Search results

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

This paper aims to present an example of the measurement of airport efficiency, a topic of great interest in civil aviation today. The methodology used is data envelopment analysis (DEA) and the Malmquist Index. The calculation of airport efficiency with up-to-date data for each period is of great importance in the context of sustainability. The study selected ten airports with high air traffic in Turkey as the sample set. The objective of this study is to evaluate the current state of airport efficiency, identify the sources of inefficiency and make appropriate policy recommendations based on the findings obtained.

The study is based on DEA and Malmquist Index Analysis. The number of personnel and terminal size data of ten selected decision-making units (DMU) are used as inputs, while passenger, cargo and aircraft traffic data are used as outputs. A five-year period, spanning from 2018 to 2022, is considered as the data set in the study.

Upon analysis of the data from the ten airports included in the study, it was found that the current input-output combination yielded efficient results, with the exception of certain characteristics, such as the impact of seasonal conditions or tourism. Concurrent with the growth in aircraft, passenger and freight traffic, the number of personnel employed at these airports has also increased. It was concluded that technological efficiency is of paramount importance for each airport, and that investments in technology should be increased.

A separate assessment was conducted for each of the ten airports included in the study sample. Each airport was evaluated in terms of its strengths and weaknesses, and areas of low efficiency were identified. Consequently, more general conclusions were reached than airport-specific evaluations.

In order to ensure the long-term sustainability of the sector, it is essential that the efficiency measurements of airports are calculated using up-to-date data on a regular basis. The results obtained from these calculations provide guidance for the strategic plans to be implemented in the long term, as well as for the solution proposals for operational problems. In this context, this study not only provides information to policymakers and airport managers about the current situation, as it includes recent data, but also contributes to the literature in this sense, as it includes policy recommendations.

Climate change significantly impacts global temperatures, posing challenges to various sectors, including aviation. The purpose of this study is to assess the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences.

This study examines the effects of rising temperatures on aircraft engine performance using real-time data from a Boeing 787-8 equipped with GEnx-1B engines, which are collected via Flight Data Recorder of the engines and were analyzed for the take-off and cruise phases on the ground. Exhaust gas temperature (EGT), fuel flow and take-off weights were evaluated.

The analysis revealed a significant increase in EGT at the cruising altitude of 38,000 ft during the summer months compared to expected standard atmospheric values. This increase, averaging over 200 °C, is attributed to global warming. Such elevated temperatures are likely to accelerate the degradation of turbine components, resulting in increased fuel consumption: higher EGT signifies inefficient engine operation, resulting in more fuel burned per unit thrust; early engine aging: elevated temperatures accelerate wear and tear on turbine components, potentially reducing engine lifespan and increasing maintenance costs and enhanced atmospheric pollution: incomplete combustion at high EGTs generates additional emissions, contributing to local air quality concerns.

The research findings have practical implications for understanding the potential operational challenges and environmental impacts of climate change on aircraft engine performance. This lets us explore mitigation strategies and adapt operational procedures to ensure sustainable regional aviation practices.

This research enhances environmental consequences by assessing the impact of climate change on aircraft performance.

The tourism sector is facing significant challenges due to global climate change. The sector is a major contributor to carbon emissions while at the same time relied upon to drive regional development in Arctic Finland. This paradoxical situation highlights the urgent need for effective mitigation and adaptation policies and governance frameworks for both climate change and tourism. Finland has set one of the world’s most ambitious national climate mitigation targets into a legally binding framework. The state aims to be the first country in the global North to reach carbon neutrality by 2035. The new Climate Change Act 2022 outlines the key requirements for climate change policy planning and related monitoring and defines the national climate objectives. However, the tourism industry’s future growth potential in the Arctic Finland relies heavily on international tourism, which is largely based on the aviation sector. Tourism therefore faces a paradoxical situation for growth thinking in the current climate change policy context. Against this backdrop, this chapter aims to explore the complex issue of sustainable tourism development governance in the context of carbon neutrality policy in Arctic Finland. Specifically, it focusses on the potential contradictions between the role and needs of the tourism sector in regional development policies, and the responsibilities outlined in the Climate Change Act. This ‘wicked problem’ requires careful consideration and innovative solutions to ensure that tourism can continue to drive economic growth while also addressing the challenges of climate change.

Blockchain is the fastest-growing technology currently being used in the aviation industry, especially in aviation maintenance, repair and overhaul (MRO) services. This study aims to create an analytic framework to assess the main factors and subfactors that have significantly influence the blockchain used in aviation MRO services. A mixed-methods approach is used to gain a comprehensive understanding of how blockchain is being adopted in aircraft maintenance facilities, Semi-structured interviews and questionnaires are used to gather data. The questionnaire is focused on the present state of the MRO industry.

Based on the literature review, a framework including four factors and 12 subfactors is developed, and the analytic hierarchy process (AHP) is then established. This study explores how these factors influence the implementation of blockchain in aviation MRO services. The five aviation MRO services providers in Taiwan, namely, “Evergreen Aviation Technologies Corporation,” “Taiwan Aircraft Maintenance and Engineering Co., Ltd.,” “Air Asia Company Ltd.,” “Aerospace Industrial Development Corp.” and “GE Evergreen Engine Services Corporation” are considered; furthermore, 55 experts working in these organizations were invited to evaluate the relative importance criteria in the AHP framework.

The results indicate that “inventory management” is the most important criterion, followed by “provisioning, procurement and sales” and “maintenance planning.” In addition, the three most important subfactors are “parts interchangeability,” “customer stock” and “SPEC2K interface for ATA SPEC 2000.”

Asia is ranked as the second most important aviation MRO service market in the world. Taiwan has the shortest flight hours in the western Pacific region, the seven major foreign cities in this area. Aviation MRO service providers located in Taiwan are the best choices for aircraft MRO in the Asia-Pacific region, indicating that Taiwan serves as a promising market development evaluation model for blockchain aviation MRO services. The results offer a comprehensive overview of the relative importance of different criteria for MRO services that use blockchains. In addition, the findings present the market potential for key players in the aviation industry, including aircraft engineers, airline companies, aircraft component manufacturers and aviation MRO service providers.

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Higher education institutions (HEIs) are increasingly aware of their role in developing and implementing a sustainable paradigm, with the potential to accelerate progress toward sustainable development (SD). Therefore, this study aims to investigate the environmental, social and governance (ESG) and sustainable development goals (SDGs) practices of the University of Southern Santa Catarina (UNISUL) and, based on this analysis, indicate how HEIs in general can contribute to the promotion of these concepts.

The study was based on the authors' experiences and participant observations made during the establishment of sustainability practices at UNISUL. Additional data collected from documentary research and literature review were analyzed qualitatively.

The results show that sustainability is present in UNISUL’s educational and management processes as actions related to all SDGs and, thus, to all ESG dimensions. On the other hand, it was noted that many actions identified in the literature are not present, with governance being the dimension with fewer practices, thus indicating possibilities for improvement. In this way, new practices were suggested.

New actions linked to SDGs and ESG at UNISUL were identified that can be adopted by other HEIs.

The primary aim of this study is to provide actionable guidance for augmenting profitability in photovoltaic power plant investments within Iran’s solar energy sector. By emphasizing prudent capital management and strategic investment decisions, our research seeks to assist emerging businesses in attaining sustained success in this domain.

This study presents a comprehensive approach to refined decision-making in Iran’s solar energy sector. Our methodology integrates the best-worst method, ArcGIS software for site selection, and the TOPSIS method for decision-making, aiming to enhance precision and reliability.

Our research has identified ten promising regions suitable for photovoltaic power plant installations in Iran. Leveraging the TOPSIS method, we have made optimal selections among these alternatives. Furthermore, our exhaustive cost analysis, incorporating factors like land prices, system maintenance, revenue estimation, and various financial scenarios, has yielded insights into project cost-effectiveness.

By filling a notable gap in the literature regarding optimal site selection and investment strategies for photovoltaic power plants in Iran, our research contributes to the sustainable development of solar energy infrastructure. Through a thorough literature review and the development of a novel methodology, we offer valuable guidance for businesses and investors seeking success in Iran’s solar energy sector. Our study represents a significant advancement by introducing a novel methodology that integrates the best-worst method, ArcGIS software, and the TOPSIS method for site selection and investment analysis. These findings furnish valuable guidance for businesses seeking success in the solar energy sector, thereby contributing to the sustainable development of renewable energy infrastructure in Iran and beyond.