Optimization model for vehicle model-based credit transfers: Evidence from fuel economy management in Taiwan

Vehicle model-based credit transfer systems are rarely adopted in vehicle energy efficiency frameworks, as they complicate credit allocation and compliance calculations, while limiting flexibility in fleet management. A few markets, such as Taiwan, have diverged from conventional credit systems by implementing frameworks that allocate credits at the vehicle model level. To address these challenges, this study developed an optimization model based on mixed-integer nonlinear programming to enhance credit transfer efficiency through the strategic selection of vehicle models for transfer. Empirical analysis of 26 real-world cases from Taiwan (2017–2022) demonstrated the effectiveness of the proposed model in improving credit allocation, minimizing excess credits, and enhancing compliance flexibility. Manufacturers with larger and more diverse fleets were shown to benefit most from optimization, owing to their broader range of transfer options. These findings provide valuable insights for policymakers and automakers seeking to refine credit mechanisms under increasingly stringent fuel economy regulations. This study addressed the challenges of vehicle model-based credit transfers, laying the foundation for future developments involving the integration of cross-category systems. One example is a potential linkage with carbon emissions trading, which could enhance regulatory adaptability and market efficiency.