Lei Ting, Wang Yi-Qi, Wang Chao
School of Economics and Management, Chang'an University, Xi'an 710064, China.
Institute of Blue and Green Development, Shandong University, Weihai 264209, China.
Huan Jing Ke Xue. 2025 Jan 8;46(1):53-65. doi: 10.13227/j.hjkx.202402137.
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.
厘清旅游交通碳排放及其影响因素的空间关联网络结构，对于中国旅游与交通行业协调碳减排治理规划、实现旅游交通行业可持续发展至关重要。基于2001—2021年的省级面板数据，测算了中国旅游交通的碳排放量，并运用改进的空间引力模型构建省级空间网络特征及其影响因素，采用社会网络分析法和QAP模型进行分析。研究表明：①中国旅游交通碳排放总量逐年缓慢增长，呈“东南高西北低”的分布格局，东西部地区差异明显。②中国旅游交通碳排放形成了“东部密集、西部稀疏”的多线程复杂网络。空间网络中“马太效应”明显，北京、上海、广东等东部省份占据核心地位，新疆、青海、黑龙江、辽宁等西北和东北省份处于边缘。③中国旅游交通板块模型的碳排放具有明显的划分结构，各板块间存在大量关联，且存在来自其他板块的碳排放空间溢出。④交通能源强度和交通结构对空间关联网络有显著正向影响，而空间地理距离、居民消费水平和旅游经济效率对空间网络有显著负向影响。
