and operational costs of sustainable technologies.	14,16,21
C9	Economic contribution	Direct and indirect economic benefits of the investment.	4,12
C10	Indoor environmental quality (IEQ)	User comfort, health, and indoor climate quality.	4,22
C11	Passenger experience	Service quality, digitalization level, and passenger satisfaction.	12,13
C12	Social impact	Social effects on employees and local communities.	10,13

The seven alternatives evaluated in this study comprehensively represent both the use of renewable energy technologies in airport operations and energy efficiency applications. Solar energy (Solar PV/BIPV), wind, geothermal, and biomass solutions, which are frequently highlighted in the literature, rely directly on renewable sources. Sustainable aviation fuels (SAF) and electric ground support equipment used in apron operations play a strategic role in reducing non-flight emissions. Cogeneration/trigeneration systems and smart energy management (AI/BMS) stand out as mature technologies that increase operational energy efficiency. Therefore, the selected alternatives directly align with the study’s focus on “evaluating renewable energy technologies in airport operations using MCDM methods” and form a comprehensive pool suitable for comparison based on criteria.

A1 – solar PV/BIPV

Solar energy systems aim to generate electricity using photovoltaic (PV) panels installed on airport terminal roofs, parking lot canopies, or suitable land areas. Additionally, building-integrated photovoltaic (BIPV) applications offer an aesthetic and functional solution for terminal facades and roof coverings. In the literature, it has been reported that at Cochin Airport, the electricity demand is largely met through full PV integration, achieving a payback period of approximately 5–6 years². It has been stated that rooftop PV and storage systems are integrated with the smart grid at Rome Fiumicino Terminal 3⁸. Additionally, it has been reported that energy savings of up to 25% have been achieved at Sharm-El Sheikh Airport through the integration of PV and building management systems (BMS)¹³. These studies demonstrate that PV and BIPV solutions are feasible and offer rapid return on investment strategies for airports.

A2 – wind energy

Wind energy aims to generate electricity using land-based turbines located outside the airport site or small-scale low-altitude/vertical-axis turbines within the site. However, from an aviation safety perspective, the application of radar systems may be limited due to constraints such as electromagnetic interference and bird migration routes. In the literature, scenario studies conducted for the planned new airport in Istanbul have suggested the inclusion of wind turbines in the energy portfolio; however, it has been noted that radar and site safety constraints pose a significant obstacle⁶. Similarly, a study on Brisbane Airport emphasized that small-scale turbines are technically feasible, but their large-scale use is limited due to radar obstacles²⁶. These findings show that although wind energy has high potential in the airport context, safety and permitting processes are the most critical limiting factors.

A3 – geothermal/GSHP

Geothermal energy and ground-source heat pump (GSHP) systems are one of the sustainable solutions used to reduce the energy requirements of terminal buildings’ heating and HVAC systems. This technology leverages the relatively constant underground temperatures throughout the year to achieve high energy efficiency in HVAC systems. Scenario analyses conducted for Istanbul New Airport in the literature have shown that GSHP applications can provide savings of 40–80% in HVAC consumption²⁷. These results demonstrate that geothermal and GSHP systems are a viable and powerful alternative for increasing energy efficiency, particularly in large-scale airport terminals.

A4 – biomass/waste-to-energy

Biomass and waste-to-energy solutions are sustainable alternatives that aim to convert organic waste generated at airports into energy. Cabin and terminal waste, particularly food waste, can be converted into biogas through anaerobic digestion (AD); the resulting biogas can be used in combined heat and power (CHP) systems to generate electricity and heat. The literature reports that food waste at Antalya Airport has been used for biogas production, with fertilizer obtained as a by-product²⁸. Similarly, applications in ASEAN countries collect cabin waste using pneumatic systems and convert it into energy². Scenario analyses conducted for Istanbul New Airport have also shown that a biogas plant has the potential to reduce CO₂ emissions²⁷. These studies demonstrate that biomass and waste-to-energy alternatives play an important role not only in energy production but also in reducing waste disposal costs and contributing to the circular economy.

A5 – AI/BMS/smart energy management

Artificial intelligence (AI)-based methods and building management systems (BMS) offer low-cost yet highly effective solutions for reducing energy consumption in airport terminals. These systems enable demand-side management and energy optimization by providing intelligent control of heating, HVAC, lighting, and other equipment. The literature reports significant savings achieved through the smart control of HVAC systems at airports in the ASEAN region². An architecture-based energy management system (EMIS) developed for Istanbul Airport has demonstrated energy savings potential of up to 70%¹⁰. Similarly, the integration of PV systems with BMS at Sharm-El Sheikh Airport has resulted in a 25% reduction in energy consumption¹³. Furthermore, studies conducted in Turkey have shown that environmental and economic benefits can be achieved by integrating measures such as LED conversions, double glazing, and insulation with BMS¹⁸. These findings demonstrate that smart energy management solutions provide a quick return on investment with low capital costs and are applicable at all scales.

A6 – SAF & e-GSE electrification

Sustainable aviation fuels (SAF) and the electrification of ground support equipment (GSE) are critical strategies for reducing direct emissions from airport operations. SAF can significantly reduce greenhouse gas emissions by blending biofuels or synthetic fuels with conventional jet fuels. On the other hand, converting GSE vehicles used in apron operations to electric power reduces noise levels and saves on maintenance costs. The literature states that Norway aims to replace 30% of its aviation fuel with SAF by 2030². Furthermore, scenarios combining the use of sustainable biofuels and PV-supported charging infrastructure with the conversion to electric GSE have been proposed²³. These studies reveal that SAF and e-GSE electrification offer comprehensive solutions that could play a fundamental role in airports achieving their carbon neutrality goals.

A7 – CHP/trigeneration

Cogeneration (CHP) and trigeneration systems are highly efficient solutions that enable the simultaneous fulfillment of electricity, heating, and cooling loads in airport terminals. These systems offer both economic and environmental advantages by ensuring the continuity of energy demand, particularly in large-scale terminals. The literature reports that CHP and trigeneration systems with a capacity of approximately 24 MW at Rome Fiumicino Airport meet the terminal’s energy needs by integrating with the smart grid⁸. Scenario analyses conducted for Istanbul New Airport also show that CO₂ emissions could be reduced by 15–39% if CHP and trigeneration technologies were used²⁷. These findings demonstrate that CHP and trigeneration systems are a reliable and mature alternative for high energy-intensive loads in airport operations.

Table 2 provides the selected alternatives and their explanations.

Table 2.

Alternatives descriptions and key sources.

	Alternatives	Description	Key source
A1	Solar PV/BIPV	Roof, parking lot, and land PV; facade/roof integrated BIPV.	2,8,13,15
A2	Wind energy	Off-site ground turbines or small-scale turbines; radar/EMI constraints.	27,26
A3	Geothermal/GSHP	Save 40–80% on HVAC loads with heat pumps.	27
A4	Biomass/waste-to-energy	Biogas + CHP from organic waste; contribution to the circular economy.	27,15,28
A5	AI/BMS/smart energy management	Savings in HVAC/lighting with advanced control algorithms.	10,13,15,18
A6	SAF & e-GSE electrification	Electrification of apron equipment; emission reduction with SAF.	15,24
A7	CHP/trigeneration	Electricity + heat + cooling; high efficiency.	8,27

The life cycle assessment (LCA)

In this study, the environmental performance of seven sustainable energy technology alternatives was evaluated using the LCA method, which is fully compliant with ISO 14040:2006 and ISO 14044:2006^29,30. The analysis followed four mandatory stages of LCA (i) definition of purpose and scope, (ii) life cycle inventory analysis (LCI), (iii) life cycle impact assessment (LCIA) and (iv) interpretation^31,32.

This study adopts a gate-to-gate system boundary focusing exclusively on operational-stage impacts (Sect. 2.3.1), and employs a passenger-normalized functional unit expressed as kWh-eq per passenger (kWh-eq/pax) (Sect. 2.3.2).

Goal and scope definition

The primary goal of this LCA study is to assess the comparative environmental performance of seven different operational energy technologies that can be implemented at major airport terminals in Turkey and to identify alternatives with the highest potential for carbon reduction and operational resource efficiency. In this context, the study was conducted as a case study of the Istanbul Airport terminal complex, which is representative of large-scale international terminal structures. In 2024, Istanbul Airport serves as a convenient example of a major airport terminal in Turkey, with 80 million passengers and a terminal area of 1.44 million m²³³. This approach provides a comparison, based on passenger-based(passenger-normalized), multi-technology, based solely on the usage stage, unlike previous airport LCA work, which mainly focuses on “cradle-to-grave” and a single technology^34–36. The study is specifically designed in the context of emerging economies, characterized by a rapid increase in the number of passengers.

The functional unit (FU) is defined as: “The provision of the total net energy service (electricity, heating, and cooling) required for the processing of a passenger at the terminal in terms of electrical equivalent (kWh-eq/pax).”

To enable consistent comparison of technologies that provide different types of useful energy, all thermal energy flows have been converted to their electrical equivalents (kWh-eq). This conversion process is based on the following references^37,38. The conversion is carried out using the relationship defined in Eq. 1:

For heatinga representative GSHP/heat pump performance coefficient (COP).

For cooling representative EER value of electric-powered cooling systems.

1

Example 1 kWh of useful thermal energy = 1/3.5 = 0.286 kWh-eq.This method allows all technologies to be compared on the same energy service basis in accordance with the defined functional unit.

The exergy coefficients adopted for thermal outputs are based on commonly used reference values in exergy-based energy analyses. Specifically, an exergy factor of 0.25 was applied for low-temperature heat (approximately 60–90 °C), reflecting typical temperature levels of district heating and CHP heat recovery systems. A coefficient of 0.20 was used for cooling (approximately 7–12 °C), consistent with absorption and district cooling applications. These coefficients represent standardized quality factors for moderate-temperature thermal energy streams relative to ambient conditions and have been widely adopted in exergy and CHP performance assessments^39–41.

System boundaries

In this study, a gate-to-gate system boundary was adopted, using a consequential LCA approach in which credits are assigned to excess renewable electricity fed into the grid via the “system expansion” method. This boundary focuses solely on operational impacts that airport operators can directly influence in the short- and medium-term^19,42. This approach is also widely adopted in airport energy studies^43,44. The scope definition and process flow used in the study are presented in Fig. 2.

Fig. 2.

System boundary diagram for gate-to-gate LCA of airport terminal energy technologies Functional unit: kWh-eq per passenger (kWh-eq/pax). System boundary: Gate-to-gate (operational phase). Excluded life-cycle stages: Capital goods, infrastructure manufacturing, maintenance, decommissioning, terminal construction, and passenger transport.

Included processes.

• Fuel and electricity supply (upstream).

• On-site energy conversion (PV, wind turbine, GSHP, CHP, e-GSE, BMS operations).

• Direct operational emissions.

• Operational water extraction/use.

• “avoided load” (avoided burden) caused by excess renewable electricity given to the grid.

Excluded processes.

• Equipment manufacturing, transportation, and installation (PV panels, turbines, heat pumps, etc.)

• Maintenance and dismantling (decommissioning) operations.

• Construction of the terminal building.

• End-of-life disposal processes for the system.

• Passenger access/transfer processes to the airport.

Excluding these processes allows the study to focus on comparing operational impacts rather than embedded impacts. Previous studies have shown that operational impacts are more dominant over the 20–30-year life cycle of airport terminals^44,45.

Life cycle inventory (LCI)

Life cycle inventory modeling was performed using the Brightway2 (v2.4.6) framework, with the USEEIO v2.0 environmental extended input–output database serving as the underlying background system^46,47. USEEIO provides a comprehensive supply chain representation, including 389 economic sectors and over 1300 elementary flows, and is entirely methodologically compatible with the TRACI 2.1 impact assessment method. This compatibility ensures the seamless application of characterization factors during the LCIA phase.

While the USEEIO database has US-specific regional representation, Turkey’s marginal electric grid mix in the study significantly reduces regional incompatibilities.

Allocation for CHP/tri-generation

The distribution of environmental loads for CHP/trigeneration systems producing multiple outputs has been determined using an energy-based allocation and an exergy-corrected weighting approach. This approach is consistent with the ISO 14,044 guideline and IEA Task 52 technical documents.

The exergy quality coefficients used in the study are as follows:

• Electricity: 1.00.

• Useful heat (80 °C): 0.25.

• Cooling (7 °C): 0.20.

Considering typical output distributions (approximately 40% electricity, 45% heat, 15% cooling): The ‘total exergy weight’ = 0.5425, ‘electricity allocation’ = 73.7%, ‘heat allocation’ = 20.7%, and ‘cooling allocation’ = 5.5% have been calculated.

Detailed tables regarding the LCI dataset and allocation calculations are presented in Supplementary Material S1–S3.

Life cycle impact assessment (LCIA)

In this study, the life cycle impact assessment was conducted at the midpoint level using the TRACI 2.1 characterisation method, which is compatible with the USEEIO v2.0 database and widely used in the LCA literature^46,48. There are three main reasons for selecting the TRACI method: (i) it includes midpoint indicators that are consistent with critical environmental pressures from a policy perspective, (ii) it eliminates data inconsistencies due to the characterisation coefficients being embedded within the USEEIO, and (iii) the selected impact categories correspond to the most regulated environmental areas within airport operations.

In this regard, four midpoint impact categories have been evaluated^49,50:

• Global Warming Potential (GWP₁₀₀, kg CO₂-eq/pax): It covers the effects of CO₂, CH₄, and N₂O emissions over a 100-year time horizon. TRACI’s GWP factors are consistent with IPCC AR5 and are directly related to airport carbon management regulations (e.g.,. ICAO CORSIA).

• Acidification Potential (kg SO₂-eq/pax): SO_x, NO_x, and NH₃ emissions represent their contribution to acid deposition. They are particularly significant in CHP and biomass scenarios, as well as in fuel supply chains.

• Particulate Matter (PM₂.₅-eq, kg/pax): It enables primary PM_2.5 emissions and precursor substances such as SO_x, NO_x, and VOC to be expressed in terms of PM₂.₅ equivalents. It has been selected because it is critical for air quality and public health at airports³⁴.

• Freshwater Withdrawal (m³/pax): It includes the amount of water used in the supply chain and, in particular, the cooling tower water demand in trigeneration/CHP systems. It has been included in the assessment due to the water-intensive operational structure of large airports.

In addition to the TRACI midpoint indicators, operational solid waste generation (kg/pax) has been included in the model as a complementary indicator, as it falls outside TRACI’s scope. This indicator is derived directly from operational assumptions linked to the scenarios.

During the characterisation phase, each elementary flow in USEEIO was multiplied by the corresponding TRACI 2.1 coefficient, and the results were aggregated on a functional unit basis (1 pax). Thus, the entire impact assessment process was conducted within a single data structure, ensuring complete methodological consistency.

For interpretation purposes, all reported GWP, AP, and PM impact values are based on a baseline scenario representing current conventional airport energy operations. All alternative technologies are evaluated under identical system boundaries, functional units, and LCIA thods, enabling consistent comparative interpretation.

Limitations

The main limitation of this study is the use of the US-based USEEIO v2.0 database in the background processes to represent an airport context in Turkey. As a baseline assumption, the Turkish marginal electricity grid mix corresponding to the year 2023 has been integrated into the model to reduce regional inconsistencies. Although the 2023 Turkey marginal electricity grid mix has been integrated into the results to reduce regional inconsistencies, country-specific supply chain differences may not have been fully reflected in the model. While this may affect absolute environmental impact values, the relative ranking of alternatives is assumed to be generally preserved due to the background system’s internal consistency.

USEEIO v2.0 was selected due to its methodological consistency and full compatibility with the TRACI 2.1 impact assessment method, which enables internally coherent system-level analyses. Input–output based background databases are widely used in comparative LCA studies, particularly when region-specific multi-sector databases are not publicly available⁴⁶ (Yang et al., 2020).

To reduce regional mismatch, the Turkish marginal electricity mix was integrated into the model, as electricity supply has been identified as one of the most influential parameters in operational-phase LCAs^51,52. Although material-intensive technologies such as PV, wind, and biomass are more sensitive to background industrial structures, the adopted gate-to-gate system boundary intentionally excludes capital goods and manufacturing stages. Therefore, while absolute impact values may vary, relative rankings are expected to remain robust under consistent functional units and system boundaries^53,54.

The second limitation of this study relates to the adoption of a gate-to-gate system boundary, which focuses on the operational phase of airport energy systems. Under this boundary, capital goods and infrastructure manufacturing processes—such as PV panel production, wind turbine manufacturing, heat pump installation, and other capital goods-related activities—are excluded from the assessment^44,45 while system-expansion credits are applied for exported electricity. This approach is appropriate for evaluating operational impacts that airport operators can directly control; however, it may under-represent the embedded environmental loads of certain generation-based technologies.

These methodological choices introduce two counteracting influences on the comparative assessment of generation-based technologies: (i) excluding manufacturing stages theoretically reduces the reported impacts of material-intensive options such as PV and wind relative to a cradle-to-gate boundary, while (ii) including export credits provides additional environmental benefits to net-exporting alternatives. Within the operational-stage comparison adopted here, however, these scope-related effects are moderated by the inherent efficiency advantages of demand-side strategies (e.g., AI/BMS, LED lighting), which directly reduce energy demand and eliminate the need for additional generation capacity.

Accordingly, while absolute impact values may vary under alternative system boundary definitions, the relative ranking of demand-side versus supply-side strategies is expected to remain robust, as operational efficiency rather than scope definition constitutes the primary driver of performance. The reported results should therefore be interpreted within this operational focus, emphasizing comparative performance rather than full life-cycle completeness.

Furthermore, the study does not include a comprehensive uncertainty analysis (e.g., Monte Carlo simulation). However, a targeted sensitivity analysis was performed for the functional unit conversion parameters (COP/EER) to verify ranking robustness (Sect. 3.1, Table 3).

Table 3.

Sensitivity of technology rankings to COP and EER variations.

Technology	Low ranks			Baseline ranks			High ranks
	GWP rank	AP rank	PM rank	GWP rank	AP rank	PM rank	GWP rank	AP rank	PM rank
A1 PV/BIPV	7	7	5	7	7	5	7	7	6
A2 Wind	5	5	3	6	5	3	6	5	3
A3 GSHP	3	3	7	3	3	7	3	3	5
A4 Biomass/WtE	4	6	4	4	6	4	4	6	4
A5 AI/BMS	1	1	1	1	1	1	1	1	1
A6 SAF & e-GSE	1	2	2	1	2	2	1	2	2
A7 CHP/Trigeneration	6	4	6	5	4	6	5	4	7

Low efficiency scenario: COP = 2.5, EER = 2.5; Baseline scenario: COP = 3.5, EER = 3.0; High efficiency scenario: COP = 4.5, EER = 4.0. Rankings are based on kWh-eq/pax values derived from Eq. (1). Lower ranks indicate better environmental performance.

Finally, performance coefficients such as COP and EER used in converting thermal energy to its electrical equivalent are based on literature values^37,50. While these coefficients are representative of typical airport energy systems, they may vary with seasonal conditions, equipment performance, and changes in operational load. This situation does not affect the usage-phase inventory data used in the study, and future studies may include scenario-based or probabilistic approaches to reflect operational variability in greater detail.

It should be noted that exergy coefficients for heat and cooling depend on system operating temperatures and the reference environment assumptions. However, the selected values fall within commonly accepted ranges for low-to-moderate temperature thermal systems and were applied consistently across all scenarios to preserve the validity of the comparative analysis.

The absence of a formal LCC and SLCA analysis in this study can be considered a methodological limitation. However, detailed and comparable economic and social data are often unavailable or subject to corporate confidentiality constraints in energy planning problems at the airport scale. Under such conditions, multi-criteria decision-making approaches based on expert opinions offer a practical and widely accepted tool in the literature to support strategic-level assessments. Therefore, the proposed hybrid framework should be considered as a strategic screening and prioritization tool rather than a detailed investment feasibility model.

The reported impact values, particularly for material- and infrastructure-intensive technologies such as PV/BIPV, are sensitive to background database characteristics. Specifically, PV/BIPV exhibits relatively high GWP (0.6736 kg CO₂-eq/pax) and water consumption (0.06334 m³/pax) values, which reflect the embodied impacts of module production, balance-of-system components, and land use within the USEEIO v2.0 background database. These values may vary under alternative database assumptions (e.g., European or Asian production chains with different energy mixes and manufacturing processes).

However, this sensitivity does not undermine the validity of the comparative assessment for two reasons: (1) all alternatives are evaluated under identical background assumptions, ensuring internal consistency and comparability; and (2) the operational-stage focus (gate-to-gate boundary) inherently limits the influence of upstream manufacturing differences, as discussed previously. Therefore, while absolute impact magnitudes are database-dependent, the relative ranking of alternatives—particularly the superior performance of demand-side strategies (AI/BMS, LED, SAF & e-GSE) over supply-side generation technologies—is expected to remain robust across different background data sources.

Regarding the qualitative evaluation phase, the study relied on a focused panel of three experts. While a larger sample size could provide a broader statistical distribution, AHP is a methodology designed to yield robust results with a small number of knowledgeable experts rather than a large statistical sample. To mitigate the risk of bias, the CR of the experts were strictly monitored, and a high level of consensus was achieved among the academic, consultant, and practitioner perspectives. Accordingly, the conclusions drawn in this study are specific to the defined system boundaries, available data, and the operational context of Istanbul Airport, and should be interpreted as case-specific insights rather than universally generalizable results.

CRITIC

In this study, the CRITIC method was used to objectively determine the environmental criterion weights from the data obtained with LCA. Developed by Diakoulaki, Mavrotas, and Papayannakis, this approach measures the amount of information carried by each criterion in multi-criteria decision-making problems based on the criterion’s contrast intensity and the level of conflict with other criteria⁵⁵. The method enables the objective calculation of weights based on statistical relationships between criteria, without requiring any subjective expert judgment.

• Step 1.
  The decision matrix is a structure in which m alternatives are evaluated according to n criteria and is expressed as follows:

  2

  Where, , represents the performance value of the . alternative under the . criterion. These values are obtained through expert opinions, statistical indicators, or criteria derived from the literature.

• Step 2.

  Data is normalized to eliminate differences arising from the measurement units of the criteria.

  For benefit-oriented criteria:

  3
  In terms of cost-related criteria:

  4

  represents the normalized value. This converts all values to the range [0,1], making the criteria comparable.

• Step 3.
  The variation of each criterion indicates how much information that criterion provides to the decision-making process:

  5

  represents the standard deviation of the ., while represents the average of the normalized values of the . criterion.

• Step 4.
  The correlation coefficients between the criteria measure the level of repetition of the information conveyed:

  6

  , j. and . are the Pearson correlation coefficients between the criteria. This value indicates that as it approaches + 1, the criteria carry a high degree of similar information, and as it approaches 0, the diversity of information increases.

• The amount of information contained in the criteria (Cj), takes into account both the level of variance and the amount of conflict with other criteria:

  7

  : j. criterion information content,

  : the amount of conflict between criteria.

• Step 6.
  Finally, these values are normalized to calculate the criterion weights:

  8

  : . weight of the criterion,

  : The sum of all weights must equal 1.

This method allows for the simultaneous evaluation of variation (contrast) and conflict (correlation) between criteria. As a result, factors with high information density and independence from other criteria are represented with higher weight.

AHP

The AHP method is used to determine the weights of the criteria evaluated qualitatively in the study. The use of the AHP method in the evaluation of economic and social criteria is a conscious methodological choice. Detailed and comparable LCC and SLCA data for airport-scale energy investments are often unavailable or subject to corporate confidentiality restrictions. Therefore, multi-criteria decision-making methods based on expert opinions are widely used in the literature to provide strategic-level decision support under data constraints. AHP is an MCDM method developed by Thomas L. Saaty and enables decision problems to be modeled in a hierarchical structure⁵⁶. The process consists of the following basic steps:

• Step 1.

  A hierarchical structure between criteria should be established.

• Step 2.

  Decision-makers should compare the criteria in pairs using Saaty’s 1–9 importance scale. The scale used in Table 4 is provided.

• Step 3.
  Equation (9) is used to create the normalized decision matrix.

  9

  ; Criterion i level of importance relative to criterion j.

  ; Normalized matrix element.

• Step 4.
  The local weight of the criterion is obtained by taking the average of each row.

  10

  ; i. local weight of the criterion.

• Step 5.
  The consistency ratio (CR) is calculated for the reliability of the method.

  11

  Where, is the random consistency index,

  ; consistency index,

  ; The largest local weight of the matrix,

  CR < 0.10, however, is considered consistent in comparisons.

• Step 6.
  In this study, the weights of environmental criteria were determined using the CRITIC method, while the weights of economic and social criteria were determined using the AHP method. In the final stage, the weights of each main criterion group were combined with the local weights of the sub-criteria to obtain global weights ().

  12

  ; i. the global (final) weight of the criterion.

  ; weight of the main criteria group (Environmental, Economic, Social).

  ; local weight of the criterion.

Table 4.

Saaty’s 1–9 importance scale⁵⁶.

Value	Definition	Description
1	Equal importance	The impact of the two criteria on the decision is considered equal.
2	Weak or slight	Based on experience, one feels a slight superiority over the other.
3	Moderate importance	One criterion is slightly more important than the other.
4	Moderate plus	Used in uncertain situations between 3 and 5.
5	Strong importance	One criterion is clearly more important than the other.
6	Strong plus	Used in situations between 5 and 7.
7	Very strong importance	The superiority of a criterion can be substantiated with evidence.
8	Very, very strong	Used for situations with a significant difference between 7 and 9.
9	Extreme importance	One criterion has absolute superiority over the other.
1/2, 1/3, …, 1/9	—	Relationships in the opposite direction of the comparison (e.g., if criterion j is more important than criterion i)

Thanks to this hybrid approach, both objective (data-based) and subjective (expert opinion-based) assessments have been taken into account.

The rationale for employing this dual weighting strategy lies in the distinct nature of the evaluation dimensions. Environmental indicators are derived from robust quantitative LCA outputs, enabling the use of the objective CRITIC method to prevent subjective bias and reflect the intrinsic data structure. In contrast, economic and social dimensions in airport strategic planning often lack standardized life cycle datasets (e.g., LCC and SLCA data availability issues) or involve intangible factors such as passenger experience. Therefore, the expert-based AHP method was essential to capture these qualitative strategic priorities. By integrating these two approaches, the global weighting scheme ensures a balanced evaluation that combines data-driven objectivity with expert-guided policy insights.

MULTIMOORA

In this study, the evaluation of alternatives was performed using the MULTIMOORA method. The method was developed by Brauers and Zavadskas and consists of three sub-methods: Ratio System, Reference Point Approach, Full Multiplicative Form⁵⁷. The combined use of these three approaches makes the method one of the most robust methods in the MCDM literature.

• Step 1.
  The decision matrix consists of the performance values of alternativesaccording to criteria.

  13

  . the performance of alternative i with respect to criterion .

• Step 2.
  To make criteria at different scales comparable, each value is normalized.

  14

  As a result of this process, all values become dimensionless and are normalized to the range [0,1].

  ; normalized value.

• Step 3.

  Ratio system.

  The weighted difference in the performance of each alternative across all criteria is calculated:

  15

  Where represents the benefit (maximization) criterion, while +1 represents the cost (minimization) criterion. Alternatives are ranked according to the obtained values.

• Step 4.

  Reference point approach.

  At this stage, the best (ideal) values for each criterion are determined as reference points, and the distance of each alternative from these values is calculated:

  16

  The alternative with the shortest distance is considered the best alternative.

• Step 5.

  Full multiplicative form.

  The overall benefit value of alternatives is calculated based on the product of all criteria:

  17

  This form is a multidimensional utility function that takes into account interactions between criteria.

  When maximization and minimization criteria are combined, the following expression is used:

  18
• Step 6.

  Final ranking.

 

The final ranking of alternatives is obtained by comparing the results of the three sub-methods (Ratio System, Reference Point, Full Multiplicative Form).

When minor ranking differences were observed among the three sub-approaches of the MULTIMOORA method (Ratio System, Reference Point, and Full Multiplicative Form), dominance relationships were evaluated by considering the consistency of the relative positions of the alternatives. Since no conflicting ranking was detected among the highest-performing alternatives, the final ranking obtained was considered sufficiently robust for strategic decision-making.

Findings

This section presents the analysis results obtained in line with the integrated sustainability assessment framework proposed in the study. First, the weighting outputs related to environmental, economic, and social criteria are provided; then, the performance assessment of renewable energy and smart airport technologies is carried out using these global weights. Throughout the analysis process, the hybrid approach, which integrates both objective and subjective information sources, has enabled a comparative examination of the multidimensional sustainability performance of the alternatives.

LCA-based environmental impact results

The life cycle-based environmental performance of seven sustainable energy technologies is compared in this section in terms of four TRACI 2.1 midpoint impact categories (GWP, AP, PM₂.₅-eq, Water Use) and operational solid waste generation. Additionally, operational energy performance indicators (EUI and RE%) that complement the LCA results have been evaluated. The analyses were conducted using Istanbul Airport terminal operations as a reference. Istanbul Airport served 80 million passengers in 2024 and represents a representative case study for large-scale international airport terminals⁴³. The results were calculated using the Brightway2–USEEIO methodology described in Sect. 2.3 and the kWh-eq/pax functional unit. Summary results for all technologies are provided in Table 5.

Table 5.

Life cycle impact indicators (kWh-eq/pax functional unit).

Technology	GWP (kg CO₂-eq/pax)	AP (kg SO₂-eq/pax)	PM₂.₅-eq (kg/pax)	Water consumption (m³/pax)	Waste (kg/pax)
AI/BMS	5.33 × 10⁻⁴	9.38 × 10⁻⁷	2.28 × 10⁻⁷	2.40 × 10⁻⁵	0.002
SAF & e-GSE electrification	5.33 × 10⁻⁴	9.38 × 10⁻⁷	2.28 × 10⁻⁷	2.40 × 10⁻⁵	0.003
Geothermal/GSHP	0.2690	3.77 × 10⁻⁴	8.83 × 10⁻⁵	0.01356	0.006
CHP/trigeneration	0.4309	5.32 × 10⁻⁴	8.45 × 10⁻⁵	0.03347	0.009
Wind energy	0.4491	5.10 × 10⁻⁴	5.25 × 10⁻⁵	0.04223	0.008
Biomass/waste-to-energy	0.3591	4.44 × 10⁻⁴	7.04 × 10⁻⁵	0.02789	0.012
Solar PV/BIPV	0.6736	7.65 × 10⁻⁴	7.88 × 10⁻⁵	0.06334	0.010

All reported GWP, AP, and PM values correspond to the baseline operational scenario and are expressed per functional unit (kWh-eq/pax), enabling direct comparison across alternatives. Results were calculated using TRACI 2.1 midpoint factors within the Brightway2–USEEIO v2.0 framework.

Operational energy performance (EUI and RE%)

The operational performance of each technology has been assessed using the EUI per passenger and renewable energy share (RE%) indicators. EUI represents the total net energy demand required for terminal operations, while RE% indicates the proportion of this demand met by renewable energy technologies. Table 6 shows that the lowest energy consumption is observed in the AI/BMS and SAF & e-GSE scenarios. Wind and PV technologies stand out in terms of renewable energy use. Although these indicators do not produce an LCA effect, they are important operational criteria in terms of the energy efficiency and sustainable energy integration of the technologies.

Table 6.

Operational performance of technologies (EUI–RE%).

Technology/operational performance	AI/BMS	SAF& e-GSE	GSHP	CHP	Biomass/waste-to-energy	Wind energy	Solar PV/BIPV
EUI (kWh/pax)	0.85	0.90	0.95	1.00	1.05	1.10	1.20
RE%	10	15	30	25	35	70	60

Global warming potential (GWP)

When assessed in terms of global warming potential, a wide range of performance is observed among technologies. The lowest carbon impact has been identified in technologies based on reducing energy consumption. Both the AI/BMS and SAF & e-GSE scenarios show values close to zero, at 0.000533 kg CO₂-eq/pax. This is a result of net energy savings and the avoided emission credits assigned to the excess electricity fed into the grid through the system expansion method.

Medium carbon impacts are observed in GSHP, CHP and wind energy technologies. The GSHP system has a relatively low GWP value of 0.269 kg CO₂-eq/pax; this performance is related to energy efficiency resulting from a high COP value (3.5). CHP/trigeneration systems emit 0.431 kg CO₂-eq/pax, while wind energy emits 0.449 kg CO₂-eq/pax. The carbon load in CHP is mainly linked to the natural gas supply chain.

The highest GWP values were measured in the Solar PV/BIPV (0.6736 kg CO₂-eq/pax) and Biomass/WtE (0.3591 kg CO₂-eq/pax) scenarios. The high carbon footprint of PV stems from the US grid mix reflected in the USEEIO database and material-intensive production processes. In the biomass scenario, biogenic CO₂ emissions were assessed as non-fossil flows in accordance with IPCC guidelines and considered carbon neutral.

Acidification potential (AP)

Acidification potential results show a similar technology ranking to GWP findings. The lowest acidification impact is observed in the AI/BMS and SAF & e-GSE scenarios, calculated at 9.37 × 10⁻⁷ kg SO₂-eq/pax for both technologies. Technologies with medium-level impacts include GSHP (3.77 × 10⁻⁴ kg SO₂-eq/pax), wind energy (5.10 × 10⁻⁴ kg SO₂-eq/pax) and CHP (5.32 × 10⁻⁴ kg SO₂-eq/pax).

The highest acidification potential was identified in Solar PV/BIPV (7.65 × 10⁻⁴ kg SO₂-eq/pax) and Biomass/WtE (4.43 × 10⁻⁴ kg SO₂-eq/pax) technologies. The high AP values in the PV and biomass scenarios are linked to the representation of SOₓ and NOₓ intensive activities such as metal processing, silicon refining, and biomass logistics in the background processes in the USEEIO database.

Particulate matter formation (PM₂.₅-eq)

A similar trend is observed in the particulate matter formation category. AI/BMS and SAF & e-GSE technologies show the lowest PM₂.₅-eq values (2.28 × 10⁻⁷ kg/pax). Technologies with medium-level impacts include wind energy (5.25 × 10⁻⁵ kg/pax), Solar PV/BIPV (7.88 × 10⁻⁵ kg/pax), CHP (8.45 × 10⁻⁵ kg/pax) and GSHP (8.83 × 10⁻⁵ kg/pax). The biomass/WtE scenario, at 7.04 × 10⁻⁵ kg/pax, also falls within the medium-high segment.

The PM₂.₅ load observed in the PV, wind and biomass scenarios stems from raw material extraction, metal processing and material transport processes. The relatively high PM value for GSHP is related to coal and natural gas production processes in the electricity grid mix.

Water consumption

There are significant differences between technologies in the water consumption category. The lowest water consumption is again seen in the AI/BMS and SAF & e-GSE scenarios, at 2.40 × 10⁻⁵ m³/pax for both technologies. GSHP shows relatively low water usage (0.01356 m³/pax). Wind energy is in the medium-high range at 0.04223 m³/pax.

The highest water consumption was observed in Solar PV/BIPV (0.06334 m³/pax) and CHP (0.03347 m³/pax) technologies. The high water demand in PV stems from USEEIO background processes that require intensive water use, particularly in silicon refining and panel production processes. CHP’s water consumption is largely dependent on the cooling tower’s ‘make-up water’ requirement. These findings confirm the water-intensive operational structure of thermal systems.

Solid waste generation (waste)

Operational solid waste generation shows significant differences between technologies. The lowest waste production is observed in the AI/BMS (0.002 kg/pax) and SAF & e-GSE (0.003 kg/pax) scenarios. GSHP (0.006 kg/pax), wind energy (0.008 kg/pax) and CHP (0.009 kg/pax) exhibit medium levels of waste production. Solar PV/BIPV (0.010 kg/pax) is also in the medium segment.

The highest waste generation was identified in the Biomass/WtE (0.012 kg/pax) scenario. This is a direct result of ash and combustion residues from the biomass combustion process. Thermal combustion-based technologies such as biomass and CHP are seen to generate a higher operational waste load compared to digital optimisation and electrification approaches.

Comparative environmental performance of technologies

When all impact categories are evaluated together, the technologies fall into three distinct performance groups. The first group includes technologies with the lowest environmental impact across all categories: AI/BMS, SAF & e-GSE, and GSHP. The superior performance of these technologies can be explained by their direct reduction in energy consumption (AI/BMS, e-GSE) and high energy efficiency (GSHP, COP = 3.5). All three technologies show the lowest values in the GWP, AP, PM₂.₅, water consumption, and waste generation categories.

The second performance group includes wind energy and CHP/trigeneration technologies. Although CHP performs relatively well in terms of carbon footprint, it is disadvantaged in terms of water consumption and waste indicators. Wind energy, on the other hand, is characterised by moderate impacts due to small supply chain burdens. Both technologies offer advantages in certain categories while having moderate impacts in others.

The third group consists of technologies with the highest environmental impact: Solar PV/BIPV and Biomass/WtE. The PV system shows the highest values, particularly in the GWP, AP, and water consumption categories. This is related to material-intensive production processes and the carbon intensity of the US grid reflected in the USEEIO. The biomass scenario stands out due to high waste generation. Although both technologies provide renewable energy production, when evaluated in terms of operational life cycle, they carry a higher environmental burden than other alternatives.

To assess the robustness of the functional unit conversion applied in Eq. (1), a sensitivity analysis was conducted by varying the COP and EER values within realistic ranges reflecting seasonal and equipment-related variability. Three efficiency scenarios were examined, representing low (COP = 2.5, EER = 2.5), baseline (COP = 3.5, EER = 3.0), and high (COP = 4.5, EER = 4.0) system performance. As shown in Table 3, while absolute kWh-eq values change across scenarios, the relative ranking of technologies remains largely stable for GWP, AP, and PM impact categories. Demand-side efficiency and electrification strategies (AI/BMS and SAF & e-GSE) consistently occupy the top positions, whereas minor rank shifts are observed only for technologies with higher thermal components, such as GSHP and CHP/trigeneration. These results confirm the robustness of the proposed functional unit design.

Quantitative evaluation criteria weighting results with CRITIC

This section presents the results of objective weighting performed using the CRITIC method, based on data obtained from LCA analysis for quantitative criteria representing environmental performance. The LCA outputs calculated for indicators such as energy consumption (EUI), renewable energy ratio, global warming potential (GWP), acidification effect (SO₂-eq), particulate matter emissions (PM10), water consumption, and waste quantity form the basic data set for the CRITIC analysis.

Step 1,2:The LCA outputs were used as the initial decision matrix, and then the data in the decision matrix were normalized using Eq. 3 and Eq. 4. Table 7 presents the normalized decision matrix.

Table 7.

Normalized decision matrix-CRITIC.

	C1	C2	C3	C4	C5	C6	C7
A1	0,2083	0,0000	1,0000	1,0000	1,0000	1	0,9090
A2	0,0000	0,4167	0,9474	0,4202	0,8698	0,9563	0,0000
A3	0,9896	0,2500	0,3608	0,3042	0,8429	0,4717	0,2727
A4	0,1042	0,3333	0,6013	0,5070	0,8356	0,7862	0,5454
A5	0,9792	1,0000	0,3340	0,3334	0,0000	0,3334	0,3636
A6	1,0000	0,0833	1,0000	1,0000	1,0000	1,0000	0,8181
A7	0,9688	0,8333	0,0000	0,0000	0,8537	0,0000	1,0000

Step 3,4,5: The standard deviation of each criterion from the normalized decision matrix was calculated using Eq. 4, while the correlation coefficient between criteria was calculated using Eq. 6. The values were calculated using Eq. 7.

Step 6: Finally, the values were normalized using Eq. 8 to calculate the weights. The calculated criterion weights are presented in Table 8. These weights objectively reveal the relative importance levels of LCA-based environmental indicators in terms of sustainability performance.

Table 8.

Criterion weights calculated with the CRITIC method.

C1	0,270912
C2	0,303373
C3	0,087382
C4	0,083629
C5	0,076726
C6	0,086995
C7	0,090984

The dominance of Use of Renewable Energy (C2: 0.303) and Energy Consumption (C1: 0.271) in the weight distribution is driven by the intrinsic data structure analysed by the CRITIC method. This method assigns higher weights to criteria with high standard deviation (contrast intensity) and low correlation with other indicators. In the decision matrix, the alternatives exhibit significant divergence in terms of renewable energy share (ranging from 10% to 70%) and energy intensity. This high variance indicates that C1 and C2 distinguish the alternatives more sharply than other indicators. In contrast, emission-based indicators (C3–C7) displayed relatively more uniform distribution patterns or high inter-correlations, resulting in lower individual weights (~ 0.08–0.09). This confirms that energy source and efficiency are the most distinguishing factors in the current dataset.

As a result of these steps, each environmental criterion was evaluated in terms of its variability, relationship with other criteria, and overall information contribution, and objective weights were obtained using the CRITIC method. These weights were then integrated with the AHP weights obtained from the qualitative criteria in the next stage and used to create the global weight set.

AHP qualitative evaluation criteria weighting results

In this study, after objectively weighting environmental criteria using the LCA-CRITIC method, economic (C8–C9) and social (C10–C12) criteria were evaluated using AHP based on expert opinions. Thus, both the measurable and qualitative dimensions of sustainability performance were comprehensively addressed.

Step 1,2: In the first step of the AHP application, a hierarchical structure containing the objectives, criteria, and sub-criteria of the study was created (Fig. 3). Following the creation of the hierarchical structure, in the second stage of AHP, the relative importance levels of the criteria were evaluated using pairwise comparisons obtained from experts. The experts compared indicators such as investment and operating costs, economic contribution, internal environmental quality, passenger experience, and social impact based on their professional experience and indicated the degree of importance of the relevant criterion in terms of sustainability. These comparisons were organized according to the scale given in Table 2, and the relative preference level was determined for each criterion pair.

Fig. 3.

AHP hierarchical structure.

Step 3.4,5:The obtained pairwise comparison matrix was normalized using Eq. 9. Subsequently, local weights were calculated for the main criteria and sub-criteria using Eq. 10. The calculated global weights and CR values are provided in Table 9a,b.

Table 9.

(a) Consolidated pairwise comparison matrix for economic criteria. (b) Consolidated pairwise comparison matrix for social technical criteria.

a
Economic Criteria	C8	C9	Global Weight
C8	1.00	0.33	0.250
C9	3.00	1.00	0.750
CR	0.000

b
Social criteria	C10	C11	C12	Global Weight
C10	1.00	2.00	0.50	0.286
C11	0.50	1.00	0.25	0.143
C12	2.00	4.00	1.00	0.571
CR	0.008

To ensure the transparency of the weighting process, the pairwise comparison matrices consolidated from the three experts are presented in this section. The geometric mean method was applied to aggregate the individual judgments of the experts (academic, consultant, and practitioner) into a single consensus matrix. Following this aggregation, the CR for each matrix was calculated using Eq. 11 to verify the reliability of the judgments. Table 9a and Table 9b show the consolidated pairwise comparison matrices for the Economic and Social criteria groups, respectively.

As seen in the tables, the CR values for both groups (0.000 and 0.008) are well below the threshold of 0.10, indicating a high level of consistency in the experts’ judgments. The Economic Contribution (C9) was evaluated as moderately more dominant than Investment Costs, while Social Impact (C12) was identified as the most critical factor within the social dimension.

Prior to aggregation, the individual consistency ratios of all three experts were checked separately and found to be within the acceptable limit (CR < 0.10). The consensus demonstrated in the aggregated matrices confirms that despite different professional backgrounds, the experts shared a similar perspective on prioritizing airport sustainability priorities.

This section explains which qualitative criteria are more dominant in sustainability assessment according to expert opinions, presenting the weights in question. These weights were then combined with the objective weights determined using the CRITIC method in the next stage to form the global criterion weights.

Creating global weights

Weights related to environmental criteria were determined using the CRITIC method, based on LCA outputs. This method produces an objective set of weights and reflects the data-based importance levels of environmental indicators, as it takes into account variance and correlation-based information content. In contrast, since economic and social criteria cannot be directly measured, the AHP method based on expert opinions was used, and subjective importance values were obtained for these criteria.

Although both methods have different data structures and evaluation logics, these weights must be combined within a common framework to establish a holistic basis for sustainability analysis. To this end, CRITIC and AHP results were normalized to the same scale, and a global weight set representing the criteria’s share in the total was created using Eq. 12 (Table 10). This process strengthens methodological integrity by simultaneously incorporating both objective and subjective information sources into the evaluation process.

Table 10.

Local and global weights of the criteria obtained through CRITIC–AHP integration.

Criterion code	Local weight	Global weight	Benefit/cost
C1	0.2709123	0.180608191	Cost
C2	0.3033725	0.202248347	Benefit
C3	0.0873816	0.058254384	Cost
C4	0.0836285	0.055752351	Cost
C5	0.0767260	0.051150696	Cost
C6	0.0869951	0.057996718	Cost
C7	0.0909840	0.060655980	Cost
C8	0.2500000	0.055555556	Cost
C9	0.7500000	0.166666667	Benefit
C10	0.2857143	0.031746032	Benefit
C11	0.1428571	0.015873016	Benefit
C12	0.5714286	0.063492063	Benefit

The resulting global weights were used in the subsequent stage to evaluate the performance of alternative renewable energy and smart airport technologies. This allowed all criteria to be made comparable under a single common level of importance, enabling the final ranking of alternatives to be achieved from a multidimensional perspective.

Table 10 shows both the local and global weights of all criteria used in the study. Local weights indicate the relative importance of criteria within their own groups, while global weights represent the final weights obtained by integrating the results from the CRITIC and AHP methods, which translate all criteria into a common evaluation scale. The “benefit” and “cost” classification in the table indicates the direction of each criterion’s evaluation structure. This structure clearly reveals which criteria have a more dominant effect in the sustainability assessment and provides a fundamental reference for the alternative ranking to be carried out in the next section.

Performance ranking of alternative energy and smart airport technologies with MULTIMOORA

At this stage, the global criterion weights obtained using the CRITIC and AHP methods were applied to the decision matrix, and alternative renewable energy and sustainable airport technologies were subjected to a multi-criteria evaluation process.

Step 1,2: The decision matrix created has been normalized using Eq. 14. The normalized decision matrix is provided in Table 11.

Table 11.

Normalized decision matrix-MULTIMOORA.

	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12
A1	0,51052	0,09295	0,00056	0,00008	0,00415300	0,00028400	0,10863	0,40756	0,36364	0,35957	0,41093	0,34698
A2	0,63064	0,32532	0,03754	0,36615	0,12808100	0,03308700	0,65175	0,34933	0,31818	0,30820	0,30820	0,29741
A3	0,06006	0,23237	0,45050	0,43938	0,15369800	0,39704600	0,48881	0,34933	0,36364	0,41093	0,35957	0,34698
A4	0,57058	0,27884	0,28131	0,31133	0,16058000	0,16085600	0,32588	0,29111	0,36364	0,30820	0,30820	0,39655
A5	0,06607	0,65064	0,46953	0,42097	0,95582200	0,50088700	0,43450	0,46578	0,40909	0,46230	0,46230	0,39655
A6	0,05406	0,13942	0,00056	0,00008	0,00415300	0,00028400	0,16294	0,34933	0,40909	0,35957	0,41093	0,44611
A7	0,07207	0,55769	0,70430	0,63145	0,14337300	0,75133000	0,05431	0,40756	0,40909	0,41093	0,35957	0,39655

Step 3: Ratio system.

The values were calculated using Eq. 15 and the alternatives were ranked. The values are given in Table 12.

Table 12.

Values calculated with ratio system, reference point approach and full multiplicative form.

A1	−0,00233	0,082442	3,53022
A2	−0,05153	0,104137	1,181373
A3	0,006852	0,028198	1,317862
A4	−0,03342	0,093289	1,035317
A5	0,054036	0,112791	1,472637
A6	0,103323	0,015152	5,861845
A7	0,058808	0,093993	1,637681

Step 4: Reference point approach.

Equation 16 was used to calculate the values and rank the alternatives. Table 12 provides the values.

Step 5: Full multiplicative form.

Equation 16 and Eq. 17 were used to calculate the values and rank the alternatives. The values are given in Table 12.

Step 6: The results of these three evaluation mechanisms were combined to create an overall ranking of the alternatives in terms of sustainability performance. This preserved the multidimensional structure of the study, analyzing both quantitative and qualitative criteria using an integrated method. The final ranking obtained provides decision-makers with a comprehensive basis for evaluation by revealing the strengths and weaknesses of the alternatives (Table 13).

Table 13.

Performance ranking obtained by integrating the MULTIMOORA sub-approaches.

	Ratiorank	RP rank	Rank FMF	Sum	Rank
A1	5	3	2	10	3
A2	7	6	6	19	7
A3	4	2	5	11	4
A4	6	4	7	17	6
A5	3	7	4	14	5
A6	1	1	1	3	1
A7	2	5	3	10	2

Table 13 shows the rankings of the alternatives obtained under the three sub-approaches of the MULTIMOORA method (Ratio System, Reference Point Approach, and Full Multiplicative Form) and the final performance ranking resulting from the integration of these rankings. The table provides the rank values obtained for each alternative in the three sub-approaches, the sum of these rankings, and the corresponding overall rank information. Thus, by considering the results based on different evaluation logics together, the relative positions of the alternatives in terms of sustainability performance have been summarized, and the final ranking structure, which will be discussed in detail in the discussion section, has been presented.

Sensitivity analysis of criterion weights

A comprehensive sensitivity analysis was conducted to test the robustness of the proposed hybrid decision support framework and to observe the effect of possible changes in criterion weights on the final ranking. Three different scenarios were created based on the assumption that the decision-maker’s priorities had changed, using the weights obtained with the CRITIC and AHP methods as a reference. These scenarios aim to measure the stability of the MULTIMOORA ranking by focusing on different sustainability dimensions:

Scenario 0 (Current Situation) The final global weights calculated in the study (Table 10) were used.

Scenario 1 (Environment-Focused) The weight coefficients of the environmental criteria group (C1–C7) were increased by 25%, and the other criteria were reduced proportionally.

Scenario 2 (Economically Focused) The weights of the investment cost and economic contribution criteria (C8–C9) were increased by 25%, simulating a situation where budget constraints are dominant.

Scenario 3 (Social and Technical Focus) The weights of the social and technical criteria (C10–C12) have been increased by 25%, testing a situation where passenger experience and social impact are prioritised.

The specific weight distributions assigned to each criterion across the five defined scenarios are detailed in Table 14. As seen in the table, Scenarios 2, 3, and 4 intensify the weights of the Environmental, Economic, and Social clusters, respectively, allowing for a comprehensive robustness check.

Table 14.

Criterion weights distribution across different sensitivity analysis scenarios.

	S0	S1	S2	S3
C1	0,180608	0,22576	0,180608	0,163652
C2	0,202248	0,252811	0,202248	0,182959
C3	0,058254	0,072818	0,058254	0,052702
C4	0,055752	0,06969	0,055752	0,050516
C5	0,051151	0,063938	0,051151	0,046502
C6	0,057997	0,072496	0,057997	0,052461
C7	0,060656	0,07582	0,060656	0,05471
C8	0,055556	0,027778	0,055556	0,050397
C9	0,166667	0,083333	0,166667	0,208333
C10	0,031746	0,015873	0,031746	0,039683
C11	0,015873	0,007937	0,015873	0,019841
C12	0,063492	0,031746	0,063492	0,079365

The results of the analysis indicate that despite significant variations in weighting scenarios, the SAF & e-GSE Electrification (A6) alternative consistently maintained the 1 st rank across all scenarios (Fig. 4). Similarly, the CHP/Trigeneration (A7) alternative stably maintained its position at 2nd rank. The fact that weight changes did not cause any deviation in the ranking of the top alternatives proves that the proposed model and the superior performance of the A6 alternative are highly insensitive to subjective weight changes and are robust. This stability stems from the fact that A6 demonstrates a balanced and high performance not only dependent on a specific criterion but across both environmental (low emission) and socio-economic dimensions.

Fig. 4.

Sensitivity analysis results showing the ranking stability of alternatives under different weighting scenarios.

Discussion

Conducting the LCA analyses used in this study at Istanbul Airport ensures that the environmental performance outputs obtained reflect the operational reality of a large-scale international airport. Previous studies on Istanbul Airport have shown that energy consumption in terminal systems is particularly high due to HVAC loads and that a large portion of CO₂ responsibility stems from these processes (ICAO 2024). The prominence of energy performance (C1) and emissions management (C2) criteria in our study is directly consistent with these findings. A similar trend has been observed in studies conducted in hot climate regions in Mexico and Saudi Arabia, emphasizing that terminal-scale energy demand is the most decisive factor in the environmental footprint^17,22.

The observed environmental performance differences among technologies should be interpreted in light of the operational system boundary adopted. While supply-chain-intensive technologies such as PV and biomass exhibit higher impacts partly due to background database characteristics, the relative superiority of demand-side efficiency and electrification strategies (e.g., AI/BMS and SAF & e-GSE) remains consistent. This finding aligns with prior LCA studies, indicating that relative technology rankings are generally preserved even when absolute impact values are influenced by background database choices. Laurent⁵⁴, Accordingly, the baseline scenario—defined as the current conventional airport energy operation—serves as a consistent reference point for interpreting differences in GWP, AP, and PM impacts among the evaluated technologies. The relative performance of PV and wind technologies observed in this study should be interpreted in light of their operational-scale contribution and system integration characteristics. Although excluding capital goods manufacturing and including system-expansion credits may appear favorable for generation-based technologies, their overall impact during airport operations remains constrained by partial system coverage, intermittency, and limited influence on terminal-wide energy demand. In contrast, demand-side efficiency and electrification strategies directly affect the entire operational energy profile of the airport, leading to more pronounced environmental benefits. As a result, the observed rankings are shaped primarily by operational effectiveness rather than by boundary-related effects, supporting the robustness of the comparative conclusions.

The circular economy and wastewater management analyses related to Istanbul Airport show that these categories are not as differentiating as energy and emissions. This is consistent with the relatively low weights assigned to waste management (C3) and water consumption (C4) criteria in our study. Therefore, the LCA approach based on Istanbul Airport demonstrates that the weights obtained using the CRITIC method are based on a sound foundation, both methodologically and in terms of the literature.

A detailed analysis of the LCA results also confirms this general trend. Various studies have shown that the environmental impacts of terminal operations are largely determined by energy consumption, HVAC loads, and the level of electrification. Kılkış²⁷, and Akyüz et al.¹⁸, state that HVAC systems play a decisive role in both energy and CO₂ emissions. In the current study, technologies focused on electrification or energy reduction, such as AI/BMS, SAF & e-GSE, and GSHP, produce the lowest environmental impact, supporting these literature findings.

LCA outputs related to production-based energy technologies also largely align with the literature. In PV and wind scenarios, the higher GWP, water consumption, and PM₂.₅ impacts stemming from USEEIO background processes are consistent with assessments highlighting the life cycle impacts of material-intensive production processes³⁴. Similarly, the high PM and waste generation of the Biomass/WtE alternative is consistent with Khaenson et al.⁵⁸, which highlights the dominant effect of particle formation in biomass combustion processes. The CHP/trigeneration system’s relatively low GWP but higher water consumption aligns with the water-intensive cooling processes highlighted by Broesicke et al.⁵⁹. This finding demonstrates the methodological consistency of our gate-to-gate model’s water consumption calculations. Accordingly, CHP performance should be interpreted within the context of these standardized exergy assumptions, emphasizing its relative comparative performance rather than absolute thermodynamic efficiency.

It should be explicitly noted that generation-based technologies such as wind and biomass are penalized in the comparative assessment due to their high upstream resource and material requirements. Although the adopted gate-to-gate system boundary excludes capital goods manufacturing, upstream resource and energy flows associated with fuel supply, processing, and operational inputs are still captured within the LCA inventory. Consequently, the higher impacts observed for wind and biomass reflect their resource-intensive supply characteristics rather than methodological bias.

The relatively high GWP and water consumption impacts observed for PV/BIPV (0.6736 kg CO₂-eq/pax and 0.06334 m³/pax, respectively; Table 5) warrant careful interpretation. These values primarily reflect the embodied environmental burdens associated with PV module manufacturing, balance-of-system components, and land-use requirements as represented in the USEEIO v2.0 background database. While these impacts are sensitive to background assumptions—particularly regional manufacturing energy mixes and supply chain structures—the adopted gate-to-gate system boundary limits the influence of upstream variability by excluding capital goods from the assessment.

More importantly, the fundamental trade-off between generation-based and demand-side strategies remains unchanged across alternative database assumptions: PV/BIPV addresses energy supply, whereas demand-side measures such as AI/BMS, LED lighting, and SAF & e-GSE directly reduce or eliminate energy demand. Comparative LCA studies consistently show that demand reduction strategies outperform supply-side substitution across diverse impact categories and background databases^60,61.

Furthermore, the water consumption associated with PV/BIPV is largely embodied in module production rather than operational use, which is minimal for PV systems. Accordingly, while absolute impact magnitudes may vary under different background assumptions, the relative ranking of technologies—and the superior performance of demand-side efficiency measures—remains robust and consistent with the energy hierarchy principle.

The general trend indicates that solutions based on energy efficiency and electrification at the airport scale provide the strongest environmental improvement; however, material- and supply chain-intensive technologies (PV, Biomass/WtE) create higher life cycle impacts during the operational phase. Findings in the literature that energy efficiency–electrification is at the heart of airport carbon reduction strategies also support this.

These findings are also consistent with the results obtained at the criteria level. Analysis based on LCA data shows that energy consumption and emissions creation create significant differences in environmental performance. The criteria of energy performance (C1) and emissions management (C2) clearly stand out in terms of environmental impact. This is consistent with studies showing that EUI is high in terminal buildings and that HVAC loads account for a large share of total energy consumption. For example, Huertas et al., showed that 50–60% of terminal energy in two Mexican airports is HVAC-related¹⁷. Analyses of Istanbul Airport also indicate a strong relationship between energy consumption and CO₂ responsibility.

The critical role of indicators such as GWP, PM10, and acidification (SO₂-eq) in the study is also supported by other academic findings. A study examining IAQ and carbon footprint together in terminal buildings showed that HVAC systems have a dominant effect on GWP, while particulate matter formation is related to fresh air ratios¹⁸. Assessments of ASEAN airports also reveal that emissions performance determines the limits of operational sustainability¹⁶.

Studies examining the applicability of renewable energy technologies at airport scale are consistent with these findings. Although the BIPV analysis conducted at Tabriz Airport in Iran resulted in a significant reduction in CO₂ emissions, findings indicating that biomass/waste-to-energy systems may increase PM and acidification effects are consistent with the findings in this study. In circular economy-based waste assessments, it has also been noted that waste-to-energy systems may create some additional environmental burdens¹⁷. All these findings strongly confirm the dominant role of energy and emission criteria in sustainability performance.

When examining criterion weights, the fact that the objective weights obtained using the CRITIC method demonstrate high information content for energy performance and emissions management is consistent with the literature. Findings indicating that terminal energy demand and carbon indicators generally exhibit the highest variance support this situation¹⁰. In qualitative criteria, the AHP results assigning high importance to social impact (C9) and indoor environmental quality (C7) indicators align with studies highlighting the prominence of passenger experience, worker safety, and community impacts in sustainability literature in recent years²². The medium weighting of economic criteria (C5 and C6) parallels NZEB transformation studies indicating that high initial investment costs are balanced by long-term performance gains⁸. Findings regarding technical criteria are consistent with studies highlighting the increasingly critical role of BMS, AI-based energy management, and IoT-based applications in sustainable airport infrastructure¹⁰.

When examining the MULTIMOORA results, SAF & e-GSE electrification (A6) ranked first in all sub-methods and demonstrated the highest overall performance. This finding is consistent with studies showing that the use of electric equipment in apron operations rapidly reduces CO₂ and PM10 values. Studies targeting the ASEAN and Adriatic regions also show that low-carbon fuels and e-GSE solutions provide the highest environmental benefits¹⁵. CHP/Trigeneration (A7) ranks second, demonstrating strong performance in terms of energy efficiency and heat recovery; however, it is expected to lag behind A6 due to fuel-related emissions. The mid-ranking of the PV/BIPV (A1) alternative stems from technical limitations such as shading, maintenance requirements, and climate dependency at the terminal scale⁹. The greater location dependency of geothermal (A3) and wind (A2) solutions, and the high PM and acidification effects produced by the biomass/waste-to-energy (A4) system, are entirely consistent with the literature.

The superior performance of the A6 alternative is also supported by the existing infrastructure capabilities of Istanbul Airport. As reported in recent sustainability reports³³, the airport has already established electric vehicle charging stations at apron areas and initiated pilot projects for SAF supply chains. This indicates that the transition to e-GSE and SAF is not merely a theoretical option but a technically feasible pathway with a high technology readiness level (TRL). The alignment of these technologies with the airport’s ‘Net Zero 2050’ targets further reinforces their strategic applicability.

Overall, the ranking of alternatives shows a high level of consistency with LCA findings and criterion weights. Energy efficiency, electrification, and low-carbon fuel use are at the forefront, while technologies with high operational loads or location dependencies are ranked lower. Moreover, the conducted sensitivity analysis confirms that the adopted functional unit design is robust to realistic variations in COP and EER values, as the relative ranking of technologies remains stable across low, baseline, and high efficiency scenarios.

This study demonstrates that the combined use of LCA-based quantitative data and expert opinion-based qualitative data in the assessment of sustainable airport technologies yields results that are consistent with the literature and methodologically robust. The dominant role of energy and emissions-focused criteria, the rising importance of the social dimension, and the decisive impact of technical feasibility on the decision-making process align with global trends. The final ranking of alternatives clearly confirms that low-carbon fuel and electrification strategies stand out as the most effective solutions in the current sustainable airport transformation.

From an application perspective, the proposed hybrid framework enables airport operators to compare renewable energy and energy efficiency technologies under conditions with data constraints. Integrating LCA-based environmental indicators with expert opinion-based assessments allows decision-makers to prioritize strategically before undertaking detailed technical and economic feasibility studies.

The final ranking reveals a distinct trade-off between operational efficiency and capital-intensive generation. The top-ranked alternative, SAF & e-GSE (A6), achieved its position by directly reducing local emissions and fuel consumption without requiring the heavy manufacturing and supply chain burdens associated with new energy infrastructure. Similarly, CHP (A7) ranked second by maximizing the efficiency of energy use (heat and power combined), trading off a dependency on natural gas for superior reliability and load coverage.

In contrast, while Wind (A2) and Biomass (A4) offer renewable generation, their lower rankings reflect the trade-off imposed by the LCA boundaries and criteria weights: their high ‘embedded’ environmental costs (e.g., particulate matter in biomass combustion or manufacturing burdens in wind turbines) and location dependencies outweighed their renewable benefits. Essentially, the decision framework favored strategies that optimize existing energy flows (efficiency and electrification) over those that purely generate new energy but come with higher upstream environmental or technical costs.

Conclusions

his study proposes a hybrid decision-support framework for evaluating sustainable airport energy and infrastructure technologies by integrating operational-phase LCA results with expert-based multi-criteria decision-making methods, using Istanbul Airport as a representative large-scale case study. Quantitative data based on LCA is used for environmental indicators (energy consumption, GWP, particulate matter, acidification, water usage, and waste production), while qualitative criteria related to other dimensions are weighted using the AHP method based on expert opinions. The CRITIC and AHP outputs are harmonized to obtain global criterion weights, and alternative technologies are systematically evaluated using the MULTIMOORA method.

The findings reveal that energy performance and emissions management at the environmental level play a decisive role in sustainability. This result is consistent with findings from various terminal-scale studies, including Istanbul Airport, which show that HVAC loads, terminal energy intensity, and CO₂ emissions constitute a large part of the environmental impact. In the social dimension, the high weighting of social impact and indoor environmental quality supports the current academic approach regarding passenger experience, employee safety, and community satisfaction. The moderate importance of economic criteria is consistent with the frequently emphasized “high initial costs–long-term energy efficiency” balance in the literature.

When examining the ranking of alternative technologies, SAF and e-GSE electrification (A6) ranked first in all evaluation methods. This technology’s ability to directly reduce apron-related CO₂ and PM emissions and increase operational efficiency explains its superior multi-dimensional performance compared to other alternatives. CHP/Trijenerasyon (A7) ranked second due to its advantages such as energy efficiency and heat recovery. Although PV/BIPV (A1) systems are a strong option environmentally, they did not reach the top rank due to technical constraints at the terminal scale. The lower ranking of solutions such as biomass/waste-to-energy, wind, and geothermal is related to additional environmental burdens in LCA outputs, dependence on the application area, and technical limitations. The overall picture shows that the balance between environmental performance and technical feasibility is critical in the evaluation of airport technologies.

The findings of this study directly contribute to the United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). The comparison of renewable and low-impact energy technologies using the integrated LCA–MCDM framework enables the identification of solutions that will increase energy efficiency in airport infrastructure and reduce dependence on carbon-intensive resources; in this respect, it contributes to SDG 7. Furthermore, the quantitative assessment of emissions across different environmental impact categories, coupled with the identification of technologies with high reduction potential, firmly ties the study to SDG 13. The proposed methodology gives decision-makers a robust tool for choosing energy solutions that will enhance the sustainability of critical transport hubs and speed up climate action.

The study has certain limitations. First, the environmental indicators are based on operational data and conditions specific to Istanbul Airport, and LCA results may vary in different climate zones or terminal designs. Second, the qualitative criteria used in the economic, social, and technical dimensions inevitably rely on specific expert assessments and assumptions; therefore, different criterion structures or alternative weighting approaches may affect the results. Furthermore, while the CRITIC method is based solely on statistical relationships between variables, AHP has a structure that quantifies subjective expert opinions. The combined use of these two methods naturally creates a hybrid methodological structure. Future studies may extend the proposed framework by incorporating formal LCC and SLCA analyses, conducting sensitivity and uncertainty analyses, or applying alternative MCDM techniques such as TOPSIS, MARCOS, or PROMETHEE to further test the robustness of the results.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

F.Ş.Y.: Conceptualization, Data curation, Formal analysis, Methodology, Writing—original draft, Writing—review and editing A.Z.: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing—original draft, Writing—review and editing.

Data availability

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

Declarations

Competing interests

The authors declare no competing interests.