Introduction

Global commercial aviation is estimated to have emitted 892–936 Mt carbon dioxide (CO2) in 20191,2,3 and is responsible for about 4% of global net anthropogenic effective radiative forcing4,5. Industry estimates suggest continued strong growth over the next two decades6,7. As demand growth has outpaced efficiency gains in the past, emissions from the sector will continue to rise unless sustainable aviation fuels can be made available at scale8,9,10. Technical and cost challenges are transition barriers to net-zero aviation8,11,12,13.

Air transport is an energy-intense activity, in which only a small share of the world population is involved14. Within the population of commercial air travelers, the percentile of the most frequent fliers accounts for approximately half of all emissions. Premium class flights are known to be five to nine times more carbon intense than those in economy class15. To be effective, policies designed to reduce the environmental impacts of aviation need to consider such complexities of growth, geographical distribution, and energy intensity.

Private aviation (PA) is the most energy-intense form of air transport, but its global scale, distribution, and energy intensity remain insufficiently understood. Sun et al.16 evaluated “250 private jets” for the period 2019–2022, concluding that emissions amounted to between 0.45 and 0.5 Mt CO2. Sobieralski and Mumbower17 assessed US private aviation during the COVID-19 pandemic (January 2019 to October 2021), finding that private air travel accounted for 6.3% of the ‘total commercial plus private aviation emissions’ in the USA in 2019, and 7.9% in 2021.

PA has received attention because of its interlinkages with climate change and emission distributions18. Users of PA have been described by industry as the “ultra-high net worth”, including about 256,000 individuals, or 0.003% of the global adult population, owning an average of US$123 million and a combined wealth of US$31 trillion19. Private aircraft can be owned, or accessed through demand chartering, membership programs, or fractional ownership. The sector has relevance for climate politics, as international aviation is not covered by efficient climate policies20. While the scale and distribution of PA remain insufficiently understood, industry expectations point to continued growth21,22.

Global climate change mitigation efforts are hampered by growth in individual economic sectors, and the energy intensity of consumption patterns of affluent population groups23. Wealth determines per capita emissions23,24, with evidence that differences in individual emissions between low and high emitters within a country are high, and up to several orders of magnitude between individuals18,25. Analyses show that global emissions continue to grow26,27,28, and that this growth is driven by the wealthiest23,29.

There are two important implications: emission reductions are particularly difficult to achieve under scenarios of continued growth in economic output or wealth30; and policies focused on CO2 will disproportionately affect less affluent population groups31,32. Given difficulties in reducing emissions in line with the Paris Agreement that are evident in most countries33, a central question is how further growth in emissions can be limited. Private air transport illustrates the policy conundrum of addressing the role of the affluent, as policymakers are reluctant to focus on the wealthy and powerful34. Against this background, the paper investigates the energy intensity of private aviation, its global scale and distribution, the role of events in attracting private aviation, patterns of individual aircraft use, as well as travel motives and growth trends.

Results

Energy intensity and distribution

Private aviation is energy-intensive. Our analysis identified 72 PA models marketed as business jets, with fuel consumption values ranging between 48 to 576 gph (equivalent to 182–2180 L per hour). For some aircraft models, this implies more CO2 per hour than emitted by an average human per year (4.5 t CO2e in 202035) (Fig. 1). Private aircraft spend 45.4% of the total flight time at >30,000 and 21.4% at >40,000 feet, suggesting that a share of emissions enters sensitive layers of the atmosphere with relevance for non-CO2 radiative forcing5,36.

Fig. 1: Private aircraft emission intensity*.
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*Highlighted models are the most popular in the global fleet of PA; see also “Methods” and Table 1.

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The total number of private aircraft in service at the end of December 2023 was 25,993. A total of 4,301,561 individual flights (legs) were made in 2023, with a total flight time—not including taxiing—of 6,474,710 hours, or 249 h per aircraft. Weighted for aircraft models, this generated 15.62 Mt CO2 (3.6 t CO2 per flight) in direct emissions. The average great circle distance flown was 865.7 km, with an average speed of 575 km/h, and an average flight time of 90 min. Almost half of all flights (47.4%) cover distances below 500 km, and 4.7% of flights are shorter than 50 km (Table 1). Less than one-third of flights (29.1%) are longer than 1000 km.

Table 1 Trip length* distribution
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The spatial distribution of private aviation is shown in Fig. 2. Most of the air traffic is taking place within the USA, followed by Europe. Central American capitals (Mexico City, Guatemala City, San José, and Panama City), as well as the Caribbean (Turks and Caicos, Anguilla, Barbados, Cancun) are highly frequented, as is the Middle East. Private air transport is less common in China and Southeast Asia, Oceania, Central and South America, except Brazil and individual capital cities (Caracas, Bogota, Montevideo, Buenos Aires). There appears to be limited private air transport in all of Africa, with the exception of South Africa and Nigeria. Some islands attract much air transport, including the Hawaiian Islands, Caribbean, Canary Islands, Cape Verde, as well as some remote destinations, including Maldives, Seychelles, Mauritius, or French Polynesia. There are also notable concentrations of air transport in specific areas: for example, the Miami area alone accounts for 6% of all PA departures.

Fig. 2: Geospatial pattern of private aviation, 2023*.
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*Lines depict direct connections between city pairs, considering 4.3 million flights.

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PA use is concentrated, with six countries accounting for more than 80% of aircraft. The USA are home to about 4% of the world population, but 68.7% of all PA are registered in the country (Table 2). Brazil has the second highest number (3.5%, 927 private aircraft), followed by Canada (2.9%, 770), Germany (2.4%, 630), Mexico (2.0%; 534), and the UK (2.0%; 522). Per capita, Malta has by far the highest density of PA (46.5 per 100,000 residents), followed by the USA (5.5), Switzerland (3.8), and Austria (2.9) (Supplementary Material, Table 2).

Table 2 National private aircraft densities
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Air transport patterns

Further insights can be derived from the analysis of spatial patterns, such as the importance of global events. Figure 3 depicts various global events and conferences taking place in 2023, including the World Economic Forum in Davos, Switzerland (16-20 January), the Super Bowl in Arizona, USA (12 February), the Conference of Parties (COP28) in Dubai (30 November–12 December), and the Cannes Film Festival in France (16-27 May). The maps illustrate that events attract considerable air transport activity, including international (WEF, COP28), national (Super Bowl), and regional (Cannes) air traffic.

Fig. 3: Global events attracting private air transport in 2023*.
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*From top left to bottom right: World Economic Forum, Super Bowl, Conference of Parties, Cannes; includes all air travel at dates of the event.

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One of the most relevant global events is the FIFA World Cup (Fig. 4). In 2022, the event took place in Qatar, attracting visitors from all over the world. This may have included affluent spectators, but also functionaries, or football stars owning PA37.

Fig. 4: FIFA World Cup*.
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*Includes all air travel at dates of the event.

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Events can attract hundreds of individual flights, and generate considerable emissions, ranging from 1.5 kt CO2 (Super Bowl) to 14.7 kt CO2 (FIFA Qatar) (Fig. 5, Table 4). Total emissions from events are higher, as affluent attendees may also use helicopters, or arrive by yacht (Cannes).

Fig. 5: Carbon-intensity of events*.
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*Includes only flights associated with the event.

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The analysis of the spatial movement patterns of individual air travelers reveals considerable differences in flight numbers, emissions, min-max, and average distances (Figs. 6 and 7). These require further investigation, as very short flights (13–72 km) suggest that aircraft are moved for parking and used for pickup/delivery (for both business and leisure; Supplementary Material, Fig. 2). An indeterminable share of flights is made empty. For example, in on-demand chartering, the nearest available jet will be sent to pick up a client19.

Fig. 6: Individuals’ air travel, 2023*.
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*Global public figures, anonymized.

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Fig. 7: Global public figures and their private air travel, 2023*.
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*List based on Yard50, includes reknowed actors, singers, and directors.

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Travel motives

The use of PA for leisure purposes is investigated for Ibiza, Spain, and Nice, France (Fig. 8), showing a clear seasonal trend of visitation peaking in summer. Arrivals are also concentrated on weekends (arrivals on Fridays, departures on Sundays), suggesting that travel motives are leisure-dominated. In Ibiza, visitation is highest in the summer (June to August).

Fig. 8: Seasonality of arrivals in Ibiza and Nice*.
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*Peaks in arrivals in Nice: 24 May, during the Cannes Film Festival.

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Data also reveal that private aircraft serve the same events. The events studied (Figs. 3–5) have political, economic, cultural, and sports foci, yet are frequented by the same aircraft (Fig. 9). For example, 172 of the 595 aircraft tail numbers appearing at the World Economic Forum (WEF) in Davos also served the Cannes Film Festival, and 65 identical tail numbers were identified at both the WEF and COP28. Of the 404 aircraft at COP28, 96 also appeared at the FIFA World Cup. It is not possible to say whether this means that events are attended by the same individuals, as aircraft are often chartered, though it would be of interest to further investigate travel motivations across political, economic, and cultural dimensions.

Fig. 9: Cross-visitation of global events*.
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*Event bubbles show the total number of aircraft, with satellites referring to identical tail numbers appearing at other events.

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Growth trends

Data for 2019–2023 confirms PA growth trends for jet numbers (6.45% per year), distances traveled (11.31% per year) and emissions (9.93% per year) (Fig. 10). In total numbers, PA numbers grew by 28.4%, distances flown by 53.5%, and emissions by 46.0%. Growth trends are distorted by the COVID-19 pandemic, as 2020 saw a reduction in flight distances and emissions, even though aircraft numbers increased consistently (from 25,993 aircraft in December 2023 to 26,454 in February 2024 alone; see Supplementary Material, Table 1), as did the average distances flown. Emissions per km stagnated. It is less clear why the total distance flown stagnated between 2022 and 2023. One explanation may be the growing use of Privacy ICAO Addresses (PIA): according to the FAA (personal information, 19 April 2024), 283 aircraft in US airspace temporarily used PIAs in 2023.

Fig. 10: Growth in private aviation and key performance indicators*.
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*Does not include distances flown under PIA use.

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Conclusions

The study provides a first understanding of global private aviation’s role in climate change. Total direct emissions of 15.6 Mt CO2 in 2023 amount to 1.7% to 1.8% of commercial aviation’s CO2 emissions. As this research only assesses direct emissions from fuel use in flight, the full impact of private aviation on climate change would have to consider the time aircraft spend taxiing, or support services such as helicopters used to reach final destinations. Non-CO2 effects from PA require further investigation5,38.

In the future, PA will become more relevant, as efficiency gains for the period 2019–2023 at around 1.25% per year (fuel use per km) are likely lower than growth rates in this market: for the period 2019–2023, our data points to a 46% increase in emissions. Industry expectations are that another 8500 business jet deliveries will be made in the period 2024–203322. PA will thus likely become increasingly important as a source of emissions in relative (share of global emissions) and absolute terms (sector’s total emissions). As sustainable aviation fuel use remains limited, and a majority of private aircraft owners do not plan to use it in the near future22, it will be necessary to regulate the sector. There are similarities to commercial aviation, where demand and supply side management have become a focus of discussions39, including reductions in air traffic13.

Findings also have relevance for distributional perspectives11,23,24,25. There is a social cost of carbon (SCC)40 that constitutes a subsidy to emitters under current policy regimes, which have only internalized a small share of the SCC35. This should also be seen in light of energy use patterns. As the analysis of travel patterns shows, 18.9% of flights are short (<200 km), and many are empty, delivery, or pickup flights. Findings also confirm that PA are used routinely, and in many instances appear to replace cars for time gains or convenience, as evident from the 4.7% share of very short flights below 50 km. As seasonal and weekly arrival peaks in popular holiday destinations show, many flights are made for leisure purposes.

The analysis shows that individuals using PA emit disproportionally more than an average human. This illustrates the sector’s relevance from distributional per capita viewpoints, and its relevance for climate policy. As many flights are made for leisure, findings thus also have relevance for tax authorities, as private aircraft expenses, including fuel, maintenance, and management costs, can be tax-deductible when the aircraft is used for business purposes (for the USA)41. To monitor PA is fundamental to understanding and managing the sector. This may become more difficult in the future, as the use of PIA is growing and will potentially lead to a declining quality of ADS-B data for emission assessments.

Methods

Inclusion of aircraft models

Private aircraft are defined as aircraft models primarily intended for transporting individuals. To identify the range of private aircraft models, marketing materials were manually analyzed. In total, 72 aircraft models were identified, based on the definition of being marketed as ‘business jets’ by manufacturers or brokers.

ADS-B data

For air traffic control and monitoring, aircrafts are required to switch on the Automatic Dependent Surveillance–Broadcast (ADS-B) transponder during flight42,43. ADS-B enhances airspace safety by offering a precise tracking of aircraft in real time. There are only very few exceptions to this rule, such as military operations. The ADS-B data was retrieved from ADS-B Exchange (https://www.adsbexchange.com), a platform that offers comprehensive and unfiltered traffic data for every aircraft on a 60 s basis. For each aircraft, the dataset contains timestamp, latitude, longitude, barometric altitude, and an indicator of whether the aircraft is grounded. For this project, the entire database for private aircraft was retrieved for the period 2019 and 2023, totaling 1.8 TB of data related to a total of 18,655,789 legs (individual flights).

ADS-B Exchange is a flight-tracking website and supplier of ADS-B data. Earlier studies have pointed to FlightRadar24 as the provider of the most comprehensive data, in comparison to OAG (Official Airline Guide) and OpenSky2, but ADS-B Exchange is the only supplier that does not filter signals under the USA’s Federal Aviation Administration’s44 Limiting Aircraft Data Displayed (LADD). To understand the scale of LADD, we examined a sample of 10% of all private flights in 2023, finding that 30% had requested LADD. ADS-B Exchange thus appears to be a more reliable provider for private aviation data than FlightRadar24. As data are continuously updated, marginal changes can occur.

There are limitations even to the use of ADS-B Exchange data. Flights are not tracked when they are using a third-party flight ID. Under the Federal Aviation Administration’s ADS-B Privacy program, aircraft registered in the US and flying in US-managed flight information regions can apply for Privacy ICAO Addresses (PIA). These are temporary ICAO aircraft addresses not assigned to the owner in the Civil Aircraft Registry (CAR)45. Legs under these ICAO numbers are not considered in the analysis, because the ADS-B exchange database relies on the original ICAO aircraft address to link it to the model that we use to determine if it is a private aircraft. According to information provided by the FAA (personal communication, 19 April 2024), 283 PIA are currently issued, indicating that around 1% of aircraft may not be covered by ADS-B Exchange temporarily. It is possible that these are used primarily by owners of larger aircraft and lead to a larger underestimate in fuel consumption. We generally find earlier ADS-B Exchange data to be noisier, potentially because of changes to the open-source software distributed by volunteers of the ADS-B Exchange network. In more recent years, PIAs affect the data, as the FAA (Personal Communication, April 19, 2024) outlines that the number of PIA applications has risen considerably since 2020.

ADS-B Exchange data is used to calculate fuel use and emissions based on flight time, as well as great circle distances based on the identification of departure/arrival airports for each leg. To assess the difference between great circle and real distance, we calculate real distances based on georeferenced flight tracks (60 s intervals). Flown distances are on average 10.1% larger than great circle distances.

Aircraft fuel use

ADS-B Exchange provides data of all aircraft with a ICAO 24-bit code, the tail number and model. A manual inspection and comparison of n = 23 aircraft with FAA, FlightAware, and FlightRadar24 was performed to assess the quality of ADS-B Exchange, but no differences were found. The country of registration was obtained from the ICAO 24-bit code based on [ICAO working paper NACC/DCA/3 – WP/05]. This dataset is available at https://private-jets.fra1.digitaloceanspaces.com/private_jets/all.csv.

Based on ADS-B Exchange data and unique hex codes, it was determined that there were 25,993 aircraft in December 2023. This compares to industry figures pointing to 23,369 business jets in 202346, 2.1% more than our number that includes 22,878 business jets, as well as 3115 turboprops. The difference can potentially be explained with the use of PIA or aircraft that are offered for sale and not in use. Table 1 shows the number of private aircraft by model, including fuel consumption in gph, as provided by aircraft brokers for these models. The 10 most popular models represent about 40% of the total fleet. The most common is the Pilatus PC-12 with an 8% market share, followed by the Bombardier CL-600 Challenger and the Piper PA-46-500TP.

For the analysis of private aircraft traffic between 2019 and 2023, all data was retrieved and stored to minimize operational impact to ADS-B exchange. The data includes the ICAO code, aircraft, timestamp, coordinates, altitude of the aircraft, and an indicator variable whether the plane is grounded at around 60 s intervals. By tracking the position of each aircraft over time, flight legs, distances, and flight times can be calculated. To calculate fuel use, we use averaged fuel economy values provided for the 72 models, in gallons per hour (gph), as retrieved from marketing materials (see https://private-jets.fra1.digitaloceanspaces.com/model/db/data.csv).

Fuel consumption is calculated for aircraft in flight, i.e., this excludes time spent taxiing or other on-the-ground emissions, and results thus need to be considered lower boundary values for fuel use. Whether an aircraft is in flight is determined based on transponder data that signals a touch-down/takeoff. Emissions of CO2 were computed based on leg duration for each flight:

$${\rm{leg}} \, {\rm{emission}} \, {\rm{kg}} \, {{\rm{CO}}}_{2}={\rm{consumption}} \, {\rm{gallonh}}* {\rm{liters}} \, {\rm{per}} \, {\rm{gallonLgallon}} \\ * {\rm{kg}} \, {\rm{per}} \, {\rm{liter}} \ {\rm{of}} \, {\rm{jet}} \, {\rm{fuel}} \, {\rm{kgL}}* {{\rm{CO}}}_{2}{\rm{emissions}} \ {\rm{per}} \, {\rm{kg}}{{\rm{CO}}}_{2}{\rm{kg}}* {\rm{leg}} \, {\rm{time}}[{\rm{h}}]$$

Where:

Liters per gallon = 3.78541 [L/gallon], kg per liter of jet fuel = 0.8 [kg/L], CO2 emissions per kg = 3.16 [kg CO2/kg jet fuel], leg time [h] computed duration of flight, and average consumption [gallon/h] obtained from the manufacturer.

The focus is on CO2, and does not consider non-CO2 warming. To assess this contribution, we use ADS-B Exchange data47,48 for an analysis of the flight time spent at altitudes >30,000 and >40,000 feet, finding that 21.4% of flight hours are spent at >40,000 feet, and 45.4% at >30,000 feet. This is not proportional to emissions at these altitudes, as larger aircraft models are more likely to fly at these altitudes. The data are available at49.

A limitation of our approach is that we use averaged LTO fuel economy values that are applied to flight times. Various earlier studies have sought to assess aviation fuel use through complex models1,2,16,17. These consider the different flight cycle elements and assign specific fuel use values to each of these; some models also consider aircraft mass and payload, and weather conditions1,2. While these models may be more accurate than the LTO fuel economy values used in this paper, discussions with pilots have pointed us to the fact that PA is characterized by specific flight styles that lead to considerable differences in fuel burn.

To assess the potential range of fuel consumption values, we consider the ranges specified by brokers for n = 18 aircraft. Average fuel consumption values are calculated for standard operating profiles, i.e., the Landing and Take-Off (LTO) cycle that considers taxiing, takeoff, climb, cruise, descent, and landing. Brokers use these values so that customers—often charter companies—can compare the fuel efficiency of different aircraft models. As fuel is one of the major operational cost factors, reliable fuel economy values have relevance for purchase decisions. Ranges provide an indication of the relevance of other factors. Over the sample of the 18 aircraft models, we find a variation in fuel consumption values of 10.1%, calculated as the standard deviation from the average consumption values available at https://private-jets.fra1.digitaloceanspaces.com/model/db/data.csv.

Signals

ADS-B data relies on responder signals to determine flight altitude and location. Coverage is generally good. Signals may however be lost in specific situations, which could lead to an incorrect allocation of airports. Calculations thus follow a set of rules (for illustration, see Supplementary Material, Fig. 1):

  1. 1.

    When the signal is “on the ground”, the location is marked as the end of the leg.

  2. 2.

    When the signal is lost for more than 5 minutes at an altitude above 10,000 feet, the last known location is considered to be the end of the leg.

  3. 3.

    When the signal is lost for more than ten hours, the last known location is marked as the end of the leg.

Uncertainties

There are various uncertainties that affect the accuracy of the calculations. Some of these have been discussed in preceding sections, this section provides a summary (Table 3), showing that there is a risk of a small error.

Table 3 Uncertainties
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Patterns

Distance and time-based flight patterns have been analyzed to determine their robustness, particularly concerning emissions. To quantify the uncertainty associated with our mean distance and emission estimates, we applied a bootstrapping methodology. We drew 10,000 bootstrap samples, each comprising 100,000 legs, from the original dataset of more than four million flights in 2023. For each bootstrap sample, the mean was calculated, and the distribution of all bootstrapped means was computed. From this distribution, we derived the empirical 95% confidence interval by taking the 2.5th and 97.5th percentiles. To report the accuracy of our estimates, we present the mean and the associated errors as indicated by the 95% confidence interval. Results show a 95% confidence interval (CI) for the average distance of 865.8 km (859.5–872.1 km), with an error of the mean that varies between a lower 6.3 km and an upper 6.4 km. For average emissions of 3632 kg, the CI is 3598–3666 kg, with a lower error of 33.88 kg and an upper error of 33.95 kg. This suggests robustness in determined flight patterns (distance) as well as emissions.

Geospatial patterns have been derived by focusing on events, individuals, and a family-owned company. For all events, the closest airports are included in the analysis, or those airports referred to by event organizers (Table 4). For example, the WEF in Davos includes Zurich, Geneva, Altenrhein, Dübendorf, Samedan, Friedrichshafen, and EuroAirport Basel. The exact dates of the event are used to determine private aviation arrivals. This can underestimate attendance, as participants may have arrived/departed on days preceding/following the event. Calculations are based on the following procedure:

  1. 1.

    Definition of all relevant airports for event

  2. 2.

    Definition of start and end date for event

  3. 3.

    Inclusion of all flights arriving at relevant airports in event period.

Table 4 Calculation of flights related to events
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To derive the net emissions associated with an event, arrivals are compared with a corresponding period prior to and after the event. For short events, this includes flights a week before/after the event, for an identical number of days (a three-day event is compared with a three-day period). For longer events, the period for comparison is chosen with a greater distance to the event (two weeks). Results for the before/after periods are added and divided by two to derive an average. Estimates of the “net” number of flights arriving for the event are then derived by subtracting the average from the total number of flights arriving during the event. This data is then used to estimate emissions associated with the event by calculating total emissions associated with all flights during the event, and subtracting the average of all flights before and after the event period.

Data for the flight patterns of public figures is derived from the database through the compilation of all flights associated with the publicly available tail number. This may underestimate flights in cases where an individual owns several aircraft, or where PIAs have been assigned. It may overestimate emissions where aircraft are made available to friends or family.

Company patterns are exemplified on the basis of a family-owned company in Denmark for which tail numbers for three aircraft are publicly known. As the company is family-owned, it is possible to explain flight patterns as partially related to publicly known second homes, and flights that appear to serve holiday destinations (for illustration see Supplementary Material, Fig. 2). Flight patterns are illustrated for a five-year period to show overlap. It is unknown who used the aircraft, which may also have been made available to non-family members within or outside the company.

Development of maps

Maps visualize air transport based on the geographic coordinate system World Geodetic System 1984 that defines latitude, longitude, and altitude. Maps are generated based on ADS-B transponder data for departure and arrival points. The projection distorts distance by increasingly stretching distances between points as they move away from the equator towards the poles, leading to visual distance exaggeration of high-latitude regions.