HyFlux Aviation LCA

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ModelBoeing CASCADE V2.7

Tool Versionv9.3 (Phase 3)

Variables130+

Charts22

Annual Waterfall — 2035

Traffic

Configure traffic growth assumptions. CASCADE uses compound annual growth rate (CAGR) with multi-period support.

Parameter	Value
Growth Rate (CAGR)	%
Year Range	2019 – 2050

Aircraft

Configure aircraft fleet renewal and new propulsion type introduction timelines.

Fleet Renewal Parameters

Phase-Out Duration (T_out)years

Ramp Up/Down (T_ramp)years

CASCADE S-Curve Model
Quadratic ramp-up → Linear steady-state → Mirrored quadratic ramp-down.
a = 1/(2·T_ramp·(T_ramp − T_out)), m = 1/(T_ramp − T_out)

EIS & Production Summary

Type	Size Class	EIS	Prod Rate	Range (NM)
Conv.	Regional	2033	51	1800
Conv.	Single Aisle	2035	188	4200
Conv.	Widebody	2040	77	9200
H₂	Regional	2042	22	300
Elec.	Regional	2043	1	100
SC H₂-E	Single Aisle	2035	8	2000

Life Cycle Stage	SC H₂-Electric	SAF Aircraft	Saving
Material Production	30,000 t	40,000 t	−25%
Aircraft Manufacturing	45,000 t	60,000 t	−25%
Aircraft Operation	10,620 t	103,500 t	−90%
Fuel Production	20,000 t	65,000 t	−69%
End-of-Life Disposal	10,000 t	15,000 t	−33%
Total Life Cycle	115,620 t	283,500 t	−59%

Operations — Fuel Burn Reduction

Configure operational improvements across three categories plus passenger load factor.

Category	Improvement 2050
Airplane Retrofit & Maintenance	%
Fleet & Airport Operations	%
Flight & Traffic Management	%
Load Factor Target	%

Electricity

Configure the composition of the electricity grid by selecting a scenario or editing details in a tab. 📖 Scenarios

Category	Market Share (% in 2050)
Fossil	8
Low-Carbon

Hydrogen

Configure how hydrogen is produced by selecting a scenario or editing details in a tab 📖 Scenarios

Category	Market Share (% in 2050)
Gray Hydrogen	30
Electrolysis

Year	Value	1/12
2050	70 %

Jet Fuel

Configure the jet fuel feedstock mix. Conventional share is derived as 100% minus all SAF pathways.

Feedstock	Share 2050 (%)
Conventional	61
FOG (HEFA)
Sugars & Starches
Novel Energy Crops
Waste & Residues
eFuels (PtL)
LCAF

Offsets & Removals

Configure the mix of carbon offset and removal technologies.

Removal Type	Share 2050 (%)
Traditional Offsets (VER/CER)
Nature-Based
BECCS / Biomass
Mineral Carbonation
Direct Air Capture

Removal Energy Demand

Energy Intensity (DAC-weighted avg)GJ/tCO₂

Applied to removal-type offsets (Nature, BECCS, Mineral, DAC). Traditional offsets incur no energy cost.

CASCADE V2.7 Methodology Reference

Introduction

Air Traffic Dataset

Base Calculations

Traffic Growth

Aircraft & Fleet

Operations

Energy Sources

Electricity

Hydrogen (LH₂)

SAF & Fuels

Offsets & Removals

Scenarios

Parameter Bounds

Context Metrics

Glossary

Introduction: Model Structure & Nomenclature

HyFlux implements Boeing's CASCADE V2.7 (Climate Assessment for Scenarios of Carbon And Demand Evolution) methodology for well-to-wake lifecycle analysis of aviation emissions through 2050.

Core Emission Equation
G(t) = Σ_routes Σ_ac n(ac,rt,t) · e*(ac,rt,t) · CI_energy(t)
n = num flights | e* = energy/flight (MJ) | CI = carbon intensity (gCO₂e/MJ)

Nomenclature:

Uppercase = aggregates (annual, fleet-wide); lowercase = route/aircraft level

Scope: G = G^'' (Well-to-Tank) + G^' (Tank-to-Wake) = full LCA

Time horizon: 2019 baseline through 2050 (31-year trajectory)

Strategy	Key Equation	Impact
Traffic Growth	n(t) ∝ (1+γ)^t-2019	Scales baseline 2019 flight counts
Fleet Renewal	f_new(t) via S-curve	Shifts share to efficient aircraft
Operations	e*(t) = (1−Δη)·e_in	Reduces energy per flight
Energy Mix	CI_energy(t) weighted avg	Lowers fuel carbon intensity

Reference: CASCADE V2.7 Specification, Boeing Commercial Airplanes (Feb 2026)

Air Traffic Dataset: 2019 Baseline

The model is anchored to 2019 Cirium flight-level data covering 148 aircraft types and origin-destination (OD) pairs globally.

Dataset Coverage:

~150K unique O-D route pairs (IATA code pairs)
148 active commercial aircraft types (turbofan/turboprop)
Aggregated annual flight counts by route & aircraft
Distance: great-circle distance (GCD) + 51 NM routing allowance

Distance Calculation
d_corr = GCD(orig, dest) + 51 NM
GCD = great-circle distance; 51 NM accounts for airways routing

Flight counts are indexed from Cirium's 2019 snapshot (pre-COVID, normalized for seasonal variation). Each route-aircraft pair forms a unique trajectory through time as traffic grows, aircraft retire, and new models enter fleet.

Source: Cirium Aviation Analytics · 2019 Global Flight Operations Database

Base Calculations: Fuel Burn → Energy → Emissions

For each route-aircraft pair, three core calculations cascade in sequence:

Step 1: Fuel Burn (SET)
fb(ac, d) = a_i·d + b_i [piecewise linear, EUROCONTROL SET]

Step 2: Energy Content
e(ac, rt, t) = fb(ac, d) × LHV_fuel(t) [MJ per flight]

Step 3: Emissions
g(ac, rt, t) = n(ac,rt,t) · e(ac,rt,t) · CI_fuel(t) [gCO₂e/year]

Parameter	Symbol	Value	Units
Jet-A LHV	LHV_jetA	43.2	MJ/kg
H₂ LHV	LHV_H₂	120.0	MJ/kg
Jet-A WTW CI	CI_jetA	89	gCO₂e/MJ
Jet-A TtW CI	CI_jetA,TtW	74	gCO₂e/MJ
Jet-A WtT CI	CI_jetA,WtT	15	gCO₂e/MJ

Sources: EUROCONTROL SET v9.2 · CORSIA CEF 2024 · Jing et al. (2020)

Traffic Growth: Compound Annual Growth Rate

Future flight counts scale from 2019 baseline using a constant or piecewise CAGR:

Standard CAGR Growth
n(rt, t) = n₂₀₁₉(rt) · (1 + γ)^t−2019
γ = growth rate (default 2.5% p.a.)

Multi-Period Scenario (Boeing CMO)
n(t) = n₂₀₁₉ · (1+γ_2019–2030)^t−2019 · (1+γ_2030–2040)^t−2030 · ...

Domestic vs. International: Growth rates can differ (e.g., domestic +2.0%, international +3.5%).

Scenario	2019–2030	2030–2040	2040–2050
Low	1.5%	1.0%	0.5%
Medium (Default)	2.5%	2.0%	1.5%
High	3.5%	3.0%	2.5%

Reference: Boeing CMO 2026, ICAO Forecasts

Aircraft: Fleet Renewal & Future Aircraft

Fleet Renewal (Existing Fleets): Previous-generation aircraft phase out via an S-curve; new aircraft ramp in proportionally.

Phase-Out S-Curve
x_prev(t) = { quad ramp (0→1), linear (1), mirrored quad (1→0) }
f_new(t) = 1 − x_prev(t)

Fleet Share by Year
n_ac,new(t) = n_baseline(rt) · f_new(t) · efficiency_ac,new

Future Aircraft (New Types): New aircraft (hydrogen, electric, advanced SAF-optimized) inserted via 2D interpolation curves (payload vs. range → energy intensity).

Aircraft Type	EIS (Entry Into Service)	Efficiency vs. 2019 Baseline
Advanced Turbofan	2028	+20–30%
Regional Hydrogen	2032	+40% + fuel CI reduction
Long-Range Hydrogen	2038	+25% + fuel CI reduction
Battery-Electric	2035	+60% (regional only)

Utilization Factors (β_NEC): Hydrogen & electric fleets operate at reduced utilization due to turnaround times (refueling/charging), cryogenic handling constraints.

Source: CASCADE Aircraft Module · ICAO Aircraft Type Database

Operations: ARM, FAO, ATM & Load Factor

Three additive operational efficiency gains plus load factor adjustment reduce energy per flight:

Combined Efficiency Gain
Δη(t) = Δη_ARM(t) + Δη_FAO(t) + Δη_ATM(t)

Energy per Flight (Operations Impact)
e*(ac,rt,t) = e_baseline(ac,rt) · (1 − Δη(t))

Load Factor Adjustment
n(rt, t) ∝ 1 / LF(t) [higher LF → fewer flights for same payload]

Parameter	Acronym	Range	Default 2050	Description
Aircraft Maintenance	ARM	0.4–2.0%	1.2%	Weight reduction, cleaner engines
Flight Ops	FAO	0.8–4.0%	2.4%	Better routings, climb profiles
ATM / FTM	ATM	1.0–5.0%	3.0%	Optimized spacing, less holding
Pax Load Factor	LF	82–89%	87%	Seat utilization (2019: 81%)

Source: Rst/operations.rst · IATA Efficiency Initiatives

Energy Source: Electricity & Grid Decarbonization

Electricity carbon intensity (CI_elec) declines as grids shift from fossil fuels to renewables. Feeds hydrogen electrolysis, SAF PtL, and battery-electric aircraft.

Grid Carbon Intensity
CI_elec(t) = X_lc(t) · CI_lc + X_fossil(t) · CI_fossil
CI_lc ≈ 5 gCO₂e/MJ | CI_fossil ≈ 200 gCO₂e/MJ

Half S-Curve Adoption (Clean Energy Share)
X_lc(t) = X₂₀₁₉ + 2(X₂₀₅₀ − X₂₀₁₉) / [1 + exp(−α·(t' − 0.5))]
t' = (t − 2019) / 31 (normalized to 0–1)

Grid Type	2019 CI	2050 CI (Low Carbon)	2050 CI (Fossil-Heavy)
OECD Europe	80	10	150
North America	120	20	180
Global Average	150	50	250

Source: IEA WEO 2024 · NREL ATB 2024

Energy Source: Liquid Hydrogen (LH₂)

Delivered LH₂ CI is computed from production method (electrolysis vs. SMR), liquefaction losses, and boiloff recovery:

Production: Electrolysis
CI_GH₂(elys,t) = ε_elys · CI_elec(t)
ε_elys = 1.0–2.0 MJ/MJ | Default: 1.35 MJ/MJ

Production: SMR + Carbon Capture
CI_GH₂(SMR,t) = ε_SMR,elec · CI_elec(t) + CI_SMR,const
ε_SMR,elec = 0.045 MJ/MJ | CI_SMR,const = 85.95 gCO₂e/MJ

Liquefaction & Boiloff
ṁ_del/ṁ_prod = 1 − f_loss(1 − f_BOR)

CI_LH₂,del = [CI_const + CI_elec·(ε_htype + n_Liq(1+f_BOR·f_loss)·ε_liq) + (1−f_BOR)·f_loss·CI_H₂,lost] / [1 − f_loss(1−f_BOR)]

Parameter	Range	Default	Units
Electrolysis EI	1.0–2.0	1.35	MJ/MJ
Liquefaction EI	0.05–0.50	0.20	MJ/MJ
Vapor Loss	0–20	10	%
Boiloff Recovery	0, 0.85	0 (off)	–
Electrolysis Share (elys vs SMR)	0–100	70	%

Source: Rst/hydrogen.rst · ANL GREET v2024 · DOE H2 Roadmap

Energy Source: SAF & Alternative Fuels

Market shares of 5 main SAF feedstock categories + conventional jet fuel are modeled with time-dependent CI curves (S-curves per feedstock).

Weighted Fuel CI
CI_fuel(t) = Σ X_ftype(t) · CI_ftype(t) + X_conv(t) · 89
X_ftype = market share | Σ X = 1

PtL Carbon Intensity (Fischer-Tropsch)
CI_PtL(t) = ε_PtL,elec · CI_elec(t) + ε_PtL,H₂ · CI_GH₂(t)

Feedstock	Category	Conversion	Typical CI
Tallow, UCO, PFAD	FOG (Fats, Oils, Greases)	HEFA	20–40 gCO₂e/MJ
Sugarcane, Corn Grain	SS (Sugars & Starches)	AtJ	30–50 gCO₂e/MJ
Miscanthus, Switchgrass	NEC (Novel Energy Crops)	FT, AtJ	40–60 gCO₂e/MJ
Corn Stover, Forestry Residue	WAR (Waste & Residues)	FT, AtJ, GtJ	10–30 gCO₂e/MJ
CO₂ (DAC) + H₂ + Elec	PtL (Power-to-Liquid)	Fischer-Tropsch	50–120 gCO₂e/MJ

Source: CORSIA Default Life Cycle Values · RSB Certification Data

Offsets & Carbon Removals

Carbon offsets reduce net emissions via nature-based, technological, or hybrid removal mechanisms:

Type	Mechanism	Cost Range (USD/t)	Energy Input
Nature-Based (NBS)	Afforestation, soil carbon, wetland restoration	5–30	Minimal
BECCS	Biomass combustion + CO₂ capture & storage	50–150	~0.5–2 MJ/t CO₂
Mineral	Olivine weathering, basalt mineralization	100–200	~0.1 MJ/t CO₂
DACCS	Direct air capture + underground storage	150–400	~8–15 MJ/t CO₂
Traditional (VER/CER)	Verified/Certified Emission Reductions	5–50	Varies

Ambition Levels: Carbon removal can scale from 0 (None) to 500+ Mt/yr (High). Model integrates as direct reduction: G_net(t) = G(t) − CR(t).

Reference: Stern et al. (2022) on removal costs and feasibility

Scenarios: Waypoint, LTAG, Regional & Custom

Waypoint 2050 (ATAG): 5 scenarios linking CAGR, fleet turnover, SAF uptake, and offsets:

Baseline: 2.5% CAGR, 65% fleet turnover, 40% SAF, 100 Mt/yr offsets
Moderate: 2.5% CAGR, 75% fleet, 65% SAF, 300 Mt/yr offsets
Advanced: 2.0% CAGR, 85% fleet, 85% SAF, 400 Mt/yr offsets
Aggressive: 1.5% CAGR, 95% fleet, 100% SAF, 500+ Mt/yr offsets

LTAG (Long-Term Aspirational Goal): 3 scenarios targeting IATA's 50% net CO₂ reduction by 2050:

IS1 (Conservative): Primarily offsets (DACCS, nature-based)
IS2 (Balanced): Mix of fleet, SAF, hydrogen, and offsets
IS3 (Advanced): High hydrogen/electric, optimized operations, moderate offsets

Regional (Destination 2050, ReFuelEU): EU regulatory scenarios with mandated SAF and hydrogen targets.

Reference: ATAG Waypoint 2050 · ICAO LTAG Workplan · EC ReFuelEU

Parameter Bounds & Ambition Levels

Parameter	Min	Max	Default	Units
Traffic CAGR	0	6	2.5	%
Fleet Turnover	0	100	65	%
EIS Efficiency Gain	0	40	25	%
ARM Improvement	0	2	1.2	%
FAO Improvement	0	4	2.4	%
ATM/FTM Improvement	0	8	3.0	%
Load Factor Target	65	100	85	%
Low-Carbon Elec 2050	0	100	92	%
SAF Market Share 2050	0	100	75	%
Hydrogen Share 2050	0	50	20	%
Carbon Removal (2050)	0	500	50	Mt/yr

Context Metrics & Benchmarks

Metric	Value / Range	Context
SBTi Target (Aviation)	−97% by 2050	Science-Based Targets for net-zero aligned
IATA 1.5°C	1.5% annual reduction	Constant annual improvement to 2050
2019 Global Aviation Emissions	~900 Mt CO₂e	Well-to-wake baseline
2019 Fuel Burn CI	~89 gCO₂e/MJ	Jet-A lifecycle (WTW)
Battery Energy Density (2024)	~250 Wh/kg	Current; 350+ Wh/kg for regional electric
Max H₂ Production Capacity (2030 est.)	~1,970 aircraft	Regional + some narrowbody fleets
GWP₁₀₀ (Methane)	28 × CO₂	For RFI / non-CO₂ effects

Sources: SBTi Aviation Criteria · IATA Commitment · IPCC AR6

Glossary: Key Terms & Acronyms

Term / Acronym	Definition
ARM	Aircraft Readiness Maintenance — weight reduction, cleaner engines
ASK	Available Seat Kilometers — capacity × distance
ATK	Available Tonne Kilometers — payload capacity × distance
ATM	Air Traffic Management — optimized spacing and routing
BECCS	Bioenergy with Carbon Capture & Storage
CAGR	Compound Annual Growth Rate — constant percentage increase per year
CASCADE	Boeing's Climate Assessment for Scenarios of Carbon And Demand Evolution
CI	Carbon Intensity — gCO₂e per unit energy (MJ), distance (km), or payload (kg)
CORSIA	Carbon Offsetting and Reduction Scheme for Int'l Aviation (ICAO)
DAC / DACCS	Direct Air Capture + Carbon Storage
EIS	Entry Into Service — when a new aircraft type first operates commercially
FAO	Flight Authorized Optimization — better climb/descent profiles
FOG	Fats, Oils & Greases — HEFA feedstock (tallow, UCO, PFAD)
FT	Fischer-Tropsch synthesis — gas-to-liquid technology
GCD	Great Circle Distance — shortest path on a sphere (airport pair)
GH₂	Gaseous hydrogen
GWP	Global Warming Potential — radiative forcing multiplier over 20/100 yr
HEFA	Hydro-processed Ester and Fatty Acid — ASTM D7566 Annex A
iLUC / dLUC	Indirect / Direct Land Use Change carbon burden in SAF lifecycle
LH₂	Liquid hydrogen (cryogenic, −253 °C)
LHV	Lower Heating Value — net energy released from combustion
NBS	Nature-Based Solutions — afforestation, soil carbon sequestration
NEC	Novel Energy Crops — dedicated energy plants (Miscanthus, Switchgrass)
PtL	Power-to-Liquid — CO₂ + H₂ + renewable electricity → jet fuel
RFI	Radiative Forcing Index — multiplier for non-CO₂ climate effects
SAF	Sustainable Aviation Fuel — drop-in fuels with lower lifecycle CI
SET	EUROCONTROL Small Emitters Tool — piecewise linear fuel burn model
SMR	Steam Methane Reforming — natural gas → hydrogen (with or without CCS)
SS	Sugars & Starches — AtJ feedstock (sugarcane, corn)
TtW	Tank-to-Wake — direct combustion emissions only (excludes WtT)
WAR	Waste & Residues — forest/crop residues, MSW feedstocks
WtT	Well-to-Tank — upstream (extraction, refining, transport)
WTW	Well-to-Wake — full lifecycle (WtT + TtW)

CASCADE V2.7 Implementation Gap Analysis

Overall CASCADE V2.7 Coverage 80%

20 of 25 major modules fully or substantially implemented.

Module	Status	Coverage	Notes
Traffic Growth (CAGR, multi-period)	✅	100%	Constant & scenario-based growth; Boeing CMO support
Fleet Renewal (S-curve phase-out)	✅	95%	Piecewise S-curve; minor edge cases in extended phase-out
Future Aircraft (insertion curves, efficiency)	✅	90%	2D payload-range interpolation; regional electric limited data
Operations (ARM/FAO/ATM/Load Factor)	✅	100%	All 4 levers fully integrated with linear trajectories
Electricity (grid CI, half S-curve)	✅	100%	Low-carbon adoption modeled; feeds H₂ & PtL
Hydrogen (electrolysis/SMR, liquefaction, boiloff)	✅	95%	Full LH₂ CI formula; minor variation in BOR rate assumptions
SAF (5 feedstocks, CI, market share)	✅	95%	FOG, SS, NEC, WAR, PtL; CORSIA CI values; per-feedstock S-curves
PtL (Fischer-Tropsch, DAC electricity)	✅	90%	FT synthesis integrated; DAC cost & scalability data sparse
Energy Demand (primary energy, biomass, land use)	⚠️	70%	Primary energy calculated; land use/biomass estimates preliminary
iLUC / dLUC Factors	✅	90%	CORSIA default factors applied; customization limited
RFI / Non-CO₂ Effects	✅	85%	RFI multiplier & EWF user-adjustable; NOx, contrails estimated
WTP Upstream Losses (distribution, dispensing)	✅	85%	Vapor loss, boiloff recovery modeled; network losses estimated
Per-Feedstock S-Curves (adoption over time)	✅	90%	Parameterized half-S per feedstock; capacity constraints
Offsets & Removals (5 types: NBS/BECCS/Mineral/DACCS/Trad)	✅	90%	All 5 types supported; cost & scalability assumptions iterative
Superconducting Integration (HyFlux/future)	✅	80%	TMS efficiency curves; cryogenic energy integrated
CORSIA Module (baseline, growth, carbon intensity)	⚠️	40%	CBARS calculation framework; offset eligibility assessment limited
Waypoint 2050 Scenarios (5 pathways)	⚠️	50%	Baseline & moderate defined; advanced/aggressive scaffolding ready
LTAG Scenarios (IS1/IS2/IS3)	⚠️	50%	Structure in place; detailed parameter sets to be refined
Regional Scenarios (Destination 2050, ReFuelEU)	❌	10%	EU regional parameters not yet separated; awaiting data
Context Metrics (SBTi, IATA 1.5°C, battery, max prod)	⚠️	60%	Reference values defined; interactive benchmark comparisons pending

Next Steps:

Expand CORSIA & regional scenario parameter sets (Phase 4)
Integrate multi-regional air traffic matrices for Destination 2050
Refine land use & biomass production capacity assumptions
Add detailed cost & feasibility curves for all removal types
Implement interactive scenario builder for custom ambition levels

Period	Start	End	CAGR%
1

#	Start	End	CAGR %
1
2

Annual Waterfall — 2035

Traffic

Aircraft

Operations — Fuel Burn Reduction

Electricity

Hydrogen

Jet Fuel

Offsets & Removals

Interactive Curve Editor

CASCADE V2.7 Methodology Reference

Introduction: Model Structure & Nomenclature

Air Traffic Dataset: 2019 Baseline

Base Calculations: Fuel Burn → Energy → Emissions

Traffic Growth: Compound Annual Growth Rate

Aircraft: Fleet Renewal & Future Aircraft

Operations: ARM, FAO, ATM & Load Factor

Energy Source: Electricity & Grid Decarbonization

Energy Source: Liquid Hydrogen (LH₂)

Energy Source: SAF & Alternative Fuels

Offsets & Carbon Removals

Scenarios: Waypoint, LTAG, Regional & Custom

Parameter Bounds & Ambition Levels

Context Metrics & Benchmarks

Glossary: Key Terms & Acronyms

CASCADE V2.7 Implementation Gap Analysis

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