Regression-Based Modeling of Flight Emissions and Per-Passenger Climate Impact

Muhammad Hadi

doi:10.36948/ijfmr.2026.v08i01.67871

Regression-Based Modeling of Flight Emissions and Per-Passenger Climate Impact

Author(s)	Muhammad Hadi
Country	India
Abstract	Accurate estimation of aircraft emissions is crucial for climate-impact assessment, policy development, and sustainability planning because aviation is a rapidly increasing contributor to global greenhouse gas emissions due to growing passenger demand and expanding air transport networks. High-precision techniques based on Quick Access Recorder (QAR) data are still unavailable due to proprietary restrictions, and many current emissions-estimation tools rely on oversimplified assumptions like fixed fuel-burn factors or idealized trajectories, which frequently underestimate real-world emissions. By modifying peer-reviewed linear regression models and fuel-flow correction equations into a useful, consumer-level application, this study suggests a lightweight and transparent framework for flight-emission estimation in order to overcome this limitation. The approach incorporates published regression coefficients, great-circle distance corrections, and standardised International Civil Aviation Organisation (ICAO) parameters to estimate fuel consumption and carbon dioxide (CO₂) emissions across Landing and Take-Off (LTO) and Climb–Cruise–Descent (CCD) flight phases instead of training an artificial intelligence model, which would require unavailable flight-recorder datasets. Repeatable emission estimates are made possible by the framework's use of publicly accessible inputs like origin-destination coordinates, aircraft type, and passenger count, which eliminate the need for limited operational data. Users can estimate total and per-passenger emissions for individual flights using an interactive web-based application that incorporates the model. This work's main contribution is to close the gap between accessible, open-source tools and high-precision academic aviation-emission models by showing that scientifically grounded regression-based techniques can produce accurate first-order emission estimates without complicated machine-learning pipelines. This supports environmental awareness, educational applications, and preliminary sustainability analyses while laying the groundwork for future extensions as data availability improves.
Keywords	Aviation emissions, fuel consumption modeling, linear regression, carbon dioxide emissions, flight sustainability, per-passenger emissions
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-04
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.67871

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Regression-Based Modeling of Flight Emissions and Per-Passenger Climate Impact

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