中国旅游业碳排放效率的空间网络结构及其效应研究

基于中国30个省区2001-2016年面板数据,采用SBM模型测度各省区旅游业碳排放效率,借助修正的引力模型和社会网络分析方法,厘清中国旅游业碳排放效率的空间网络结构及其效应。研究表明：① 中国旅游业碳排放效率的空间关联渐趋紧密,网络发育程度日益完善,但距理想状态仍有差距;② 各省区网络中心性指标分异性逐步减小,上海、北京、江苏等省区排名稳居前列,重庆、福建、内蒙古等省区排名波动上升,宁夏、青海、山西等省区排名相对滞后;③ 网络整体呈核心区由东部沿海向中部及西南地区持续扩展,而边缘区范围逐步收缩态势;④ 网络密度与旅游业碳排放效率呈正相关,与旅游业碳排放效率差异构成负相关关系,网络等级度和网络效率则与之相反,网络中心性各指标的提升均能显著增强旅游业碳排放效率。

Spatial Network Structure of Carbon Emission Efficiency of Tourism Industry and Its Effects in China

The carbon emission efficiency of China's tourism industry presents complex spatial correlation characteristics for the interaction of multiple factors. It is of great significance for energy saving and emission reduction of tourism industry to clarify the comprehensive structure scenario of carbon emission efficiency in China's tourism industry. Based on panel data of 30 provinces in mainland China from 2001 to 2016, this article uses the SBM model to measure the carbon emission efficiency of tourism industry. Then, the method of social network analysis and the modified gravity model are applied to examine the characteristics of spatial network and its effects on carbon emission efficiency in China's tourism industry. The results show that: 1) The network relationship number and network density of tourism carbon emission efficiency fluctuate upwards. While the network grade and network efficiency decline gradually. The spatial correlation of carbon emission efficiency in China's tourism industry has been significantly strengthened. The network structure tends to be mature, but there is still much space for improvement from the ideal state. 2) From the analysis results of network centrality, the diversity of network centrality indicators in various provinces gradually decreases. Shanghai, Beijing, Jiangsu and other provinces rank steadily in the forefront, Chongqing, Fujian, Inner Mongolia etc. present a fluctuant rising trend, Ningxia, Qinghai, Shanxi and others rank relatively backward. 3) According to the analysis results of core-periphery structure, the network as a whole shows the trend of core districts expansion from eastern coastal areas to central and southwestern China, while the scope of the periphery shrinks gradually. 4) The network density is positively proportional to the tourism carbon emission efficiency and negatively correlated with the difference of tourism carbon emission efficiency. While the network grade and network efficiency are contrary to network density. The promotion of individual centrality indicators including degree, closeness and betweenness can significantly enhance the carbon emission efficiency of tourism industry. In order to promote regional cooperative emission reduction in tourism industry, targeted policies should be formulated according to the effects of network structure.