| 中国省际旅游交通碳排放空间关联网络及影响因素 |
| 摘要点击 2103 全文点击 184 投稿时间：2024-02-27 修订日期：2024-04-08 |
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| 中文关键词 旅游交通 碳排放 空间网络结构 社会网络分析 影响因素 |
| 英文关键词 tourism transportation carbon emissions spatial network structure social network analysis influencing factors |
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| 中文摘要 |
| 厘清旅游交通碳排放空间关联网络结构及其影响因素对中国旅游交通行业统筹规划碳减排治理工作,实现旅游交通产业可持续发展至关重要. 基于2001~2021年省际面板数据,测算中国旅游交通碳排放量,利用修正后的空间引力模型构建中国省际旅游交通碳排放空间关联网络,结合社会网络分析法和QAP模型对空间网络结构特征及其影响因素进行分析. 结果表明： 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,地区差异明显. ②中国旅游交通碳排放网络已形成“东密西疏”的多线程、复杂化的关联形态. 空间网络中的“马太效应”较明显,北京、上海和广东等东部省份居于核心主导地位；新疆、青海、黑龙江和辽宁等西北和东北省份居于边缘位置. ③中国旅游交通碳排放块模型划分结构明显,各板块关联关系较多且均接收来自其他板块的碳排放溢出. ④交通能源强度和交通运输结构对空间关联网络具有显著的正向影响；空间地理距离、居民消费水平和旅游经济效益则对空间网络产生显著的负向影响. |
| 英文摘要 |
| 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. |
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