Private aviation is making a growing contribution to climate change

Global commercial aviation emitted roughly 892–936 Mt CO₂ in 2019 and contributes about 4% of global anthropogenic effective radiative forcing. Demand growth is expected to remain strong and has historically outpaced efficiency gains, implying rising emissions absent large-scale deployment of sustainable aviation fuels, which face technical and cost barriers. Air transport participation is skewed toward a small share of the global population, with frequent flyers responsible for a disproportionate share of emissions and premium classes being far more carbon intensive than economy. Within this context, private aviation (PA) is the most energy-intensive form of air transport, yet its global scale, distribution, and energy intensity are insufficiently understood. The sector is closely linked to affluence and climate politics, as international aviation lacks effective climate policies and PA use is concentrated among ultra-high-net-worth individuals. This paper investigates PA’s energy intensity, global scale and distribution, the role of events in attracting PA, seasonal and weekly usage patterns, travel motives, and growth trends, to inform targeted and effective policy responses.

Prior work using ADS-B and other data sources has quantified commercial aviation emissions and climate impacts, but global private aviation remains less well studied. Sun et al. evaluated 250 private jets (2019–2022), estimating 0.45–0.51 Mt CO₂, while Sobierski and Mumbower analyzed US private aviation (Jan 2019–Oct 2021), finding substantial growth during COVID-19 and an increased US share of global PA emissions. Broader literature highlights that aviation growth tends to outpace efficiency gains; that premium and frequent flying disproportionately drive emissions; and that emissions are highly unequal across populations, with affluent groups responsible for outsized shares. Policy analyses note limitations of current international mechanisms (e.g., CORSIA) and emphasize the need for more effective mitigation strategies that consider distributional effects. These strands motivate a comprehensive, global, data-driven assessment of PA’s scale, intensity, geography, and drivers, including events and leisure travel.

Data source and scope: The study uses Automatic Dependent Surveillance–Broadcast (ADS-B) data from ADS-B Exchange, an unfiltered, comprehensive global flight-tracking platform providing 6-second resolution records. The dataset covers private aircraft worldwide for 2019–2023 and includes timestamp, latitude, longitude, barometric altitude, ICAO 24-bit code, tail number, and aircraft model. A total of 25,993 private aircraft were identified in service by December 2023. For 2023, 4,301,561 flights (legs) and 6,474,810 flight hours (excl. taxi) were analyzed. Identification of private aircraft: Aircraft models marketed as business jets and relevant turboprops were compiled (72 models). The fleet distribution by model was cross-checked against industry figures; the top 10 models account for ~40% of the fleet, led by the Pilatus PC-12. Handling data gaps and privacy programs: The FAA’s LADD can restrict data on some platforms, but ADS-B Exchange provides unfiltered data. A 10% flight sample indicated 30% LADD requests, supporting the choice of ADS-B Exchange. Privacy ICAO Addresses (PIA) temporarily obfuscate some US-registered aircraft; 283 PIAs (≈1% of aircraft) were reported (Apr 19, 2024). Flights using temporary PIA IDs could be undercounted when linkage to model is not possible. ADS-B signal loss and coverage gaps were handled with rules for leg termination (on-ground signals; >15 minutes loss above 10,000 ft; >10 hours loss). Emissions calculation: Flight legs were reconstructed from positional and altitude data to obtain leg durations and great-circle distances (actual distances on average ~10% longer). Average fuel burn (gallons per hour) for each of the 72 models was sourced from manufacturer/broker materials. Direct CO₂ emissions per leg were computed as fuel (gal/h × hours) × 3.78541 L/gal × 0.8 kg/L jet fuel (note: paper cites 0.48 kg/L, and 3.16 kg CO₂/kg fuel) × 3.16 kg CO₂/kg fuel. Taxiing and non-CO₂ effects were excluded from primary estimates. Cruise-altitude exposure was characterized via time spent ≥30,000 ft and ≥40,000 ft. Event analysis: For selected 2023 global events (World Economic Forum, Super Bowl, COP28, Cannes Film Festival) and the 2022 FIFA World Cup, relevant airports and exact event dates were defined. All arrivals during event windows were counted and compared to matched pre/post periods; net event-associated flights and emissions were derived by subtracting the average of before/after periods from the event-period totals. Seasonality and motive inference: Arrival distributions by month and weekday were analyzed for leisure destinations (e.g., Ibiza, Nice) to infer leisure dominance (peaks in summer; weekend patterns). Statistical uncertainty: Bootstrapping (samples of 100,000 legs) produced empirical 95% confidence intervals for average 2023 flight distance (mean 865.8 km; 95% CI 859.5–872.1 km) and per-flight emissions (mean ~3,632 kg CO₂; 95% CI ~3,598–3,666 kg). Mapping: Routes and airport pairs were visualized using WGS84 coordinates; map projection limitations at high latitudes were noted. Data and code availability: Data links for 2019–2023 legs and GitHub repository for code are provided.

- Energy intensity: Across 72 PA models, average fuel consumption spans 86–576 g CO₂-eq per unit basis (corresponding to ~182–1200 g CO₂ per flight hour as cited), with some aircraft emitting per hour amounts comparable to or exceeding annual per-capita emissions in some contexts (~4.5 t CO₂ in 2020). - Altitude profile: 45.4% of flight time occurs at ≥30,000 ft and 21.4% at ≥40,000 ft, indicating potential relevance of non-CO₂ radiative forcing from high-altitude operations. - Scale in 2023: 25,993 aircraft made 4,301,561 flights, totaling 6,474,810 flight hours (excl. taxi). Weighted by model fuel burn, direct emissions were 15.62 Mt CO₂ in 2023, averaging ~3.6 t CO₂ per flight. Average great-circle distance was 865.7 km, with mean speed 575 km/h and flight time ~90 minutes. - Trip-length distribution (2023): 47.4% of flights were <500 km; 4.7% were <50 km; 29.1% were >1000 km. Real distances are on average ~10% longer than great-circle distances. - Geography and concentration: Private aviation is heavily concentrated in the USA and Europe. The USA accounts for 68.7% of registered aircraft (~18,163). Six countries account for >80% of the fleet. Per capita, Malta has the highest density (46.5 per 100,000), followed by the USA (5.45), Switzerland (3.8), and Austria (2.85). Miami accounts for ~6% of all PA departures. - Events: Major events attract significant PA activity. Estimated total CO₂ from event-associated flights: FIFA World Cup 2022 (Qatar) 14.7 kt; World Economic Forum (Davos) 7.5 kt; Cannes Film Festival 4.0 kt; COP28 (Dubai) 3.8 kt; Super Bowl (Arizona) 1.5 kt. - Usage patterns and motives: Seasonality shows summer peaks; weekend concentration (Fri arrivals, Sun departures) suggests leisure dominance. Short very-low-distance flights (13–27 km) indicate repositioning, parking, pickup/delivery; an indeterminable share are empty legs. Cross-visitation analysis shows the same aircraft frequently serving political, economic, cultural, and sports events. - Growth: PA emissions increased by ~46% from 2019 to 2023. Industry expects strong growth ahead, with ~8,500 additional business jet deliveries projected for 2024–2033. - Relative contribution: Direct PA CO₂ in 2023 corresponds to ~1.7%–18.5% of 2019 commercial aviation CO₂, depending on comparison bounds and estimates used.

The study addresses the knowledge gap on PA’s global scale, distribution, and energy intensity by leveraging unfiltered ADS-B data and model-specific fuel burn to quantify direct CO₂ emissions and usage patterns. Findings reveal that PA is highly concentrated geographically (especially in the USA), exhibits strong leisure and event-driven demand, and includes a large share of short flights—many of which may be repositioning or convenience trips—leading to high emissions per passenger compared to commercial aviation. The altitude profiles underscore the likely importance of non-CO₂ climate effects, which would increase PA’s total climate impact beyond direct CO₂. The 46% emissions growth from 2019–2023, together with projected fleet expansion and limited near-term availability and uptake of sustainable aviation fuel, suggests PA’s absolute and relative contribution to aviation climate impacts will grow without policy intervention. These insights reinforce the need for targeted regulation that addresses both demand (e.g., managing discretionary and short flights, event-related surges) and supply (fuel standards, operational measures), while considering distributional fairness given PA’s association with affluent users. The results provide an empirical basis for policymakers to design measures that more effectively internalize climate costs and curb high-intensity segments of air transport.

This study provides a first global quantification of private aviation’s direct CO₂ emissions and patterns using ADS-B data. In 2023, PA emitted an estimated 15.62 Mt CO₂, equal to roughly 1.7%–18.5% of commercial aviation’s 2019 CO₂ emissions, with a large share of short-haul and leisure/event-driven flights and a marked concentration in the USA. Efficiency gains (~1.25% per year in 2019–2023, fuel use per km) trail demand growth, and emissions rose by ~46% over the study period. With industry expecting ~8,500 new business jet deliveries in 2024–2033 and limited near-term availability or adoption of sustainable aviation fuel, PA is poised to become more significant both in share and in absolute emissions. Because estimates exclude taxiing, empty legs quantification, lifecycle emissions, and non-CO₂ effects, the true climate impact is higher. Regulation of the sector is therefore necessary, paralleling discussions in commercial aviation on demand- and supply-side measures. Future research should refine non-CO₂ forcing estimates for PA, incorporate taxiing and lifecycle emissions, better quantify empty legs and PIA-covered operations, and evaluate policy instruments’ effectiveness and equity implications.

- Coverage and identification: Some flights are not tracked when third-party flight IDs are used. FAA Privacy ICAO Addresses (PIA) temporarily mask aircraft identities; ~283 PIAs (~1% of aircraft) were active, likely biasing results downward, especially for larger jets. LADD restrictions affect some providers, but ADS-B Exchange offers unfiltered data. - ADS-B signal and coverage: Occasional signal losses and incomplete coverage can misallocate endpoints or miss legs; rule-based heuristics mitigate but do not eliminate errors. - Fleet size uncertainty: Industry counts differ by ~2% from ADS-B-derived figures; differences may reflect PIA use or aircraft for sale/not in use. - Fuel burn estimation: Use of model-average fuel economy (gph) applied to leg time does not capture flight phase, aircraft mass/payload, weather, or specific operating practices; estimated error within ~10% based on SD. - Scope of emissions: Taxiing, ground support, and lifecycle emissions (aircraft manufacturing, infrastructure) are excluded; results represent lower-bound direct in-flight CO₂ only. - Non-CO₂ effects: Radiative forcing from NOx, contrails, and water vapor at altitude is not included in the main estimates, likely understating climate impact. - Biofuels: Low current usage; any lower-lifecycle-intensity fuels are not accounted for, though likely <1% share. - Event attribution: Event windows and airport sets may undercount participants arriving earlier/later or via alternative airports; netting method reduces but does not remove confounding traffic. - Individual/company analyses: Tail-number-based attribution may miss flights when multiple aircraft are owned or PIA is used, and may over-attribute when aircraft are loaned to others.