This study aims to evaluate the environmental, economic and social sustainability of next-generation commercial supersonic transport and develops the Supersonic Sustainability Transition Assessment Model (SSTAM) to distinguish near-term actionable pathways from options dependent on wider socio-technical readiness.
SSTAM integrates ISO 14040/44-aligned life-cycle assessment, triple bottom line indicators, multi-criteria decision analysis, scenario and Monte Carlo uncertainty testing and socio-technical systems/multi-level perspective interpretation. Reference, best-case and worst-case scenarios were modelled using regulatory, NASA/OEM, life-cycle inventory and peer-reviewed evidence.
Supersonic aircraft show CO2 emissions about 2.3–2.6 times higher than subsonic benchmarks, with additional high-altitude non-CO2 effects. Passenger-normalized unit operating cost ranges from US$0.118 to 0.190/RPK against unit revenue of US$0.150–0.170/RPK, producing positive margins only in the reference and best-case scenarios. Energy intensity reduction, verified low-carbon SAF uptake and noise/contrail-aware operations emerge as no-regrets pathways; liquid hydrogen propulsion and overland low-boom permissions remain conditional on infrastructure, policy, certification, market and community safeguards.
SSTAM moves beyond parallel sustainability frameworks by linking environmental performance, economic feasibility, social-equity outcomes, uncertainty robustness and socio-technical readiness through explicit pathway-classification rules for sustainable supersonic aviation.
1. Introduction
Commercial supersonic transport (SST) is re-emerging after the Concorde/Tu-144 era, enabled by advances in aerodynamics, lightweight materials, propulsion, low-boom design and alternative fuels. Concorde nevertheless demonstrated that technical capability alone does not ensure commercial viability when operating cost, premium fares, market access and noise restrictions remain constraining (Abeyratne, 2002; Banke, 2018; Liu, 2023; National Air and Space Museum, 2026) NASA’s X-59 QueSST programme is now generating low-boom and community-response evidence relevant to future ICAO and FAA approaches, while concepts such as Boom’s Overture link high-speed service with SAF use and lower-carbon operations (Coen et al., 2022; Harding, 2021; NASA, 2023). SST therefore requires assessment as a wider sustainability transition rather than only an aircraft-performance problem.
The environmental case remains contested. Relative to advanced subsonic aircraft, SST is expected to require greater cruise energy and to impose higher climate burdens through fuel burn, high-altitude NOx emissions, and stratospheric H2O effects (Davis and Kharina, 2018; Eastham et al., 2022; Matthes et al., 2022; Zhang et al., 2023). Sonic boom and airport noise further affect route permissions, public acceptance and environmental justice exposure (Rötger et al., 2024; Ghosh and Bressman, 2024). Consequently, speed and aircraft efficiency must be evaluated alongside climate impact, cost, regulation, community exposure and distributional effects.
Mitigation options differ substantially in maturity and dependency. Low-boom shaping and trajectory optimization can reduce ground-level boom signatures (Goel and Jawahar, 2022). SAFs, particularly HEFA-based pathways, offer a nearer-term decarbonization option, but benefits depend on feedstock, life-cycle carbon intensity, allocation, scale and cost (International Air Transport Association [IATA], 2020; Cabrera and Melo de Sousa, 2022; Watson et al., 2024). Liquid hydrogen may offer deeper long-term reductions but requires cryogenic storage, aircraft redesign, low-carbon electricity and airport infrastructure (Su-ungkavatin et al., 2023). Hybrid-electric and variable-cycle concepts face weight, thermal-management and high-Mach integration constraints (Schäfer et al., 2019; Onilede, 2025; Skinner et al., 2025; Yang, 2024).
Three evidence gaps motivate this study. Firstly, supersonic-specific life-cycle assessments remain limited and often extrapolate from subsonic aviation, leaving uncertainty around altitude-dependent emissions, sonic-boom externalities, material burdens and maintenance requirements (Rupcic et al., 2023; Bahman, 2023; Matthes et al., 2022). Second, environmental, techno-economic, noise and equity assessments are commonly conducted separately, obscuring cross-pillar trade-offs (Markatos and Pantelakis, 2023; Parolin et al., 2024). Third, policy timing, fuel infrastructure, certification, overland restrictions, airport readiness and public acceptance are often discussed conceptually rather than operationalized as transition conditions (El Bilali, 2019; Geels, 2020; Wang et al., 2022).
These gaps reflect a broader need for integrated transport-sustainability assessment. Tetteh et al. (2025) call for stronger integration of environmental, economic and social indicators. For next-generation SST, emissions, cost viability, infrastructure readiness, noise exposure, public acceptance and equity therefore need to be evaluated jointly.
To address this need, the study develops the Supersonic Sustainability Transition Assessment Model (SSTAM). SSTAM integrates cradle-to-grave LCA outputs, TBL indicators, multi-criteria decision analysis (MCDA), scenario and Monte Carlo uncertainty analysis and STS/MLP interpretation. Rather than stacking these frameworks, the model converts them into explicit decision rules for transition readiness.
SSTAM classifies pathways as no regrets when they remain robust across uncertainty ranges, perform positively or neutrally in at least two sustainability pillars and do not worsen equity exposure or major noise burden. Energy intensity reduction, verified low-carbon SAF uptake and noise/contrail-aware operations are candidate no-regrets measures. Conditional pathways may offer substantial benefits but depend on enabling conditions such as policy approval, certification, airport infrastructure, low-carbon energy, market viability or community safeguards; liquid hydrogen propulsion and overland low-boom permissions are principal examples.
The contribution is therefore a transition-readiness assessment rather than a new aircraft or propulsion design. SSTAM asks whether proposed sustainability measures remain credible when environmental performance, economic feasibility, social exposure, equity, uncertainty and socio-technical readiness are considered together, providing a decision basis for regulators, OEMs, airlines, airports and fuel providers.
2. Literature review
2.1 Supersonic aircraft development, sustainable fuels, and system innovation
Commercial SST has returned to the development agenda after Concorde demonstrated both the feasibility and the persistent constraints of high-speed passenger service: high fuel intensity, operating cost, sonic boom, route restrictions and environmental acceptability (Shachtman, 2003; National Air and Space Museum, 2026; Liu, 2023). Current concepts, including NASA’s X-59 QueSST and Boom Supersonic’s Overture, emphasize low-boom design, aerodynamic refinement, advanced materials, and alternative-fuel compatibility (Harding, 2021; NASA, 2023). NASA’s community-response program is particularly relevant to future overland operating rules (Rathsam and Cliatt, 2019; Coen et al., 2022).
SST nevertheless remains environmentally challenging. Higher cruise energy demand, high-altitude NOx, and stratospheric H2O can increase climate impacts relative to advanced subsonic aircraft (Davis and Kharina, 2018; Matthes et al., 2022; Rupcic et al., 2023). Sonic boom and airport noise also influence route permissions, exposure, and public acceptance (Rötger et al., 2024; Ghosh and Bressman, 2024). A credible sustainability assessment must therefore combine climate, economic, regulatory, and social considerations.
SAF, liquid hydrogen, and advanced propulsion illustrate different transition profiles. HEFA-based SAF can reduce life-cycle emissions without complete aircraft or airport redesign, although performance depends on feedstock, life-cycle carbon intensity, scale, cost, and high-speed compatibility (IATA, 2020; Cabrera and Melo de Sousa, 2022; Watson et al., 2024). Liquid hydrogen has larger infrastructure and safety dependencies, while hybrid-electric and variable-cycle concepts remain constrained by weight, thermal management and high-Mach integration (Su-ungkavatin et al., 2023; Schäfer et al., 2019; Onilede, 2025; Skinner et al., 2025; Yang, 2024).
Accordingly, SST sustainability is best treated as a portfolio problem. Efficiency improvement, verified low-carbon SAF, and operational optimization are closer to near-term implementation, whereas liquid hydrogen, overland low-boom permissions, and advanced propulsion architectures require stronger certification, infrastructure, policy, market and community readiness.
2.2 Sustainability assessment and research gaps
Existing assessments remain fragmented across environmental, technological, economic, and regulatory lenses. Supersonic LCA studies often extrapolate from subsonic aviation and incompletely represent altitude-dependent non-CO2 effects, sonic-boom externalities, material burdens, and life-cycle maintenance (Bahman, 2023; Matthes et al., 2022; Rupcic et al., 2023). Studies of fuels and propulsion likewise rarely integrate noise, affordability, equity, infrastructure, and transition feasibility within one decision model (Markatos and Pantelakis, 2023; Parolin et al., 2024).
As summarized in Appendix A, Table A1, [1], the central gap is the absence of an integrated transition-readiness model linking environmental credibility, economic viability, social acceptability, and socio-technical feasibility. SSTAM addresses this gap by combining LCA, TBL indicators, MCDA, uncertainty analysis, and STS/MLP interpretation to distinguish robust no-regrets measures from pathways dependent on enabling policy, infrastructure, energy, certification, market, or community conditions.
2.3 Theoretical basis and conceptual framework
This study combines TBL, LCA, socio-technical systems (STS), and the multi-level perspective (MLP) in a decision-oriented structure. TBL defines environmental, economic, and social dimensions (Elkington, 1998), while LCA supports life-cycle impact estimation across materials, manufacturing, fuels, operations, maintenance and end-of-life processes (Cucurachi et al., 2019; Sitarska, 2025). Although the triple bottom line has been criticized for conceptual breadth and difficulties in integrating its dimensions into a unified evaluative construct (Norman and MacDonald, 2004), SSTAM operationalizes these dimensions through measurable indicators, scenario comparison, uncertainty testing and explicit pathway-classification rules. STS frames SST as an interacting system of technology, infrastructure, regulation, economics, organizations, and users (Malan, 2018; Sony and Naik, 2020). MLP interprets how niche innovations interact with regime constraints and landscape pressures such as certification, boom regulation, fuel infrastructure, net-zero targets and public concern (Geels, 2020; El Bilali, 2019).
Figure 1 presents the resulting process-based framework. Secondary evidence supports LCA inventory construction; LCA outputs are linked with TBL indicators for environmental impact, economic feasibility, noise, equity and community acceptance; and MCDA, scenario comparison, sensitivity analysis and uncertainty testing classify pathways. STS/MLP then maps the results to aviation-specific transition conditions, consistent with integrated transport-sustainability assessment (Tetteh et al., 2025) and performance-based resilience logic (Amekudzi-Kennedy et al., 2024).
The framework places Interpretive Lenses, S T S and M L P, above a six-stage analytical pipeline. The M L P levels are Niche innovations, Socio-technical regime and Landscape pressures. The S T S elements are Technology, Infrastructure, Policy and regulation, Market, Organizations, and Users and communities. These lenses interpret and map results from the analytical pipeline and do not replace it. Stage 1, Data Sources, includes O E M and N A S A data, I C A O, F A A and E A S A, L C A databases, and peer-reviewed literature. Stage 2, L C A Inventory Construction, includes goal and scope, system boundary, functional units and impact categories. Stage 3, T B L Indicators, includes environmental, economic cost and revenue, social and equity, and noise and community exposure. Stage 4, M C D A plus Sensitivity and Uncertainty Analysis, includes scenario comparison, weighting and normalization, Monte Carlo and robustness testing. Stage 5, Pathway Classification, separates No-regrets pathways from Conditional pathways. No-regrets are defined as robust across scenarios and uncertainty. Conditional pathways depend on enabling infrastructure, policy, energy systems or community safeguards. Stage 6, Decision-Oriented Outputs, includes climate-cost-equity trade-offs, policy readiness, industry guidance and transition priorities.Integrated framework for assessing sustainable supersonic aviation transitions using LCA, TBL and STS/MLP perspectives
The framework places Interpretive Lenses, S T S and M L P, above a six-stage analytical pipeline. The M L P levels are Niche innovations, Socio-technical regime and Landscape pressures. The S T S elements are Technology, Infrastructure, Policy and regulation, Market, Organizations, and Users and communities. These lenses interpret and map results from the analytical pipeline and do not replace it. Stage 1, Data Sources, includes O E M and N A S A data, I C A O, F A A and E A S A, L C A databases, and peer-reviewed literature. Stage 2, L C A Inventory Construction, includes goal and scope, system boundary, functional units and impact categories. Stage 3, T B L Indicators, includes environmental, economic cost and revenue, social and equity, and noise and community exposure. Stage 4, M C D A plus Sensitivity and Uncertainty Analysis, includes scenario comparison, weighting and normalization, Monte Carlo and robustness testing. Stage 5, Pathway Classification, separates No-regrets pathways from Conditional pathways. No-regrets are defined as robust across scenarios and uncertainty. Conditional pathways depend on enabling infrastructure, policy, energy systems or community safeguards. Stage 6, Decision-Oriented Outputs, includes climate-cost-equity trade-offs, policy readiness, industry guidance and transition priorities.Integrated framework for assessing sustainable supersonic aviation transitions using LCA, TBL and STS/MLP perspectives
Within this framework, no-regrets pathways remain beneficial across scenarios and uncertainty, perform positively or neutrally in at least two pillars and do not worsen equity exposure or major noise burden. Conditional pathways require enabling infrastructure, policy, certification, energy-system readiness, market viability or community safeguards.
3. Methodology
3.1 Research design
This study used a document-based comparative design to assess sustainability-transition readiness for next-generation SST, an appropriate approach for pre-commercial concepts where proprietary design, engine-test, flight-test, and airline operational data remain limited. Low-boom and alternative-fuel SST concepts were compared with advanced subsonic A350/787-type benchmarks using representative mission stages and scenario assumptions. The analytical structure integrates LCA, TBL assessment, MCDA, uncertainty testing, and STS/MLP interpretation.
3.2 Data sources and functional units
Secondary evidence was drawn from regulatory, technical, life-cycle inventory, industry and peer-reviewed sources (Appendix H, Figure A1), including NASA/OEM concept material, ICAO/FAA/EASA documents, aircraft-emissions data sets, LCA databases, SAF and hydrogen studies, airline/airport reports and energy data sets. The literature corpus covered 2018–2025 and was identified through Scopus, Web of Science and IEEE Xplore. As documented in Appendix I, Figure A2, 482 records were identified; 126 duplicates were removed, 356 unique records were screened, 82 full texts were assessed and 30 studies were included. Records were retained when they provided relevant evidence on LCA/TBL indicators, SST technologies, fuels, emissions, noise, economics, infrastructure or transition readiness.
Inputs were organized by aircraft/mission performance, materials and manufacturing, fuel and hydrogen pathways, high-altitude emissions, airport/community noise, economics, social/equity indicators, and policy variables. Table 1 summarizes source types, parameters, assumptions, uncertainty ranges and analytical roles. The primary environmental functional unit was g CO2-eq/RPK for passenger-normalized comparison; g CO2-eq/mission was used for route-level burden. Economic scenario outputs were also expressed on an RPK basis to maintain a common passenger-normalized denominator.
Condensed data inventory and assumptions matrix for the integrated LCA–TBL assessment
| Input domain | Key parameters | Main source basis and assumptions | Role in the model |
|---|---|---|---|
| Aircraft mission and performance | Stage length, seating capacity, load factor, Mach number, cruise altitude, SFC, L/D, block fuel | NASA/OEM concepts, peer-reviewed SST studies, and A350/787-type benchmarks; uncertainty: ±10%–30% | Defines mission profile, RPK denominator, energy intensity, fuel burn and g CO2- eq/mission |
| Materials, manufacturing and end-of-life | Aluminium, titanium, composites, engine materials, process energy, recycling, disposal | LCI databases, aircraft-materials literature and technical reports; advanced SSTs assumed to require lightweight/high-temperature materials; uncertainty: ±20%–40% | Estimates embodied emissions, production impacts, resource demand and circularity benefits |
| Fuel and hydrogen pathways | Jet-A, HEFA-SAF, FT-SAF, ATJ-SAF, LH2 carbon intensity, electrolysis, grid intensity, liquefaction, boil-off | GREET, eco-invent, SAF/LH2 studies, ASTM-related literature and energy-system reports; modelled on a well-to-wake basis; uncertainty: ±15%–50% | Determines fuel-cycle emissions, fuel cost, pathway comparison and LH2 conditional viability |
| Operations, maintenance and high-altitude emissions | Mission fuel burn, lubricants, spare parts, MRO energy, NOx effects, stratospheric H2O non-CO2 forcing | Airline/MRO reports, atmospheric studies, ICAO/CAEP literature, and technical sources; non-CO2effects treated separately from direct CO2 uncertainty: ±10%–50% | Captures dominant use-phase impacts, GWP100, energy demand and altitude-sensitive climate effects |
| Noise, social, equity and policy variables | L_den, SEL, PLdB, exposed population, affordability, accessibility, equity exposure, sentiment, unit operating cost, unit revenue, carbon cost, SAF mandate, overland permission | FAA/EASA/ICAO noise standards, NASA X-59 sources, policy documents, airline reports, and equity literature; social indicators use document/survey synthesis proxies; uncertainty: ±10–40% or binary policy switch | Supports TBL social and economic assessment, MCDA integration, community acceptance and pathway classification |
| Quality and validation | Unit checks, source hierarchy, pedigree scoring, benchmark comparison, sensitivity testing | Regulatory, NASA/OEM, database and peer-reviewed sources prioritized when values conflicted | Supports transparency, reproducibility, uncertainty treatment and robustness testing |
The primary functional unit is g CO2-eq/RPK, and the secondary functional unit is g CO2-eq/mission. The full data inventory, units, allocation rules and detailed assumptions are provided in Appendix B, Table A2
Data were harmonized to common units (e.g. MJ, kg, km, RPK, USD/RPK, dB, PLdB and g CO2-eq). Where SST-specific data were unavailable, assumptions were bounded using published concept studies, advanced-subsonic benchmarks, regulatory data sets and LCI sources. Conflicting values were resolved through a source hierarchy prioritizing regulatory and OEM/NASA evidence, followed by intergovernmental data sets, peer-reviewed studies, LCI databases and vetted technical reports. Parameter uncertainty reflected source quality and technological representativeness (Table 1; Appendix B, Table A2).
3.3 Analytical procedures
3.3.1 Life-cycle assessment and inventory construction.
The LCA followed ISO 14040/44 principles and used the cradle-to-grave system boundary in Figure 2: raw materials/components, manufacturing/assembly, well-to-wake fuel pathways, operations/maintenance, high-altitude emissions, and end-of-life treatment. Airport infrastructure was outside the core LCA boundary but entered the TBL and STS/MLP interpretation when readiness affected pathway feasibility. The LCI was organized by mission profile, aircraft performance, materials, manufacturing, fuel/hydrogen pathways, operations/maintenance, high-altitude emissions, end-of-life, and quality validation; detailed parameters and uncertainty ranges are provided in Appendix C, Table A3.
The life-cycle flow proceeds from Raw Materials and Components, including aluminium, titanium and composites, through Manufacturing and Assembly of the airframe and engine, Fuel Pathways from well to wake, Operations and Maintenance, and finally End-of-Life recycling and recovery. Raw materials cover mining and processing, prepregs, fasteners and avionics. Manufacturing covers airframe and nacelles, engine build, Q A and testing, and plant energy. Fuel pathways include Jet A, S A F using H E F A, F T and A T J, and liquid hydrogen production followed by liquefaction, boil-off and distribution. Operations and Maintenance covers mission profiles including Mach, altitude and stage length, in-service fuel burn, and M R O parts and energy, with cruise-phase atmospheric impacts including nitrogen oxides to ozone and stratospheric water. End-of-Life covers disassembly, material recovery of aluminium, titanium and composites, and waste treatment. Inputs include electricity mix and grid E F, heat and process energy, and feedstocks including lipids, F T syngas, ethanol and water. Recovered materials return toward earlier life-cycle stages as avoided burden. Outputs include emissions to air comprising carbon dioxide, nitrogen oxides, sulphur oxides and water, plus solid and liquid wastes. Reporting units for all stages are F U 1, grams carbon dioxide equivalent per R P K, and F U 2, grams carbon dioxide equivalent per mission.Cradle-to-grave LCA system boundary for a supersonic passenger aircraft
The life-cycle flow proceeds from Raw Materials and Components, including aluminium, titanium and composites, through Manufacturing and Assembly of the airframe and engine, Fuel Pathways from well to wake, Operations and Maintenance, and finally End-of-Life recycling and recovery. Raw materials cover mining and processing, prepregs, fasteners and avionics. Manufacturing covers airframe and nacelles, engine build, Q A and testing, and plant energy. Fuel pathways include Jet A, S A F using H E F A, F T and A T J, and liquid hydrogen production followed by liquefaction, boil-off and distribution. Operations and Maintenance covers mission profiles including Mach, altitude and stage length, in-service fuel burn, and M R O parts and energy, with cruise-phase atmospheric impacts including nitrogen oxides to ozone and stratospheric water. End-of-Life covers disassembly, material recovery of aluminium, titanium and composites, and waste treatment. Inputs include electricity mix and grid E F, heat and process energy, and feedstocks including lipids, F T syngas, ethanol and water. Recovered materials return toward earlier life-cycle stages as avoided burden. Outputs include emissions to air comprising carbon dioxide, nitrogen oxides, sulphur oxides and water, plus solid and liquid wastes. Reporting units for all stages are F U 1, grams carbon dioxide equivalent per R P K, and F U 2, grams carbon dioxide equivalent per mission.Cradle-to-grave LCA system boundary for a supersonic passenger aircraft
Inventory construction applied a documented source hierarchy, allocation rules and uncertainty checks. Multi-output processes used source-specific energy, mass or economic allocation; SAF co-products followed the allocation treatment of the underlying database or source. Impact categories included GWP100, cumulative energy demand, resource and water use, and other categories where data were available. NOx- related ozone effects and stratospheric H2O forcing were characterized separately from direct CO2 because cruise altitude affects radiative forcing. Uncertainty was evaluated through parameter ranges, pedigree checks, benchmark comparison, one-way sensitivity analysis and Monte Carlo simulation.
3.3.2 Triple bottom line assessment.
The TBL assessment operationalized environmental, economic, and social pillars. Environmental indicators included GWP100, non-CO2 effects, fuel pathways, and noise-related externalities. Economic indicators included CAPEX/OPEX, passenger-normalized unit operating cost and unit revenue, fuel and carbon-compliance costs, and infrastructure exposure. Social indicators included airport noise, boom exposure, affordability, accessibility, equity exposure, public favorability, safety perception, complaint risk, and workforce effects. Indicators were direction-aligned before MCDA integration; lower values were favorable for burdens such as emissions, cost, noise, and equity exposure, whereas higher values were favorable for access, perception, employment, and operating margin.
3.3.3 Supersonic Sustainability Transition Assessment Model and socio-technical systems/multi-level perspective interpretive coding.
SSTAM integrates LCA outputs, TBL indicators, MCDA normalization, scenario/sensitivity/Monte Carlo analysis, and STS/MLP interpretation into one decision logic. Findings were coded under aviation-specific STS categories—certification, fuel infrastructure and supply chain, airline economics, airport noise governance, overland boom regulation, organizational coordination, and community acceptance—then interpreted across MLP landscape, regime, and niche levels.
Landscape pressures included net-zero targets, carbon pricing, SAF mandates, and public concern; regime constraints included certification rules, airline cost structures, noise governance, overland boom bans, and limited SAF/LH2 infrastructure; niche innovations included low-boom aircraft, verified SAF, LH2 concepts, contrail-aware operations, and environmental-justice safeguards.
A pathway was classified as no regrets when its ranking was preserved in at least 75% of Monte Carlo draws or remained stable across sensitivity ranges, it performed positively or neutrally in at least two TBL pillars, and it did not increase equity exposure or major noise burden relative to the reference case. Conditional pathways were those whose favorable performance depended on enabling assumptions such as low-carbon electricity, fuel infrastructure, regulatory approval, certification progress, policy support, market viability, or community safeguards.
3.4 Scenario, sensitivity, and validation
Three scenarios were applied consistently. The reference case represented near-term low-boom design, 0%–50% HEFA-SAF blends, baseline routing, regional grid intensities, current overland-boom restrictions, and CORSIA-aligned carbon prices. The best case combined 100% SAF or green LH2, higher propulsion efficiency, optimized operations, contrail avoidance, higher load factors, stronger carbon pricing, and improved recycling. The worst case assumed fossil Jet-A or grey hydrogen, conservative aerodynamic performance, lower load factors, boom-related detours, no contrail management, weak policy support, and higher community exposure.
Table 2 summarizes the operational decision rules and illustrative pathways. Energy intensity reduction, verified low-carbon SAF, optimized climb/cruise/descent, noise/contrail-aware operations, improved maintenance, and lightweighting were assessed as candidate no-regrets measures; LH2 propulsion, overland low-boom permissions, airport hydrogen infrastructure, and major advanced-propulsion changes were treated as conditional where enabling requirements remained unresolved.
Operational criteria for classifying no-regrets and conditional pathways
| Classification | Operational definition | Decision criteria | Illustrative SST pathways |
|---|---|---|---|
| No-regrets pathway | A pathway that remains beneficial across scenarios and uncertainty ranges without requiring major unresolved infrastructure, policy or community preconditions | Ranking preserved in ≥ 75% of Monte Carlo draws; positive or neutral performance in ≥ 2 TBL pillars; no increase in equity-exposure ratio or major noise burden; feasible under current or near-term aviation regime conditions | Energy-intensity reduction; verified low-carbon SAF uptake; optimized climb/cruise/descent; noise-aware and contrail-aware operations; improved maintenance and lightweighting |
| Conditional pathway | A pathway that may provide substantial sustainability benefits but only if enabling technical, policy, infrastructure, energy or community conditions are met | Performs favourably only under specific assumptions; depends on infrastructure readiness, regulatory approval, low-carbon energy systems, supportive policy or environmental-justice safeguards | Liquid hydrogen propulsion; overland low-boom permissions; large-scale SAF mandates without cost support; airport hydrogen infrastructure; advanced propulsion requiring major certification and infrastructure change |
Sensitivity testing focused on fuel pathway and carbon intensity, energy intensity, load factor, Mach number, cruise altitude, stage length, mission frequency, specific fuel consumption, lift-to-drag ratio, SAF logistics, LH2 storage/distribution, grid intensity, carbon price, SAF mandates, and overland low-boom policy. One-way tests used ±10%–30% or pathway-specific ranges; Monte Carlo simulation used evidence-based distributions for uncertain parameters. Outputs were summarized using scenario medians and 5th–95th percentile intervals where available, together with sensitivity rankings and cross-pillar joint displays.
Validation was documentary, analytical, and benchmark-based rather than experimental. Table 3 summarizes source triangulation, benchmark comparison, uncertainty intervals, sensitivity-driver testing, and robustness criteria. Assumptions were checked against NASA/OEM concepts, ICAO emissions references, FAA/EASA/ICAO noise logic, NASA low-boom targets, LCA databases, fuel-pathway studies, and A350/787-type benchmarks. Conflicts were resolved through source hierarchy and pedigree scoring. Robustness required ≥ 75% ranking preservation, positive/neutral performance in at least two TBL pillars, and no worsening of equity exposure or major noise burden. Future confirmation requires proprietary design/test data, airline operations, airport monitoring, and primary community-response evidence.
Validation evidence, uncertainty treatment and robustness criteria for scenario-based pathway classification
| Validation dimension | Evidence/check applied | Uncertainty treatment | Role in interpretation |
|---|---|---|---|
| Source triangulation | NASA/OEM concepts, ICAO, FAA/EASA, LCA databases, peer-reviewed studies, fuel-pathway sources and subsonic benchmarks were compared | Conflicting values were resolved using a predefined source hierarchy and pedigree scoring | Strengthens credibility of secondary-data inputs and reduces reliance on single-source assumptions |
| Benchmark comparison | Supersonic concept assumptions were compared with A350/787-type benchmarks, ICAO emissions references, FAA/EASA/ICAO noise logic and NASA low-boom targets | Deviations from benchmark ranges triggered review of assumptions and parameter bounds | Supports engineering plausibility of fuel burn, emissions, cruise and noise assumptions |
| Uncertainty intervals | Scenario outputs were reported using medians and 5th–95th percentile intervals where available | Monte Carlo simulation and sensitivity ranges were used for uncertain parameters | Avoids overreliance on deterministic point estimates |
| Sensitivity drivers | Fuel pathway, energy intensity, load factor, fuel carbon intensity, Mach number, cruise altitude, SAF share, carbon price, LH2 grid intensity and overland permission were tested | One-way sensitivity testing used ±10%–30% or pathway-specific ranges | Identifies which assumptions most influence emissions, cost, noise, equity and pathway classification |
| Robustness criteria | Pathway rankings, TBL pillar performance and equity/noise outcomes were checked across scenarios | Robust classification required ≥ 75% ranking preservation in Monte Carlo draws, positive/neutral performance in ≥ 2 pillars and no worsening of equity exposure or major noise burden | Distinguishes no-regrets pathways from conditional pathways |
4. Results
4.1 Life-cycle assessment findings
The LCA indicates substantially higher life-cycle burdens for SST than for advanced subsonic benchmarks. Passenger-normalized CO2 emissions are approximately 2.3–2.6 times greater, driven mainly by higher cruise fuel burn. Including high-altitude non-CO2 effects, particularly NOx-related ozone formation and stratospheric water vapor, increases estimated climate forcing by nearly 50% (Table 4).
LCA emissions and resource contribution by life-cycle stage
| Lifecycle stage | CO2 emissions (Mt) | Non-CO2 effects (Mt CO2-eq) | Energy demand (PJ) | Water use (Mm³) | End-of-life recovery (%) |
|---|---|---|---|---|---|
| Raw materials and components | 0.8 | 0.20 | 12 | 4 | 30 |
| Manufacturing and assembly | 0.6 | 0.10 | 9 | 3 | 40 |
| Fuel pathways (well-to-wake) | 3.5 | 1.40 | 55 | 18 | – |
| Operations and maintenance | 6.0 | 2.80 | 78 | 22 | – |
| End-of-life | 0.2 | 0.05 | 3 | 1 | 60 |
Manufacturing and assembly remain material-intensive, with aluminum and advanced composites accounting for a large share of embodied energy and titanium carrying a high processing burden. Operations constitute the largest single contributor across several life-cycle impact dimensions, although the relative contribution varies by impact category (Appendix D, Table A4). End-of-life recovery provides a smaller offset and depends strongly on material-specific recycling potential.
Hydrogen pathways create additional water and energy demands through electrolysis, liquefaction, and boil-off management, whereas Jet-A retains higher greenhouse-gas intensity. Appendix E, Table A5 shows that operations and maintenance have the largest illustrative inventory-stage energy demand, while end-of-life treatment remains important for circularity through metal and component recovery. These values are interpreted as scenario-based inventory inputs rather than universal fleet averages.
4.2 Economic analysis and scenario-linked feasibility
The economic pillar assessed passenger-normalized operating cost, revenue adequacy, fuel-pathway sensitivity, and scenario feasibility. Unit operating cost was US$0.140/RPK in the reference case, US$0.118/RPK in the best case, and US$0.190/RPK in the worst case. Appendix J, Figure A3, shows that total unit operating cost is lowest in the best-case scenario and highest in the worst-case scenario. The adverse case is driven primarily by higher energy intensity, effective fuel cos,t and weaker carbon-policy support.
Revenue adequacy was evaluated on the same RPK denominator. As summarized in Table 5, unit revenue was US$0.160/RPK in the reference case, US$0.170/RPK in the best case and US$0.150/RPK in the worst case. The resulting operating margins were +US$0.020/RPK, +US$0.052/RPK, and −US$0.040/RPK, respectively. Appendix K, Figure A4 illustrates the unit revenue–cost relationship and corresponding margin. Commercial viability therefore depends on keeping energy intensity, fuel price, load factor, and carbon-compliance cost within ranges that can be supported by premium demand.
Passenger-normalized unit revenue, unit operating cost and operating margin by scenario
| Scenario | Unit revenue (US$/RPK) | Unit operating cost (US$/RPK) | Operating margin (US$/RPK) | Margin status |
|---|---|---|---|---|
| Reference | 0.16 | 0.14 | 0.02 | Positive |
| Best case | 0.17 | 0.118 | 0.052 | Positive |
| Worst case | 0.15 | 0.19 | −0.040 | Negative |
Fuel-pathway assumptions explain much of the scenario separation. Energy intensity was 5.0 MJ/RPK in the reference case, 3.0 MJ/RPK in the best case, and 6.5 MJ/RPK in the worst case; paired with effective fuel prices of US$12/GJ, US$15/GJ, and US$14.62/GJ, the implied fuel-cost components were US$0.060/RPK, US$0.045/RPK, and US$0.095/RPK (Table 6). Efficiency gains can therefore offset higher low-carbon fuel prices, whereas high energy intensity rapidly erodes margin.
Fuel pathway and energy assumptions by scenario
| Scenario | Energy intensity (MJ/RPK) | Effective fuel price (US$/GJ) | Implied fuel cost (US$/RPK) | Interpretation |
|---|---|---|---|---|
| Reference | 5.0 | 12.00 | 0.060 | Mixed Jet-A/SAF conditions with moderate energy intensity |
| Best-case | 3.0 | 15.00 | 0.045 | Efficiency gains offset higher low-carbon fuel price |
| Worst-case | 6.5 | 14.62 | 0.095 | Higher energy intensity and fuel-cost exposure weaken viability |
Economic feasibility is inseparable from environmental and social constraints. Figure 3 shows higher noise exposure under adverse assumptions, while Figure 4 shows how boom corridors and airport-noise contours may overlap with vulnerable communities. Route restrictions, noise governance, and community safeguards can therefore affect both operating feasibility and economic performance.
The line graph compares noise levels for Departure L den in decibels, Arrival L den in decibels and Cruise P L d B. The vertical axis ranges from about 60 to 85 decibels and P L d B in intervals of 5. For Departure L den, Reference is about 65 decibels with an error range of about 62 to 68, Best-case is about 63 decibels with a range of 60 to 66, and Worst-case is about 68 decibels with a range of 65 to 72. For Arrival L den, Reference is about 63 decibels with a range of 60 to 66, Best-case is about 61 decibels with a range of 58 to 64, and Worst-case is about 66 decibels with a range of 63 to 70. For Cruise P L d B, Reference is about 78 P L d B with a range of 76 to 80, Best-case is about 75 P L d B with a range of 73 to 77, and Worst-case is about 82 P L d B with a range of 80 to 84.Noise exposure by scenario
The line graph compares noise levels for Departure L den in decibels, Arrival L den in decibels and Cruise P L d B. The vertical axis ranges from about 60 to 85 decibels and P L d B in intervals of 5. For Departure L den, Reference is about 65 decibels with an error range of about 62 to 68, Best-case is about 63 decibels with a range of 60 to 66, and Worst-case is about 68 decibels with a range of 65 to 72. For Arrival L den, Reference is about 63 decibels with a range of 60 to 66, Best-case is about 61 decibels with a range of 58 to 64, and Worst-case is about 66 decibels with a range of 63 to 70. For Cruise P L d B, Reference is about 78 P L d B with a range of 76 to 80, Best-case is about 75 P L d B with a range of 73 to 77, and Worst-case is about 82 P L d B with a range of 80 to 84.Noise exposure by scenario
The map uses Longitude in arbitrary units on the horizontal axis, ranging from about 0.5 to 1.1, and Latitude in arbitrary units on the vertical axis, ranging from about 0.4 to 1.0. Background cells encode Vulnerability from 0.0 to 1.0. Two locations are labelled Coastal Hub A, one near longitude 0.63 and latitude 0.52 and another near 0.74 and 0.81. Coastal Hub B lies near longitude 1.03 and latitude 0.75. Concentric airport noise contours surround the two Coastal Hub A locations and are labelled L den equals 55 decibels and L den equals 65 decibels. A modelled boom corridor curves from the lower left towards the upper right, with a surrounding boom buffer labelled L P L d B swath. The corridor passes close to the lower Coastal Hub A and continues between the upper Coastal Hub A and Coastal Hub B.Environmental-justice overlay of noise and boom exposure
The map uses Longitude in arbitrary units on the horizontal axis, ranging from about 0.5 to 1.1, and Latitude in arbitrary units on the vertical axis, ranging from about 0.4 to 1.0. Background cells encode Vulnerability from 0.0 to 1.0. Two locations are labelled Coastal Hub A, one near longitude 0.63 and latitude 0.52 and another near 0.74 and 0.81. Coastal Hub B lies near longitude 1.03 and latitude 0.75. Concentric airport noise contours surround the two Coastal Hub A locations and are labelled L den equals 55 decibels and L den equals 65 decibels. A modelled boom corridor curves from the lower left towards the upper right, with a surrounding boom buffer labelled L P L d B swath. The corridor passes close to the lower Coastal Hub A and continues between the upper Coastal Hub A and Coastal Hub B.Environmental-justice overlay of noise and boom exposure
The social indicators in Appendix F, Table A6, reinforce this interaction: affordability, accessibility, complaint risk, public favorability, safety perception, and equity exposure vary materially across scenarios. The best case combines wider access and lower exposure burden, whereas the worst case couples a negative operating margin with higher complaint and equity risks. Long-thin, high-yield routes are therefore credible only when efficiency, fuel-cost management, route screening, noise control, and equity safeguards are addressed together.
4.3 Social impact assessment (triple bottom line – social pillar)
The social assessment examined community noise exposure, environmental justice, and public acceptance. Figure 3 shows the lowest departure, arrival, and cruise exposure in the best case and the highest in the worst case. Cruise boom exposure remains near the low-boom range in the reference and best cases but exceeds 80 PLdB in the worst case, indicating greater complaint risk. Environmental justice was examined through the potential overlap of airport noise, modelled boom corridors, and community vulnerability. Figure 4 provides the main-article spatial illustration: optimized routing and procedures reduce overlap in the best case, whereas conservative operating conditions increase potential burden in the worst case.
Table 7 groups the social indicators into exposure equity, affordability equity, accessibility equity, community acceptance and socio-economic co-benefit. Lower population exposure, affordability burden, complaint rate and equity-exposure ratio are favorable; higher accessibility, public favorability, safety perception and workforce effects are favorable. Public favorability, safety perception and complaint response are document/survey-synthesis proxies because no primary public survey was conducted. They indicate scenario-based stakeholder-response tendencies rather than directly measured public opinion.
Social and equity indicators used in the assessment of supersonic aviation scenarios
| Equity category | Indicator | Unit/scale | Direction | Reference | Best case | Worstcase | Notes/source mapping |
|---|---|---|---|---|---|---|---|
| Exposure equity | Population within L_den ≥ 65 dB airport contours | Thousand persons | ↓ better | 85 | 70 | 120 | Quantitative noise-exposure estimate from airport noise modeling; supports Figure 3 |
| Exposure equity | Population within L_den ≥ 55 dB airport contours | Thousand persons | ↓ better | 320 | 290 | 400 | Wider community noise-exposure estimate; supports Figure 3 |
| Exposure equity | Population within PLdB ≥ 75 boom corridor | Thousand persons | ↓ better | 60 | 45 | 95 | Modelled sonic-boom corridor exposure; spatial overlap with community vulnerability is illustrated in Appendix L, Figure A5 |
| Exposure equity | Equity-exposure ratio | Ratio | ↓ better | 1.25 | 1.1 | 1.4 | Indicates whether vulnerable populations are disproportionately exposed; spatial overlap between noise/boom exposure and community vulnerability is illustrated in Appendix L, Figure A5 |
| Affordability equity | Affordability index: round-trip fare ÷ median monthly income | % | ↓ better | 140 | 120 | 170 | Fare-access proxy linked to passenger-normalized unit revenue and operating-cost assumptions and premium-market affordability |
| Accessibility equity | Accessibility coverage: viable city pairs with ≥ 2 h time saving | Count | ↑ better | 18 | 26 | 10 | Network-access proxy showing where passengers receive meaningful time-saving benefits |
| Community acceptance | Complaint rate | Complaints per 10,000 flights | ↓ better | 22 | 15 | 38 | Complaint-risk proxy derived from modelled noise/boom exposure and literature-based response relationships |
| Community acceptance | Public favorability/sentiment | −1 to +1 index | ↑ better | 0.05 | 0.2 | −0.15 | Document/survey synthesis proxy; not based on primary survey data |
| Community acceptance | Safety perception | Likert 1–5 | ↑ better | 3.6 | 3.9 | 3.2 | Expert/document-coding proxy for perceived safety and public confidence; not directly measured through primary survey |
| Socio-economic co-benefit | Workforce impact | FTE per aircraft, direct and indirect | ↑ better | 450 | 520 | 380 | Employment proxy linked to aircraft operations, maintenance and supply-chain activities |
4.4 Scenario and sensitivity results
The scenarios clearly separate economic performance. As summarized in Table 8 and illustrated in Appendix K, Figure A4, the reference and best cases retain positive operating margins, whereas the worst case produces a negative margin. Unit operating cost rises from US$0.118/RPK in the best case to US$0.140/RPK in the reference case and US$0.190/RPK in the worst case; unit revenue ranges from US$0.150 to US$0.170/RPK. Corresponding margins are US$0.052/RPK, US$0.020/RPK, and −US$0.040/RPK for the best, reference and worst cases.
Scenario results with uncertainty and robustness interpretation
| Indicator | Best case | Reference | Worst case | Main sensitivity driver | Robustness interpretation |
|---|---|---|---|---|---|
| Unit operating cost (US$/RPK) | 0.118 | 0.14 | 0.19 | Fuel cost, energy intensity, load factor, carbon cost | Positive margin is robust only under best-case and reference assumptions |
| Unit revenue (US$/RPK) | 0.170 | 0.16 | 0.15 | Premium demand, load factor, route viability | Revenue adequacy weakens under adverse market and operating assumptions |
| Operating margin (US$/RPK) | 0.052 | 0.02 | −0.040 | Unit revenue–cost spread, fuel price, carbon cost | Worst-case margin is negative, showing commercial fragility |
| Departure L_den (dB), median range | 63 | 65 | 68 | Take-off thrust, operating procedures, route design | Noise exposure remains manageable only with optimized procedures |
| Arrival L_den (dB), median range | 61 | 63 | 66 | Approach profile, airport procedures | Best-case assumptions reduce community exposure |
| Cruise PLdB | 75 | 78 | 82 | Low-boom design, routeing, altitude, overland permission | Worst-case exceeds low-boom acceptability targets and raises complaint risk |
| Population within L_den ≥ 65 dB | 70,000 | 85,000 | 120,000 | Airport operations, route density, exposure contours | Exposure burden increases under conservative operating assumptions |
| Population within PLdB ≥ 75 | 45,000 | 60,000 | 95,000 | Boom corridor design, overland routeing, population overlap | Environmental-justice concerns increase in the worst-case scenario |
| Equity exposure ratio | 1.1 | 1.25 | 1.4 | Route location, vulnerable-population overlap, noise/boom exposure | No-regrets classification requires no worsening relative to reference |
Noise and equity outcomes follow the same directional pattern. Departure L_den ranges from about 63–68 dB, arrival L_den from 61 to 66 dB and cruise PLdB from 75 in the best case to 82 in the worst case. Population within L_den ≥ 65 dB increases from about 70,000 to to 120,000 persons, while population within PLdB ≥ 75 rises from about 45,000 to 95,000. Table 8 shows the equity-exposure ratio improving to 1.10 in the best case and worsening to 1.40 in the worst case. Appendix L, Figure A5 provides a conceptual spatial illustration of how airport noise and boom corridors may overlap with communities of differing vulnerability; it is not a site-specific exposure inventory.
Sensitivity analysis identifies fuel pathway and energy intensity as the strongest drivers, followed by operational profile and regulatory change (Figure 5). A ±20% shift in fuel pathway or energy intensity changes unit operating cost by approximately ±US$0.012/RPK and produces proportional changes in GWP100; a 10% load-factor reduction raises per-RPK environmental and cost metrics by about 11%. Table 9 maps these findings to STS/MLP levels. Energy intensity reduction, verified low-carbon SAF and noise/contrail-aware operations remain the strongest no-regrets pathways, whereas LH2 propulsion and overland low-boom permissions remain conditional on low-carbon energy, infrastructure, certification, regulation, market viability and environmental-justice safeguards.
The chart is titled Sensitivity Drivers Impact. The horizontal axis is labelled Key Sensitivity Drivers, and the vertical axis is labelled Impact on Index delta. Fuel Type has an impact value of 0.35. Operational Profiles has an impact value of 0.25. Regulatory Changes has an impact value of 0.20.Sensitivity drivers and impact on scenario outcomes
The chart is titled Sensitivity Drivers Impact. The horizontal axis is labelled Key Sensitivity Drivers, and the vertical axis is labelled Impact on Index delta. Fuel Type has an impact value of 0.35. Operational Profiles has an impact value of 0.25. Regulatory Changes has an impact value of 0.20.Sensitivity drivers and impact on scenario outcomes
Mapping of findings to STS elements and MLP levels
| MLP level | STS element | Key finding | Transition interpretation |
|---|---|---|---|
| Landscape pressures | Policy/regulation | Net-zero targets, SAF mandates, carbon pricing | SST must align with climate-policy and fuel-transition requirements |
| Landscape pressures | Users/communities | Public concern over boom, noise, climate and equity | Community acceptance is a core transition condition |
| Landscape pressures | Market/economics | Fuel-price volatility and carbon-cost exposure | Economic viability depends on fuel and policy stability |
| Regime constraints | Policy/regulation | Overland boom bans and certification limits | Low-boom approval remains conditional on regulation and evidence |
| Regime constraints | Infrastructure | Uneven SAF supply and limited LH2 readiness | SAF is nearer-term; LH2 depends on major infrastructure development |
| Regime constraints | Market/economics | Positive margins only in reference and best-case scenarios | SST economics remain fragile under adverse cost assumptions |
| Regime constraints | Organizations | Airlines, airports, OEMs, fuel suppliers and regulators must coordinate | Deployment requires cross-sector governance and operational alignment |
| Niche innovations | Technology | Low-boom design, efficient propulsion, lightweighting | These support no-regrets improvement in fuel burn, noise and cost |
| Niche innovations | Infrastructure | LH2 storage, liquefaction and airport handling | Hydrogen remains conditional on low-carbon energy and airport readiness |
| Niche innovations | Policy/regulation | PLdB governance, contrail-aware routing EJ safeguards | Policy innovation is needed for responsible deployment |
| Niche innovations | Users/communities | EJ screening and exposure mitigation | Social legitimacy depends on avoiding disproportionate burdens |
| Niche innovations | Market/economics | Verified SAF uptake and efficiency gains | These strengthen cost and emissions performance across scenarios |
Overall, the STS/MLP mapping reinforces the SSTAM classification: measures that improve multiple pillars without major unresolved regime change are more robust, while LH2 and overland low-boom deployment remain contingent on wider infrastructure, policy, certification and community conditions.
5. Discussion
5.1 Interpretation and comparison with literature
The results support a portfolio-transition interpretation of sustainable supersonic aviation. Lower energy intensity, verified low-carbon SAF, optimized operations and noise/contrail-aware procedures improve performance across more than one sustainability dimension, consistent with evidence that transport technology can improve energy and carbon efficiency (Zhao, 2024). The LCA findings also reinforce prior work showing that operational fuel use and high-altitude non-CO2 effects remain central climate concerns for SST (Davis and Kharina, 2018; Matthes et al., 2022; Eastham et al., 2022). Existing studies have addressed fuel burn, sonic boom, SAF, hydrogen and economics separately (Coen et al., 2022; Cabrera and Melo de Sousa, 2022; Su-ungkavatin et al., 2023); SSTAM extends this evidence by testing whether proposed pathways remain credible across environmental, economic, social and institutional conditions.
Verified low-carbon SAF emerges as the most practical near-term decarbonization option because it can reduce fuel-cycle emissions without complete aircraft or airport redesign, although benefits remain sensitive to feedstock, lifecycle carbon intensity, scale and cost (Cabrera and Melo de Sousa, 2022; Watson et al., 2024). Liquid hydrogen is more conditional because it depends on low-carbon electricity, cryogenic storage, airport infrastructure, safety systems and certification (Su-ungkavatin et al., 2023). Social feasibility is equally important: low-boom design and optimized procedures can reduce exposure, but adverse routing and high-thrust assumptions can concentrate burdens in vulnerable communities. Route governance should therefore integrate noise monitoring, public disclosure, complaint response and environmental-justice screening.
The principal contribution of SSTAM is the explicit distinction between no-regrets and conditional pathways. Energy intensity reduction, verified low-carbon SAF and optimized noise/contrail-aware operations remain beneficial under uncertainty and do not worsen equity exposure. LH2 propulsion and overland low-boom permissions can offer benefits, but only when infrastructure, low-carbon energy, certification, regulation, market viability and community safeguards are sufficiently mature. The framework therefore converts sustainability evidence into a transition-readiness decision rule rather than treating each technology in isolation.
5.2 Practical implications and implementation pathways
Practical implementation should prioritize measures that improve performance without creating major new dependencies. OEMs should focus on lower mission fuel burn through aerodynamic efficiency, reduced specific fuel consumption, lightweighting, thermal management and low-boom shaping. Airlines should target long-thin, high-yield routes while maintaining positive passenger-normalized revenue–cost margins through load-factor discipline, optimized flight profiles and verified SAF offtake. These measures align with SSTAM’s no-regrets logic because they can improve multiple pillars under a range of assumptions.
Airports and fuel suppliers should scale SAF logistics before committing to capital-intensive LH2 systems. Hydrogen deployment should proceed through staged readiness gates covering low-carbon electricity, liquefaction, cryogenic storage, safety, emergency response, demand certainty and community risk. Regulators should align climate accountability, market formation, noise governance and equity protection through life-cycle MRV, carbon-intensity reporting, SAF policy, carbon pricing, differentiated charges and adaptive PLdB-based low-boom rules. Expanded overland operations should be conditioned on route-specific exposure assessment, public disclosure and environmental-justice safeguards.
A pathway should advance when it retains acceptable economic performance under sensitivity bounds, performs positively or neutrally in at least two sustainability pillars and does not worsen equity exposure or major noise burden. Otherwise, it should remain conditional until enabling policy, infrastructure, energy-system readiness, certification evidence, market viability and community safeguards are demonstrated. Appendix G, Table A7 summarizes stakeholder-specific actions.
5.3 Limitations and future work
The study relies on public documents, secondary data sets, technical reports, regulatory material and peer-reviewed literature. This is appropriate for pre-commercial SST concepts but cannot provide the precision of proprietary design, engine-test, flight-test or airline operational data. Uncertainty remains in fuel burn, propulsion performance, material composition, maintenance, manufacturing burdens, costs and non-CO₂ climate effects, while social and equity indicators partly rely on document/survey-synthesis proxies rather than primary community observations.
Future work should validate SSTAM with OEM, engine, flight-test, airline, airport and community-response data; refine atmospheric and route-specific modelling; and test the classification rules across SAF-only, LH2, hybrid-electric, variable-cycle and low-boom concepts. Public surveys, expert elicitation and environmental-justice mapping would further strengthen low-boom acceptability and equity assessment.
6. Conclusion
This study developed and applied the SSTAM to assess the sustainability readiness of next-generation passenger SST. By integrating LCA outputs, TBL indicators, MCDA, uncertainty testing and STS/MLP interpretation, SSTAM provides a decision-oriented basis for distinguishing robust sustainability measures from options dependent on wider transition conditions.
The results show that SST remains environmentally and socio-technically constrained. Fuel intensity, high-altitude NOx stratospheric H2O, and noise remain important burdens, while economic performance is positive only under the reference and best-case assumptions. Energy intensity reduction, verified low-carbon SAF and noise/contrail-aware operations emerge as no-regrets pathways because they remain robust under uncertainty, support at least two sustainability pillars and do not worsen equity exposure. LH2 propulsion and overland low-boom permissions remain conditional on low-carbon energy, airport infrastructure, certification, regulation, market viability and community safeguards.
The study therefore reframes supersonic sustainability as a transition-readiness problem rather than a technology-performance question. This distinction can help regulators, OEMs, airlines, airports and fuel providers separate measures suitable for near-term action from those requiring stronger enabling systems before responsible deployment.
Ethical considerations
This study used only publicly available, non-personal secondary data and documentary sources. No human participants, identifiable personal information, interviews or surveys were involved, and informed consent was therefore not applicable. Sources, assumptions, system boundaries and uncertainty treatments are documented to support transparency and reproducibility. The analysis is reported as a scenario-based assessment, and limitations arising from incomplete manufacturer and operational data are explicitly acknowledged.
Note
All appendices (Appendices A–L), including supplementary tables and figures, are provided in the Supplementary Materials file accompanying this article.
References
Supplementary material
The supplementary material for this article can be found online.
