摘要
文章通过利用长江经济带11个省市2010—2019年旅游业面板数据测算其旅游业碳排放强度,借助SDM模型,探究旅游业碳排放强度的时空演变及影响因素。结果表明:旅游业碳排放强度整体明显降低,其中以云南省、四川省和贵州省的碳排放强度下降幅度最大;碳排放强度在研究时期向着较高碳排放强度区域集中在中游地区的趋势发展,呈现碳排放强度“中间高,两侧低”的空间格局;产业结构与贸易依赖水平的间接效应和总效应分别表现出显著的抑制、促进效应,表明产业结构优化显著促进本地及相邻地区的旅游业碳排放强度下降,而贸易依赖水平提升效果相反。最后,文章对所得结论提出建议,旨在为旅游业实现低碳科学发展提供决策参考。
This paper calculates carbon emission intensity of tourism by using the panel data of tourism in 11 provinces and cities along the Yangtze River Economic Belt from Year 2010 to 2019.Besides,the temporal and spatial characteristics and influencing factors of tourism carbon emission intensity are explored with SDM model.The results reveals the following:(1)Overall tourism carbon emission intensity of the Yangtze River Economic Belt has been significantly reduced.Among them,Yunnan Province,Sichuan Province and Guizhou Province show the largest decline in carbon emission intensity.(2)Carbon emission intensity of the Yangtze River Economic Belt tends to focus on mid-stream region with higher carbon emission intensity,presenting a spatial pattern of carbon emission intensity“high in the middle and low on both sides”.(3)Both indirect effect and total effect of industrial structure and trade dependence level obviously show inhibition effect and promotion effect respectively,indicating that the optimization of industrial structure significantly promotes decline of tourism carbon emission intensity in local and adjacent areas,and increase of trade dependence level will lead to the opposite effect.Finally,recommendations are put forward to the conclusions obtained,aiming to provide a decision-making basis for scientific low-carbon development of tourism industry.
作者
伍卓
林宣任
WU Zhuo;LIN Ren-xuan(University of South China,Hengyang 421001,China)
出处
《南华大学学报（社会科学版）》
2022年第3期25-36,共12页
Journal of University of South China(Social Science Edition)
基金
湖南省教育厅科学研究优秀青年项目“供给侧改革背景下中国制造业嵌入全球价值链的碳排放效应与减排策略研究”资助(编号:19B493)
湖南省社会科学成果评审委员会项目“湖南省贸易隐含碳排放实证评估及其碳减排责任研究”资助(编号:XSP19YBC028)。
关键词
长江经济带
碳排放强度
时空演变
影响因素
SDM模型
the Yangtze River Economic Belt
tourism carbon emission intensity
temporal and spatial characteristics
influencing factors
SDM model
