Evaluating airports’ Sustainable Development Goals by using multi-criteria decision making methodologies

Abstract

BACKGROUND:

The recent growth of the aviation industry, which poses significant environmental challenges, has heightened the pressure on the sustainability of airports. Airport sustainability requires a holistic approach that encompasses economic, social, environmental, and operational aspects. In this regard, the United Nations’ 17 Sustainable Development Goals (SDGs) Agenda provides a roadmap for the aviation industry. However, despite recognizing the importance of SDGs, aviation authorities and airports often fail to effectively integrate them into their activities and annual reports.

OBJECTIVE:

This study aims to evaluate the significance of SDGs for airports and select the airport that prioritizes SDGs the most using Multi-Criteria Decision Making (MCDM) methodologies.

METHODS:

This study introduces a novel approach that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods, which are MCDM techniques, to enhance airport sustainability. The SWARA method is employed to evaluate and assign weights to the SDGs in the context of airports.

RESULTS:

SDG 8 holds the highest level of significance among the goals concerning airports, while SDG 14 falls outside the scope of airport sustainability aspects. Then, five international airports that have been designated as green airports by aviation authorities and assessment organizations are selected, and the optimal alternative is determined using the WASPAS method, considering the weights obtained through SWARA.

CONCLUSION:

Dallas/Fort Worth International Airport is the top choice due to its successful implementations and reports aligning with the SDGs.

Keywords

SDGs airports ergonomics SWARA WASPAS MCDM

1 Introduction

Aviation is one of the key drivers of economic and social development. Pre-pandemic data from 2019, as reported by the International Civil Aviation Organization (ICAO) in 2020, revealed that approximately 4.5 billion passengers and 61 million tons of cargo were transported by air [1]. However, this growing demand for air transportation brings significant responsibilities for the industry, particularly in terms of environmental sustainability. As the fastest-growing sector of the global economy in recent years, aviation has been associated with various environmental issues, including air pollution, noise pollution, depletion of natural resources, high energy consumption, disruption of local ecosystems, and carbon and greenhouse gas emissions [2 –5].

On the other hand, airports are vital components of the aviation industry, providing essential services, facilities, and supporting infrastructures for the smooth and continuous operation of air transport. To achieve sustainability, airports must adopt a holistic approach that encompasses economic sustainability, operational efficiency, conservation of natural resources, and addressing social challenges [6, 7].

Sustainability is established upon a harmonious balance between social engagement, economic progress, and environmental protection. The United States Environmental Protection Agency defines sustainability as the creation of conditions that enable both humans and nature to support present and future generations while maintaining a direct or indirect commitment to the natural environment [8]. The United Nations’ 2030 Agenda outlines a comprehensive plan for attaining these objectives, which encompass eradicating poverty, safeguarding the environment, promoting peace and prosperity for all, and establishing a framework for local and global sustainable development practices [9]. However, as noted by Beguin and Duarte [10], the human labor aspect of sustainable development often receives less attention compared to the environmental and economic dimensions. It can be argued that the work dimension is sometimes overlooked in discussions on sustainable development. The Sustainable Development Goals (SDGs) seek to redirect global development towards a more sustainable trajectory, and this includes enhancing employee awareness of environmental issues and promoting the creation of environmentally-friendly workplaces within specific sectors.

The COVID-19 pandemic in 2020 had severe consequences on human life, the economy, the environment, energy, and transportation. This situation has magnified the significance of the 2030 targets in ensuring the sustainability of all sectors [11 –15]. Within this context, the global aviation sector has a role to play in promoting sustainable operations by embracing the SDGs and cultivating a positive corporate image [16]. Aviation authorities acknowledge the importance and benefits of implementing the SDGs, and they actively support their integration within airports [17 –19]. While research and discussions have predominantly focused on the environmental impacts of airports, there has been limited exploration of the significance of SDGs in the airport industry airports [20 –24]. Table 1 provides a general overview of the studies conducted on the SDGs in the aviation industry and airports.

Table 1
General review on research related to the United Nations’ Sustainable Development Goals (SDGs)

Authors Related SDGs Summary points

[25] SDG 6, SDG 7, SDG 9, SDG 12 The authors analyzed how airport sustainability fits into the SDGs across four categories -energy, water, construction materials, and indoor environmental quality- and give real-world case studies addressing these concerns

[26] SDG 6, SDG 7, SDG 12 The authors argued that the dimensions of sustainability evaluate the connection between SDGs and sustainable practices in airports of selected ASEAN countries.

[27] SDG 7, SDG 13 This research highlights that the aviation industry needs financing to enable it to adopt sustainability in a line with sustainable energy and climate action.

[28] SDG 11, SDG 17 The authors investigated financial and non-financial reports of Italian airports to analyze how airports adopt organizational, accounting, and reporting practices to meet the SDGs.

[29] SDG 4 The authors analyzed how human resources and the necessary knowledge, skills, and competencies in the aviation industry can be used to increase the productivity and competitiveness of aviation training schools.

[30] SDG 5 The study addresses the challenges in aviation, including gender equality and equal opportunities for employees, in terms of social benefit diversity within the framework of sustainable development in aviation.

[31] SDG 6, SDG 14 This study discussed the data and information on the environmental impacts of aircraft deicing discharges.

[32] SDG 7 This paper discusses the solar at-gate example, which demonstrates the opportunities associated with maximizing airport electrification and supplying the new electricity demand with clean energy for carbon emission reductions consistent with the global efforts to address climate change.

[33] SDG 8, SDG 9 This study examines the effects of airports on regional economic development.

[34] SDG 8, SDG 9 The study estimates the effects of airport infrastructure on relative sectoral employment at the metropolitan-area level, using data from the United States.

[35] SDG 7, SDG 9 This article explores background information on jet fuel use, quality standards and best practices, airport infrastructure, and options for providing airports with Sustainable Airport Fuel (SAF).

[36] SDG 9 The authors investigated the effect of organizational readiness, innovation and airport size and ownership on digital change at airports

[37] SDG 11, SDG 12 This paper discusses developing an airport-specific green rating framework.

[38] SDG 12 This study conducts a systematic review of the various aspects of airport waste management,

[39] SDG 13, SDG 16 This review article explores how climate scholars view the role of structural and agentic approaches in reducing these emissions.

[40] SDG 7, SDG 9, SDG 15 The paper estimates the ILUC emission intensity for seventeen SAF pathways considered by the ICAO, covering five ASTM-certified technologies, nine biomass-based feedstocks, and four geographical regions.

Authors	Related SDGs	Summary points
[25]	SDG 6, SDG 7, SDG 9, SDG 12	The authors analyzed how airport sustainability fits into the SDGs across four categories -energy, water, construction materials, and indoor environmental quality- and give real-world case studies addressing these concerns
[26]	SDG 6, SDG 7, SDG 12	The authors argued that the dimensions of sustainability evaluate the connection between SDGs and sustainable practices in airports of selected ASEAN countries.
[27]	SDG 7, SDG 13	This research highlights that the aviation industry needs financing to enable it to adopt sustainability in a line with sustainable energy and climate action.
[28]	SDG 11, SDG 17	The authors investigated financial and non-financial reports of Italian airports to analyze how airports adopt organizational, accounting, and reporting practices to meet the SDGs.
[29]	SDG 4	The authors analyzed how human resources and the necessary knowledge, skills, and competencies in the aviation industry can be used to increase the productivity and competitiveness of aviation training schools.
[30]	SDG 5	The study addresses the challenges in aviation, including gender equality and equal opportunities for employees, in terms of social benefit diversity within the framework of sustainable development in aviation.
[31]	SDG 6, SDG 14	This study discussed the data and information on the environmental impacts of aircraft deicing discharges.
[32]	SDG 7	This paper discusses the solar at-gate example, which demonstrates the opportunities associated with maximizing airport electrification and supplying the new electricity demand with clean energy for carbon emission reductions consistent with the global efforts to address climate change.
[33]	SDG 8, SDG 9	This study examines the effects of airports on regional economic development.
[34]	SDG 8, SDG 9	The study estimates the effects of airport infrastructure on relative sectoral employment at the metropolitan-area level, using data from the United States.
[35]	SDG 7, SDG 9	This article explores background information on jet fuel use, quality standards and best practices, airport infrastructure, and options for providing airports with Sustainable Airport Fuel (SAF).
[36]	SDG 9	The authors investigated the effect of organizational readiness, innovation and airport size and ownership on digital change at airports
[37]	SDG 11, SDG 12	This paper discusses developing an airport-specific green rating framework.
[38]	SDG 12	This study conducts a systematic review of the various aspects of airport waste management,
[39]	SDG 13, SDG 16	This review article explores how climate scholars view the role of structural and agentic approaches in reducing these emissions.
[40]	SDG 7, SDG 9, SDG 15	The paper estimates the ILUC emission intensity for seventeen SAF pathways considered by the ICAO, covering five ASTM-certified technologies, nine biomass-based feedstocks, and four geographical regions.

Most airports have already integrated the SDGs into their annual reports and underscore the importance of these objectives. However, the annual reports of airports tend to concentrate on the SDGs that directly align with their long-term goals, resulting in a limited exploration of all 17 goals presented in Table 1. This lack of comprehensive examination is a common occurrence. Moreover, there has been no systematic weighting of the SDGs carried out by aviation authorities or airports. Consequently, the current findings reveal a significant gap in the existing literature, as there has been no previous study that comprehensively evaluates all 17 SDGs from the perspective of airports. Thus, there is a notable deficiency in the literature in terms of describing the importance of the SDGs specifically with regard to airports.

In this study, the 17 SDGs were weighted using the Step-wise Weight Assessment Ratio Analysis (SWARA) method, which is a Multi-Criteria Decision Making (MCDM) approach. Subsequently, five airports considered sustainable were selected by aviation authorities and these 5 airports were compared using the weights of the SDGs and the WASPAS method. The selection of these airports was based on three criteria: (1) possessing LEED (Leadership in Energy and Environmental Design) certification, an internationally recognized standard for environmentally friendly buildings, (2) being acknowledged as environmentally friendly by Skytrax, an international air transport rating agency, and (3) being labeled as green airports by aviation authorities.

The SWARA method is widely recognized as an effective approach that enables decision-makers (DMs) to prioritize their preferences. Its key feature lies in estimating the importance ratios of criteria during the criteria weighting stage, allowing for the aggregation of expert opinions. The SWARA method has found significance in collecting information from experts and consolidating their insights [41]. It has been successfully applied in various sustainability studies [42 –44] and has been utilized in different decision problems within the aviation industry. For instance, it has been employed for assessing airport service quality [45], prioritizing sustainable design requirements [46], and aiding in location selection [47]. Previous research has utilized the SWARA technique for decision-making in aviation involving pilots, aircraft maintenance technicians, air traffic controllers, and others [48 –50]. However, there has been a notable absence of research focusing on airports and sustainability using the SWARA technique.

The WASPAS method is known for its superior ranking accuracy compared to other MCDM methods, while also offering simplified mathematical calculations. This approach has been applied in various fields, including construction site selection [51], aircraft type selection [52], assessment of airport ground access modes [53] and evaluation of airlines based on service quality [54]. Furthermore, the SWARA and WASPAS methods have been employed in a range of other studies, such as the assessment of digital health interventions [55], evaluation of sustainable suppliers in manufacturing companies [56].

Based on the literature review conducted, it is evident that the SDGs have not been approached as a MCDM problem specifically in the context of airports. Moreover, neither the SWARA nor the WASPAS methods have been implemented for this purpose. Thus, this study proposes a novel hybrid approach that combines the SWARA and WASPAS methods to evaluate the achievement of Sustainable Development Goals in the airport domain. This approach aims to fill the existing research gap and provide valuable insights into the assessment of SDGs within the airport industry.

The study is structured as follows: Section 2 provides a concise overview of the 17 SDGs and the selection of sustainable airports. Section 3 outlines the research methodology and presents the steps involved in applying the SWARA and WASPAS methods. Section 4 describes the application of these methods in the study. Section 5 presents a comprehensive discussion of the findings and concludes with overall conclusions and recommendations for future research.

2 Methodology

In this section, a comprehensive explanation of the calculation steps involved in the SWARA and WASPAS methods is provided.

First of all, DMs are determined as DM_k ={ DM₁, DM₂, … DM_l }; (k=1,2, . . . ,l). The criteria are defined as C_j ={ C₁, C₂, … C_n }; (j=1,2, . . . ,n) and alternatives as A_i ={ A₁, A₂, … A_m }; (i=1,2, . . . ,m).

Where DM_k is kth participant, C_j is jth criterion and A_i is ith altenative.

2.1 SWARA method

The SWARA method, developed by Kersuliene et al. [57], is an export-oriented strategy used to evaluate and assign weights to criteria. It enables the numerical estimation of experts’ or interest groups’ opinions and knowledge in various domains such as economics, management, policy, and environmental sustainability. This method allows decision-makers to play a crucial role in assessing the significance of each criterion, taking into account the current environmental and economic conditions [58].

The steps involved in determining weights using the SWARA method, as outlined by Prajapati et al. [59], are as follows:

Step 1. Identification of prioritization of criteria

Each decision-maker ranks the criteria based on their importance (from the most important to the least important) according to their own expertise.

Step 2. Evaluation of the relative importance

Starting from the second criterion, each decision-maker determines the relative importance of criterion j in relation to the previous criterion (j - 1) for each particular criterion. This ratio is commonly referred to as the average comparative importance value, as described by Keršuliene et al. [57] and Stanujkic et al. [60].

Where $s_{j}^{k}$ is the comparative importance of jth criterion for kth DM and (0≤ $s_{j}^{k}$ ≤1).

Step 3. Determination of the coefficient ( $k_{j}^{k}$ )

$k_{j}^{k}$ is determined for each DM using Equation (1). $k_{j}^{k} = {1 j = 1 | s_{j}^{k} + 1 j > 1}$ (1)

Where $k_{j}^{k}$ is the coefficient of jth criterion for kth DM.

Step 4. Determination of recalculated weight

In this step, Equation (2) is applied to determine the recalculated weight of each criterion for every DM as follows: $q_{j}^{k} = {1 j = 1 | \frac{k_{j - 1}^{k}}{k_{j}^{k}} j > 1}$ (2)

Where $q_{j}^{k}$ is the recalculated weight of jth criterion for kth DM.

Step 5. Calculation of the relative weights of the criteria

The relative weight of each criterion is determined using Equation (3). $w_{j}^{k} = \frac{q_{j}^{k}}{\sum_{j = 1}^{n} q_{j}^{k}}$ (3)

Where $w_{j}^{k}$ expresses the relative weight of criterion j for each DM. Then, the final weight w_j is obtained a geometric mean of $w_{j}^{k}$ . w_j is average relative weight of criterion j.

2.2 WASPAS method

The WASPAS method was initially developed by Zavadskas et al. [61] by integrating the Multi-Criteria Decision Making (MCDM) methodologies Weighted Sum Model (WSM) and Weighted Product Model (WPM). This method utilizes the performance values and criterion weights of alternatives based on criteria to solve the problem. Moreover, the method can ensure consistency in alternative rankings by conducting sensitivity analysis within its own framework [62].

The steps of the WASPAS method are outlined below [63].

Step 1. Developing the decision matrix

The decision matrix is constructed using the alternatives and criteria defined in Equation (4). $X = [\begin{matrix} x_{11} & x_{12} & \dots & x_{1 n} \\ x_{21} & x_{22} & \dots & x_{2 n} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ ⋮ & ⋮ & ⋮ & ⋮ \\ ⋮ & ⋮ & ⋮ & ⋮ \\ x_{m 1} & x_{m 2} & \dots & x_{m n} \end{matrix}]$ (4) where x_ij represents the performance of the i^th alternative with regards to the j^th criterion, m refers to the number of the alternatives and n is the number of the criteria.

Step 2. Developing the normalized decision matrix

The decision matrix formed in the previous step is normalized using Equations (6) as follows: ${\bar{x}}_{ij = \frac{x_{ij}}{maks x_{ij}}} for benefical criteria$ (5) ${\bar{x}}_{i j} = \frac{m i n x_{i j}}{x_{i j}} for non - benefical criteria$ (6) where ${\bar{x}}_{ij}$ represents the normalized value of x_ij.

Step 3. Determination of relative importance of alternatives based on WSM

The relative significances of the i^th are evaluated using Equation (7) as follows: $Q_{i}^{(1)} = \sum_{j = 1}^{n} {\bar{x}}_{ij} w_{j}$ (7)

Where Q_i is total relative importance of i^th alternative for WSM.

w_j is obtained SWARA method.

Step 4. Determination of relative importance of alternatives based on WPM

The relative significances of i^th are evaluated using Equation (8) as follows: $Q_{i}^{(2)} = \prod_{j = 1}^{n} ({\bar{x}}_{ij})^{w_{j}}$ (8) where Q_i is total relative importance of i^th alternative for WSM.

w_j is obtained SWARA method.

Step 5. Determination of total relative importance of alternatives by combing the WSM and WPM

A joint generalized criterion of weighted aggregation of additive and multiplicative methods is evaluated using Equation (9) as follows:

$\begin{matrix} Q_{i} = [0.5 Q_{i}^{(1)} + 0.5 Q_{i}^{(2)}] \\ = [0.5 \sum_{j = 1}^{n} {\bar{x}}_{ij} w_{j} + 0.5 \prod_{j = 1}^{n} {({\bar{x}}_{ij})}^{w_{j}}] \end{matrix}$ (9)

The WASPAS technique develops a more generalized equation for determining the overall relative significance of the i^th alternative in order to improve accuracy and effectiveness of the decision-making process.

$\begin{array}{l} Q_{i} = [Q_{i}^{(1)} + (1 -) Q_{i}^{(2)}] \\ = [\sum_{j = 1}^{n} {\bar{x}}_{i j} w_{j} + (1 -) \prod_{j = 1}^{n} {({\bar{x}}_{i j})}^{w_{j}}] \end{array}$ (10) where λ value is 0, WASPAS method is transformed to WPM, and when the value of λ is 1, it becomes WSM method [61]. The alternatives are ranked according to the Q values obtained in the previous step. The best alternative is determined as the highest Q value.

3 Application

In this study, the best alternative among the evaluated airports was selected based on the 17 SDGs published by the UN. The objective is to assess the importance of SDGs in relation to airport sustainability and promote the implementation of sustainability practices and necessary measures in airports.

In the first part of the study involved applying the SWARA method, a weighting technique, to determine the level of importance of the SDGs. In the next section, the WASPAS method, a selection method, was utilized to assess and compare different airports in order to identify the best alternative.

3.1 SWARA

Step 1. Identification of prioritization of criteria

First, a panel of DMs was formed, consisting of experts in the field of aviation. The panel included two academics with more than 25 years of experience and consultancy roles, one quality manager from an international airport, and two ground services supervisors (one from Istanbul and the other from Antalya). Each DM was assigned a unique numerical identifier from 1 to 5, as indicated in Table 2. The DMs were then asked to individually rank the 17 SDG criteria based on their perceived importance in relation to airports. The rankings provided by the DMs are presented in Table 2.

Table 2
Sustainable Development Goals (SDGs) evaluation criteria

Criteria DM₁ DM₂ DM₃ DM₄ DM₅

C ₁ SDG 1: No Poverty 14 15 4 15 15

C ₂ SDG 2: Zero Hunger 17 14 8 12 14

C ₃ SDG 3: Good Health and Well-being 11 9 12 10 12

C ₄ SDG 4: Quality Education 15 7 11 9 10

C ₅ SDG 5: Gender Equality 13 6 14 8 7

C ₆ SDG 6: Clean Water and Sanitation 10 11 16 16 11

C ₇ SDG 7: Affordable and Clean Energy 9 4 9 5 4

C ₈ SDG 8: Decent Work and Economic Growth 1 1 1 2 1

C ₉ SDG 9: Industry, Innovation and Infrastructure 5 16 2 1 2

C ₁₀ SDG 10: Reduced Inequality 6 5 15 3 6

C ₁₁ SDG 11: Sustainable Cities and Communities 2 10 3 4 5

C ₁₂ SDG 12: Responsible Consumption and Production 3 2 6 13 3

C ₁₃ SDG 13: Climate Action 4 3 13 11 8

C ₁₄ SDG 14: Life Below Water 16 17 17 17 17

C ₁₅ SDG 15: Life on Land 12 13 10 14 13

C ₁₆ SDG 16: Peace and Justice Strong Institutions 8 12 5 7 16

C ₁₇ SDG 17: Partnerships to achieve the Goal 7 8 7 6 9

Criteria		DM₁	DM₂	DM₃	DM₄	DM₅
C ₁	SDG 1: No Poverty	14	15	4	15	15
C ₂	SDG 2: Zero Hunger	17	14	8	12	14
C ₃	SDG 3: Good Health and Well-being	11	9	12	10	12
C ₄	SDG 4: Quality Education	15	7	11	9	10
C ₅	SDG 5: Gender Equality	13	6	14	8	7
C ₆	SDG 6: Clean Water and Sanitation	10	11	16	16	11
C ₇	SDG 7: Affordable and Clean Energy	9	4	9	5	4
C ₈	SDG 8: Decent Work and Economic Growth	1	1	1	2	1
C ₉	SDG 9: Industry, Innovation and Infrastructure	5	16	2	1	2
C ₁₀	SDG 10: Reduced Inequality	6	5	15	3	6
C ₁₁	SDG 11: Sustainable Cities and Communities	2	10	3	4	5
C ₁₂	SDG 12: Responsible Consumption and Production	3	2	6	13	3
C ₁₃	SDG 13: Climate Action	4	3	13	11	8
C ₁₄	SDG 14: Life Below Water	16	17	17	17	17
C ₁₅	SDG 15: Life on Land	12	13	10	14	13
C ₁₆	SDG 16: Peace and Justice Strong Institutions	8	12	5	7	16
C ₁₇	SDG 17: Partnerships to achieve the Goal	7	8	7	6	9

Step 2. Evaluation of the relative importance

The DMs individually evaluated each criterion relative to the previous one and assigned separate scores ( $s_{j}^{k}$ ) to indicate the relative importance levels of the criteria. These scores ranged between 0 and 1.00.

Step 3. Determination of the coefficient ( $k_{j}^{k}$ )

The coefficient ( $k_{j}^{k}$ ) for each criterion was calculated using Equation (1) and the results, along with the corresponding ( $s_{j}^{k}$ ) values, are presented in Table 3.

Table 3

Relative importance and coefficient of criteria for each decision-maker (DM)

Criteria	$s_{j}^{1}$	$s_{j}^{2}$	$s_{j}^{3}$	$s_{j}^{4}$	$s_{j}^{5}$	$k_{j}^{1}$	$k_{j}^{2}$	$k_{j}^{3}$	$k_{j}^{4}$	$k_{j}^{5}$
C ₁	0.01	0.1	0.01	0.1	0.01	1.01	1.10	1.01	1.10	1.01
C ₂	0.2	0.05	0.1	0.1	0.15	1.20	1.05	1.10	1.10	1.15
C ₃	0.1	0.01	0.1	0.15	0.01	1.10	1.01	1.10	1.15	1.01
C ₄	0.1	0.01	0.2	0.01	0.01	1.10	1.01	1.20	1.01	1.01
C ₅	0.15	0.01	0.15	0.1	0.01	1.15	1.01	1.15	1.10	1.01
C ₆	0.05	0.05	0.25	0.01	0.2	1.05	1.05	1.25	1.01	1.20
C ₇	0.01	0.1	0.05	0.1	0.01	1.01	1.10	1.05	1.10	1.01
C ₈				0.05		1.00	1.00	1.00	1.05	1.00
C ₉	0.1	0.01	0.05		0.1	1.10	1.01	1.05	1.00	1.10
C ₁₀	0.05	0.05	0.2	0.15	0.05	1.05	1.05	1.20	1.15	1.05
C ₁₁	0.01	0.1	0.01	0.01	0.01	1.01	1.10	1.01	1.01	1.01
C ₁₂	0.05	0.1	0.05	0.2	0.15	1.05	1.10	1.05	1.20	1.15
C ₁₃	0.01	0.25	0.01	0.05	0.1	1.01	1.25	1.01	1.05	1.10
C ₁₄	0.15	0.01	0.1	0.01	0.1	1.15	1.01	1.10	1.01	1.10
C ₁₅	0.01	0.01	0.01	0.2	0.15	1.01	1.01	1.01	1.20	1.15
C ₁₆	0.01	0.1	0.1	0.02	0.15	1.01	1.10	1.10	1.02	1.15
C ₁₇	0.15	0.05	0.15	0.01	0.05	1.15	1.05	1.15	1.01	1.05

Step 4. Determination of recalculated weight

The recalculated weights ( $q_{j}^{k}$ ) for each criterion are obtained by applying Equation (2), and the results are presented in Table 4.

Table 4

Calculated weights of criteria for each decision-maker (DM)

Criteria	$q_{j}^{1}$	$q_{j}^{2}$	$q_{j}^{3}$	$q_{j}^{4}$	$q_{j}^{5}$	$w_{j}^{1}$	$w_{j}^{2}$	$w_{j}^{3}$	$w_{j}^{4}$	$w_{j}^{5}$
C ₁	0.509	0.393	0.934	0.310	0.387	0.044	0.040	0.087	0.030	0.037
C ₂	0.335	0.432	0.639	0.490	0.391	0.029	0.044	0.060	0.047	0.037
C ₃	0.597	0.582	0.456	0.566	0.517	0.051	0.059	0.043	0.055	0.049
C ₄	0.462	0.617	0.502	0.651	0.626	0.040	0.062	0.047	0.063	0.059
C ₅	0.514	0.623	0.393	0.658	0.731	0.044	0.063	0.037	0.063	0.069
C ₆	0.656	0.504	0.262	0.306	0.522	0.056	0.051	0.025	0.030	0.049
C ₇	0.689	0.661	0.609	0.745	0.783	0.059	0.067	0.057	0.072	0.074
C ₈	1	1	1.000	0.952	1.00	0.086	0.101	0.094	0.092	0.095
C ₉	0.849	0.389	0.952	1.000	0.909	0.073	0.039	0.089	0.096	0.086
C ₁₀	0.808	0.630	0.327	0.828	0.738	0.069	0.064	0.031	0.080	0.070
C ₁₁	0.990	0.529	0.943	0.820	0.775	0.085	0.054	0.088	0.079	0.073
C ₁₂	0.943	0.909	0.808	0.409	0.791	0.081	0.092	0.076	0.039	0.075
C ₁₃	0.934	0.727	0.452	0.539	0.664	0.080	0.074	0.042	0.052	0.063
C ₁₄	0.402	0.385	0.238	0.303	0.306	0.034	0.039	0.022	0.029	0.029
C ₁₅	0.591	0.454	0.603	0.341	0.449	0.051	0.046	0.056	0.033	0.043
C ₁₆	0.696	0.458	0.849	0.724	0.336	0.060	0.046	0.080	0.070	0.032
C ₁₇	0.703	0.588	0.703	0.738	0.633	0.060	0.059	0.066	0.071	0.060

Step 5. Calculation of the relative weights of the criteria

The final weights of the SDGs are calculated by using Equation (3), as shown in Table 4 for each DM.

Then, final weight w_j is obtained geometric mean of $w_{j}^{k}$ . w_j and ranking of criteria as shown Table 5.

Table 5

Final weights and ranking

Criteria	w _j	Ranking
C ₁	0.047	13
C ₂	0.043	15
C ₃	0.051	12
C ₄	0.054	11
C ₅	0.055	10
C ₆	0.042	16
C ₇	0.066	5
C ₈	0.093	1
C ₉	0.077	2
C ₁₀	0.063	7
C ₁₁	0.076	3
C ₁₂	0.073	4
C ₁₃	0.062	8
C ₁₄	0.031	17
C ₁₅	0.046	14
C ₁₆	0.057	9
C ₁₇	0.063	6

According to the obtained criteria weights, the final ranking is as follows: C₈ > C₉ > C₁₁ > C₁₂ > C₇ > C₁₇ > C₁₀ > C₁₃ > C₁₆ > C₅ > C₄ > C₃ > C₁ > C₁₅ > C₂ > C₆ > C₁₄ . From Table 5, it can be observed that the most significant criterion is SDG 8: Decent Work and Economic Growth. The second and third important criteria are SDG 9: Industry, Innovation and Infrastructure, and SDG 11: Sustainable Cities and Communities. It has been determined that the least important criterion in terms of airport sustainability is SDG 14: Life Below Water.

3.2 WASPAS

At this stage, using the weights determined by the SWARA method in the previous step, five airports were compared in terms of their implementation of SDGs, and the best airport alternative was determined.

Step 1. Developing the decision matrix

First, a decision matrix was created for the five selected airports, incorporating the 17 SDG criteria.

Airports develop their sustainability practices based on their geographical regions, national legal regulations, and cultural differences. While the specific practices may vary, the adoption of sustainable development goals is essential to ensure the long-term viability of airports globally. For this study, five airports were selected based on their certification as sustainable airports by international aviation authorities and organizations.

Singapore Changi Airport (A₁)

Singapore Changi Airport, established in 1981 and located in Changi, Singapore, is the largest airport in Southeast Asia, serving 382,000 aircraft movements and accommodating 68.3 million passengers annually. The airport has made significant strides in sustainability and has been awarded Level Three of the Airport Carbon Accreditation. This achievement reflects their efforts in carbon footprint mapping, implementing emission reduction plans, and engaging stakeholders to minimize the airport’s carbon emissions. Notably, Singapore Changi Airport has recently announced its plans to introduce a fully electric fleet of baggage handling tractors at Terminal 4, demonstrating their commitment to reducing greenhouse gas emissions [64].

Munich International Airport (A₂)

Munich International Airport, established in Munich, Germany in 1992, is a prominent hub in Europe and the second-largest airport in terms of traffic volume. Annually, it serves approximately 48 million passengers and handles 471,000 aircraft movements. The airport is recognized for its advanced renewable energy initiatives, notably its block heat and power facilities that utilize environmentally friendly natural gas to generate more than half of its annual energy requirements. Munich International Airport is also notable as the world’s first major commercial airport to incorporate energy-efficient LED technology throughout its premises. Additionally, the airport actively utilizes renewable energy sources such as biogas, bioethanol, and solar energy within its operations. Furthermore, it employs alternative biofuels and electricity to power airport vehicles and equipment, demonstrating its commitment to sustainable practices [65].

Dallas/Fort Worth International Airport (A₃)

Dallas/Fort Worth International Airport, situated in Texas, United States, commenced operations in 1973 and has since become one of the busiest airports worldwide. Annually, it serves approximately 75 million passengers and handles 720,000 aircraft movements. In 2019, the airport achieved a significant milestone by becoming the first carbon-neutral airport in North America, as certified by the Airports Council International (ACI) for a period of three years. Dallas/Fort Worth International Airport has implemented numerous environmental strategies to reduce its carbon footprint. These include utilizing 100% renewable electricity and adhering to green building standards for all new construction projects. Furthermore, the airport focuses on optimizing the energy efficiency of its existing facilities to minimize its environmental impact [66].

Oslo-Gardermoen Airport (A₄)

Oslo-Gardermoen Airport, situated in the capital city of Oslo, Norway, was inaugurated in 1998. It ranks as the second busiest airport among Scandinavian countries, serving approximately 28 million passengers and handling 254,000 aircraft movements annually. The airport is operated by Avinor Group. Oslo-Gardermoen Airport has taken significant strides in promoting sustainable aviation practices. Through collaborations with airlines and fuel suppliers such as Neste, SkyNRG, Lufthansa Group, KLM, and SAS, it achieved a remarkable milestone by becoming the world’s first international airport to introduce biofuels into its fuel supply system and make them available to airlines. In 2019, the airport phased out the use of fossil fuels with low priority in its energy supply, replacing them with biofuels. Moreover, Oslo-Gardermoen Airport has transitioned to utilizing the biofuel HVO (Hydrogenized Vegetable Oil) for energy production since 2020, while operating its backup power units solely on fossil diesel [67].

Amsterdam Schiphol Airport (A₅)

Amsterdam Schiphol Airport, located in the capital city of The Netherlands, Amsterdam, was initially established as a military airport in 1916. Over time, it has transformed into one of the largest airports globally, handling an impressive annual passenger volume of 71.7 million and accommodating 496 thousand aircraft movements. The airport is operated by the Royal Schiphol Group, and it actively supports sustainable aviation initiatives promoted by the European Union (EU). Furthermore, Amsterdam Schiphol Airport collaborates with other airport management entities such as the Single European Sky Project. The airport is also actively involved in sustainable aviation policy and partnerships, including participation in the European CO2 Emissions Trading Scheme (EU ETS) and the Clean Skies for Tomorrow (CST) initiative. The CST initiative aims to foster the alignment of the aviation value chain with sustainable aviation fuels in order to facilitate the transition toward a more sustainable aviation industry [68].

Decision-makers were requested to provide scores for the applications implemented at the airports, and subsequently, an initial decision matrix was formulated based on their collective assessments. Table 6 presents the initial decision matrix, reflecting the evaluations and opinions of the decision-makers.

Table 6
The initial decision matrix

Airports C ₁ C ₂ C ₃ C ₄ C ₅ C ₆ C ₇ C ₈ C ₉ C ₁₀ C ₁₁ C ₁₂ C ₁₃ C ₁₄ C ₁₅ C ₁₆ C ₁₇

A ₁ 3 1 3 5 3 5 4 5 4 3 3 4 4 5 2 3 3

A ₂ 1 1 4 3 4 4 3 4 4 2 3 4 3 1 5 2 3

A ₃ 2 1 5 4 5 4 4 4 5 5 5 4 4 2 4 4 5

A ₄ 1 1 2 2 2 3 5 5 4 3 3 5 4 5 5 2 3

A ₅ 1 1 3 2 2 2 3 3 3 4 4 4 4 1 2 3 4

Airports	C ₁	C ₂	C ₃	C ₄	C ₅	C ₆	C ₇	C ₈	C ₉	C ₁₀	C ₁₁	C ₁₂	C ₁₃	C ₁₄	C ₁₅	C ₁₆	C ₁₇
A ₁	3	1	3	5	3	5	4	5	4	3	3	4	4	5	2	3	3
A ₂	1	1	4	3	4	4	3	4	4	2	3	4	3	1	5	2	3
A ₃	2	1	5	4	5	4	4	4	5	5	5	4	4	2	4	4	5
A ₄	1	1	2	2	2	3	5	5	4	3	3	5	4	5	5	2	3
A ₅	1	1	3	2	2	2	3	3	3	4	4	4	4	1	2	3	4

Step 2. Developing the normalized decision matrix

The initial decision matrix, as presented in Table 6, was subjected to a normalization process to create the normalized decision matrix, as shown in Table 7. The normalization method employed in this study, indicated by Equation (5), was utilized to ensure that all criteria are utility-based and appropriately scaled.

Table 7

The normalized decision matrix

Airports	C ₁	C ₂	C ₃	C ₄	C ₅	C ₆	C ₇	C ₈	C ₉	C ₁₀	C ₁₁	C ₁₂	C ₁₃	C ₁₄	C ₁₅	C ₁₆	C ₁₇
A ₁	1	1	0,6	1	0,6	1	0,8	1	0,8	0,6	0,6	0,8	1	1	0,4	0,75	0,6
A ₂	0,333	1	0,8	0,6	0,8	0,8	0,6	0,8	0,8	0,4	0,6	0,8	0,75	0,2	1	0,5	0,6
A ₃	0,667	1	1	0,8	1	0,8	0,8	0,8	1	1	1	0,8	1	0,4	0,8	1	1
A ₄	0,333	1	0,4	0,4	0,4	0,6	1	1	0,8	0,6	0,6	1	1	1	1	0,5	0,6
A ₅	0,333	1	0,6	0,4	0,4	0,4	0,6	0,6	0,6	0,8	0,8	0,8	1	0,2	0,4	0,75	0,8

Step 3. Determination of relative importance of alternatives based on WSM

The weighted sum of the normalized scores of the airports based on the criteria was calculated using the weight values obtained from the SWARA method, as shown in Table 4. The calculations were performed using the weight values w_j and Equation (7). The results of these calculations are presented in Table 8.

Table 8

Relative significance values based on the Weighted Sum Model (WSM)

Airports	C ₁	C ₂	C ₃	C ₄	C ₅	C ₆	C ₇	C ₈	C ₉	C ₁₀	C ₁₁	C ₁₂	C ₁₃	C ₁₄	C ₁₅	C ₁₆	C ₁₇
A ₁	0,047	0,043	0,031	0,055	0,033	0,042	0,053	0,093	0,061	0,038	0,045	0,058	0,062	0,031	0,018	0,043	0,038
A ₂	0,016	0,043	0,041	0,033	0,043	0,034	0,039	0,075	0,061	0,025	0,045	0,058	0,047	0,006	0,046	0,029	0,038
A ₃	0,032	0,043	0,051	0,043	0,054	0,034	0,053	0,075	0,077	0,063	0,076	0,058	0,062	0,012	0,037	0,057	0,063
A ₄	0,016	0,043	0,021	0,022	0,022	0,025	0,066	0,093	0,061	0,038	0,045	0,073	0,062	0,031	0,046	0,029	0,038
A ₅	0,016	0,043	0,031	0,022	0,022	0,017	0,039	0,056	0,046	0,050	0,061	0,058	0,062	0,006	0,018	0,043	0,051

Step 4. Determination of relative importance of alternatives based on WPM

The weighted product of the normalized scores of the airports based on the criteria, using the weight values obtained from the SWARA method (shown in Table 4) and Equation (8), is calculated. The results of these calculations are presented in Table 9.

Table 9

Relative significance values based on the Weighted Product Model (WPM)

Airports	C ₁	C ₂	C ₃	C ₄	C ₅	C ₆	C ₇	C ₈	C ₉	C ₁₀	C ₁₁	C ₁₂	C ₁₃	C ₁₄	C ₁₅	C ₁₆	C ₁₇
A ₁	1,000	1,000	0,974	1,000	0,972	1,000	0,985	1,000	0,983	0,968	0,962	0,984	1,000	1,000	0,959	0,984	0,968
A ₂	0,949	1,000	0,989	0,973	0,988	0,991	0,967	0,979	0,983	0,944	0,962	0,984	0,982	0,952	1,000	0,961	0,968
A ₃	0,981	1,000	1,000	0,988	1,000	0,991	0,985	0,979	1,000	1,000	1,000	0,984	1,000	0,972	0,990	1,000	1,000
A ₄	0,949	1,000	0,954	0,952	0,951	0,979	1,000	1,000	0,983	0,968	0,962	1,000	1,000	1,000	1,000	0,961	0,968
A ₅	0,949	1,000	0,974	0,952	0,951	0,962	0,967	0,953	0,962	0,986	0,983	0,984	1,000	0,952	0,959	0,984	0,986

Step 5. Determination of total relative importance of alternatives by combing WSM and WPM

The values determined by the weighted sum and weighted multiplication methods of each alternative were calculated using Equation (10). The results of the alternatives evaluated using different λ values are shown in Table 10.

Table 10

Alternatives assessment with different λ values

	λ = 0	Rank	λ = 0, 1	Rank	λ = 0, 2	Rank	λ = 0, 3	Rank	λ = 0, 4	Rank	λ = 0, 5	Rank	λ = 0, 6	Rank	λ = 0, 7	Rank	λ = 0, 8	Rank	λ = 0, 9	Rank	λ = 1	Rank
A ₁	0,768	2	0,770	2	0,773	2	0,775	2	0,778	2	0,780	2	0,783	2	0,785	2	0,787	2	0,790	2	0,792	2
A ₂	0,646	4	0,650	4	0,653	4	0,656	4	0,659	4	0,663	4	0,666	4	0,669	4	0,673	4	0,676	4	0,679	4
A ₃	0,877	1	0,879	1	0,880	1	0,881	1	0,882	1	0,884	1	0,885	1	0,886	1	0,887	1	0,889	1	0,890	1
A ₄	0,683	3	0,688	3	0,693	3	0,697	3	0,702	3	0,707	3	0,711	3	0,716	3	0,721	3	0,725	3	0,730	3
A ₅	0,602	5	0,606	5	0,610	5	0,614	5	0,618	5	0,622	5	0,625	5	0,629	5	0,633	5	0,637	5	0,641	5

4 Results and discussion

When examining the WASPAS results based on the SWARA weightings, it was observed that the order of the alternatives did not change for different λs values. According to Table 10, the order of the alternatives is A_3> A_1 > A_4 > A_2 > A₅. It appears that A₃ is the airport that implements the best practices for the 17 SDGs.

According to the results of the SWARA method, it has been determined that SDG8 ( C₈ ) is given higher priority than other SDGs in the context of airports. This is consistent with the understanding that airports contribute significantly to employment and job opportunities, as well as their economic impact on the surrounding regions through commercial activities. Research studies have highlighted the role of airports in generating employment [69], and their contribution to regional economic development [70]. The second most significant goal, SDG9 ( C₉ ), focuses on developing resilient infrastructure, promoting inclusive and sustainable industrialization, and fostering innovation. This emphasizes the importance of aviation as a driver of innovation and highlights the significance of airports as key components of the “Airport-Industry-City” concept, which encompasses urban infrastructure and Aerotropolis structures [71].

The objective of SDG11 ( C₁₁ ) for airports is to establish a safe, secure, and sustainable green airport community. This goal emphasizes the importance of airports supporting a sustainable transport system by investing in transport infrastructure. It also highlights the significance of fostering an airport community where all stakeholders work together in harmony to enhance sustainability across various aspects [72]. SDG8, SDG9, and SDG11 all encompass the social, economic, and environmental dimensions of sustainability, and their significance is emphasized in the annual reports of airports. Given that airports inherently encompass standard elements of sustainable development initiatives, it is expected that these goals would be among the top three priorities.

According to the methodology employed in the study, SDG14 ( C₁₄ ), was determined to be the least important goal in the context of airports, which is consistent with its limited emphasis in airports’ annual reports. However, it is worth noting that there are airports worldwide that are located directly on seafront land or built into the sea, such as Hong Kong International Airport and other international airports. Despite this, the underwater impacts of aviation activities have not been adequately addressed by airports and the aviation industry. It is essential for aviation authorities and the industry to place greater importance on SDG14 and address the challenges associated with sustainability practices in relation to sea and ocean impacts. While this study evaluated the 17 SDGs in terms of their importance for airports, it is crucial for airports to prioritize all SDGs in their 2050 Agenda. They should actively work towards achieving these goals in an integrated manner, considering the economic, social, and environmental dimensions of sustainability.

In accordance with the results obtained from the WASPAS method, the airports were ranked as follows: Dallas/Fort Worth International Airport ( A₃ ), Singapore Changi Airport ( A₁ ), Oslo-Gardermoen Airport ( A₄ ), Munich International Airport ( A₂ ), and Amsterdam Schiphol Airport ( A₅ ). Dallas/Fort Worth International Airport was identified as the most successful in implementing SDG applications. One of the key reasons for its selection is its extensive network interaction with various airport stakeholders, including airlines, ground handling agencies, maintenance companies, government entities, and non-governmental organizations. By collaborating with these stakeholders, Dallas/Fort Worth International Airport has successfully implemented numerous sustainability practices and social responsibility projects.

Sustainability practices, such as collaborations and initiatives, support almost all SDGs, and their relationships with the implementations are transparently and comprehensively described in their annual reports. Similar to Dallas/Fort Worth International Airport, Singapore Changi Airport, which ranked second according to the method, demonstrates numerous successful sustainability practices. It is noteworthy that Singapore Changi Airport places significant emphasis on the SDGs in its annual reports. The airport acknowledges the crucial role of the SDGs in airport sustainability and effectively integrates them into its reports and operations. On the other hand, Oslo-Gardermoen Airport stands out with its remarkable and successful environmental sustainability initiatives. However, in comparison to the other two airports, Oslo-Gardermoen Airport ranks third as it places the least emphasis on the SDGs in its annual reports. Conversely, Munich International Airport and Amsterdam Schiphol Airport mention the SDGs solely in their corporate goals and priorities within their annual reports. However, when compared to other airports in the study, these airports rank last in terms of their applications, as they do not adequately support all SDGs.

The results of this study have demonstrated that the best airports excel in implementing successful initiatives for specific SDGs, such as SDG6 ( C₆ ), SDG7 ( C₇ ), SDG11, SDG 15 ( C₁₅ ) taking into account geographical conditions and cultural differences (Çelem et al., 2021). However, it has been observed that the importance of social development and equality goals, such SDG1 ( C₁ ), SDG2 ( C₂ ), SDG3 ( C₃ ), SDG10 ( C₁₀ ), and SDG16 ( C₁₆ ), which are not solely environmental goals, is not adequately emphasized in the annual reports of airports (Avinor Oslo Airport, 2020; Royal Schipol Group, 2020; Munich Airport, 2019). The UN Sustainable Development Goals for 2030 provide a roadmap for the aviation industry, which bears significant environmental responsibilities. Airports should align their strategic plans with these 17 goals to achieve sustainability in all dimensions and recognize their role in addressing social, economic, and environmental challenges. Furthermore, airports should embrace their contribution to the SDGs within an operational context and provide more comprehensive details in their Sustainability Reports.

5 Conclusion

As airports play a vital role as service providers on a global scale, they have a significant impact on economic, social, and environmental aspects of development. It is crucial for decision-makers in the aviation sector to effectively manage resources and develop sustainable and efficient airport management methodologies. The United Nations’ Sustainable Development Goals have a profound influence on various industries, including aviation, and many airports establish their sustainability goals and publish annual sustainability reports aligning with these SDGs. Despite the importance of achieving sustainability in the aviation industry, there is a scarcity of studies specifically focusing on airport sustainability practices related to the UN SDGs. Existing research in the literature mostly adopts qualitative methods to assess environmental criteria of the SDGs through general comparisons and evaluations of operations and organizations. In contrast to previous studies, this research proposes a hybrid approach using multi-criteria decision analysis (MCDA) as a tool to evaluate the SDGs in the context of airports. The SWARA method is initially employed to assign weights to the 17 SDGs based on their relative importance for airports. Subsequently, the WASPAS method is utilized using the calculated weights to determine the best alternative among the airports considered in the study.

This study highlights the importance of airports prioritizing the UN SDGs to enhance their sustainability performance and contribute to the achievement of the 2030 Agenda targets. It emphasizes the need for airports to establish new strategic and tactical targets, as well as performance indicators, to effectively work towards their sustainability goals. The findings of this study can serve as a valuable guide for aviation authorities, airport managers, municipalities, and researchers in the field to align their efforts with the specified sustainability goals and make progress toward a more sustainable future.

The limitation of this study pertains to its exclusive reliance on the analysis of the most recent annual sustainability reports of airports. To enhance the comprehensiveness of future investigations, it is recommended to broaden the scope by incorporating airlines into the evaluation process and employing diverse MCDM methods. Such an expanded approach would enable a more comprehensive assessment of the significance of SDGs for airlines and facilitate the identification of airlines exhibiting exemplary sustainability practices. By considering multiple stakeholders within the aviation industry, these extended studies would provide a more nuanced and holistic understanding of sustainability endeavors in this sector.

Ethical approval

Not applicable.

Informed consent

Not applicable.

Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

This article is a part of the Master’s thesis by Beste Pelin Çelem, entitled “Evaluation of United Nations 2030 Sustainable Development Goals in Airports using Multi-Criteria Decision Making Methods”.

Funding

None to report.

References

International Civil Aviation Organization (ICAO). Annual report of the ICAO Council: 2020. [accessed January 21, 2022]. Available from: https://www.icao.int/annual-report-2020/Pages/default.aspx

Harrison

, Masiol

, Vardoulakis

. Civil aviation, air pollution, and human health. Environmental Research Letters. 2015;10(4):041001. doi: 10.1088/1748-9326/10/4/041001.

Basner

, Clark

, Hansell

, Hileman

, Janssen

, Shepherd

, Sparrow

. Aviation noise impacts: state of the science. Noise & health. 2017;19(87):41. doi: 10.4103/nah.NAH-104-16.

Zaporozhets

, Levchenko

, Synylo

. Risk and exposure control of aviation impact on the environment. doi: 10.20998/2522-9052.2019.3.02.

Andrejiová

, Grincova

, Marasová

. Study of the percentage of greenhouse gas emissions from aviation in the EU-27 countries by applying multiple-criteria statistical methods. International Journal of Environmental Research and Public Health. 2020;17(11):3759. doi: 10.3390/ijerph17113759.

Upham

, Mills

. Environmental and operational sustainability of airports: Core indicators and stakeholder communication. Benchmarking: An International Journal. 2005;12(5):498–518. doi: 10.1108/14635770510615069.

Koc

, Durmaz

. Airport corporate sustainability: An analysisof indicators reported in the sustainability practices. Procedia-Social and Behavioral Sciences. 2015;181:158–170. doi: 10.1016/j.sbspro.2015.04.877.

Environmental Protection Agency (EPA). What is sustainability? [accessed January 27, 2022]. Available from: https://www.epa.gov/sustainability/learn-about-sustainability#what (accessed January 27, 2022).

Pfeiffer

, Middeke

, Tambour

. 2030 Agenda for Sustainable Development: Implications for Official Statistics. Work. 2017;2017:911–918.

10.

Béguin

, Duarte

. Work and sustainable development. Work. 2017;57(3):311–313.

11.

Nundy

, Ghosh

, Mesloub

, Albaqawy

, Alnaim

. Impact of COVID-19 pandemic on socio-economic, energy-environment, and transport sector globally and sustainable development goal (SDG). Journal of Cleaner Production. 2021;312:127705. doi: 10.1016/j.jclepro.2021.127705.

12.

Anholon

, Rampasso

, Dibbern

, Serafim

, Quelhas

. COVID-19 and decent work: A bibliometric analysis. Work. (Preprint). 2022;1-9.

13.

Pavlovic-Veselinovic

. Ergonomics as a missing part of sustainability. Work. 2014;49(3):395–399.

14.

Bidarbakhtnia

. SDG progress assessment; comparing apples with what? Statistical Journal of the IAOS. (Preprint). 2022; 1-6.

15.

Sharma

. Sustainable Consumption and Production in India and Its Global Impact: A Complex System Approach to Its Solution. Work. 2020;2020:19–30.

16.

Elhmoud

, Kutty

. Sustainability assessment in the aviation industry: A mini-review on the tools, models, and methods of assessment. In Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management, Harare, Zimbabwe. 2021;7-10.

17.

International Civil Aviation Organization (ICAO). ICAO and the United Nations Sustainable Development Goals [accessed January 21, 2022]. Available from: https://www.icao.int/annual-report-2020/Pages/default.aspx

18.

Airports Council International (ACI). Airports Council International supports United Nations’ Global Sustainable Development Goals [accessed January 21, 2022]. Available from: https://aci.aero/2015/09/29/airports-council-international-supports-united-nations-global-sustainable-development-goals/

19.

European Organization for the Safety of Air Navigation (EUROCONTROL). [accessed January 30, 2022]. Available from: https://www.eurocontrol.int/news/eurocontrol-and-airport-regions-council-strengthen-cooperation-reduce-environmental-impact

20.

Lambert

, Champelovier

, Blanchet

, Lavandier

, Terroir

, Márki

, Griefahn

, Iemma

, Janssens

, Bisping

. Human response to simulated airport noise scenarios in home-like environments. Applied Acoustics. 2015;90:116–125. doi: 10.1016/j.apacoust.2014.11.009.

21.

Neto

RFM

, Calijuri

, de Castro Carvalho

, da Fonseca Santiago

. Rainwater treatment in airports using slow sand filtration followed by chlorination: efficiency and costs. Resources, Conservation and Recycling. 2012;65:124–129. doi: 10.1016/j.resconrec.2012.06.001.

22.

Washburn

, Begier

, Wright

. Collisions between eagles and aircraft: an increasing problem in the airport environment. Journal of Raptor Research. 2015;49(2):192–200. doi: 10.3356/rapt-49-02-192-200.1.

23.

Wan

, Peng

, Wang

, Tian

, Xu

. Evaluation of airport sustainability by the synthetic evaluation method: A case study of Guangzhou Baiyun international airport, China, from 2008 to 2017. Sustainability. 2020;12(8):3334. doi: 10.3390/su12083334.

24.

Greer

, Rakas

, Horvath

. Airports and environmental sustainability: a comprehensive review. Environmental Research Letters. 2020;15(10):103007. doi: 10.1088/1748-9326/abb42a.10.1016/j.jairtraman.2020.101949.

25.

Sperry

, Bender

. Airport buildings: A key opportunity for sustainability in aviation. Journal of Airport Management. 2020;14(3):234–245.

26.

Sreenath

, Sudhakar

, Yusop

. Sustainability at airports: Technologies and best practices from ASEAN countries. Journal of Environmental Management. 2021;299:113639. doi: 10.1016/j.jenvman.2021.113639.

27.

Dube

, Nhamo

, Chikodzi

. COVID-19 pandemic and prospects for recovery of the global aviation industry. Journal of Air Transport Management. 2021;92:102022. doi: 10.1016/j.jairtraman.2021.102022.

28.

Di Vaio

, Varriale

. SDGs and airport sustainable performance: Evidence from Italy on organizational, accounting, and reporting practices through financial and non-financial disclosure. Journal of Cleaner Production. 2020;249:119431. doi: 10.1016/j.jclepro.2019.119431.

29.

Angelov

. Roads for improving the quality of aviation education. Trans Motauto World. 2019;4(1):36–39.

30.

Dimitriou

, Sartzetaki

. Social Dimensions of Aviation on Sustainable Development. In: Sustainable Aviation. Palgrave Macmillan. 2020;173-191. doi: 10.1007/978-3-030-28661-3_9.

31.

Swietlik

. The Environmental Impacts of Airport Deicing–Water Quality. Environmental Protection Agency Washington Dc Office of Water. Available from: https://apps.dtic.mil/sti/pdfs/ADA554090.pdf.

32.

Barrett

, Caldera-Petit

. Decarbonising gate operations through clean energy solutions. Journal of Airport Management. 2021;15(2):160–171.

33.

Florida

, Mellander

, Holgersson

. Up in the air: The role of airports for regional economic development. The Annals of Regional Science. 2015;54(1):197–214. doi: 10.1007/s00168-014-0651-z.

34.

Sheard

. Airports and urban sectoral employment. Journal of Urban Economics. 2014;80:133–152. doi: 10.1016/j.jue.2014.01.002.

35.

Moriarty

, Kvien

. US Airport Infrastructure and Sustainable Aviation Fuel (No. NREL/TP-5400-78368). National Renewable Energy Lab (NREL), Golden, CO (United States). 2021. Available from: https://www.nrel.gov/docs/fy21osti/78368.pdf.

36.

Halpern

, Mwesiumo

, Suau-Sanchez

, Budd

, Bråthen

. Ready for digital transformation? The effect of organisational readiness, innovation, airport size and ownership on digital change at airports. J Air Transp Manag. 2021;90:101949.

37.

Ramakrishnan

, Liu

, Yu

, Seshadri

, Gou

. Towards greener airports: Development of an assessment framework by leveraging sustainability reports and rating tools. Environmental Impact Assessment Review. 2022;93:106740. doi: 10.1016/j.eiar.2022.106740.

38.

Sebastian

, Louis

. Understanding waste management at airports: A study on current practices and challenges based on literature review. Renewable and Sustainable Energy Reviews. 2021;147:111229. doi: 10.1016/j.rser.2021.111229.

39.

Dolsak

, Prakash

. Different approaches to reducing aviationemissions: reviewing the structure-agency debate in climate policy. Climate Action. 2022;1(1):1–9. doi: 10.1007/s44168-022-00001-w.

40.

Zhao

, Taheripour

, Malina

, Staples

, Tyner

. Estimating induced land use change emissions for sustainable aviation biofuel pathways. Science of the Total Environment. 2021;779:146238. doi: 10.1016/j.scitotenv.2021.146238.

41.

Aghdaie

, Zolfani

, Zavadskas

. Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Engineering Economics. 2013;24(1):5–17. doi: 10.5755/j01.ee.24.1.2822.

42.

Ghenai

, Albawab

, Bettayeb

. Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renewable Energy. 2020;146:580–597. doi: 10.1016/j.renene.2019.06.157.

43.

Rani

, Mishra

, Krishankumar

, Mardani

, Cavallaro

, Soundarapandian Ravichandran

, Balasubramanian

. Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS). Symmetry. 2020;12(7):1152. doi: 10.3390/sym12071152.

44.

Albawab

, Ghenai

, Bettayeb

, Janajreh

. Sustainability performance index for ranking energy storage technologies using multi-criteria decision-making model and hybrid computational method. Journal of Energy Storage. 2020;32:101820. doi: 10.1016/j.est.2020.101820.

45.

Pamucar

, Yazdani

, Montero-Simo

, Araque-Padilla

, Mohammed

. Multi-criteria decision analysis towards robust service quality measurement. Expert Systems with Applications. 2021;170:114508.

46.

Kaya

, Erginel

. Futuristic airport: A sustainable airport design by integrating hesitant fuzzy SWARA and hesitant fuzzy sustainable quality function deployment. J Clean Prod. 2020;275:123880.

47.

Ulutas

, Karakus

, Topal

. Location selection forlogistics center with fuzzy SWARA and CoCoSo methods. J Intell Fuzzy Syst. 2020;38(4):4693–4709.

48.

Yazgan

, Üstün

. Application of analytic network process:weighting of selection criteria for civil pilots. J Aeronaut SpaceTechnol. 2011;5(2):1–12.

49.

Yazgan

, Yilmaz

. Prioritisation of factors contributing to human error for airworthiness management strategy with ANP. Aircraft Eng Aerospace Technol. 2018;91(1):78–93.

50.

Bongo

, Alimpangog

KMS

, Loar

, Montefalcon

, Ocampo

. An application of DEMATEL-ANP and PROMETHEE II approach for air traffic controllers’ workload stress problem: A case of Mactan Civil Aviation Authority of the Philippines. J Air Transp Manag. 2018;68:198–213.

51.

Turskis

, Zavadskas

, Antucheviciene

, Kosareva

. A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. Int J Comput Commun Control. 2015;10(6):113–128.

52.

Deveci

, Öner

, Ciftci

, Özcan

, Pamucar

. Intervaltype-2 hesitant fuzzy Entropy-based WASPAS approach for aircrafttype selection. Appl Soft Comput. 2022;114:108076.

53.

Pamucar

, Deveci

, Canítez

, Lukovac

. Selecting an airportground access mode using novel fuzzy LBWA-WASPAS-H decision makingmodel. Eng Appl Artif Intell. 2020;93:103703.

54.

Ghorabaee

, Amiri

, Zavadskas

, Turskis

, Antucheviciene

. A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria. J Air Transp Manag. 2017;63:45–60.

55.

Mardani

, Saraji

, Mishra

, Rani

. A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak. Appl Soft Comput. 2020;96:106613.

56.

Alrasheedi

, Mardani

, Mishra

, Rani

, Loganathan

. An extended framework to evaluate sustainable suppliers in manufacturing companies using a new Pythagorean fuzzy entropy-SWARA-WASPAS decision-making approach. J Enterp Inf Manage. 2022;35(2):333–357.

57.

Kersuliene

, Zavadskas

, Turskis

. Selection of rationaldispute resolution method by applying new step-wise weightassessment ratio analysis (SWARA). J Bus Econ Manag. 2010;11(2):243–258.

58.

Zolfani Hashemkhani

, Yazdani

, Zavadskas

. An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Comput. 2018;22:7399–7405.

59.

Prajapati

, Kant

, Shankar

. Prioritizing the solutions of reverse logistics implementation to mitigate its barriers: A hybrid modified SWARA and WASPAS approach. Journal of Cleaner Production. 2019;240:118219. doi: 10.1016/j.jclepro.2019.118219.

60.

Stanujkic

, Karabasevic

, Zavadskas

. A framework for the selection of a packaging design based on the SWARA method. Engineering Economics. 2015;26(2):181–187. doi: 10.5755/j01.ee.26.2.8820.

61.

Zavadskas

, Turskis

, Antucheviciene

, Zakarevicius

. Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika. 2012;122(6):3–6.

62.

Chakraborty

, Zavadskas

. Applications of WASPAS method in manufacturing decision making. Informatica. 2014;25(1):1–20. doi: 10.1088/1748-9326/10/4/041001.

63.

Karabasević

, Stanujkić

, Urosević

, Maksimović

. An approach to personnel selection based on SWARAand WASPAS methods. Bizinfo (Blace). 2016;7(1):1–11.

64.

Changi Airport Group. Sustainability Report 2019/20. Available from: https://www.changiairport.com/content/dam/cacorp/sustainability/sustainable-changi/sustainability-report/CAG-Sustainability%20Report%20FYpdf.

65.

Munich Airport. Sustainability Program. Available from: https://report2019.munich-airport.com/sustainable-development/sustainability-program.html.

66.

Dallas Fort Worth International Airport. Environmental, Social, and Governance Report. Available from: www.eastmoney.com

67.

Avinor Oslo Airport. Environmental Report. Available from: https://avinor.no/globalassets/_oslo-lufthavn/om-oslo-lufthavn/miljo-og-lokalsamfunn/miljodokumenter/miljoarsrapport-2020-engelsk.pdf.

68.

Royal Schipol Group. Annual Report. Available from: https://2020.annualreportschiphol.com/.

69.

Blonigen

, Cristea

. Air service and urban growth: Evidence from a quasi-natural policy experiment. J Urban Econ. 2015;86:128–146.

70.

Feng

, Bai

. Coordinated development of airport economy in China: impact of “Airport-Industry-City” coordination on regional economic performance. Ann Oper Res. 2021;1-20.

71.

Bolat

, Durmaz

. Havalimanlarínda yeni dönem aerotropolisyapílar: Istanbul Havalimaní örneği. Journalof Awareness. 2020;5(3):375–390.

72.

Babaoglu

, Kürkcüoğlu

. Life In-Between Flows:A Study on Airport Cities and Changing Trends in Metropolitan Areas. Megaron. 2020;15(2).