The remainder of the paper is structured as follows: Section “Literature review” provides a review of existing research on airport energy sustainability, renewable energy technologies, and investment trends. Section “Proposed methodology” outlines the research methodology, including expert data collection and the MCDM model structure. Section “Analysis results” presents the evaluation results and rankings of investment alternatives. Section “Discussion” discusses the key findings, compares the results with existing models, and provides policy implications. Section “Conclusion” concludes the study by outlining its limitations and proposing directions for future research. By addressing both theoretical and practical dimensions, this study contributes to the growing discourse on sustainable airport infrastructure and provides a replicable decision framework for energy investment planning.
The increasing energy demands of airports necessitate the adoption of sustainable energy harvesting technologies to reduce operational costs and carbon footprints. Various energy harvesting solutions are being explored for smart airports, including piezoelectric energy from passenger foot traffic, solar panel-integrated runways, wind energy from aircraft jet blast, thermal energy recovery from aircraft operations, and electromagnetic energy harvesting in airport vehicles. These technologies contribute to achieving carbon neutrality and improving energy efficiency in airport operations.
Piezoelectric energy harvesting has emerged as a promising innovation for airports, leveraging mechanical vibrations from passenger foot traffic and moving aircraft to generate electricity through electromechanical transduction. Correia and Ferreira found that embedding piezoelectric materials in high-traffic areas such as terminal walkways and runway sections with frequent aircraft movement could achieve an energy conversion efficiency of approximately 10-18%, producing up to 20 kWh per day per 100 m. However, limitations related to material fatigue, energy dissipation losses, and high installation costs currently impede large-scale deployment. De Fazio et al. further estimated that kinetic energy harvesting from vehicular transit, particularly through piezoelectric roadways near airport perimeters, could supply up to 30% of an airport’s sensor and lighting power needs. Still, the development of high-durability piezoelectric ceramics and polymer composites is crucial for improving long-term efficiency and energy yield. Additionally, the Transportation Research Board highlighted that kinetic energy capture from aircraft deceleration using piezoelectric and regenerative braking systems could contribute significantly to airport sustainability by recovering energy otherwise lost as heat during landing, though advancements in high-power-density materials are required for widespread implementation.
Solar panel-integrated runways and taxiways have also been explored as a means to enhance airport energy self-sufficiency through photovoltaic energy conversion. Minja and Mushi designed a hybrid renewable energy system for international airports, incorporating monocrystalline and bifacial solar photovoltaics (PV) as primary power sources. Their study estimated that a 10 MW solar farm deployed on underutilized airport land could offset up to 15% of an airport’s annual electricity demand while integrating with battery storage for grid stabilization. Yıldız, Yilmaz, and Celik examined solar energy applications for airport ground support equipment (GSE) and found that transitioning to solar-powered electric tugs, baggage carts, and de-icing trucks could lead to a 60% reduction in fossil fuel consumption, generating operational cost savings exceeding $3 million annually in large airports. Yadav et al. extended this analysis by evaluating hybrid solar-wind airport energy systems, demonstrating that airports adopting these technologies could cut overall CO₂ emissions by 20-35% within five years, depending on regional solar irradiance levels and wind availability. Salata, Dell’Olmo, and Ciancio further explored agri-voltaic applications near airport facilities, proposing a dual-use model where solar panel installations on farmland adjacent to airports could maximize energy generation while mitigating glare risks for aircraft operations.
Wind energy capture from aircraft jet blast has been identified as a potential supplementary energy source for airports. Baxter investigated the feasibility of micro wind turbines placed near active taxiways and found that small-scale vertical-axis wind turbines (VAWTs) could generate between 2 and 5 MWh per unit annually, harnessing airflow from departing aircraft to power nearby airport infrastructure. Goh et al. proposed an adaptive energy management strategy integrating wind-assisted energy capture with existing solar power systems, revealing that high-traffic airports handling over 50 million passengers per year could derive up to 20-30% of their energy needs from wind-assisted power systems. However, technical challenges such as turbine placement, wake turbulence effects, and compliance with air safety regulations must be addressed before large-scale implementation.
Thermal energy recovery from aircraft and ground operations presents another opportunity for improving airport energy efficiency. Khalil and Dincer explored a holistic energy system integrating waste heat recovery from aircraft auxiliary power units (APUs) to support terminal heating and cooling. Their findings suggest that repurposing 20-25% of exhaust heat from APUs and ground service equipment could substantially enhance airport-wide energy efficiency, particularly in climates with high heating and cooling demands. Ziegler and Nixon analyzed the feasibility of geothermal heat pumps for airport pavement snowmelt and terminal climate control, estimating that a 5 MW geothermal system could reduce winter heating costs by 40% in colder regions. These thermal recovery technologies offer a stable and renewable alternative to traditional heating and cooling systems, particularly when integrated with district energy networks within large airport hubs. Kokkinos and Emmanouilidou also highlighted the role of waste-to-energy applications in producing sustainable aviation fuel (SAF) through thermal gasification and pyrolysis processes, reinforcing the importance of multi-functional energy systems in sustainable airport operations.
Electromagnetic energy harvesting in airport vehicles has also gained traction as a method for enhancing sustainability in ground operations. De Fazio et al. examined inductive wireless charging systems embedded in taxiways and parking zones to power electric ground support vehicles, reporting an energy transfer efficiency of 85-90%, outperforming conventional plug-in charging systems in terms of reliability and convenience. Camilleri and Batra explored kinetic energy recovery from landing aircraft, proposing the integration of electromagnetic braking systems into runways to convert deceleration forces into usable electricity. Their study concluded that kinetic energy recovery systems could capture up to 8-12 MWh annually in major international airports, though infrastructure modifications and investment in high-efficiency electromagnetic coils would be required for practical implementation. These findings align with the increasing emphasis on integrating renewable energy solutions to enhance airport sustainability. The combination of energy harvesting technologies, innovative energy management strategies, and renewable energy investments will play a crucial role in advancing sustainable aviation industry practices
The aviation industry is undergoing a transformative shift toward sustainability, with airports investing heavily in green infrastructure and energy-efficient technologies. Sustainable airport development strategies focus on integrating renewable energy, improving operational efficiency, and reducing carbon emissions. Green investment in airports is driven by regulatory frameworks, financial incentives, and the growing recognition of environmental responsibility. Aviation contributes approximately 2.5% of global energy-related CO₂ emissions, making investment in sustainable practices critical. According to the Business Council for Sustainable Energy, global clean energy investments reached $1.7 trillion in 2023, with a substantial portion allocated to airport sustainability projects. The International Energy Agency reported that solar PV investments in aviation surpassed $500 billion in 2024, reflecting the industry’s commitment to renewable energy.
Investments in airport sustainability initiatives include the adoption of alternative energy sources, energy efficiency programs, and the integration of smart energy management systems. Aksoy et al. utilized a fuzzy decision-making model to assess investment strategies for green flight activities, finding that solar and wind energy integration in airports could reduce operational costs by 25-30% over a 10-year period. Becken et al. explored the benefits of preferential land access for clean energy adoption in airports, highlighting that airports with dedicated renewable energy zones could achieve up to 40% energy self-sufficiency. Energy-efficient infrastructure improvements have also become a priority in airport investment strategies. According to Bahman (2023), life cycle assessments of airport sustainability projects indicate that airports implementing energy-efficient HVAC systems, LED lighting, and smart building automation can achieve energy savings of 15-20% annually. Chourasia et al. emphasized that sustainable airport planning should incorporate digital energy monitoring tools, reducing electricity consumption by up to 12% through AI-based demand response systems.
Green financing mechanisms play a crucial role in supporting sustainable airport development. Dua and Guzman analyzed the role of green bonds and carbon credit trading in aviation, estimating that the global market for sustainable aviation financing will exceed $100 billion by 2030. The World Economic Forum reported that airports investing in clean hydrogen and electric ground support equipment could cut emissions by 20-35% within five years. Additionally, Alharasees et al. applied the AHP methodology to evaluate smart energy investment strategies for airports, concluding that on-site renewable energy projects had the highest cost-effectiveness ratio compared to other sustainability initiatives.
Operational strategies are also evolving to support energy-efficient airport development. Sigler et al. demonstrated that optimizing airport shuttle routes through machine learning algorithms led to a 9% reduction in fuel consumption and emissions at Dallas Fort Worth International Airport. Similarly, Szaruga and Załoga assessed non-efficient airport units and found that integrating energy recovery systems into airport infrastructure could increase overall energy performance by 8-12%. Industry reports further highlight the increasing momentum behind airport sustainability investments. According to Deloitte Insights, the renewable energy market in aviation is expected to grow at a CAGR of 7.2% between 2025 and 2030, with airports playing a key role in the adoption of new energy-efficient technologies. The Boeing Cascade Climate Impact Model emphasized that sustainable aviation fuels (SAFs) and hydrogen-based energy solutions could contribute to a 50% reduction in aviation emissions by 2050.
Effective decision-making is important to optimize investments in sustainable energy projects within airports. The complexity of selecting and prioritizing energy solutions requires advanced analytical methods that integrate multiple criteria, expert opinions, and data-driven approaches. Two primary methodologies — Multi-Criteria Decision-Making (MCDM) and hybrid artificial intelligence (AI) with fuzzy logic — have emerged as valuable tools in assessing and ranking investment options for green airport development. Multi-Criteria Decision-Making (MCDM) methods have been widely applied in aviation energy investments to evaluate the trade-offs between cost, efficiency, sustainability, and implementation feasibility. Lotfi et al. demonstrated that resilience-based MCDM approaches could optimize renewable energy site selection, ensuring risk-aware investment decisions. Similarly, Li et al. applied a hybrid MCDM model incorporating the hysteresis band principle to evaluate renewable energy projects, concluding that integrating multiple energy sources enhances sustainability. Wang et al. proposed an MCDM framework for selecting the most suitable renewable energy technologies, finding that decision models incorporating financial, environmental, and technical criteria improved energy investment strategies. Additionally, Effatpanah et al. conducted a comparative analysis of five widely-used MCDM methods, revealing that hybrid models provide superior accuracy in ranking clean energy solutions for airport operations.
The application of MCDM in aviation has been particularly relevant for evaluating solar, wind, and hybrid energy solutions. Rezk et al. applied MCDM techniques to concentrated solar thermal power projects, determining that large-scale solar farms in airport environments could achieve levelized costs of electricity (LCOE) as low as $50 per MWh, making them one of the most cost-effective solutions. Garcia-Orozco et al. highlighted the role of MCDM in optimizing offshore renewable energy systems, providing insights that can be adapted for aviation-specific sustainability projects. Bandira et al. demonstrated the effectiveness of Geographic Information System (GIS)-based MCDM models in selecting solar farm locations for airport campuses, achieving 20-30% higher energy yield optimization through site-specific evaluation.
Hybrid artificial intelligence (AI) and fuzzy logic approaches offer advanced decision-making capabilities by incorporating real-time data and predictive modeling into energy investment strategies. AI-based models can analyze vast datasets, improve energy efficiency forecasting, and enhance decision accuracy. Alexandridis et al. explored distributed AI modeling in smart airport energy management, demonstrating that AI-driven simulations could optimize 10-15% of annual airport energy consumption. Singh et al. identified challenges in AI adoption for sustainability management and proposed integrated AI-fuzzy decision models to enhance airport infrastructure investments. Additionally, Kou et al. introduced a novel AI-based fuzzy decision-making framework for financing electric vehicle charging infrastructure investments. Their model incorporates a dimension reduction algorithm to calculate expert weights, a spherical fuzzy decision matrix, spherical fuzzy CRITIC for weighting criteria, and spherical fuzzy RATGOS for ranking financing strategies. The study found that ‘potential income’ is the most effective criterion, and among innovative financing strategies, ‘blockchain technology’ is the most sustainable solution. This framework can be adapted to airport energy systems to improve financial decision-making, optimize resource allocation, and ensure sustainable infrastructure investments.
Fuzzy logic approaches have been particularly useful in handling uncertainty in energy decision-making. Yayla et al. introduced an AI-based HVAC control system for airport terminals, achieving a 25% reduction in energy consumption by dynamically adjusting ventilation and temperature settings based on real-time occupancy and weather conditions. Rauch and Hen examined AI-driven airport digital transformation strategies, concluding that integrating AI into airport sustainability initiatives could lead to long-term operational cost savings exceeding $5 million annually for major hub airports. Additionally, Dulhare and Rasool proposed a smart airport system utilizing AI and IoT to enhance energy efficiency and passenger flow, with projected improvements in overall airport energy optimization by 12-18%.
Industry reports highlight the increasing reliance on MCDM and AI-based models for energy investment decisions. The International Energy Agency reported that AI-driven decision models have accelerated clean energy project implementation by 30%, reducing delays caused by conventional feasibility assessments. According to Bloomberg New Energy Finance, AI-integrated sustainability frameworks in aviation are projected to increase energy efficiency by 20-25% by 2030, contributing to significant cost reductions in airport operations. Furthermore, the World Economic Forum identified MCDM and AI as key enablers in achieving net-zero aviation emissions, predicting that by 2040, over 60% of airport sustainability investments will be optimized through AI-enhanced decision-making models. The literature and industry analyses underscore the growing importance of advanced decision-making techniques in sustainable airport energy investments. As the aviation industry progresses toward decarbonization, integrating MCDM and AI-based approaches will be essential for maximizing the efficiency, feasibility, and financial viability of renewable energy projects. Future research should explore the combination of deep learning algorithms with fuzzy multi-criteria decision models to further enhance the precision of investment strategies in green airport development.
