Abstract:

Climate change is a universal challenge facing mankind around the world. According to the Intergovernmental Panel on Climate Change (IPCC), it is crucial to restrict the increase in global temperatures to no more than 1.5 ℃ above pre-industrial levels. Consequently, mitigating climate impacts and reducing carbon emissions have gained global consensus as integral components of sustainable development. The Chinese government has suggested that to effectively combat climate change, we must prioritize high-quality development, create new quality productive forces that are appropriate for local conditions, actively and steadily advance carbon peaking and carbon neutrality goals, and achieve low-carbon and green development. As a key sector in the nation's economic growth, tourism plays a pivotal role in achieving green and low-carbon transitions. Given that the tourism sector is one of the primary contributors to carbon dioxide emissions worldwide, carbon emissions have received increasing attention. This study, based on the author’s calculations, reveals that China’s overall carbon emissions in 2019 reached 12290 Mt, of which 744 Mt came from tourism, making up 6.05% of the country’s total carbon emissions for the year. Moreover, studies have estimated that by 2025, global tourism carbon emissions will surge to 6500 Mt, with a growth rate exceeding 40%. Therefore, it is practical to determine how to boost the tourism industry while reducing the considerable disparities in economic development, industrial composition, and availability of tourism resources across different regions of the country. China’s regional tourism carbon emissions exhibit a clear spatial imbalance in their distribution. In this context, identifying the characteristics of the spatial distribution of carbon emissions from regional tourism in China is essential for formulating effective emission-reduction strategies. This study establishes a progressive research framework of “Database Construction-Spatial Correlation Deconstruction-Collaborative Strategy Generation”: First, based on the “tourism consumption stripping coefficient” method, the study proposes the “tourism value-added stripping coefficient” method to further optimize tourism carbon emission estimation, accurately measure regional tourism carbon emissions, and provide data support for spatial correlation analysis. Subsequently, from the perspective of the new development philosophy that empowers high-quality tourism development, it integrates the vision of innovative, coordinated, green, open, and shared development as a guarantee for regional high-quality tourism development. Combining these with indicators of industrial development quality and scale, this study innovatively constructs a spatial correlation expansion gravity model for tourism carbon emissions based on high-quality development. Finally, a synergistic effect model was constructed to quantify the degree of synergy in regional tourism’s carbon reduction. Based on the node characteristics of each region in the spatial correlation network and their synergy with neighboring regions, differentiated tourism carbon reduction policies were proposed. The research findings indicate the following: 1) With regard to temporal and spatial evolution characteristics, China’s regional tourism carbon emissions have shown a significant growth trend (2005-2019); however, the growth rate has slowed since 2010, reflecting the inhibitory effect of policy-driven industrial structure transformation on carbon emissions. The spatial focus has undergone a dynamic shift from east to west, forming a phased distribution pattern characterized by “horizontal dispersion—vertical concentration.” However, between 2020 and 2021, due to the impact of the public health event, the spatial focus of China's regional tourism carbon emissions shifted towards the northeast, with the distribution trend exhibiting “horizontal concentration—vertical dispersion.” 2) In terms of network structure evolution, the correlation network displayed a single-core radiating structure centered on the Beijing-Tianjin-Hebei region. However, the overall network density and network correlation degree were relatively low, indicating that the tourism carbon emission correlation network structure was relatively loose and that regional coordination mechanisms need to be strengthened. 3) In terms of individual network characteristics, regions such as Shanghai, Beijing, and Jiangsu have become network hubs thanks to their economic and technological advantages, combining the dual attributes of “pilot zone potential” and “bridge function.” These regions can drive emission reductions in the surrounding areas through pilot demonstrations while promoting optimal resource allocation and information flow, thereby improving network operational efficiency. 4) Regarding synergistic effects, the overall level of synergy in national tourism carbon reduction was relatively low, with significant regional differences. It is imperative that the eastern region strengthen its leading role, the central region consolidate Jiangxi's benchmark effect, the western region enhances cross-regional cooperation, and the northeastern region focuses on internal synergy. Differentiated strategies should be formulated to optimize the overall emission-reduction efficiency. In light of the above findings, this study proposes the following suggestions: First, fostering regional collaboration, including closer ties between core regions such as the “Beijing-Tianjin-Hebei region” and the “Yangtze River Delta” and their surrounding areas, is necessary. Through technology transfer, talent exchange, and resource sharing, the spatial correlation network is expected to be reinforced, thus helping reduce tourism carbon emissions in adjacent areas. In addition, the status of core node regions, including Shanghai, Beijing, and Jiangsu, in the tourism carbon emission spatial correlation network should be further consolidated and enhanced. Support should be provided for establishing pilot platforms for tourism carbon trading in areas such as Beijing and Shanghai and exploring the operation of market mechanisms, including carbon sink trading and carbon inclusion. Finally, a differentiated emission reduction strategy characterized by “Eastern Leadership-Central Benchmarking-Western Collaboration-Northeastern Integration” should be established. Specifically, the eastern region will focus on upgrading the tourism industrial structure and building a modern tourism industrial chain; the central region will deepen the demonstration effect of areas like Jiangxi and create pilot projects for carbon-sink trading; the western region will set up a “carbon-sink+culture and tourism” integrated demonstration zone and establish a cross-regional carbon-offset mechanism; and the northeastern region will construct a “ice and snow tourism+industrial heritage” low-carbon transition zone and promote coordinated governance among resource-based cities.

Key words: Gravitational model, tourism carbon emission, temporal and spatial pattern, network characteristics, Collaborative effect of tourism carbon reduction

摘要：

协同推进碳减排是旅游业实现“双碳”目标和高质量发展的必然选择，然而旅游业碳减排协同效应的经验证据较少。从创新、绿色、协调、共享、开放等5个维度剖析新发展理念，创新构建基于高质量发展的旅游业碳排放空间关联拓展引力模型。并在“旅游消费剥离系数”法基础上，进一步优化旅游业碳排放估算方法，从而更加科学地解释旅游碳排放的网络特征。最后，对旅游碳减排协同效应进行分析。结果表明，1）时空演变上，以2010年为时间节点，可将旅游碳排放增长趋势划分为显著上升期和平缓发展期，并在空间上呈现出“横向分散－纵向集中”趋势，碳排放重心“自东向西”发生偏移。2）整体网络上，中国区域旅游碳排放关联网络始终保持以“京津冀”为中心的“单核”式空间网络结构。3）个体网络特征上，北京、上海、江苏等地区凭借经济、技术等优势成为网络中的核心节点，有望通过自身影响力为空间关联网络稳定运行提供保障。4）旅游碳减排协同效应上，中国区域整体协同水平较低，中、西部地区协同水平较高，东部地区协同水平最低。

关键词: 引力模型, 旅游碳排放, 时空格局, 网络特征, 旅游碳减排协同效应