The Sustainable Aviation Technology Dashboard: A Platform to Explore Alternative Energy Pathways

Introduction

Aviation accounts for 2.5% of global CO_2 emissions [1, 2, 3], yet its broader footprint is substantial, contributing both to climate change and to premature mortality [4, 5]. Although efficiency gains have been achieved through new technologies [6, 7] and improvements in air traffic management [8, 9], projected traffic growth could drive emissions to 2000 Mt CO_2 by 2050 [10]. In addition to CO_2, aircraft release a range of greenhouse gases and pollutants [11, 12, 13, 14].

Facilitating long-distance air travel requires substantial energy and power, posing a substantial challenge for integrating sustainable energy carriers into the existing aviation ecosystem [15]. Conventional aviation fuels, such as Jet A, have historically met these demands owing to their high gravimetric and volumetric energy densities. For future alternatives to be viable on a large scale, they must offer comparable or superior performance. However, energy density alone is not sufficient to determine suitability. The adoption of alternative energy carriers must also consider a broader set of criteria, including life-cycle GHG emissions, safety and certification standards, infrastructure readiness, supply chain complexity, and overall economic feasibility. A comprehensive well-to-wake assessment, encompassing both upstream (production and distribution) and downstream (operational) impacts, is therefore essential to evaluate the sustainability of each candidate. For instance, the practical deployment of battery-electric or hydrogen-based propulsion is currently constrained by aircraft weight sensitivity and airworthiness requirements. Nevertheless, several technological pathways are under active exploration. Despite significant integration challenges, replacing conventional jet fuel with low-carbon or carbon-free alternatives remains one of the most promising strategies for achieving long-term aviation decarbonization [16, 17].

Ensuring that all stakeholders have access to recent technological developments and can interpret them to make informed decisions is as critical as the breakthroughs themselves. Successful technology adoption hinges not only on innovation but also on effective dissemination of research outcomes across industry, government, and academia. The premature failure of several aviation start-ups demonstrates the consequences of investing in emerging technologies without a comprehensive understanding of their market potential or operational readiness [18]. Transparent communication of results is essential, as it fosters cross-sector collaboration and enables the validation of findings across independent efforts.

However, access to reliable scientific data and credible aircraft performance estimates remains limited. These insights are often confined within proprietary research repositories or paywalled publications, restricting public and cross-disciplinary engagement. Although a few platforms attempt to visualize prospective developments in flight operations, their emphasis on fleet-level metrics can obscure the technological and environmental implications at the aircraft level, thereby limiting their utility in guiding innovation and policy.

The SAT Dashboard is designed as a scenario analysis and visualization tool for rapidly comparing alternative aviation energy pathways under user defined assumptions and openly documented datasets. Its outputs should therefore be interpreted as first order estimates intended for research, education, and policy exploration. The governing equations, data sources and model assumptions implemented in the dashboard are documented in a companion methodology document distributed with the software repository [19].

Recognizing the need for transparency in scientific communication, this paper presents the architecture at the core of the Sustainable Aviation Technology Dashboard (SAT Dashboard), an interactive web-based platform developed and maintained by the Laboratory for Electric Aircraft Design and Sustainability (LEADS) at the University of Illinois at Urbana-Champaign. The SAT Dashboard is designed to evaluate the integration of emerging energy carriers into future aircraft architectures and to assess their broader societal and environmental implications. It enables users to assess the feasibility of various technologies by quantifying key metrics, including projected market penetration, airport operations, Cost per Available Seat Mile (CASM), Life Cycle Analysis (LCA), and emissions. By offering a system-level perspective grounded in technical and economic modeling, the SAT Dashboard facilitates informed decision-making and serves as a communication bridge between academia, industry, policymakers, and the public.

Implementation and architecture

Achieving net-zero emissions in aviation by 2050 demands not only ambitious targets but also a detailed understanding of the enabling technologies that will drive this transformation. While numerous strategic frameworks and climate action roadmaps have emerged in the literature in recent years [20, 21, 22, 23], few publicly accessible analytical tools offer comprehensive, technology-specific assessments that rigorously account for the physical constraints inherent to aircraft architectures, energy carriers, and operational parameters. Moreover, existing tools predominantly emphasize European aviation contexts, whereas the SAT Dashboard distinguishes itself by explicitly incorporating upstream feedstock supply chain requirements and by targeting U.S. domestic aviation operations. The spatial resolution at the regional crop production level provided within the dashboard enables systematic analysis of geo-economic factors that fundamentally inform policy formulation at the state level.

To address this critical gap, LEADS at the University of Illinois at Urbana-Champaign has developed the SAT Dashboard, an interactive platform that synthesizes engineering data, economic metrics, and operational feasibility to assess the future of low-emissions air travel. First introduced in March 2024, the dashboard has averaged more than 1,300 user engagements per month, indicating sustained interest from users across research, government, and industry contexts. Developed with funding from the U.S. Department of Energy, the dashboard has evolved considerably beyond its initial release. Its scope now spans the entire U.S. domestic aviation sector and incorporates a range of critical metrics, including CASM, Projected Lifecycle CO_2 Emissions, and the feasibility of integrating emerging propulsion systems. These metrics are derived from validated aircraft performance models and are supported by operational data from the Bureau of Transportation Statistics [24], enabling scenario analyses rooted in realistic mission profiles. This paper presents the tool architecture and functional capabilities of the SAT Dashboard. The methodology document [19] in the GitHub repository provides the detailed equations, data sources, unit conversions, and implementation assumptions for the Electrification, Energy-eX(ploration), SAF, Hydrogen, cost, and emissions modules.

The SAT Dashboard is organized into four main modules: Electrification, Sustainable Aviation Fuel (SAF), Hydrogen, and Energy-eX(ploration), each designed to explore a distinct technological pathway. The core modules of each energy carrier are illustrated in Figure 1. Each module enables users to evaluate aircraft performance, fuel consumption, emissions, and costs under technological and infrastructure constraints.

Figure 1

Figure 2 presents the flowchart for the Electrification route feasibility workflow. The workflow evaluates candidate routes using representative aircraft parameters, battery specific energy, battery mass fraction, pack voltage, C-rate limits, passenger displacement, route distance, monthly temperature, and adoption assumptions. The corresponding range model, battery pack sizing procedure, current limit feasibility criterion, passenger displacement calculation, and temperature-based feasibility filters are detailed in Ref. [19].

Figure 2

The Electrification module of the SAT Dashboard focuses on the potential of battery-powered propulsion systems for future aircraft. This section evaluates current commercially available battery technologies and visualizes their suitability for aviation through a series of interactive plots and maps. In the Battery Technology section (Figure 3), users can filter battery datasets by manufacturer brand and electrochemical chemistry. The resulting data are visualized through a customizable scatter plot interface (Figure 3a), enabling users to conduct comparative analyses of any two performance metrics, including energy density, power density, capacity, maximum discharge power, or voltage, across the x- and y-axes. The dashboard further facilitates multi-dimensional comparative assessment of battery cells across six performance metrics simultaneously via radar chart visualization, as illustrated in Figure 3b. This graphical representation elucidates the relative advantages and limitations of each battery chemistry, thereby informing optimal battery selection for mission-specific operational requirements. To augment the technical performance assessment, the dashboard incorporates a geospatial visualization of ongoing battery research and development initiatives worldwide (Figure 3c). This interactive cartographic interface presents global data disaggregated by sector, encompassing industry, academia, and governmental institutions, and stratified by battery technology type. Through systematic mapping of the contemporary technological landscape, this functionality promotes collaborative research by identifying geographic concentrations of specialized expertise and thematic research foci.

Figure 3

The Electric Motor section enables the exploration of state-of-the-art motors under development for aviation applications. As shown in Figure 4, this module enables users to visualize a curated database of electric motors under development, sourced from publicly available manufacturer data and academic publications. The tool is designed to provide a flexible and intuitive interface for analyzing motor performance characteristics critical to the electrification of flight. Users can filter the dataset by selecting specific manufacturers or motor types, such as permanent magnet, induction, or axial flux motors. Additionally, the x- and y-axes of the scatter plot can be configured to display a wide range of performance metrics, including power output, specific power, torque density, rotational speed, voltage, and cooling method.

Figure 4

Battery configurations selected in the Battery Technology module are passed to the U.S. Domestic Operations module to evaluate their operational viability in commercial aviation scenarios. As shown in Figure 5, the SAT Dashboard supports scenario analysis of electric propulsion deployment across the U.S. domestic air transportation network. Users can vary battery mass fraction, propulsive efficiency, operating month, airline adoption, and fleet adoption to assess their effects on route feasibility and passenger allocation. The feasibility assessment applies sequential filters for battery current capability, aircraft range, passenger displacement, airline selection, aircraft class, temperature limits, and adoption fraction, with the associated equations and implementation assumptions provided in Ref. [19]. Figure 5a illustrates how these parameters affect the set of viable routes, while Figure 5b shows the use of monthly origin and destination temperature data to account for battery operating temperature constraints.

Figure 5

The Techno-Economics Analysis section, shown in Figure 6, extends the operational assessment from the U.S. Domestic Operations section by evaluating electric aircraft deployment from infrastructure and market perspectives. Using route-level passenger demand, route distance, and the feasible route set, this section estimates the number of passengers allocated to electric aircraft, identifies the busiest airports in the feasible network, and compares the share of operations served by alternative-energy and conventional fleets. The dashboard also reports an energy source cost metric, with the corresponding cost formulation provided in Ref. [19].

Figure 6

Finally, the Climate & Emissions Impact section, illustrated in Figure 7, compares monthly fleet emissions for the selected alternative-energy scenario with a conventional Jet-A baseline. In the current Electrification and Energy-X implementation, routes identified as feasible for electric operation are treated as having zero direct operational emissions, while infeasible routes remain assigned to Jet-A aircraft and contribute to the conventional emissions inventory. Upstream electricity generation emissions are not included. The Jet-A emissions calculation and emission factor assumptions are provided in Ref. [19].

Figure 7

Mirroring the structure of the Electrification tab, the SAF section provides a comprehensive exploration of the technical potential and environmental impact of SAF integration into current and future aircraft operations. This module enables users to investigate a wide variety of sustainable fuels, assessing both their production characteristics and their implications for flight performance and climate impact.

The corresponding SAF route allocation workflow is shown in Figure 8. The SAF Technology tab allows users to filter fuels by production pathway, feedstock type, maximum blend ratio, life cycle emission factor, volumetric energy density, and cost, while also customizing the displayed metrics to compare fuel options. The tab also includes a global SAF Research Map that summarizes ongoing development efforts across academia, industry, and government. The operational deployment of SAF is then evaluated in the U.S. Domestic Operations tab, shown in Figure 9, where user defined fuel selections, cumulative fuel use sliders, maximum blend ratios, airport adoption assumptions, and fleet adoption percentages determine which routes are assigned to SAF operation. Unlike the Electrification and Hydrogen modules, the SAF module does not impose a range or passenger capacity penalty, reflecting the dashboard assumption that SAF is used as a drop-in or near-drop-in fuel subject to blend-ratio and airport availability constraints. The blend-ratio treatment, route allocation procedure, feedstock land-use calculation, and SAF emissions equations are provided in Ref. [19].

Figure 8

Figure 9

In the subsequent tab, users can investigate the regional implications of SAF deployment by selecting a specific U.S. state as the feedstock origin. This enables evaluation of the localized impact of variables such as feedstock crop type, fleet adoption rate, airport-level infrastructure readiness, and SAF production cost. The Techno-economic Analysis section builds on this foundation by quantifying the number of passengers that can be transported using SAF as the primary energy source. It identifies the busiest airports that would adopt SAF and estimates the percentage of flights that would use SAF. Lastly, these operational and economic inputs culminate in an environmental analysis. The model estimates cumulative tailpipe CO_2 emissions for the entire fleet and incorporates an LCA to evaluate well-to-wake environmental impacts.

The Hydrogen module provides a platform for exploring future operational scenarios involving hydrogen powered aircraft. The hydrogen feasibility workflow is shown in Figure 10. Similar in structure to the Electrification and SAF modules, this section allows users to vary the share of the fleet assigned to hydrogen operation, while the airport adoption selector limits hydrogen service to selected origin airport sets. The workflow combines passenger capacity, estimated fuselage volume, hydrogen storage volume fraction, hydrogen fuel mass, aircraft range, passenger displacement, airport availability, and fleet adoption assumptions to distinguish LH₂ feasible routes from routes that remain assigned to conventional operation. A central consideration in this module is the low volumetric energy density of LH₂, which creates constraints on onboard fuel storage and passenger capacity. The dashboard evaluates this tradeoff using a simplified volume based passenger displacement model and a screening level hydrogen range equation. Users can therefore assess domestic operational feasibility, system level tradeoffs, airport adoption potential, energy source cost, and emissions across the U.S. aviation network. The polynomial aircraft parameter regressions, atmospheric model, hydrogen range relation, passenger displacement calculation, and hydrogen emissions treatment are provided in Ref. [19].

Figure 10

The Energy-eX(ploration) module provides a flexible environment for evaluating future electrochemical cell assumptions and their implications for aircraft operations. This module follows the same route feasibility structure as the Electrification module, but instead of selecting a commercial battery cell from the dashboard database, users directly specify cell-level properties such as nominal voltage, capacity, maximum C-rate, specific energy, and operating temperature range. The module is therefore best interpreted as a parametric future battery impact predictor. Users can dynamically adjust cell and system level assumptions to evaluate how improved specific energy, current capability, voltage, and thermal operating limits affect feasible routes, passenger allocation, energy source cost, and emissions. The governing equations are identical to those used in the Electrification module and are documented in the methodology document [19].

Quality control

The SAT Dashboard is an interactive web application designed for data visualization, primarily developed with the Plotly Dash framework. Tests for the SAT Dashboard are not currently implemented, as the application is primarily an interactive framework that manipulates Pandas dataframes derived from literature sources. However, the underlying architecture is built with a focus on reliability and maintainability. Dash is an open-source Python framework built on Flask, plotly.js, and ReactJS. Applications in Dash consist of two main components: Layout and Callbacks. Layout defines the visual structure and appearance of the dashboard. This is constructed using HTML components and interactive core components such as dropdowns, sliders, and input fields. Callbacks provide interactivity by linking user actions (e.g., selecting an item from a dropdown) to updates in the dashboard’s layout or data. Callbacks are Python functions that are triggered by changes in component properties. The SAT Dashboard is hosted on Render, a cloud application platform that ensures availability by running routine zero-downtime maintenance. Render provides cross-platform hosting and automated service restarts, ensuring the web application remains accessible.

Primary aircraft operations and performance datasets are sourced from publicly available and authoritative databases, including the U.S. Bureau of Transportation Statistics. Electric motor performance data are obtained from publicly released results of the ARPA-E ASCEND program [25]. Battery characteristics are compiled from manufacturer datasheets and publicly available specification repositories referenced within the codebase, capturing current-state assumptions for specific energy, power capability, and efficiency. Agricultural feedstock availability, crop production volumes, and regional yield data used in lifecycle and fuel pathway analyses are sourced from the U.S. Department of Agriculture (USDA) [26]. Additional energy system and technology assumptions are derived from openly accessible government and institutional datasets, ensuring transparency and reproducibility of the analyses. Where applicable, energy consumption and cost models are cross-checked against conventional aircraft benchmarks to ensure consistency with established performance trends and industry-accepted reference values.

To improve traceability, the dashboard documentation distinguishes between direct model inputs, internally derived quantities, and auxiliary reference properties. Direct inputs include route, fuel, battery, hydrogen, and SAF pathway data used in calculations. Derived quantities include range, battery sizing, passenger displacement, SAF land use, energy source cost metrics, and emissions. Auxiliary properties provide contextual fuel comparisons and are not used directly unless stated. The companion methodology document defines the governing equations, units, and data treatment for these calculations [19].

Operating system

The Sustainable Aviation Technology Dashboard is a free, web-based tool that can be accessed directly online without any sign-up or installation requirements. Step-by-step video tutorials are provided to demonstrate the execution workflow and expected application behavior, and are available at https://www.youtube.com/watch?v=uf_OyVi-j6Q and https://www.youtube.com/watch?v=R_4eqRuHGaM.

Programming language

The Sustainable Aviation Technology Dashboard is programmed in Python, compatible with Python 3.8 and above.

Additional system requirements

No specific requirements.

Dependencies

There are no external libraries, frameworks, or version-specific dependencies required.

List of contributors

Matteo Guidotti orcid.org/0009-0003-9231-644X

Ph.D. Candidate, Department of Aerospace Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States. Design, development and testing, data collection.

Matthew A. Clarke orcid.org/0000-0001-9250-9493

Assistant Professor, Department of Aerospace Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States. Design, development and testing, data collection.

Software location

Archive

• Name: GitHub (release v1.0.0)

• Persistent identifier: https://github.com/leadsgroup/Technology_Dashboard/releases/tag/v1.0.0

• Licence: AGPL-3.0 license

• Publisher: The LEADS team

• Version published: v1.0.0

• Date published: 24/08/25

Code repository

• Name: GitHub

• Persistent identifier: https://github.com/leadsgroup/Technology_Dashboard

• Licence: AGPL-3.0 license

• Date published: 24/08/25

Language

The source code is written in Python, and all documentation and supporting files are in English.