中国旅游产业集聚与碳排放空间关联性
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摘要
为厘清旅游产业集聚对碳排放的影响程度,采用区位熵和分解加总法分别测算2001—2016年中国旅游产业集聚度和碳排放强度,继而运用重心分析、空间自相关法探究其空间演变及内在关联特征,最后构建二者的回归模型。研究结果表明:(1)旅游产业集聚和旅游业碳排放强度在空间上分布不均衡,旅游产业集聚呈现出东中部高、西部低的分布格局,而旅游业碳排放强度与其相反;(2)旅游产业集聚重心分布在河南省驻马店市与南阳市及其交界地带,其演变轨迹整体向西北小幅偏移,移动距离约为112.362 km;旅游业碳排放强度的重心分布在陕西省商洛市、安康市和湖北省十堰市境内,其重心轨迹整体呈现出南略偏东的态势,移动距离约为256.734 km;(3)旅游产业集聚会显著降低旅游业碳排放强度;旅游产业集聚和旅游业碳排放强度存在空间负相关关系;高集聚-低排放类型主要分布在北京、天津、上海、江苏、浙江、广东、贵州和云南等省区;低集聚-高排放类型分布在新疆、内蒙古、宁夏、甘肃以及青海等省区。
The location quotient and decomposition method are used to estimate the degree of tourism industry agglomeration and the intensity of tourism CO_2 emissions from 2001 to 2016. The approach of the center of gravity analysis and the spatial autocorrelation are applied to explore the spatial evolution and intrinsic correlation. A regression model of two issues is constructed to clarify the impact of tourism industry agglomeration on tourism CO_2 emissions. Results show that:(1) The agglomeration of tourism industry and the intensity of tourism CO_2 emissions are in an unbalance spatial distribution. The agglomeration of tourism industry is characterized by high-value in the east and central part, and low-value in the west, and the intensity of tourism CO_2 emissions is opposite.(2) The concentration of tourism industry is distributed in Zhumadian City and Nanyang City and the junction zone in Henan Province. The overall trajectory of the tourism industry is slightly shifted to the northwest. The moving distance is about 112.362 km. The center of gravity of tourism CO_2 emissions intensity is distributed in Shangluo City, Ankang City in Shaanxi Province,Shiyan City in Hubei Province. The center of gravity of trajectories shows a tendency toward the south to the east. The moving distance is about 256.734 km.(3) The tourism industry agglomeration will reduce the intensity of tourism CO_2 emissions. There is a spatial negative correlation between tourism industry agglomeration and tourism CO_2 emissions intensity. High agglomeration-low emissions are mainly distributed in Beijing, Tianjin, Shanghai, Jiangsu,Zhejiang, Guangdong, Guizhou, Yunnan and so on. Low agglomeration-high emissions are mainly distributed in the northwestern regions such as Xinjiang, Inner Mongolia, Ningxia, Gansu and Qinghai.
The location quotient and decomposition method are used to estimate the degree of tourism industry agglomeration and the intensity of tourism CO_2 emissions from 2001 to 2016. The approach of the center of gravity analysis and the spatial autocorrelation are applied to explore the spatial evolution and intrinsic correlation. A regression model of two issues is constructed to clarify the impact of tourism industry agglomeration on tourism CO_2 emissions. Results show that:(1) The agglomeration of tourism industry and the intensity of tourism CO_2 emissions are in an unbalance spatial distribution. The agglomeration of tourism industry is characterized by high-value in the east and central part, and low-value in the west, and the intensity of tourism CO_2 emissions is opposite.(2) The concentration of tourism industry is distributed in Zhumadian City and Nanyang City and the junction zone in Henan Province. The overall trajectory of the tourism industry is slightly shifted to the northwest. The moving distance is about 112.362 km. The center of gravity of tourism CO_2 emissions intensity is distributed in Shangluo City, Ankang City in Shaanxi Province,Shiyan City in Hubei Province. The center of gravity of trajectories shows a tendency toward the south to the east. The moving distance is about 256.734 km.(3) The tourism industry agglomeration will reduce the intensity of tourism CO_2 emissions. There is a spatial negative correlation between tourism industry agglomeration and tourism CO_2 emissions intensity. High agglomeration-low emissions are mainly distributed in Beijing, Tianjin, Shanghai, Jiangsu,Zhejiang, Guangdong, Guizhou, Yunnan and so on. Low agglomeration-high emissions are mainly distributed in the northwestern regions such as Xinjiang, Inner Mongolia, Ningxia, Gansu and Qinghai.
引文
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