Taoyu Chen

Achieving urban carbon neutrality requires not only ambitious mitigation targets but also urban conditions capable of supporting implementation. However, whether cities possess such transition capacity remains insufficiently understood. Here, we develop an empirical framework to evaluate urban transition capacity across 336 Chinese cities by integrating carbon emission characteristics, policy and technology, ecological endowment, and energy potential. We identify substantial heterogeneity: 40 cities are classified as high-capacity, while most cities fall into medium- or low-capacity groups with systematically different obstacle compositions. High-capacity cities exhibit stronger conditions across all four capacity dimensions, but weighted obstacle structures shift progressively across groups. Policy and technology represent the largest relative obstacle component throughout, whereas the contribution of carbon emission characteristics increases markedly as transition capacity declines. Together, these results link multidimensional capacity assessment with group-specific obstacle diagnostics across a large urban system. The findings question the suitability of uniform mitigation strategies across heterogeneous urban contexts and support capacity-oriented governance that aligns transition priorities with city-specific conditions.

Demand-side management is increasingly important for low-carbon transport governance. However, many studies assume relatively stable travel preferences and pay limited attention to behavioural changes under sudden external shocks. This study proposes an Event–Behaviour–Resilience framework and applies Natural Language Processing to Sina Weibo data to examine travel responses to extreme heat and refined oil price adjustments. The results show asymmetric response patterns. Oil price increases were associated with cost-based low-carbon substitution, with new-energy vehicle intentions accounting for 64.4% of the share. In contrast, extreme heat was associated with both trip reduction and motorised travel. Travel reduction reached 52.4%, while ride-hailing or taxi responses accounted for 24.6%. A quadratic fitting analysis identified 38.0–39.0 °C as an observed transition interval, within which high-carbon motorised willingness began to exceed low-carbon slow mobility willingness. Group-level analysis showed unequal behavioural flexibility. While 80.0% of the general population reduced travel under extreme heat, the forced mobility group showed limited travel reduction and maintained a high level of low-carbon willingness at 86.87%. XGBoost-SHAP results indicated that temperature, emotional valence, and behavioural constraints contributed to low-carbon mobility intention. These findings suggest that behavioural responses can help identify spatial interventions for low-carbon transport, especially in relation to heat exposure, mobility flexibility, and access to adaptive travel options.