Urban disaster resilience has become central to risk governance as climate change, compound hazards, and interdependent urban systems intensify pressures on cities. Publication-based reviews describe visible research outputs but reveal less about how research priorities are formulated and funded. This study examines 26 National Natural Science Foundation of China projects initiated during 2014–2024 and retrieved using three deliberately focused keywords: “urban disaster,” “urban resilience,” and “disaster resilience.” Descriptive statistics, text-assisted coding, temporal analysis, knowledge mapping, and a paradigm matrix were applied across six dimensions: hazard types, urban systems, resilience capacities, research methods, spatial scales, and paradigm orientations. Within this selected corpus, funding activity became concentrated after 2019, while research attention broadened from disaster prevention, risk identification, and vulnerability assessment toward resilience assessment, adaptive governance, community capacity, infrastructure networks, and coupled urban systems. Nevertheless, indicator frameworks, spatial evaluation, and model-based measurement remained dominant. Recovery trajectories, compound hazard interactions, social equity, and cross-system governance received comparatively limited attention. These findings indicate an agenda-level reorientation within the selected NSFC-funded corpus, rather than a universal transformation of Chinese urban disaster research. A more mature resilience agenda requires stronger integration of compound risks, recovery processes, equity, and coupled system governance.

Road transport is an important source of carbon emissions worldwide, yet the factors driving these emissions may differ under varying geographical conditions. Plateau regions are characterized by high altitude and strong spatial constraints, but their transport carbon emission mechanism remains insufficiently understood. Taking Qinghai Province on the Qinghai–Tibetan Plateau as a case, this study estimates road transport CO₂ emissions from 2003 to 2022 using annual statistical data. It constructs a multidimensional indicator system covering economic development, industrial structure, energy use, and road transport activity, and applies correlation analysis, PCA, and LASSO regression to diagnose variable relationships and identify key drivers. The results show that GDPI, VATSP, CVO, and TPT have stable positive effects on road transport CO₂ emissions, indicating that economic expansion, transport services, vehicle ownership, and passenger mobility are the dominant drivers. Industrial and energy-related variables have more indirect and stage-dependent effects: NMI and NFMO are negatively associated with emissions, whereas NMEC has a weak positive effect. These findings suggest that, under the dispersed spatial development and long-distance transport dependence of plateau regions, emissions are more directly shaped by economic and transport activity than by short-term changes in energy structure. Low-carbon transport policy in Qinghai should therefore combine transport-demand management, more efficient transport organization, public-transport improvement, and gradual transport electrification. The results provide evidence for emission-reduction strategies in high-altitude and ecologically fragile regions.