Urban areas play a critical role in reconciling carbon reduction with welfare enhancement under global carbon neutrality goals. This study evaluates the Carbon Welfare Performance (CWP) of 284 Chinese cities (2000−2023) using a novel two-stage Super-Network Slacks-Based Measure (Super-NSBM) model. By decomposing CWP into green production efficiency (GPE) and welfare transformation efficiency (WTE), we identify significant structural imbalances: while national CWP showed modest growth, this was solely driven by a 38.2% increase in WTE, whereas GPE declined by 31.5%. Spatially, CWP evolved from fragmented clusters to a multi-polar pattern. Dagum Gini decomposition reveals that regional disparities are primarily attributable to cross-regional overlaps rather than simple east-west divisions, with GPE being the main source of inequality. Slack analysis further identifies three inefficiency patterns, which are high redundancy–high emissions, capital inefficiency–structural mismatch, and high output–environmental shortfall, providing a basis for targeted policy interventions. These findings integrate welfare dimensions into carbon efficiency evaluation and offer valuable insights for fostering low-carbon, welfare-enhancing urban transitions in China and other developing economies. • A two-stage Super-NSBM model evaluates urban Carbon Welfare Performance. • CWP decomposed into green production and welfare transformation efficiencies. • Welfare transformation drives growth, while green production remains a bottleneck. • Regional disparities in CWP stem mainly from cross-regional overlaps. • Three inefficiency patterns reveal risks of resource misallocation in low-carbon transition. • Welfare transformation drives growth, while green production remains a bottleneck. Regional disparities in CWP stem mainly from cross-regional overlaps. • Regional disparities in CWP stem mainly from cross-regional overlaps. • Three inefficiency patterns reveal risks of resource misallocation in low-carbon transition.
Zhang et al. (Wed,) studied this question.