[Spatial Correlation Network and Influencing Factors of Carbon Emissions from Inter-provincial Tourism Transportation in China].

Clarifying the spatial correlation network structure from tourism transportation carbon emissions and its influencing factors is crucial for China's tourism and transportation industry to coordinate the planning of carbon reduction governance and realize the sustainable development of the tourism transportation industry. Based on inter-provincial panel data from 2001 to 2021, China's carbon emissions from tourism transportation were measured, and the modified spatial gravity model was used to construct characteristics of provincial spatial networks and their influencing factors, which were analyzed using the social network analysis method and the QAP model. The study showed that ① China's total carbon emissions from tourism and transportation have been growing slowly year by year, showing a distribution pattern of "high in the southeast and low in the northwest," with obvious differences between the eastern and western regions. ② China's carbon emissions from tourism and transportation formed a multi-threaded and complex network of "dense in the east and sparse in the west." The "Matthew effect" in the spatial network was obvious, with eastern provinces such as Beijing, Shanghai, and Guangdong dominating the core and the northwestern and northeastern provinces such as Xinjiang, Qinghai, Heilongjiang, and Liaoning on the periphery. ③ China's carbon emissions from the tourism transportation block model had a clear division structure, and each block had a large number of correlations and received a spatial overflow of carbon emissions from other blocks. ④ Transportation energy intensity and transportation structure had a significant positive effect on the spatial correlation network, while spatial geographic distance, residents' consumption level, and tourism economic efficiency had a significant negative effect on the spatial network.