Techno-economic analysis resting on nonlinear systems theory provides a robust framework for decarbonising commercial and public fleets. Standard linear models fail to capture the feedback loops, threshold effects and phase transitions inherent in freight electrification, minor perturbations may cascade into system-wide regime shifts. Accordingly, this study embeds five nonlinear mechanisms: Wright’s Law cost learning, logistic grid decarbonisation, experience-curve hydrogen price decline, an electrification-grid feedback loop and tipping-point detection, within a techno-economic and optimized simulation for Australian heavy long-haul freight, which accounts for approximately 22% of transport emissions. The model provides three principal outputs, which are the identification of a sequencing problem for green hydrogen deployment, quantification of a persistent emissions-economics inversion across the scenario space and provision of a time-indexed decision framework that maps transition year, Heavy Fleet choice, technology scenario to ROI and cumulative emissions, which aims at serving as a quantitative procurement guide for operator capital planning across the 2025-2035 time period. In addition, the model evaluates cumulative lifecycle emissions, total cost of ownership and return on investment relative to diesel across 270 scenarios over a 25-year horizon, using SEEA-aligned Australian emission factors, NREL ATB 2024 vehicle costs and DCCEEW grid projections. Under nonlinear dynamics with Mid technology assumptions and a 10-year operating lifetime from 2025, battery-electric vehicles deliver emissions approximately 62% below diesel (ROI +108% nonlinear vs +96% linear) with Wright’s Law reducing the cost premium by AUD 33k; fuel-cell vehicles on grey hydrogen achieve ROI +329% nonlinear with emissions 14% below diesel; and diesel hybrid recovers its capital premium within the first operating year. Fuel-cell vehicles on green hydrogen amplify emissions to 49% above diesel under a 2025 transition, a sequencing problem resolving later than linear projections suggest. Sensitivity analysis confirms diesel price, electricity price and annual utilisation as dominant economic sensitivities, while the emissions-economics inversion is robust across all tested ranges. Tipping-point analysis identifies crossover years, providing a critical decision-support tool for fleet transitions aligned with Net Zero 2050. The novelty of this paper lies in the integration of the nonlinear SEEA-aligned Australian emission factors with fleet transition ROI analysis across multiple technology learning pathways and transition timing scenarios, which is an integration and model scheme which has been established and implemented in the Australian heavy freight context from longitudinal dynamic dimensions.
Singh et al. (Thu,) studied this question.