中国旅游业碳排放效率的空间格局及其影响因素

Spatial pattern and influencing factors of carbon dioxide emissions efficiency of tourism in China

旅游业碳排放效率作为旅游业绿色全要素生产率指标,是衡量旅游业碳排放与旅游经济增长之间关系的重要工具。借助SBM模型测算了中国省际旅游业碳排放效率,并利用ESDA和GWR方法分析了旅游业碳排放效率的空间格局及其影响因素的时空异质性。结果表明:中国旅游业碳排放效率呈现缓慢提升态势,但总体水平仍较低。旅游业碳排放效率的空间集聚特征明显,形成了以上海为中心的高值集聚区和以西北省份为中心的低值集聚区。旅游业碳排放效率及其空间格局演化是多因素共同作用的结果,旅游经济规模对中西部地区旅游业碳排放效率的提升作用较强;城镇化的促进作用逐步减弱,且在多数省份开始产生抑制作用;技术效应的提升作用高值区从中东部转移至华北和东北地区;旅游业产权结构对南部地区的推动作用也逐步凸显;结构效应主要对西南地区起促进作用。这为优化和提升旅游业碳排放效率提供了理论依据。

Combining a strong tourism economic performance and less environmental effect is one of the important objectives of current Chinese tourism development. As a total factor productivity index of green tourism, carbon dioxide emissions efficiency of tourism has become an important tool which measures the relationship between carbon dioxide emission of tourism industry and tourism economic growth. Based on the panel data about provincial input-output of tourism industry, the carbon dioxide emissions efficiency of tourism of 30 provinces in China's Mainland is measured by the Slack-Based Measure （SBM） considering the unexpected output. In addition, the exploratory spatial data analysis model （ESDA） is applied to analyze the spatial distribution characteristics of carbon dioxide emissions efficiency of tourism. At last, the paper analyzes the influencing factors, and their spatial-temporal heterogeneity through the geographical weighted regression model （GWR）. The key results are as follows： （1）Carbon dioxide emissions efficiency of tourism in China is low, but recorded obvious progress over the observed period. A low average carbon dioxide emissions efficiency of tourism of 0. 38 was calculated in 2001, increasing to almost 0.47 in 2011. The carbon dioxide emissions efficiency of tourism of only a few provinces achieves the best production frontier, while most of provinces need to optimize the input-ontput of tourism. （2） Spatial distribution characteristics are also significant. The carbon dioxide emissions efficiency of tourism has significant characteristics in spatial correlation which are still spatial structure with Shanghai strengtheni as the center of the high value ng constantly. Local spatial pattern, which initially formed the agglomeration regions and provinces in Northwest China as the center of low concentrated area, is relatively stable, and has not changed substantively over the observed period. （3） Carbon dioxide emissions efficiency of tourism is affected by a combination of factors. Tourism economic scale, technology effect, the level of urbanization, property right structure of tourism and industrial structure on carbon dioxide emissions efficiency of tourism have a spatial-temporal heterogeneity, which causes the migration and accumulation of dioxide emissions efficiency of tourism.（4）The spatial-temporal heterogeneity are recognized by geographical weighted regression model. The tourism economy scale has an obvious promoting effect on carbon dioxide emissions efficiency of tourism, especially in the Central and Western area. The promoting effect of urbanization gradually is weakening, and with the rapid development of urbanization, the inhibition effect exists in most provinces. The technology effect has also an obvious promoting effect, and the high value region concentrates from the Central and Eastern area to the North and Northeast of China. The property right structure of tourism exhibits a clear south-north division, while it has an obvious promoting effect in Southern and an obvious inhibition effect in Northern China. The industrial structure effect has an obvious promoting effect in the Southwest and an obvious inhibition effect in the North and Northeast of China. These results provide a theoretical basis for implementation of differentiation strategy in order to promote carbon dioxide emissions efficiency of tourism in China.