我国旅游业碳排放的空间关联性及其影响因素

为全面厘清我国旅游业碳排放的空间网络结构特征，基于2000-2015年我国各省（自治区、直辖市）旅游业碳排放相关数据（不含西藏自治区和港澳台地区数据，下同），结合修正的引力模型，构建旅游业碳排放的空间关联关系；采用社会网络分析法，深入剖析我国旅游业碳排放的空间关联性及其影响因素.结果表明:①2000-2015年，我国旅游业碳排放的空间网络关联度始终为1，其网络关系数与网络密度持续增加，而网络等级度与网络效率平稳下降.②上海市、浙江省、江苏省、北京市、天津市等东部经济发达地区在网络中处于核心位置，对旅游业碳排放空间关联性的影响显著；而海南省、云南省、广西壮族自治区、青海省、吉林省等在网络中居于边缘位置，对旅游业碳排放空间关联性的影响微弱.③广西壮族自治区、贵州省、新疆维吾尔自治区等归为"净溢出"板块，河北省、甘肃省、陕西省等处于"经纪人"板块，北京市、天津市、内蒙古自治区等归为"双向溢出"板块，江苏省、浙江省、上海市等属于"净受益"板块.④空间邻接关系、城镇化水平差异在1%的显著性水平上对旅游业碳排放空间关联性起正向促进作用，旅游消费水平差异和产业结构差异分别在5%和10%的显著性水平上与旅游业碳排放空间关联性呈正相关，能源消耗差异在1%的显著性水平上与其呈负相关.研究显示，我国整体旅游业碳排放空间关联性愈趋紧密，但仍存在较大改良空间，各板块间的空间关联性有待进一步加强. 

Spatial Correlation of Carbon Emissions in Tourism Industry and Its Influencing Factors in China

In order to fully clarify the spatial network structure characteristics of carbon emissions in China's tourism industry, based on the data of carbon emissions (excluding the data of Tibet Autonomous Region, Hongkong, Macao and Taiwan, the rest is the same) from the tourism industry of various provinces, autonomous regions and municipalities directly under the central government in China from 2000 to 2015, and by combining them with the improved gravity model, this paper constructs the spatial network correlation of carbon emissions in tourism industry. Social network analysis method is applied to the analysis of the spatial correlation of carbon emissions in tourism industry and its influencing factors in China. The results show that:(1) During the investigation period, the spatial network correlation degree of carbon emissions in the tourism industry is always 1, the number of its network relations and the network density continue to rise, while the network hierarchy and efficiency steadily decline. (2) The eastern developed regions such as Shanghai City, Zhejiang Province, Jiangsu Province, Beijing City, Tianjin City, etc. are at the core position of the network, which have a significant impact on the spatial correlation of carbon emissions in tourism industry. Relatively backward regions such as Hainan Province, Yunnan Province, Guangxi Zhuang Autonomous Region, Qinghai Province, Jilin Province, etc. are at the edge position of the network, and have little effect on the spatial correlation of carbon emissions in the tourism industry. (3) Regions such as Guangxi Zhuang Autonomous Region, Guizhou Province and Xinjiang Uygur Autonomous Region are classified as 'net spillover plate'; Hebei Province, Gansu Province, Shaanxi Province and so on are in the 'agent plate'; Beijing City, Tianjin City and Inner Mongolia Autonomous Region are classified as 'bidirectional spillover plate'; Jiangsu Province, Zhejiang Province as well as Shanghai City belong to the 'net benefit plate'. (4) The spatial adjacency relation and urbanization level disparity have a positive effect on the spatial correlation of tourism carbon emissions at a significant level of 1%; The differences of tourism consumption level and industrial structure are positively correlated with the spatial correlation of tourism carbon emissions at the significant level of 5% and 10%; The energy consumption disparity has a negative correlation with the spatial correlation of tourism carbon emissions at the significant level of 1%. The research shows that the spatial correlation of carbon emissions in tourism industry is closely related, yet there is still much space for improvement. The spatial correlation between the plates needs to be further strengthened.