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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.

Air transport accounts for nearly 40% worth of the global trade cargo volume, where more than 50% of the air cargo is carried on passenger flights. Therefore, this paper aims to focus on identifying the influencing factors for both passenger and cargo demand-driven networks to smoothen the global supply chain.

The data for the study was collected through literature reviews and interviews with industry experts. The analytical hierarchy process was used to analyze the expert's opinions on the critical factors affecting air cargo demand growth. Regression analysis was conducted using the selected variables to develop a model to calculate air cargo demand growth.

According to the expert opinion, it was identified that facilities under airport capacities and facilities are mainly affected by the air cargo carried by combi carriers. The model was developed considering the air connectivity index and air cargo demand at destination variables.

The factors identified here are mainly related to the current situation in Sri Lanka. Applying this methodology to other economic zones will add new factors related to their economic contexts and could be generalized as the influencing factors for the growth of air cargo demand by finding more results.

Previous studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from combi and all-cargo carriers together. More than 98% of air cargo trades in Sri Lanka are happening through combi carriers. Hence, Sri Lanka will be a best case study to analyze the behavior of combi carriers.

One of the sectors most affected by the variable weather events caused by climate change and global warming is the aviation sector. Especially in aircraft accidents, weather events increasing with climate change and global warming are effective. The purpose of this study is to determine how much the change in weather conditions caused by global warming and climate changes affect the aircraft in the world between the years 2010 and 2022.

In this study, it was investigated which weather events were more effective in aircraft crashes by determining the rates of air events and aircraft crashes in aircraft crashes with a passenger capacity of 12 or more that occurred between 2010 and 2022.

It is clearly seen that increasing weather conditions with global warming and climate change increase the effect of weather conditions in aircraft crashes.

The difference of this study from other studies is the evaluation of the data of the past 12 years, in which the increasing consequences of global warming and climate change have been felt more. It also reveals the necessity of further research on the effects of weather conditions on aircraft.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

A quick search of the headlines of major newspapers reveals a treasure trove of technology procurement gone wrong. While the private sector seems to adopt and implement new technology seamlessly and quickly to deliver for customers, the government struggles to accomplish technology purchases and integrations with the same ease. As governments in the United States are looking to retain their current workforce and attract the next generation of workers, the technological capabilities and ethos of governments will be paramount. With nearly every industry being transformed by technology and Generation T being the first generation to have an ingrained “technology first” mindset, the ability of governments to attract these workers depends, in large part, on the ability to transform their government technology culture, policies, and practices.

In this chapter, the authors examine the administrative branch and observe two key components at the root of most technology failures: poor organizational structure in the bureaucracy and the lack of an empowered Chief Information/Technology Officer. Building upon case studies from Massachusetts and California, this chapter looks at the factors related to failure or success to understand the technology procurement culture. The chapter concludes by presenting four key “best practice” principles of public policy and administration that can be implemented by almost any governmental entity to improve their acquisition and implementation of technology.

To assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.

Case studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.

The results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.

Inherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.

Adding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.

One novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.

Goa, a tiny state located along the western coast of India, is rich in cultural heritage and biodiversity. It boasts of a good network of roads and also some rustic experiences. However, the entry of new technology in terms of transport is difficult due to the strong links of people with nature and the desire to retain the natural setting for future generations. Thus, the devices or machines must also be resilient and sustainable in the long run. Good governance and infrastructural support can work wonders in the long run if this is supported.

Conversely, smart mobilities must be powered up through hydro, wind, solar, hydrogen, coal and lithium batteries. While the former four are ideal as they are renewable, the latter, which is coal-generated energy and lithium batteries, can deter nature as they have a large carbon footprint. While hydrogen if created through green systems will be more feasible and can be more sustainable to run electric vehicles (EVs). To top it up, the start-up India mission also has played a significant role in helping smart mobilities businesses to thrive in India. Therefore, the need to adopt smart mobilities dependent on green energy is important for this sector to succeed. This chapter also enlightens the reader through a case study about how BLive, an Indian EV start-up introduced smart mobilities in Goa, their mode of implementation, operations and challenges faced.

As the operational entity of China Yiwu Commodity Market, Yiwugou is designed to integrate online and offline business to upgrade the physical market by relying on and serving the physical market. It aims to highlight Yiwu Market where every physical shop is related to an online shop, thus protecting honest trade. The strong support from more than 70,000 physical shops owned by Yiwugou ensures the first-hand supply that poses a problem for most e-commerce merchants, and equips Yiwugou with competitive advantage. In terms of marketing, Yiwugou is now aiming at commodity markets across the country through the “Center Plan”, and advertising in public space such as airports. Relying on physical market, Yiwugou Hall distributes commodities with Yiwu's features and superior sources of goods to other places, and connects local market players to Yiwu market, establishing an unobstructed supply channel.

The purpose of this study is to investigate the effect of the COVID-19 pandemic on key performance metrics of accommodation properties by elaborating on the roles of business models (i.e. franchised, chain-managed and independent hotels, and the sharing economy) and state-level restrictions in the US.

The pandemic is considered a variable interference against the average daily rate, occupancy and revenue per available room, which permits the examination of the before and after effects of the pandemic. The panel data model is used to examine the effect of the recent pandemic on the accommodation sector in the USA.

The results showed that chain-managed hotels were the most adversely impacted by the COVID-19 pandemic, while independent hotels were the least adversely impacted. Interestingly, and consistent with emerging consumer needs suggested by spatial distance theory, the pandemic does not have significant negative effects on Airbnb. The adverse impact of the pandemic on hotels was exacerbated in more restrictive states, while Airbnb remained immune to regulatory differences.

This study addresses the dearth of research on the types, roles and efficacy of business models in the accommodation industry and makes important theoretical contributions to the study of business model resilience in the accommodation industry, leveraging the resource-based theory of the firm and spatial distance theory.

The findings of this study make a significant contribution to the extant literature on the resilience of business models in the accommodation industry and have important implications for hotels, Airbnb owners, accommodation brands and destination and health policymakers. They demonstrate that a lower level of corporate control and greater flexibility in brand and operational standards allow for a more effective response to business disruptions such as a global pandemic.