Strengthening research on urban tourism competitiveness is vital in evaluating the current situation and potential of urban tourism, maintaining the sustainable development of the tourism economy and assisting in the regional macro decision making. In this study, an index system evaluation of urban tourism competitiveness in city agglomerations across the Guanzhong Plain is established by collecting cross-section data from the years 2017 and 2010. The entropy value method is adopted to determine the index weight. Cluster analysis is performed and the spatial-temporal pattern and evolution laws of urban tourism competitiveness among city agglomerations in the Guanzhong Plain are analyzed and the geographic detector utilized to discuss the influencing factors. Results show that the spatial gradient difference of urban tourism competitiveness of agglomerations in the Guanzhong Plain is significant. In 2010, it presented the characteristic of ‘the high and middle levels having a zonal distribution from east to west, and the low level was distributed along the north and south wings’. In 2017, the characteristic of ‘polarization’ became highly prominent, that is, the scope of high-level and low-level cities expanded and the scope of medium-level cities decreased. Urban tourism competitiveness in city agglomerations across the Guanzhong Plain exhibited a trend of ‘strengthening in the east, weakening in the west’. The competitiveness of resources and management shifted aggressively and supporting factors competitiveness underwent a slight change. The urban tourism competitiveness of city agglomerations in the Guanzhong Plain is generally low, while the urban tourism competitiveness of Xi’an had an absolute advantage in city agglomerations of the Guanzhong Plain. According to the cluster analysis results, resources and management competitiveness, supporting factors competitiveness, demand conditions competitiveness, situational conditions competitiveness and urban tourism competitiveness of Xi’an in 2010 and 2017 were all at an extremely high level, which was relatively higher than the index values of other cities in the city agglomerations of the Guanzhong Plain. Tourism resources, service support capacity, infrastructure support capacity, tourism income scale, tourism reception scale and economic development power are the core influencing factors of urban tourism competitiveness among city agglomerations in the Guanzhong Plain. The single factor explanatory power of destination management indicates a downward trend while the single factor explanatory power of the ecological environment condition shows an upward trend. Tourism resources are the leading interactive factor of urban tourism competitiveness, and destination management and ecological environment condition are the most significant indicators for the collaborative effect.

As a strategic infrastructure supporting high-quality economic and social development, computing infrastructure plays a pivotal role in enabling green transitions. Using panel data from Chinese prefecture-level cities spanning 2007 to 2023 and leveraging the staggered commissioning of 12 National Supercomputing Centers as a quasi-natural experiment, this paper employs a time-varying difference-in-differences (DID) approach to estimate the effect of computing infrastructure on urban green total factor productivity (GTFP). The results indicate that the operation of supercomputing centers has a statistically significant positive effect on urban GTFP, with a magnitude equivalent to approximately 0.83 times the sample standard deviation of GTFP, a finding that remains robust to alternative dependent variable specifications, the exclusion of other policy shocks, and placebo tests. Mechanism analysis reveals that computing infrastructure facilitates green development through three channels: fostering green technological innovation, optimizing energy efficiency, and strengthening environmental regulation. Heterogeneity analysis shows that the positive effect is more pronounced in coastal cities, small-to-medium-sized cities, and regions with weaker digital infrastructure. Spatial analysis further uncovers a distance-decay pattern, with a siphoning effect within a 50 km radius and a spillover effect between 50 km and 200 km from the supercomputing center. This study provides empirical evidence on the environmental consequences of the computing economy and offers policy implications for optimizing computing infrastructure deployment to facilitate green transitions.