Policy stringency and cost-effective strategies for low-carbon transition: A sequential conditional robust data envelopment analysis

Identifying cost-effective strategies is essential for enabling a smooth and efficient low-carbon transition, while previous studies often neglect policy stringency, potentially biasing low-carbon transition costs. This paper develops a novel sequential conditional nonparametric shadow price estimation framework that incorporates the effect of continuous policy stringency and captures the dynamic accumulation effects of best performers over time. Order-m partial frontier estimation technique is employed to ensure robust results against outliers. The proposed approach is applied to identify cost-effective low-carbon transition strategies of 271 Chinese cities over the 2001–2021 period. A comparison with conventional approaches without policy considerations reveals that neglecting policy stringency leads to an overestimation of low-carbon transition costs. Despite significant disparities in low-carbon transition costs among city types, energy mix transformation is still the most cost-effective strategy across the majority of cities and during most years. These findings offer policymakers valuable insights for designing targeted and cost-effective low-carbon transition pathways.