Abstract network modeling

, 28

Africa

, 28, 361–362

Air Bucharest

, 348

Air Canada

, 345–347

Air Caraibes

, 360–361

Air cargo

, 320

Air China (CA)

, 106

Air France-KLM Group

, 122

Air India

, 62

Air Italy

, 361–362

Air Line Pilots Association (ALPA)

, 360–361

Air Quality Index (AQI)

, 220–221

Air Tahiti

, 361–362

Air traffic management (ATM)

, 92

Air transport companies

, 249–250

Air Transport International (ATI)

, 13–14

Air transportation

, 11, 20, 24, 342

employees

, 3–5

networks

, 94–95

supply chain

, 39

as vector for COVID-19 transmission

, 178–179

Air Transportation Act (1978)

, 22

Air travel effects on spread of viruses

, 26–29

Air travel hubs

, 176, 188

AirAsia

, 16, 149, 154

Aircraft design

, 92

Airline Deregulation Act of 1978

, 30, 323

Airline employment

, 342

case study of US Airline Labor Market

, 354–359

global impact of pandemic on

, 345–353

government support and recent changes

, 351–353

implications for future

, 362–364

industrial actions in postpandemic airline industry

, 359–362

overall employment impacts

, 345–351

Airline industry

, 6–7, 71–72, 342–343

background and data

, 74–77

descriptive statistics

, 85

descriptive statistics on daily return

, 75

empirical analysis

, 77–87

nonmarket strategies in

, 58–60

Airline networks

, 8, 12, 118

broad picture

, 123–124

differences by airline business model

, 124–131

and industry structure

, 249–253

literature review

, 119–121

methodology

, 122–123

reaction

, 131–138

results

, 123–139

Airline-within-Airline strategies (AinA)

, 149, 153

Airlines

, 6, 8, 24, 29, 146, 217

alliances

, 59

attributes

, 84–87

data analysis

, 178

differences by airline business model

, 124–131

executives

, 61–62

impacts and responses

, 301–309

operations

, 12–17

passengers

, 1, 50

reactions in airlines markets

, 34–35

resilience

, 14

schedules

, 287–288

stock prices

, 33

Airport Act (1986)

, 22

Airport cost analysis

, 216

channels of covid-19 effect

, 233–234

data

, 220–223

empirical research strategy

, 218

empirical results

, 225–233

gaps and contributions

, 217–218

large hubs

, 232–233

literature review

, 216–218

medium hubs

, 232–233

robustness check

, 234

summary statistics

, 223–225

translog cost function

, 218–220

Airports

, 1, 12, 29, 216

bases

, 119–120

closures

, 119

connectivity

, 37–38

outputs

, 12

short-run costs

, 216

Alaskan Airlines

, 252

Amazon

, 13–14

American Airlines (AA)

, 96, 252, 302

American Rescue Plan Act

, 355–356

Amerijet International airlines

, 259

Animal spirit

, 21

Anthrax

, 20

Artificial intelligence (AI)

, 364

ASEAN+3

, 101–102

Asia Pacific

, 16, 361–362

Asian aviation markets

, 95

Asiana

, 361–362

Association of Flight Attendants (AFA)

, 360–361

Attenuating effect

, 166–167

Available freight ton-miles (ARFTM)

, 325

Available revenue passenger miles (ARPM)

, 325

Available seat kilometers (ASK)

, 122–123

Average Shortest Path Length (ASPL)

, 97–100

Average variable costs

, 231–232

Aviation

, 27–28, 92, 118

fuel taxes

, 55

industry

, 146, 216–217

sector

, 1, 12

Azul

, 348

Backhauls

, 320

analysis

, 329–336

data and analysis

, 323–329

interpretation of findings

, 337–338

load factors

, 15, 333

theoretical motivation

, 321–323

Bankruptcy

, 54, 353

Bargaining

, 6–7

conflictual

, 7

partnership

, 7

strategies

, 58

Behavioral social sciences

, 23

Beijing Capital Airport

, 162

Betweenness centrality (BC)

, 97, 99–100

Big Four airlines

, 355

Big Three airlines

, 147

Bioeconomics

, 21

Biological concepts

, 21

Bivariate regression

, 84, 86

Breakpoints

, 336

British Airways

, 7, 64, 353

Brussels Airlines

, 361–362

BTS T-100

, 220–221

Buffer absorbability

, 101

Bureau of Economic Analysis (BEA)

, 11, 181

Bureau of Labor Statistics (BLS)

, 221

Bureau of Transportation Statistics (BTS)

, 11, 181, 220–221, 251, 288, 355

Business legitimacy

, 60–61

Business models

, 120

Business resilience

, 284, 287–288

country-level comparisons

, 294–295

measures

, 289–290, 294, 296

for US airlines

, 296

US airport system analysis

, 295–296

Cabin crew

, 63, 344

Capacity management

, 15

Capital asset pricing model (CAPM)

, 8, 72–73

Carbon taxes

, 119

Cargo

, 12, 252

Cargo airlines

, 359

Cargo carrier

, 14–15

Cargo-only carriers

, 8, 13–14, 249

Cargo-only companies

, 249, 251

Cathay Pacific

, 353

Census Bureau

, 11, 181

Center

, 97–100

Centers for Disease Control and Prevention (CDC)

, 221–222

Certification Activity Tracking System (CATS)

, 220

Charter Airlines

, 345–347

Chengdu Airlines

, 153

Chief Executive Officers (CEOs)

, 61–62

China

, 146–147

FSC–LCC competition before pandemic

, 149, 156–157, 169

LCC development

, 149–156

overall impact of pandemic and government’s interventions

, 157–162

China Airlines

, 360

Chinese LCC

, 149

Choropleths

, 184–189

Cirium databases

, 292–294

Civil Aviation Administration of China (CAAC)

, 10–11, 106, 147

Closeness centrality

, 97–100

Clustering coefficient (CC)

, 97–100

Coasian transactions costs of trade

, 23

Combination (Combo) companies

, 273

Combination carriers

, 249

Combo carriers

, 13–14

Commercial Aircraft Corporation of China (COMAC)

, 153

Commercial airlines

, 39

Commodities transported

, 321

Communication, navigation, and surveillance (CNS)

, 92

Communism

, 21

Competition and commonality of networks

, 127–131

Complex networks

, 95

analysis

, 101–109

historical aviation crises

, 93

literature review

, 95–97

preliminaries

, 97–101

theory

, 94–95

Compliance

, 61–62

“Conflictual” nonmarket strategy

, 63

Consolidated Appropriations Act

, 355–356

Consumer confidence

, 3

Consumer price index (CPI)

, 3

Contractual services

, 12, 218–219

Conventional t-tests

, 33

Coping strategy

, 323

Coronavirus Aid, Relief, and Economic Security Act (CARES Act)

, 62, 217, 305, 308, 355–356

Coronavirus disease 2019 pandemic (COVID-19 pandemic)

, 1, 6, 8, 20, 50, 71–72, 92, 118, 146, 176, 216, 248–249, 285–286, 320, 342

airline networks and

, 8–12

airline operations and

, 12–17

and airports

, 217

and aviation industry

, 216–217

business and economic impact of covid-19 on airline industry

, 52–57

cases

, 1–2

channels of covid-19 effect

, 233–234

COVID-19-related policies

, 222–223

cumulative cases and deaths

, 2

deaths, by region

, 2

delta variant

, 74

effect

, 220

measures

, 221–222

and networks

, 120–121

overview

, 1–3

policies

, 216

related deaths

, 1–2

shock

, 73

spread

, 11

transmission

, 176–178

uncertainty

, 72

vaccine

, 3

Coronavirus Response and Relief Supplemental Appropriation Act (CRRSAA)

, 217

Corporate social responsibility (CSR)

, 57–58

Correlations analysis

, 189–191

Cost elasticity

, 228

Cost share

, 223–225

County

, 11, 179

CovidCareMap

, 11, 181

“Cream skimming” approach

, 155–156

Critical flights

, 55–56

Cross-section of stock returns

, 77–78

Cross-sectional regressions

, 82–84

Cumulative abnormal return (CAR)

, 73

Data envelopment analysis (DEA)

, 13

Decomposition

average variable costs

, 231–232

full sample

, 229

methodology

, 12–13

of percentage change in average variable costs

, 219–220

of percentage change in total and average variable costs

, 228–232

TVC

, 230

Degree

, 97–100

Degree centrality

, 97–100

Degree distribution

, 97–100

Delegitimation cascade

, 60–62

Delegitimization cascade

, 7

Delta AirLines (DL)

, 8, 96, 250, 252, 302, 356–357

Diameter

, 97–100

Diphtheria

, 20

Direct output effect

, 220

Disciplinary approaches

, 287

Discrete business-model categories

, 120

Disease importation risk

, 178

Dispersal model

, 27–28

Disruptions

, 3, 94

Domestic flights

, 95

Domestic markets

, 121–122

Domestic risk of COVID-19 transmission

, 176

Domestic traffic

, 118

Double Pareto Law

, 100–101

Double-log specification

, 195–196

Dow Jone US Airline Index

, 77

Dynamic resilience

, 5

E-commerce

, 92, 251

EasyJet

, 7, 32, 64, 121, 135–136

Ebola

, 28

and Marburg hemorrhagic fevers

, 20

Eccentricity

, 97–100

Economic impacts

, 3–5

macroeconomic indicators

, 4

US Airline Carrier Indicators

, 4

Economic institutions

, 38–39

Economic man

, 21

Economics Intelligence Unit (EIU)

, 72–73, 76

Economics interactions

, 24–29

Economist Intelligence Unit

, 8

Egypt Air

, 351–352

Emergency Use Authorization (EUA)

, 74

Emirates Airlines

, 59

Emission reduction programs

, 59

Empirical model

, 78–79, 179–180, 265, 270

Empirical research strategy

, 218

Empirical strategy

, 288–291

Employment

, 3, 342

Endogeneity

, 204–205

concerns

, 192

Environmental, social, and governance (ESG)

, 73–74, 96–97

Envoy

, 356–357

Epidemics/pandemics

, 24–29

Equity markets

, 7

Etihad Airlines

, 59

EUROCONTROL

, 123

Europe

, 3, 9

European air traffic networks

, 100–101

European airlines

, 6–7

in context of Covid-19

, 60–64

European Common Aviation Area (ECAA)

, 14, 285

European Court of Justice

, 22–23

European Union

, 37, 321

EU–US Open Skies Agreement of 2007

, 22

Ewa Air

, 361–362

Excess returns

, 73

F-statistic

, 205

Fascism

, 21

Federal Aviation Administration (FAA)

, 220, 288

Federal Express

, 251, 259

Financial resources

, 85

Financial subsidies

, 10–11

Finnair

, 6–7, 135

Firms

, 58, 287

Fleet capacity

, 14

Flight delay propagation

, 101

Flight operations

, 92

Fractional logit regression model

, 267

Freight

, 3, 5, 12

Fronthaul load factors

, 15, 332

Frontier Airlines

, 259

Frontier theory

, 253–254

Full-service carriers (FSCs)

, 8, 32, 35, 75, 96, 102, 146–147, 249–250, 284–285

consolidation and rationalization

, 302–308

FSC–LCC competition before pandemic

, 149–156

market contact between LCC and FSC during pandemic

, 168–169

route choice behavior of LCC and FSC during pandemic

, 162–168

Full-service network carriers (FSNCs)

, 120, 131, 135, 345, 347

Garuda Air

, 351–352

Generalized AutoRegressive Conditional Heteroskedasticity model (GARCH model)

, 96

Genoeconomics

, 21

Global airline network

, 100–101

Global Efficiency (GE)

, 97–100

Global epidemic and mobility model

, 178

Governance

, 22–23, 35, 38

Government support programs (CARES Act)

, 16

Graph theory

, 97–100

Great Recession

, 30–31, 248

Gross domestic product (GDP)

, 3, 31, 58–59, 118, 157, 343

Guangzhou Baiyun Airport

, 162

Gulf Air

, 351–352

Hawaii Airlines

, 80

Hawaiian Airlines

, 13–14

Herfindahl–Hirschman index (HHI)

, 124

Heterodox economics

, 6, 20–21

economic institutions

, 38–39

economics interactions

, 24–29

institution environment and governance

, 35–38

layers of economic institutions

, 22

market challenges confronting airlines

, 31–34

market clearing implication of covid-19 for air travel

, 30–31

new institutional economics

, 21–24

reactions in airlines markets

, 34–35

High-speed rail (HSR) tickets

, 155

High-speed trains (HSTs)

, 95

Homo economicus

, 21

Hong Kong influenza pandemic strain

, 27

Hub airports

, 119–120

Hub classification scheme

, 295

Hub-and-spoke networks

, 119

Hub-and-spoke system

, 250

Human immunodeficiency virus (HIV)

, 20

Hypothetical performance trajectory

, 290

Iberia Express

, 361–362

IndiGo

, 104

Industry sectoral analysis

airline networks and industry structure

, 249–253

data

, 259–265

empirical methods

, 265–270

modeling airline performance during pandemic

, 253–259

results

, 270–275

Industry-specific levies

, 55

Infectious disease outbreaks

, 20

Influenza

, 20

Input effect

, 220

Input prices

, 221

Institution environment

, 35–38

Institutions

, 6, 8, 23

Instrumental variable (IV) approach

, 204

Integrators

, 13

Intensive care unit (ICU)

, 176

Interjet

, 360–361

Internal/external short-and long-term responses

, 97

International Air Transport Association (IATA)

, 51, 92, 118, 216–217

International Airlines Group (IAG)

, 122

International Civil Aviation Organization (ICAO)

, 92, 94, 216–217

International Civil Aviation Organization Plus (ICAO+)

, 16, 345

International transmission

, 179

International Union of Railways (UIC)

, 177

Interval type-2 fuzzy analytic hierarchy process (IT2FAHP)

, 97

Interval type-2 fuzzy decision-making trial and evaluation laboratory (IT2FDEMATEL)

, 97

Invasive meningococcal disease

, 20

Iran Air

, 351–352

Iraqi Airways

, 361–362

Italian airport network

, 100–101

JetBlue

, 35

JetBlue Airways

, 259

JetSmart

, 345–347

John Hopkins Git Hub depository

, 11, 180

Kaiser Family Foundation (KFF)

, 222

Kalitta Airlines

, 259

Kenya Airways

, 361–362

Labor relations

, 360–362

Labor unions

, 344

Large hubs

, 232–233

Largest US carriers

, 302

Lassa fever

, 20

LATAM

, 360

Legitimacy

, 60

Linear Point-to-Point System

, 250–251

Load factors

, 334–336

Lockdown measures

, 146

Low-cost carriers (LCCs)

, 8, 59–60, 75, 96, 102, 120, 135, 138, 146–147, 249–250, 296, 345, 347

development

, 149–156

market contact between LCC and FSC during pandemic

, 168–169

route choice behavior of LCC and FSC during pandemic

, 162–168

Lufthansa Group

, 122

Lynden Air Cargo

, 259

Macro-macro level studies

, 32

Macroeconomic indicators

, 3

Magnitude of drop

, 290

Magnitude of recovery

, 290

Maintenance personnel

, 344

Malmquist index

, 273–275

Malmquist productivity

, 13–14

Malmquist total factor productivity

, 267–268

Managerial preparedness of US commercial airline management teams

, 96–97

Market challenges confronting airlines

, 31–34

Market clearing

implication of covid-19 for air travel

, 30–31

processes

, 23

Market contact between LCC and FSC during pandemic

, 168–169

Market index

, 71–72

Market return

, 73–74

Market risk

, 82

Market strategies

, 6–7, 50

Mean Absolute Percentage Error (MAPE)

, 291

Mean Error (ME)

, 291

Mean Percentage Error (MPE)

, 291

Mean Square Error (MSE)

, 291

Measles

, 20

Medical supply transport

, 55–56

Medium hubs

, 232–233

Metrics

, 97–98, 100

Micro-micro studies

, 29

Middle East

, 16

Multi-airport system (MAS)

, 163

“Multi-compartment” aircraft

, 252

Multi-output translog cost function

, 216

Multicriteria Data Envelopment Analysis (MCDEA) model

, 97

National Association of Securities Dealers Automated Quotations (NASDAQ)

, 75–76

National Aviation System (NAS)

, 288

Natural experiment

, 20

Negative attributes

, 220–221

Network disruptions

, 52–54

Network economies

, 119–120

Network metrics

, 9

Network science

, 112–113

methodological approach

, 9

methodology

, 8

metrics

, 9

Network structures

, 119–120

Networks as critical elements of airline strategies

, 119–120

New institutional economics (NIE)

, 5–6, 20–21

in context

, 21–24

New marketing planning methods

, 96

New Normal, emerging trends in

, 138–139

New York Stock Exchange (NYSE)

, 75–76

Non-aeronautical revenue

, 216, 220

Nonbargaining

, 6–7

approaches

, 58

compliance

, 7

selective avoidance

, 7

Nonengagement in political markets

, 58

Nongovernmental organizations (NGOs)

, 57–58

Nonmarket strategies

, 6–7, 50

in airline industry

, 58–60

business and economic impact of covid-19 on airline industry

, 52–57

choices of European airlines in context of Covid-19

, 60–64

economic profit/loss by subsector in aviation 2020

, 53

European Airlines bankruptcies

, 54

evolution of revenue passenger kilometers

, 53

financial support

, 56

net profit and loss of airlines

, 51

overview

, 57–58

Nonparametric Mann–Whitney test

, 33

Nonparticipation in political markets

, 58

Nonsystematic risk

, 8

Normalization

, 179–180

North America

, 3, 333

Official Airline Guide (OAG)

, 9–10, 122, 284

databases

, 292–294

Omni Air International

, 259

Onset of pandemic

, 290

Open Skies Agreements (OSAs)

, 154

Operating capacity

, 14

Operating revenues (OR)

, 265

Organization for Economic Cooperation and Development (OECD)

, 118, 364

Organizational resilience

, 287–288

Organized labor

, 359

Origin-destination (O-D) pairs

, 10, 94, 119–120, 179

Output effect

, 220

Pandemic beta

, 72–73, 79, 81–82

with airline attributes

, 84–87

Pandemic response strategies

, 5–6

Pandemic uncertainty

, 73

Pandemic uncertainty index (PUI)

, 8, 72–73, 76

Pandemics

, 320

Partnership

, 62

Passenger airlines

, 284

Passenger and all-cargo schedule analysis

, 301–302

Passenger carrier

, 301–302

Passenger taxes

, 55

Passenger traffic analysis

, 28

Passenger-only carriers

, 13–14, 249

Passenger-only companies

, 251

Passengers

, 146

Paths

, 97–100

Performance measures

, 249

Performed departures

, 292, 302

Periphery

, 97–100

Persistent effect

, 166–167

Personal protective equipment (PPE)

, 55–56, 301–302

Perspex dividers

, 62

Philippines Airlines (PAL)

, 348

Piedmont Airlines

, 360–361

Pilots

, 345

Point-to-point networks

, 119

Point-to-point system

, 250–251

Polar Air Cargo

, 259

Policy intervention effect

, 220

Political market capitalism

, 21

Population density

, 189–191

Positive outputs

, 220–221

Postpandemic airline industry, industrial actions in

, 359–362

Preighters

, 342–343

Prevention strategies

, 121

Producer price indexes (PPI)

, 221

Productivity

, 249

Psychology

, 23

Public education campaigns

, 208

Public good

, 50

Public health policymakers

, 38–39

Public policy instruments

, 119

“Puppy dog” approach

, 147

Pure Technical Efficiency

, 230

Qantas

, 361–362

Qatar Airways

, 54, 59

Quasi-fixed capital effect

, 220

Quasi-fixed factor

, 221

Quasi-maximum likelihood method

, 267

Radius

, 97–100

Rate of recovery

, 290

Real GDP

, 3

Redundancy

, 101–102

Regional airlines

, 350–351

Regression

, 11

Regression analysis

, 192–194

Relative measures

, 291

Relative risk assessment

, 11

Repatriation flights

, 55–56

Reputation management

, 59, 101–102

Resilience

, 5–6, 15

Resource-allocation studies

, 23

Retrenchment measures

, 96–97

Revenue freight ton-miles (RFTM)

, 325

Revenue passenger miles (RPM)

, 325

Revenue ton-miles (RTM)

, 265

Risk distribution

, 188–189

Risk index

, 178–179

Risk management

, 73

Risk of spread

, 196–197

Risk premium

, 73, 84

Robustness check

, 234

Robustness tests

, 195–197

Rolling-Hub System

, 250

Root Mean Square Error (RMSE)

, 291

Root Mean Square Percentage Error (RMSPE)

, 291

Route choice

, 149

Route entry restrictions

, 11

Rubella

, 20

Rule-based methodology

, 14

Ryanair

, 7, 32, 59–60, 64, 121

S&P 500 Index

, 3

Sabre Market Intelligence

, 102

Sargan tests

, 205

SARS-CoV-2

, 1

SAS

, 125–126

Scale economies

, 119–120

Scale technical efficiency

, 261–263

Scale-dependent measures

, 291

Scarring

, 290

Schedule gap measurement

, 290–291

Scheduled departures

, 292, 302

Scheduling process over time

, 308–309

Schweiterman study

, 323

Scope economies

, 119–120

Seasonal influenza

, 26–27, 178

Selective avoidance

, 62

September 11 2001, attacks

, 28, 50

7-point Likert scale

, 177–178

Severe acute respiratory syndrome (SARS)

, 20, 28–29, 92, 94

Shanghai Pudong Airport

, 162

Shock duration

, 296

Short run costs

, 12

Short-run cost analysis

, 221

Shortest path

, 97–100

Sideral Air

, 348

Simultaneity

, 204–205

Singapore Airlines

, 50

Situational Crisis Communication Theory (SCCT)

, 96–97

Smallpox

, 20

Socialism

, 21

South African Airways Pilots Association

, 361–362

South America

, 3

Southwest Airlines (WN)

, 302

Spatiotemporal evolution

, 112

SpiceJet

, 348

Spirit Airlines

, 252–253

Spreading diseases

, 6

Spring Airlines

, 146–147, 149

Sri Lankan Airlines

, 351–352

Standard Deviation of the Error

, 291

Static resilience

, 5

Stock data

, 75

Stock markets

, 96

valuations

, 32–33

Stock price

, 77

Strategic management

, 60

Strikes

, 101

Stringency scores

, 15, 334, 336

Subsidies

, 10

Subway crowding

, 177–178

Sun Country Airlines

, 259

Supply variations

, 125–127

Susceptible-Exposed-Infectious-Recovered-based models

, 178

Susceptible-exposed-infectious-removed (SEIR)

, 6

Sustainable Aviation Fuels (SAF)

, 139

SWISS

, 34–35

Systematic risk

, 8

T-100 International Segment table

, 324

T100 Market database

, 181

TAAG

, 361–362

TAROM

, 351–352

Technical change

, 254–255

Technical efficiency

, 13–14, 249, 253–254

change

, 268

Thai Airways

, 353

Ticketing and sales

, 345

Tobit model

, 266

Total factor productivity (TFP)

, 14, 267–268, 273, 275

Total variable costs (TVC)

, 220–221, 230

Tourism

, 30

Traditional statistical methods

, 33

Traffic flow

, 15, 336

Transaction costs

, 23

Transavia

, 125–126

Translog (flexible form) cost function

, 12, 218, 220

Transport-related policy measures

, 177

Transportation network analysis

, 40, 176

Transportation systems

, 177–178

Travel restrictions

, 146

Travel risk analysis

, 192

Tuberculosis

, 20

Tunisia Air

, 361–362

Two-by-two matrix

, 7, 96–97

Two-stage least squares (2SLS) model

, 176–177, 204–205

Ultra-low cost carriers (ULCCs)

, 252–253

UN International Civil Aviation Organization (ICAO)

, 56–57

Unemployment rate

, 3

Union Bank of Switzerland (UBS)

, 55

Unions

, 360

United Airlines (UA)

, 96, 302, 356–357

United Parcel Service (UPS)

, 251

US air transportation network

, 180

data source

, 180–184

empirical approach and data sample

, 179–184

endogeneity and simultaneity

, 204–205

key variables in study

, 181

policy implications

, 205–208

results

, 184–197

review of related work

, 177–179

summary statistics

, 180, 182, 184

US airline industry

, 73

US Airline Labor Market, case study of

, 354–359

US airline schedule planning

, 284–285

business resilience measures

, 289–290, 294, 296

changes

, 298–309

COVID-19 pandemic

, 285–286

data sources and selected variables

, 292–294

empirical strategy

, 288–291

literature review

, 286–287

organizational and business resilience

, 287–288

schedule gap measurement

, 290–291

US airline stocks

, 8

US airport system analysis

, 295–296, 298, 301

US BTS T-100 ex post databases

, 292

US Civil Reserve Air Fleet (CRAF) Program

, 39

US Coronavirus Aid, Relief, and Economic Security Act (CARES)

, 14–15

US Department of Justice

, 22–23

US Environmental Protection Agency (EPA)

, 220–221

Value-added taxes (VAT)

, 55

Vande Bharat Mission

, 62

Variable returns to scale (VRS)

, 265

Vector-borne disease airline importation risk model

, 178

Vueling

, 136

Western European market

, 119

WestJet

, 325

“Wild Your Weekends” program

, 159–160

Wizz Air

, 32, 121

Workload

, 12, 225

World Health Organization (WHO)

, 1, 23–24, 73, 92, 94

Worldwide air transportation network (WATN)

, 121

Worldwide airport network

, 100–101

Wu-Hausman tests

, 205

Wuhan Municipal Health Commission

, 1

X-inefficiency

, 40

Yahoo Finance

, 8