A three-stage evaluation of airline low-carbon competitiveness

Abstract

Achieving deep decarbonisation in aviation requires a systematic understanding of how airlines differ in their carbon efficiency and what operational or technological factors drive these differences. This study proposes an integrated three-stage analytical framework that combines a non-oriented SBM-DDF model, the Global Malmquist–Luenberger (GML) productivity index, Random Forest regression and a cloud-model heterogeneity assessment to evaluate the low-carbon competitiveness of 20 major global airlines from 2018 to 2022. The SBM-DDF model benchmarks multi-input–multi-output environmental efficiency, while the GML index captures dynamic productivity changes and decomposes them into efficiency change and technical change. Random Forest analysis identifies the key operational determinants of carbon efficiency, and the cloud model characterises heterogeneity in performance level, volatility and uncertainty. Results reveal pronounced cross-airline and intertemporal heterogeneity. Mean efficiency declined markedly during 2020–2021 and rebounded in 2021–2022, although absolute CO2-slack reductions lagged behind relative technical efficiency improvements. GML analysis shows that productivity changes were modest and mainly driven by managerial and operational efficiency rather than technological progress, indicating the limited short-run impact of fleet renewal on low-carbon competitiveness. Airline-level patterns demonstrate consistently strong and stable performance among Singapore Airlines, EasyJet and Cathay Pacific. Random Forest results identify revenue tonne-kilometres (RTK), employment scale and fleet size as dominant drivers of emission efficiency, while the cloud-model heterogeneity typology reveals four distinct groups ranging from efficient-stable to inefficient-volatile. Policy implications emphasise the need for performance-sensitive benchmarks in ETS and SAF-crediting schemes, and for managerial efficiency improvements to complement long-run technological transitions.