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

Accurate estimation of aircraft emissions is crucial for climate-impact assessment, policy development, and sustainability planning. This study develops a lightweight, transparent framework for flight-emission estimation by adapting validated regression models and fuel-flow correction equations into a consumer-level application. The approach incorporates published regression coefficients, great-circle distance corrections, and standardized ICAO parameters to estimate fuel consumption and CO₂ emissions across Landing and Take-Off (LTO) and Climb–Cruise–Descent (CCD) flight phases. Users can estimate total and per-passenger emissions using an interactive web-based application with publicly accessible inputs. This work bridges the gap between accessible, open-source tools and high-precision aviation-emission models, demonstrating that scientifically grounded regression-based techniques can produce accurate first-order emission estimates without complicated machine-learning pipelines.