Pathways analysis to reducing aircraft emissions for China-Foreign routes

China’s aviation sector has grown rapidly, with international (China-foreign) routes among the fastest growing globally. This growth has driven significant increases in pollutant emissions, raising concerns for climate and local air quality. While most studies emphasize CO₂, aircraft also emit CO, HC, NOx, SO₂, and PM2.5, which have health and environmental impacts and contribute to climate change via non-CO₂ effects. The research question is how two principal decarbonization pathways—Emissions Trading Schemes (ETS) and Sustainable Aviation Fuels (SAFs)—individually and jointly affect climate-relevant emissions and concentrations on China-foreign routes through 2100. The study’s purpose is to calculate actual multi-pollutant emissions for these routes, translate climate-relevant species into carbon dioxide equivalent concentration (CDEC), and quantitatively compare the effectiveness of ETS, SAFs, and their combination. This is important for designing effective policy mixes to achieve aviation climate goals (e.g., CNG2020, carbon neutrality timelines) while accounting for both CO₂ and non-CO₂ climate forcers.

- Non-CO₂ aircraft emissions (CO, HC, NOx, SO₂, PM2.5) significantly affect health and climate near airports and globally. Traditional methods (ICAO BFFM2/FOA, EPA, EMEP) are better suited to LTO-cycle inventories and have limitations for cruise phases and integration with CO₂ accounting. Cui et al. proposed a Modified BFFM2-FOA-FPM method enabling combined CO₂ and non-CO₂ estimates with low error versus official data. - Climate metrics: Since early work on ODP and HGWP, the Global Warming Potential (GWP), particularly GWP100, has become the standard metric to express emissions as CO₂-equivalents. GWP integrates radiative forcing over a time horizon relative to CO₂, but it does not directly include environmental absorption dynamics. - Aviation policy context: EU ETS expanded to aviation (legislated 2008; effective 2012) to internalize climate costs amid rising aviation CO₂. ETS primarily targets CO₂ and provides economic incentives rather than direct multi-gas control. - SAFs: Considered a key long-term decarbonization option compared with battery-electric (currently constrained). Literature evaluates SAFs via life cycle assessment and costs; potential for climate-neutral aviation with synthetic biomass fuels, green hydrogen, and e-fuels. - Gap: Few studies systematically compare ETS and SAFs for aviation, particularly quantifying impacts on both CO₂ and non-CO₂ pollutants for a specific, fast-growing market like China-foreign routes. This study addresses that gap by integrating emissions accounting with concentration modeling and CDEC.

Study scope: China-foreign routes, using detailed flight data to compute emissions and then model atmospheric concentrations and CDEC for 2023–2100 under multiple scenarios (baseline, ETS-only, SAFs-only, and combined ETS+SAFs). Data sources: - Flight activity (aircraft type, frequency, distance, time, airlines, transfers): VariFlight.com. - Engine characteristics: ICAO Aircraft Engine Emissions Databank (EASA-hosted). - Historical route-level CCD and LTO emissions (2014–2019) from prior work; 2020–2021 in supplementary tables. Emissions calculation: - Pollutants: CO₂, CO, HC, NOx, SO₂, PM2.5. - Method: Modified BFFM2-FOA-FPM method to estimate both CO₂ and non-CO₂ emissions across flight phases with aircraft- and engine-specificity. Concentration and CDEC modeling: Aviation FAIR-GWP concentration method - Framework: Builds on FAIR model (v1.0/1.3) to simulate CO₂ and non-CO₂ (CH₄, N₂O) concentrations considering environmental absorption, then converts to carbon dioxide equivalent concentration (CDEC) using GWP factors. - CO₂: Modeled with FAIR box model partitioning to geological processes, deep ocean, biosphere, and mixed layer with partition fractions αᵢ and time constants τᵢ. CO₂ concentration C_CO₂ computed from emissions E_CO₂ via equations (1)–(3), solved using 100,000 Monte Carlo simulations; includes temperature feedback parameters r_c, r₁, r₀. - CH₄ and N₂O: Derived from HC and NOx emissions, assuming CH₄ is 0.4 of aviation HC and N₂O is 0.29 of aviation NOx (validated to yield CO₂e/CO₂ ≈ 1.7 under GWP100). Concentrations computed with lifetimes τ_CH4 = 9.3 years and τ_N2O = 121 years using mass conversion (equations (5)–(6)). - CDEC: C_E = C_CO₂ + 25*C_CH₄ + 298*C_N₂O (GWP100 factors for time horizon through 2100). Scenario design (2023–2100): - Baseline: Annual emissions fixed at the average of 2014–2021 (reflecting rise through 2019 and COVID-19 dip 2020–2021; chosen to match expected near-term rise then long-term decline pattern). - ETS-only (CNG2020-aligned) sub-scenarios: Two neutrality timelines with recovery and peak years: • Neutrality by 2035: 2023–2026–2035; 2024–2027–2035; 2025–2028–2035 (recover to 2019 levels, peak, then reach CO₂ neutrality in 2035). • Neutrality by 2040: 2023–2030–2040; 2024–2030–2040; 2025–2030–2040. ETS primarily constrains CO₂; CH₄ and N₂O assumed unchanged relative to baseline unless implied by CO₂ linkage. - SAFs-only sub-scenarios, with performance assumptions: NOx and SO₂ emissions set to 0; PM2.5 reduced by 30%; CO₂ and CH₄ reduced by 50–90% (average 70%). Deployment cases: • 2025–50%: reach 50% SAF blend in 2025 and maintain. • 2025–50% + 2030–100%: reach 50% in 2025, 100% in 2030. • 2025–50% + 2035–100%: reach 50% in 2025, 100% in 2035. - Combined ETS + SAFs: Nine combinations pairing ETS neutrality timelines with SAF deployment cases. Analysis outputs: Annual concentration trajectories for CO₂, CH₄, N₂O and CDEC; comparisons across scenarios in absolute concentration changes and as percentages of baseline by 2100.

- Baseline emissions and concentrations: • Historical CO₂ emissions on China-foreign routes rose from 28.15 Mt (2014) to 45.93 Mt (2016), then slowed with CNG2020-related measures; COVID-19 reduced CO₂ to 7.60 Mt (2020) and 7.56 Mt (2021). • Concentration changes (2023–2100): CO₂ growth reaches 7.52E-07 ppm by 2100; N₂O reaches 5.65E-10 ppb by 2100; CH₄ rises to 5.71E-10 ppb by 2032 then stabilizes at 5.65E-10 ppb from 2033. CDEC grows from 9.65E-09 ppm (2023) to 7.53E-07 ppm (2100). CO₂ drives most of CDEC, with N₂O the next largest contributor. - ETS-only scenarios: • Neutrality by 2035: CO₂ concentration growth by 2100 reduces to 1.35E-07, 1.38E-07, 1.40E-07 ppm (2023–2026–2035; 2024–2027–2035; 2025–2028–2035), i.e., 17.96–18.61% of baseline. CDEC similarly reduces to 1.36E-07, 1.39E-07, 1.41E-07 ppm (18.06–18.70% of baseline). • Neutrality by 2040: CO₂ concentration growth by 2100 is 2.78E-07, 2.52E-07, 2.27E-07 ppm (2023–2030–2040; 2024–2030–2040; 2025–2030–2040), i.e., 30.27–37.10% of baseline. CDEC is 2.80E-07, 2.53E-07, 2.29E-07 ppm (30.37–37.18% of baseline). • CH₄ and N₂O concentrations remain essentially unchanged versus baseline; reductions in CDEC are driven by CO₂ control under ETS. - SAFs-only scenarios: • CO₂ concentration by 2100: 4.99E-07 ppm (2025–50%), 2.62E-07 ppm (2025–50%+2030–100%), 2.80E-07 ppm (2025–50%+2035–100%) corresponding to 66.94%, 34.94%, and 37.18% of baseline. • CH₄ by 2100: 4.59E-11, 2.12E-11, 2.12E-11 ppb, i.e., 8.13%, 3.75%, 3.75% of baseline. • N₂O by 2100: 1.67E-09, 3.87E-10, 5.50E-10 ppb, i.e., 53.43%, 12.35%, 17.56% of baseline. • CDEC by 2100: 4.99E-07, 2.63E-07, 2.80E-07 ppm across the three SAF deployment cases. SAFs are more effective than ETS at reducing non-CO₂ (CH₄, N₂O), yielding lower CDEC than ETS-only in the best case. - Combined ETS + SAFs: • Strongest reductions observed, especially with 2035 neutrality plus rapid SAF scale-up. In 2025–2028–2035 + 2025–50% + 2030–100%, CO₂ concentration and CDEC reach 7.74E-08 and 7.76E-08 ppm by 2100 (~10.3% of baseline for CO₂/CDEC, among the best outcomes). • Across nine combined scenarios, CO₂ by 2100 ranges roughly 7.74E-08 to 1.94E-07 ppm (≈10.30–25.76% of baseline), with corresponding CDEC reductions to ≈10.30–25.75% of baseline. • CH₄ and N₂O follow reductions similar to SAFs-only cases; the combined approach most strongly impacts CO₂ and CDEC. - Overall ranking: Combined ETS+SAFs > SAFs-only > ETS-only for reducing multi-gas climate impact (CDEC), with ETS more targeted at CO₂ and SAFs delivering substantial benefits for non-CO₂ gases.

The findings show that CO₂ is the dominant contributor to aviation climate impact on China-foreign routes, with N₂O second in importance. ETS effectively reduces CO₂ and thus lowers CDEC but leaves non-CO₂ largely unchanged. SAFs reduce CO₂ and substantially cut CH₄ and N₂O, leading to greater overall CDEC reductions than ETS alone in the best SAF deployment scenarios. The combination of ETS and SAFs yields the greatest reductions, particularly when carbon neutrality is achieved by 2035 and 100% SAFs are adopted by 2030. This indicates that policy designs that pair market-based CO₂ controls with rapid SAF deployment can most effectively mitigate aviation’s multi-gas climate impacts. The modeling provides a quantitative basis for prioritizing accelerated neutrality timelines and ambitious SAF blending targets for international routes originating in China, with potential transferability to other regions.

This paper contributes a systematic comparison of ETS and SAFs for aviation emission reduction on China-foreign routes, integrating detailed emissions accounting with an Aviation FAIR-GWP concentration framework to compute CDEC to 2100. Key contributions include: (1) quantification of CO₂ and non-CO₂ pollutant emissions and concentrations under realistic recovery and neutrality scenarios, (2) demonstration that SAFs provide broader multi-gas benefits than ETS alone, and (3) evidence that combining ETS with rapid SAF deployment achieves the largest reductions, especially under a 2035 neutrality timeline. Policy implications: Prioritize combined ETS+SAFs strategies, accelerate neutrality targets to 2035 where feasible, and support pathways to reach 100% SAF use by 2030–2035. Ensure complementary measures address non-CO₂ effects alongside CO₂ pricing. Future research: Incorporate cost dynamics (aircraft modifications, maintenance, carbon prices, SAF price trajectories), differentiate SAF pathways by feedstock/technology and life-cycle performance, and explicitly treat uncertainties and sensitivity ranges in emissions, chemistry, and deployment timelines.

- Economic factors not modeled: costs of aircraft modifications and maintenance for SAFs, carbon prices, and SAF price trajectories, which could affect scenario feasibility and deployment pace. - SAF heterogeneity not captured: differences in emission-reduction properties across feedstocks and processing technologies are not considered. - Uncertainty analysis omitted: uncertainties in emissions, atmospheric processes, technology adoption, and policy implementation are not explicitly treated; future work should incorporate uncertainty quantification and sensitivity analyses.