Balancing climate action for greater transport decarbonisation: An avoid-shift-improve driven network data envelopment analysis framework
研究开发了一个避免-转移-改进驱动的网络数据包络分析框架，用于衡量爱尔兰26个郡的交通脱碳进展，并作为资源分配决策支持工具，发现大城市和农村地区表现差异显著。
Transport decarbonisation requires allocating limited resources across competing strategies. The Avoid–Shift–Improve framework categorises these strategies to reduce transport's reliance on fossil fuels. This study develops an Avoid–Shift–Improve driven network data envelopment analysis (DEA) framework to measure county-level progress toward transport decarbonisation. The framework also serves as a decision-support tool for allocating resources efficiently to advance transport decarbonisation. Using data from Ireland's 26 counties, we integrate DEA with a Stackelberg leader–follower game and the best–worst method. The DEA component provides an objective, mathematically defined measure of relative efficiency. The best–worst method incorporates expert judgement on the economic and environmental impact of actions within the Avoid–Shift–Improve hierarchy. This hierarchical approach, reflecting expert consensus, prioritises transformational measures that reduce travel demand (Avoid), followed by strategies that shift remaining trips to low-carbon modes (Shift), while technological improvements (Improve) play a more limited role. Results reveal disparities in county performance. Dublin leads due to its relatively well-developed public transport and active mobility infrastructure. Smaller counties such as Longford and Leitrim also perform strongly despite their rural character. By contrast, large-area counties including Cork, Mayo, and Kerry underperform, reflecting structural challenges of dispersed settlement and high car dependency. The analysis highlights that larger counties achieve lower efficiency scores, while links with emissions and expenditure are weaker, underscoring the role of spatial scale and carbon lock-in in shaping outcomes. The framework is scalable to other regional and national contexts and can support economically rational, socially inclusive climate policy. • Develops a network DEA model for hierarchical importance of subunits in parallel structure. • Employs the Avoid–Shift–Improve framework to assess transport decarbonisation progress. • Integrates DEA, Stackelberg game, and BWM to evaluate emission-reduction strategies. • Prioritises and harmonises transport climate actions to maximise decarbonisation gains. • Provides a decision-support tool for policy-driven transport decarbonisation.