旅游交通碳排放的空间结构与情景分析

旅游业作为全球第一大产业,是影响气候变化的重要因素之一,旅游碳排放的相关研究近年来已经引起学者们的关注.选择了九寨沟风景区、西安碑林博物馆、南京珍珠泉风景区3个旅游交通模式差异明显的案例地为例,根据实地问卷调查数据估算了九寨沟风景区、西安碑林博物馆、珍珠泉旅游风景区2010年的旅游交通碳排放总量分别为654.18,108.44和15.92 Gg.通过比较九寨沟、西安碑林和珍珠泉的碳排放累积曲线,得出不同旅游平均距离的景区的碳排放结构均衡度有所不同,旅游平均距离偏低景区的碳排放结构最不均衡.同时,旅游景区的交通碳排放在距离上具有分段性,不同旅游平均距离的景区交通碳排放的空间结构具有明显的差异性.通过4种不同的交通情景分析发现,旅游平均距离高和距离中等的景区对飞机的碳减排敏感度较高,旅游平均距离偏低的景区自驾车的碳减排效果最为明显.研究结果为旅游管理部门根据碳排放结构有针对性的制定差异化的旅游交通碳减排政策提供了参考和借鉴.

Analysis on spatial structure and scenarios of carbon dioxide emissions from tourism transportation

Tourism is a rapidly increasing industry and has recently attracted attention as an important contributor to climate change through greenhouse gas emissions. Recently, researchers have focused on the impact of carbon emissions from tourism. Among the greenhouse gases, CO₂ is the main contributor to global warming. Previous energy consumption studies have indicated that CO₂ emissions from tourism can be categorized into three sections, namely transportation, accommodation and activities. Among these sections, tourism transportation accounts for the most significant proportion of the energy consumption and CO₂ emissions. This paper focuses on three scenic spots, the Jiuzhaigou Scenic Area, Xi'an Beilin Museum and Nanjing Pearl Spring Resort, and applies a bottom-up approach to determine the CO₂ emissions related to tourism transportation in these areas in 2010. We investigated the visitors to these three scenic spots by questionnaires using stratified sample method, collected 1404 available samples, and estimated the total amount of carbon emissions in each scenic spot. The results indicated that CO₂ emissions from aviation are higher than those of all other transportation methods. The total CO₂ emissions at the Jiuzhaigou Scenic Area, Xi'an Beilin Museum and Pearl Spring Resort were 386.27, 215.71 and 10.45 Gg, respectively. The higher carbon emissions at Jiuzhaigou were attributed to the spot's long average travel distances. To analyze the mechanism of CO₂ emissions from tourism transportation more thoroughly, we used the carbon cumulative curve to describe the spatial structure of the three scenic spots. The carbon cumulative curve analysis suggested that the average travel distance was the most important factor controlling CO₂ emissions, influencing not only the balance structure, but also the spatial structure of CO₂ emissions from tourism transportation. The tourist attractions with low travel distances had the most unbalanced structure of carbon emissions. Also, the spatial structure of carbon emissions in different subsections of travel distance had significant discrepancies. Finally, we completed a scenario analysis to explore policies to reduce CO₂ emissions from tourism transportation. When we compared four different traffic scenarios, we found that the tourist attractions with high and moderate average travel distances were sensitive to a reduction policy that decreased the proportion of emissions from aviation. Therefore, low-carbon travel modes such as the high speed railway could be promoted as an alternative to aviation, and economic compensation such as discounted tickets could be provided for low-carbon tourists to reduce CO₂ emissions. In contrast, tourist attractions with low average travel distances are sensitive to the policy of switching from private cars to public vehicles. Consequently, we can implement reduction policies like encouraging tourists to travel by bus or bicycle and using carpools to decrease the average load factor. These results have important implications for tourism management departments to understand the structure of CO₂ emissions and establish targeted policies to reduce CO₂ emission from tourist transportation.