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

This paper develops a transparent, regression-based framework for estimating aircraft flight emissions and per-passenger climate impact, designed to be usable without proprietary airline data. It responds to two limitations in existing tools: high-precision methods rely on Quick Access Recorder (QAR) data that is restricted to airlines and research institutions, while many accessible tools use oversimplified fixed fuel-burn factors or idealised trajectories that underestimate real-world emissions. Rather than train a machine-learning model, which the author found impossible because no public dataset contains the necessary variables of passenger load, flight distance and actual fuel burn, the study adapts peer-reviewed linear-regression models and fuel-flow correction equations into a practical, consumer-level application. The method incorporates published regression coefficients, great-circle distance corrections and standardised ICAO parameters to estimate fuel consumption and CO2 emissions across the Landing and Take-Off (LTO) and Climb-Cruise-Descent (CCD) phases of flight. Because its inputs are publicly available, origin-destination coordinates, aircraft type and passenger count, the framework produces repeatable estimates and is embedded in an interactive web-based application that returns total and per-passenger emissions for a given flight. The paper's focus is on closing the gap between accessible open-source calculators and rigorous academic emission models, showing that scientifically grounded regression techniques can deliver accurate first-order estimates without complex machine-learning pipelines or restricted data. Spanning aerospace engineering, environmental science and data modelling, it positions the tool as useful for environmental awareness, education and preliminary sustainability analysis, while remaining extensible as richer aviation data becomes available, addressing aviation's fast-growing contribution to greenhouse-gas emissions.