旅游碳足迹空间化

Reducing carbon emissions and transitioning to a low-carbon economy are important propositions for human sustainability. Since it is closely related with high carbon emissions, international travel makes a substantial contribution to the global carbon emissions. To comprehensively explore the influence of international travel on carbon emissions and develop a sustainable development plan, this paper studies the temporal and spatial distribution and evolution of the carbon footprint of inbound tourism in China's 30 provinces between 2007 and 2017. In this study, comprehensive calculations and spatial models are adopted to reveal the temporal and spatial characteristics. The results show that the carbon footprint of inbound tourism in China has been increasing continuously from 2007 to 2017. While the carbon footprint increased by 1.94-fold, from 5.623 million tons to 10.8809 million tons, it presented obvious fluctuations by initially increasing rapidly and then dropping slightly. From the perspective of the contributions of various tourism components on the carbon footprint, transportation and post and telecommunications account for the largest proportions. In the past ten years, the variations in the carbon footprint of inbound tourism in most provinces and cities in China were not very extreme, but maintained a relatively stable state. In the spatial dimension, the carbon footprint of China's inbound tourism tends to decrease from the southeast to the northwest. The highest coefficient of variation is in Ningxia and the lowest is in Liaoning. Based on these results, recommendations are put forward for sustainable development plans in some major cities and provinces for the future.

Beijing is an important hub for global tourism, but the extent of tourism’s contribution to Beijing’s carbon footprint remains unclear. We integrated an environmentally extended multiregional input–output model and the tourism satellite account in a study to estimate the dynamics of Beijing’s tourism-related carbon footprint in the post-financial crisis period. Our findings indicate that from 2007 to 2012, whereas the carbon footprint of inbound tourists in Beijing steadily decreased, that of domestic tourists increased. The composition of carbon footprints for the consumption activities of inbound and domestic tourists differed substantially. We also traced the spatial distribution of carbon sources associated with tourism consumption in Beijing. In light of our findings, we offer recommendations to target the adoption of low-carbon consumption patterns by domestic tourists, and energy optimization of service suppliers by increasing energy use efficiency and the renewable energy ratio. In addition, we recommend that public and government should seek to lower energy costs and reduce carbon emissions throughout the life cycle of commodities. We conclude that the government and tourism authorities should actively promote carbon and wider environmental awareness, and that producers must seek to improve the efficiency of their energy use by reducing carbon emissions at source.

No abstract

The tourist carbon footprint (TCF) is the measure of the total amount of carbon dioxide (CO 2) tourists emit by travelling from origin to destination and by participating in tourism – and leisure – related activities considering all relevant sources, sinks and storage within the spatial boundary of the destination. This paper presents a method of assessing the part of TCF associated with tourist transport at the tourist destination and proposes iso-pollutant contours as the most effective method of mapping TFC in relation to hotel location by using the prefecture of Attica in Greece as an example. The paper demonstrates for the first time how important is hotel location as a determinant factor of TCF and also proposes measures to reduce CO 2 emissions through the implementation of policies that are environmentally friendly and are aiming to facilitate the transport of the tourists and promote the use of public transport.

As a nature reserve with extremely vulnerable ecological system, Jiuzhai Valley must strike a balance between climate change, tourism growth and environmental protection in the future development. Carbon footprint is a key tool to measure the environmental impact of human activities. The key factors of carbon emission reduction in Jiuzhai Valley Nature Reserve can be found through carbon footprint evaluation and targeted carbon emission reduction measures can be put forward. In this paper, the carbon emission inventory based on mixed life cycle is introduced to calculate the carbon emissions within the boundary of Jiuzhai Valley Nature Reserve, evaluate its carbon footprint and find out its spatial distribution pattern in order to find out the key factors and links in the improvement of the low-carbon management in Jiuzhai Valley. The study found that the commercial activities of operating companies providing services to tourists (such as transportation and catering) are a major part of carbon emissions, followed by tourist tours and the management and maintenance activities of the Authority, the residential carbon emissions which come from the village daily life and private car driving. The low carbon emission reduction in Jiuzhai Valley should focus on the optimization of land use, the control of the number of residents and tourists, and the sharing of responsibility for emission reduction.

Purpose This study explores the spatial and temporal relationship between tourism activities and transportation-related carbon dioxide (CO 2 ) emissions in the United States (US) from 2003 to 2022 using advanced geospatial modeling techniques. Design/methodology/approach The research integrated geographic information systems (GIS) to map tourist attractions against high-resolution annual emissions data. The analysis covered 3,108 US counties, focusing on county-level attraction densities and annual on-road CO 2 emission patterns. Advanced spatial analysis techniques, including bivariate mapping and local bivariate relationship testing, were employed to assess potential correlations. Findings The findings reveal limited evidence of significant associations between tourism activities and transportation-based CO 2 emissions around major urban centers, with decreases observed in Eastern states and the Midwest, particularly in non-coastal areas, from 2003 to 2022. Most counties (86.03%) show no statistically significant relationship between changes in tourism density and on-road CO 2 emissions. However, 1.90% of counties show a positive linear relationship, 2.64% a negative linear relationship, 0.29% a concave relationship, 1.61% a convex relationship and 7.63% a complex, undefined relationship. Despite this, the 110% national growth in tourism output and resource consumption from 2003–2022 raises potential sustainability concerns. Practical implications To tackle sustainability issues in tourism, policymakers and stakeholders can integrate emissions accounting, climate modeling and sustainability governance. Effective interventions are vital for balancing tourism demands with climate resilience efforts promoting social equity and environmental justice. Originality/value This study’s innovative application of geospatial modeling and comprehensive spatial analysis provides new insights into the complex relationship between tourism activities and CO 2 emissions. The research highlights the challenges in isolating tourism’s specific impacts on emissions and underscores the need for more granular geographic assessments or comprehensive emission inventories to fully understand tourism’s environmental footprint.

This study investigates the influence of urban tourists’ behaviour on the environmental performance of a destination, particularly in terms of carbon emissions. Tourist-related emissions are shaped by their choices and behaviours, impacting the overall carbon footprint of the locations they visit. To assess this impact, we introduce a methodology for quantifying greenhouse gas emissions linked to tourists’ energy consumption. This approach considers key tourism components—activities, accommodation, and transportation—analysing their roles in emissions across a trip’s temporal and spatial dimensions. By integrating tourists’ spatiotemporal behaviour with emissions data, our framework offers insights that can support local climate-responsive urban and tourism policies. We empirically apply the proposed model to the destination of Donostia/San Sebastián (Spain), where the primary travel sequences of visitors are analysed. We utilise cartographic techniques to map the environmental footprints of different tourist profiles, such as cultural and nature tourists. The findings indicate that visitors primarily motivated by nature and outdoor recreation constitute the segment with the highest greenhouse gas emissions (with a minimum footprint of 30.69 kg CO2-equivalent per trip), followed by cultural tourists, and finally, other categories of visitors. The results highlight the practical applications of the proposed model for sustainable tourism management, providing valuable guidance for urban planners and policymakers in mitigating the environmental impacts of tourism.

Purpose – The research is based on the thesis that if the impacts of tourism on the environment were reduced to a local carrying capacity of the environment, then the global impact of tourism on the environment would be fully sustainable. In this light, the purpose of this research is to measure the local impact of tourism related road traffic on the environment on the example of the Učka Nature Park. Methodology – The carbon footprint of road traffic in the Učka tunnel was calculated using the carbon footprint methodology. The footprint was measured in the period from 2015 to 2020 on a monthly basis in order to gather database for analysing the seasonality of CO2 emissions, taking into account the local biocapacity of the environment. Findings – The total carbon footprint of traffic in the Učka tunnel from 2015 to 2020 is the result of an average volume of 3,204,375 vehicles per year. This amount of road traffic emitted an average of 2934.3 tons of CO2 per year. On a yearly basis 4.45% of the total biocapacity of the Učka Nature Park or 687.9 lha is needed to absorb carbon emissions from the Učka tunnel. The share of tourism in the total carbon footprint of road traffic in the Učka tunnel during the observed period at the annual level is 30.5%. Contribution – The paper contributes to the discussion of the local impact of tourism related to road traffic. Specifically, the paper aims to raise awareness and encourage the scientific community to research more local case studies that will measure the concrete impact of tourism on the environment. The applied contribution of the work is expressed through the measured value of the total and specifically separated tourist carbon footprint and contributes to the expansion of the database that would enable objective, measurable and sustainable spatial management.

No abstract

No abstract

Reducing carbon emissions is crucial to the sustainable development of tourism. However, there are no consistent conclusions about the nexus between tourism and carbon emissions. Considering the possible nonlinear and spatial effects of tourism on carbon emissions, this paper employed spatial econometric models combined with quadratic terms of explanatory variables to explore the nexus between them using Chinese provincial panel data from 2003 to 2016. The main results are as follows: (1) There is a significant inverse U-shaped relationship between tourism development and carbon emissions. In the provinces whose tourism receipts are relatively low, the effects of tourism on carbon emissions are positive but decrease gradually as the tourism receipts increase and then shifts to negative and continues decreasing gradually when the tourism receipts beyond the critical value. (2) For the geographical proximity and industrial relevance, one province's tourism development not only affects its carbon emissions but also affects its neighbors' carbon emissions through spatial lag effect (indirect effect) which is also inverse U-shaped. (3) Carbon reduction policies, sustainable education, and transportation infrastructure all have significant moderating effects on the relationship between tourism and carbon emissions, but the moderating effect of the management efficiency of tourism is not statistically significant. Furthermore, improvements to the sustainable education and transportation infrastructure not only strengthen the direct negative effect of tourism on carbon emissions but also strengthen the indirect negative effect of tourism on carbon emissions. This study not only advances the existing literature but is also of considerable interest to policymakers.

Tourism-with its social, economic, and ecological dimensions-can be an important driver of sustainable development of alpine communities. Tourism is essential for local people's incomes and livelihoods, but it can also have a major impact on the local environment, landscape aesthetics, and (mainly through tourist transport) global climate change. A project currently underway is developing the Austrian mountain municipality of Alpbach into a role model for competitive and sustainable year-round alpine tourism using an integrated and spatially explicit approach that considers energy demand and supply related to housing, infrastructure, and traffic in the settlement and the skiing area. As the first outcome of the project, this article focuses on the development of the Model of Alpine Tourism and Transportation, a geographic information system-based tool for calculating, in detail, energy consumption and greenhouse gas emissions resulting from travel to a single alpine holiday destination. Analysis results show that it is crucial to incorporate both direct and indirect energy use and emissions as each contributes significantly to the climate impact of travel. The study fills a research gap in carbon impact appraisal studies of tourism transport in the context of alpine tourism at the destination level. Our findings will serve as a baseline for the development of comprehensive policies and agendas promoting the transformation toward sustainable alpine tourism.

No abstract

Anthropogenic climate change is an imminent threat. In order to curb the effects of climate change, economic industries including tourism must assess their contributions to the overall phenomenon and develop creative solutions. As carbon dioxide and other greenhouse gas emissions represent a major reason why global tourism is contributing to climate change, carbon footprinting can help identify which aspects of individual tourism events are least sustainable. This case study seeks to assess the total carbon footprint of four seasons of American college football. The fan journey to the college football spectate represents a tourism experience and therefore can and should be assessed for its ecological impact. The subject of this case study is the University of Tennessee, an institution with one of the largest football stadiums in the United States. Using an extensive geographic sample of ticketing data from Tennessee’s home games during the 2015–2018 seasons, a carbon footprint was estimated for each game, each contributing polluter, and each season. The total season footprint over the four years was estimated to be 154,717,114 kg CO2eq. This study presents both a methodology for studying spectator sporting events in sport tourism and a framework by which tourism can begin to assess its contributions to the global carbon footprint. It also demonstrates grounded consequences for often trivialized ideas of fandom and place-based identity. Additionally, it highlights the need for tourism organizations and governments to consider policy and management practices that account for and reduce the impact of greenhouse gas emissions.

This study conducts in-depth research on geographic information visualization and the sustainable development of low-carbon rural slow tourism under artificial intelligence (AI) to analyze and discuss the visualization of geographic information and the sustainable development of low-carbon slow tourism in rural areas. First, the development options related to low-carbon tourism in rural areas are discussed. Then, a low-carbon rural slow tourism recommendation method based on AI and a low-carbon rural tourism scene recognition method based on Cross-Media Retrieval (CMR) data are proposed. Finally, the proposed scheme is tested. The test results show that the carbon dioxide emissions of one-day tourism projects account for less than 10% of the total tourism industry. From the proportion, it is found that air transport accounts for the largest proportion, more than 40%. With the development of time, the number of rural slow tourists in Guizhou has increased the most, while the number of rural slow tourists in Yunnan has increased to a lesser extent. In the K-means clustering model, the accuracy of scenario classification based on the semantic features of scene attributes is 5.26% higher than that of attribute likelihood vectors. On the Support Vector Machine classifier, the scene classification accuracy based on the semantic features of scene attributes is 19.2% higher than that of the scene classification based on attribute likelihood vector features. CMR techniques have also played a satisfying role in identifying rural tourism scenarios. They enable passengers to quickly identify tourist attractions to save preparation time and provide more flexible time for the tour process. The research results have made certain contributions to the sustainable development of low-carbon rural slow tourism.

Urban-intensive areas are responsible for an estimated 80% of greenhouse gas emissions, particularly carbon dioxide. The urban–rural fringe areas emit more greenhouse gases than urban centers. The purpose of this study is to analyze the spatial pattern and driving factors of carbon emissions in urban–rural fringe mixed-use communities, and to develop planning methods to reduce carbon emissions in communities. This study identifies mixed-use communities in East Asian urban–rural fringe areas as industrial, commercial, tourism, and rental-apartment communities, subsequently using the emission factor method to calculate carbon emissions. The statistical information grid analysis and geographic information systems spatial analysis method are employed to analyze the spatial pattern of carbon emission and explore the relationship between established space, industrial economy, material consumption, social behavior, and carbon emission distribution characteristics by partial least squares regression, ultimately summing up the spatial pattern of carbon emission in the urban–rural fringe areas of East Asia. Results show that (1) mixed-use communities in the East Asian urban–rural fringe areas face tremendous pressure to reduce emissions. Mixed-use community carbon emissions in the late urbanization period are lower than those the early urbanization. (2) Mixed-use community carbon emission is featured by characteristics, such as planning structure decisiveness, road directionality, infrastructure directionality, and industrial linkage. (3) Industrial communities produce the highest carbon emissions, followed by rental-apartment communities, business communities, and tourism communities. (4) The driving factor that most affects the spatial distribution of carbon emissions is the material energy consumption. The fuel consumption per unit of land is the largest driver of carbon emissions. Using the obtained spatial pattern and its driving factors of carbon emissions, this study provides suggestions for planning and construction, industrial development, material consumption, and convenient life guidance.

Purpose Although extensive studies have examined the link between tourism and carbon emissions, the impact of tourism on carbon emissions remains controversial. In contrast to prior studies, this study aims to investigate the effects of tourism on carbon emissions at the city level and the underlying moderating mechanism. Design/methodology/approach This study designs an econometric model drawing on panel data for 313 city-level regions in China from 2001 to 2019. This study also performs rigorous robustness tests to support the regression results. In addition, the temporal and spatial heterogeneity is analyzed based on which this study discusses the moderators of the effects of tourism on carbon emissions. Findings The results show that both tourist arrivals and tourism revenue significantly impact carbon emissions. Also, there exists a significant temporal and spatial heterogeneity of these effects. Economic development significantly enhances while green technology and tertiary industry development suppress the positive relationship between tourism and carbon emissions. Moreover, regarding the impact on carbon emissions, an explicit substitution exists between tourism and tertiary industry development. Originality/value For the first time, this study quantitatively estimates the moderators of tourism’s impact on carbon emissions and concludes the moderating effects of economic growth, technological progress and industrial structure, thus furthering the theoretical understanding of the heterogeneity of tourism’s association with carbon emissions. The study also fills a technical gap in previous studies by demonstrating the reliability of the findings through various robustness tests. This is also the first empirical study to systematically examine the relationship between tourism and carbon emissions in China.

Tourism is a non-negligible source of greenhouse gas (GHG) emissions. Using South Tyrol (ST) – a small region with a tourism-intensive economy situated in the North of Italy – as a case study, this article discusses a multiregional input–output (MRIO) framework for calculating the direct and indirect emissions embodied in tourist consumption of goods and services at a subnational level. Compared to more standard single-region implementations of the input–output approach, MRIO analysis offers a more accurate depiction of the amount of emissions, that is, embodied in imports, because it acknowledges that in the modern economy supply chains often stretch across multiple borders and that the carbon intensity of production can vary widely from one location to another. Operationalizing the framework has become relatively straightforward since a number of new global MRIO databases have become available in recent years. Furthermore, the analysis could easily be extended to other environmental externalities of tourism, where the model’s capability to explicitly account for spatial spillovers might also be of interest. The modelling exercise at the heart of the article suggests that, over the course of 2010, the process of producing the goods and services consumed by tourists in ST resulted in 1092 kt CO 2 e of GHGs being emitted into the atmosphere. This is equivalent to average emissions of 191 kg CO 2 e per overnight visitor, 38 kg CO 2 e per night or 0.316 kg per euro of tourist expenditure. Direct emissions account for about one-fourth of the total. Almost four-fifths of total emissions appear to be the result of productive activities sited outside ST itself.

Ecosystem Services (ESs) assessment is increasingly considered the constitutive metric to embrace the social, ecological, and economic spheres. Spatially explicit ES assessments can integrate and standardize different types of information, making them comparable. In this context, a multiple coastal and marine ESs assessment in the Northern-Central Adriatic Sea was carried out, considering seven ESs. Two cultural (tourism and recreational boating), two regulating (carbon sequestration and coastal erosion prevention potential), and three provisioning (mussel, whitefish aquaculture, and industrial fishery) ESs have been measured. The spatial analysis described (un)sustainable human uses of ecosystems in the area. (De)coupling of ES capacities and flows and synergies and tradeoffs among ESs were analyzed. Results indicate spatial agreement for capacities, while contrasting results emerged from the analysis across flows and of the capacity-flow balance. The evidence of a geographical pattern and areas of high, medium, and low capability to provide ESs across the study area was highlighted, suggesting the need for implementing the natural resources management. Some coastal provinces maximize a single ES at the detriment of other ESs, and other provinces built mimics of Nature through artificial facilities. These strategies are not far-sighted in the view of conserving the supply of the whole ESs set. These findings might be useful in the context of the Marine Strategy Framework Directive (MSFD), and for the implementation of the Maritime Spatial Planning (MSP) in the Northern-Central Adriatic Sea.

The tourism economy is regarded as an effective way to realize regional sustainable development. Hence, it is of great significance to explore whether and how tourism economy can alleviate regional carbon emission intensity. To this end, a structural equation model (SEM) reflecting the multiple pathways of the carbon emission reduction effect of tourism economy was constructed based on 92 tourism-dependent cities in China, and the existence and formation mechanism of the carbon emission reduction effect of tourism economy were empirically tested. The main findings are as follows: (1) The tourism economy has a significant carbon emission reduction effect in China. Although the direct impact of tourism economy on carbon emission intensity is significantly positive, the indirect impact is significantly negative and stronger than the direct impact. (2) The carbon emission reduction effect of tourism economy presents multiple pathways characteristics. There are single intermediary pathways such as Tourism Economy → Environmental Regulation → Carbon Emission Intensity, Tourism Economy → Opening-Up → Carbon Emission Intensity, and dual intermediary pathways such as Tourism Economy → Opening-Up → Industrial Development → Carbon Emission Intensity, Tourism Economy → Opening-Up → Innovation Capacity → Carbon Emission Intensity. (3) The formation mechanism of the carbon emission reduction effect of tourism economy presents obvious spatial heterogeneity.

Investment and construction of energy and transport-related infrastructure are closely linked to the achievement of sustainable development goals. China’s infrastructure-led foreign investment, technical integration, and tourism with Belt and Road Initiative (BRI) countries have maintained exponential growth. This growth certainly has an impact on economic development mode and environmental sustainability. Therefore, this study examines the impact of infrastructure-led Chinese outward foreign direct investment, tourism development, and technology innovation on carbon emissions across the selected BRI node countries and respective regions. This study employs cross-sectional autoregressive distributive lag model to deal with parameters and cross-sectional heterogeneity. The results exhibit that foreign direct investment and technology innovation reduces carbon emissions in the long run, while tourism development and its interaction with foreign direct investment led to higher emissions in the overall BRI sample. In contrast, the regional estimates show significant variations in the magnitude and direction of the relationship, where foreign direct investment produces an emissions-increasing effect in South Asian and MENA countries. Moreover, the results support the validity of the Environmental Kuznets Curve hypothesis in overall and regional samples. These results are also endorsed by common correlated effects means group estimator and imply relevant policies.

This study analyses sustainability and competitiveness through measurements of efficiency, using data envelopment analysis. It constructs a meta-frontier non-radial directional distance function (meta-frontier NDDF) approach, which is then used to define a tourism development index and a tourism sustainability index. Using these indexes, the paper evaluates the efficiency of the tourism sector and its dynamic evolution for 27 cities in the Yangtze River Delta, China, (YRD) from 2010 to 2019. Considering regional heterogeneity, this paper analyzes the meta-frontier, group-frontier efficiency and technology gap ratio of urban tourism in the YRD, and explores the competitiveness of the cities. The results show that the more traditional measure of tourism efficiency, namely the tourism development index, which does not take account of the sector’s undesirable output (i.e., the negative impacts of carbon emissions from travel), produces overestimates. This study highlights the following practical implications: The increasing competition among tourism destinations requires tourism industry managers to determine the appropriate allocation of resources to promote the sustainable development of urban tourism. In the context of the need for global ‘carbon neutrality’, more consideration should be given to the negative impact of tourism on the natural environment to enhance the competitiveness of tourist destinations.

The tourism industry is seen as having great potential, but tourism development and tourism activities may increase energy consumption and environmental pressure. Based on the provincial panel data of China from 2000 to 2017, we calculate the energy and carbon emission performance by using the non-radial distance function (NDDF) and further investigate the impact of tourism industry agglomeration on energy and carbon emission efficiency by combining the panel fixed effect model, mediation effect model and quantile regression. Our research results show that there is an inverted U-shaped relationship between tourism industry agglomeration and energy and carbon emission efficiency, and tourism industry agglomeration can improve energy and carbon emission efficiency at present. At the same time, the impact of tourism industry agglomeration on energy and carbon emission efficiency has regional heterogeneity. The industrial structure upgrading plays an important role in the process of tourism industry agglomeration. In addition, with the improvement of energy and carbon emission performance, the impact of tourism industry agglomeration is also different. These findings suggest that policymakers should promote tourism industry agglomeration to realize energy conservation and emission reduction. The Chinese government should focus on the tourism resources and advantages of different regions and formulate differentiated regional policies to improve ecological performance.

The purpose of this study is to analyze the regional differences and spatial-temporal evolutionary characteristics of carbon dioxide emissions of tourism in China during 2001–2022 from the perspective of carbon sink effect by using an econometric modeling approach. The findings show that CO2 emissions from China's tourism industry exhibit non-equilibrium spatial characteristics or non-linear relationships. Secondly, the results of Theil's index show that the intra-regional differences in tourism CO2 emissions are greater than the inter-regional differences and exhibit a gradient of ‘West > Central > East’. Finally, under the condition of considering the existence of spatial regional heterogeneity, it is found that tourism CO2 emissions have significant spatial agglomeration, with high CO2 emission regions primarily clustered within the eastern region and the economically developed regions, and low CO2 emission regions distributed centrally within the central region and less developed areas of the tourism economy. It is worth noting that the intra-regional differences mainly come from the eastern belt, and that there are large regional differences in tourism CO2 emissions within the eastern region, while the western region is the next largest and the central region the smallest.

No abstract

No abstract

This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO 2 ) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.

High-quality tourism development under the “double carbon” target (the peaking of carbon emissions and achievement of carbon neutrality) is an important path to achieving low-carbon emissions in the tourism industry and is vital for improving the industry’s carbon emissions efficiency. Using spatial and temporal panel data for 11 prefecture-level cities in Jiangxi Province from 2000 to 2020, a spatial Durbin model and a threshold model were constructed to assess the spatial spillover and threshold effects that high-quality tourism development has on the carbon emission efficiency of the tourism industry. The three key results were as follows. (1) There is a non-linear relationship between the carbon emission efficiency of tourism and the high-quality development trend of tourism, with differences in spatial distribution. (2) Coordinated development, green development, and open development all have significant positive direct effects on the carbon emission efficiency of tourism. Innovation-driven and coordinated development have a positive spillover effect on the carbon emission efficiency of tourism. In contrast, green development, open development, and shared results have a negative spatial spillover effect. (3) When the scale of the tourism economy crosses the first threshold in the second stage and the structure of tourism investment crosses the second threshold in the third stage, the ability of the tourism quality development to enhance the tourism carbon emission efficiency is the largest. When the tourism investment structure and tourism carbon emission intensity cross a single threshold, the role of the tourism quality development level in enhancing the tourism carbon emission efficiency decreases. Accordingly, three types of countermeasures are proposed: solving development problems, tapping into positive spillovers, and scientifically describing the impact of thresholds. The ultimate goal of this is to provide theoretical references and innovative ideas for promoting green, low-carbon, and high-quality development of tourism in Jiangxi Province and elsewhere.

No abstract

在碳中和、碳达峰的时代背景下，提高旅游业碳排放效率对实现旅游产业高质量发展具有重要的实践价值。基于2001-2018年中国省际（自治区、直辖市）面板数据，首先利用区位熵和Super-SBM模型分别测算中国省际旅游产业集聚水平和旅游业碳排放效率，并探究二者空间演变趋势和关联特征；其次，运用空间杜宾模型分析旅游产业集聚对旅游业碳排放效率的影响及其空间溢出效应。结果表明：（1）研究期内，中西部地区的旅游产业集聚水平明显提高，东部地区则无明显变化；除河北省、山西省、内蒙古自治区等地区外，其他省份的旅游业碳排放效率均无显著变化。整体上看，二者高水平地区的空间分布变化大致均呈现出以现有集聚区为中心向周边扩散的趋势。（2）旅游产业集聚能显著提高旅游业碳排放效率，并且具有正向空间溢出效应，而旅游业碳排放效率的负向空间溢出效应则会抑制其他地区旅游业碳排放效率的提高。（3）经济发展、产业结构、城镇化、对外开放、技术进步和环境规制均能不同程度促进旅游业碳排放效率，但城镇化作用效果不显著，旅游业产权结构则显著抑制旅游业碳排放效率，经济发展和城镇化均具有正向空间溢出效应，产业结构呈现出较强的负向空间溢出作用，技术进步和对外开放的正向空间溢出效应并不显著。

Tourism green production efficiency serves as the foundation for assessing the mutual coupling performance of the tourism economy and the ecological environment. In this paper, the tourism carbon sink is included in the measurement framework, and the TGPE of 41 cities in the Yangtze River Delta region from 2011 to 2019 is estimated by the Super-SBM model. Furthermore, kernel density estimate, spatial autocorrelation, Markov chain and spatial Durbin model are further integrated to explore its spatio-temporal evolution process, spatial effects and influencing factors. The results show that 1) TGPE in the Yangtze River Delta has been increasing during the study period. The high-efficiency and low-efficiency areas of the TGPE have a bipolar pattern characterized by “low–low convergence” and “high–high convergence.” 2) There is considerable spatial variation in TGPE from north to south. The number of hot spots and sub-hot spots increases in volatility, whereas the number of sub-cold spots and cold spots decreases. 3) Although cities with low levels of TGPE have a higher probability of moving to the next level, grade transformation across hierarchies is difficult to attain. When considering the factor of adjacent types and the influence of spatial lag on the transfer probability. 4) The positive spatial spillover effects of TGPE is significant. At the same time, economic development level, transport accessibility and tourism industry agglomeration have positive spillover effects on neighboring cities. Conversely, urbanization level and openness level have negative spillover effects.

In recent years, China has increasingly emphasized green development. Therefore, it is of theoretical and practical significance to study the green economic effect and carbon reduction effect of tourism development for the transformation of economic development. Using the superefficient EBM to measure the green economic efficiency of 280 cities from 2007–2019, we rely on the spatial Durbin model to explore the spatial spillover utility and nonlinear characteristic relationship of tourism development on green economic efficiency and carbon emission intensity and test the mediating effect of carbon emission intensity. The findings are as follows: (1) Under the exogenous shock test of the “low-carbon city” pilot policy, the spatial spillover effect of tourism development on urban green economic efficiency and carbon emission intensity is robust to spatial heterogeneity. (2) The spatial spillover effects of tourism development on the green economic efficiency and carbon emission intensity of cities show a nonlinear characteristic relationship of “U” and “M” shapes. After tourism development reaches a certain high level, the green economy effect and carbon emission reduction effect are significantly increased. (3) Carbon emission intensity has a significant mediating effect on the impact of tourism development on urban green economic efficiency.

Tourism ecological security is an important basis for measuring the realization of the “double carbon” goal of regional tourism. Based on the drivers, pressures, state, impact and response model of intervention (DPSIR), an evaluation index system of tourism ecological security in the old revolutionary region of the Dabie Mountains is constructed. The entropy technique for order of preference by similarity to ideal solution (TOPSIS) method, spatial variation model, standard deviation ellipse model and gray dynamic model are used to explore the spatial and temporal evolution characteristics of the tourism ecological security level in the old revolutionary region of the Dabie Mountains from 2001 to 2020, and to forecast its future spatial development pattern. The study shows that (1) the average value in tourism ecological security in that region is 0.3153. Moreover, the comprehensive index increased from 0.2296 in 2001 to 0.4302 in 2020, which shows a steady improvement. The security status has improved from insecure to critically secure; (2) the number of municipalities that are insecure or relatively insecure in the region is gradually decreasing, while the number of municipalities that are located within critically secure and relatively secure cities and towns in the region is gradually decreasing. Moreover, an increasing number of cities and towns are critically secure and safe, and the whole region is now in the critical transition period between an average to low level to an average to high level of tourism ecological security; (3) the degree of spatial variation in tourism ecological security is increasing, the features of spatial differentiation are more obvious, and the overall spatial pattern of “Hubei > Henan > Anhui” is presented. (4) The spatial distribution pattern for tourism ecological security is “southeast-northwest”, and the spatial distribution range has undergone the process of “convergence to diffusion”. (5) The spatial distribution pattern in tourism ecological security is “southeast-northwest”, and the spatial distribution range has undergone a process of “convergence to diffusion”. This shows expansion toward the southeast that reflects a certain spatial spillover effect and “convergence” toward the northwest, with no obvious spatial spillover effect.

The flows of people and material attributed to international tourism exert a major impact on the global environment. Tourism carbon emissions is the main indicator in this context. However, previous studies focused on estimating the emissions of destinations, ignoring the embodied emissions in tourists' origins and other areas. This study provides a comprehensive framework of a tourism telecoupling system. Taking China's international tourism as an example, we estimate the carbon emissions of its tourism telecoupling system based on the Tourism Satellite Account and input-output model. We find that (1) the proposal of a tourism telecoupling system provides a new perspective for analyzing the carbon emissions of a tourism system. The sending system (origins) and indirect spillover system (resource suppliers) have been ignored in previous studies. (2) In the telecoupling system of China's international tourism, the emission reduction effect of the sending system is significant. (3) The direct spillover system (transit) and indirect spillover system's spatial transfer effects of environment responsibility are remarkable. (4) There is a large carbon trade implied in international tourism. This study makes us pay attention to the carbon emissions of tourists' origins and the implied carbon trading in tourism flows.

In recent years, with the continuous improvement in the economic conditions of our people, people pay more and more attention to the spiritual aspect of consumption. Therefore, tourism has developed by leaps and bounds, and the tourism economy has become an important form of economic growth in China. However, as the global climate continues to deteriorate, people have begun to seek a sustainable development path, and the concept of low carbon tourism has been put forward, which requires hotels to make certain changes in their management mode in order to adapt to the concept of low carbon tourism in the new era. Since carbon trading is an important means for the promotion of carbon dioxide emission reduction, this paper explores the emission reduction effect and transmission mechanism of the carbon trading pilot through a spatial double difference model based on the study of spatial characteristics. The experiment shows that carbon trading not only effectively promotes local CO2 emission reduction, but also has a certain spillover effect on the surrounding areas. In addition, carbon trading can promote the economic growth of the pilot areas and the neighboring regions, and drive CO2 emission reduction at the same time. The paper concludes with an analysis of how to strengthen policy and behavioral guidance, improve government regulatory mechanisms, reduce environmental pollution in hotel tourism, and ensure that the model of hotel management meets the needs of the industry from the perspective of low carbon tourism under the situation of information symmetry and asymmetry.

The ability of a low-carbon strategy to promote tourism development is critical to achieving economic development and environmental protection. Taking China's low-carbon city pilot (LCCP) policy as a quasi-natural experiment, this study evaluates the low-carbon strategy's effect on tourism development by adopting the difference-in-difference method with 275 cities'panel data from 2002 to 2019. The results verify that tourism has significantly developed, driven by the LCCP policy, and this finding is robust even after multiple tests. Mechanism and heterogeneity analyses show that LCCP policy drives tourism development by improving air quality, promoting industrial structure upgrading, and enhancing green technology innovation. Additionally, the pilot construction has a positive spatial spillover impact on the tourism industry in surrounding cities, reflecting the demonstration effect of the pilot city on the neighbouring cities.

The tourism industry’s explosive growth has triggered severe carbon emission issues, making enhancing tourism carbon efficiency (TCE) a pressing concern for achieving sustainable tourism development. The widespread application of artificial intelligence (AI) in tourism presents new opportunities. This study applies the Environmental Kuznets Curve (EKC) theory to examine the pathways and mechanisms of AI’s impact on TCE, with a focus on China. The findings reveal that AI significantly enhances TCE, where improvements in tourism labor productivity, the rationalization of the tourism industry structure, and advancements in tourism technology are the key channel mechanisms. Heterogeneity tests indicate that AI substantially boosts TCE in eastern developed regions and areas with deficient tourism resource endowments. Furthermore, AI exhibits significant spatial spillover effects, enhancing both local and neighboring regions’ TCE. These insights provide crucial policy implications for utilizing AI to promote China’s sustainable tourism industry.

No abstract

No abstract

No abstract

China's Low-Carbon City Pilot (LCCP) policy is an important emission reduction measure, and its development is of great significance for enhancing the ecological efficiency of tourism industry. This study, for the first time at the urban level, adopts Difference-in-Differences (DID) and spatial DID models to systematically analyze the impact of the LCCP policy on ecological efficiency of tourism. The study effectively addresses the issue of endogeneity and reveals the spatial spillover effects and regional heterogeneity, providing new theoretical and policy foundations for the coordinated development of the environment and economy. The results show that the LCCP policy can significantly improve ecological efficiency of tourism, with this result remaining robust after a series of tests. This positive effect is more pronounced in western cities, while it is relatively weaker in central and eastern regions. Mechanism tests indicate that technological innovation and industrial structure optimization are the primary driving factors. Additionally, the policy has a significant negative spillover effect on the tourism ecological efficiency of surrounding areas. This research provides empirical evidence and policy insights for better utilizing the LCCP policy to promote the sustainable development of the tourism industry and lays the foundation for policymakers to design industrial incentive mechanisms that balance environmental protection and economic development.

No abstract

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Against the background of global climate change, agricultural ecosystems face extreme weather, resource shortages, and carbon emission pressures, necessitating green transitions. Rural tourism, a key driver of rural revitalization, injects momentum into green agriculture through ecological resource monetization, low-carbon technology adoption, and industrial restructuring. This study evaluates rural tourism and agricultural green development levels in Jiangxi Province (2008–2022) using the entropy weight method and explores their spatiotemporal coordination via a coupling coordination degree model and spatial autocorrelation analysis. The study reveals the following: (1) Rural tourism and agricultural green development in Jiangxi Province demonstrate an upward trend overall, though with significant regional disparities. Regions such as Nanchang and Jiujiang exhibit higher coordination levels, while areas like Pingxiang and Xinyu persistently cluster in low-value agglomerations. (2) The coupling coordination degree transitions from “marginal imbalance” to “intermediate coordination”, with Nanchang City achieving “good coordination” status in 2022, forming a high-value radiation zone encompassing Nanchang, Jiujiang, and Yichun. Low-value regions remain constrained by inadequate resource exploitation and technological lag. (3) Global spatial autocorrelation analysis reveals significant positive agglomeration effects (Moran’s I values range from 0.148 to 0.312). Local spatial associations show coexisting patterns of ‘high-high’ synergy and ‘low-low’ lock-in”. The study proposes targeted policy interventions, industrial convergence enhancement, and regional coordination mechanism optimization to mitigate spatial disparities and foster high-quality synergetic development. This study establishes theoretical foundations for agricultural green transition integrated with rural tourism development while offering referential pathways for analogous regions confronting climate change challenges.

Introduction Controlling over-tourism has emerged as a pressing concern, attracting significant recent attention. Investigating this issue through the analysis of the impacts of marine green energy investment (MGEI), fintech (FT), and tourism concentration (TC) on carbon footprint (CF) and coastal water pollution (CWP) at tourist destinations is crucial. Methods This study employs the Spatial Method of Moment Quantile Regression (SMMQR) model to examine the effects of these indicators on two environmental metrics in coastal regions of China, validated through Moran's I analysis, Local Indicators of Spatial Association (LISA) Cluster Maps, and robustness checks. Results Results reveal strong positive spatial autocorrelation, with dominant High-High (HH) clusters for both environmental indicators, concentrated in areas such as Shanghai, Guangzhou, and Sanya, indicating significant environmental pressures. TC and FT exacerbate CF (6.215-13.185 and 0.715-2.110) and CWP (5.210-10.145 and 2.045-4.570), whereas MGEI exhibits mixed CF (-3.078-4.042) and CWP impacts (-3.038-6.858), driven by spatial dependencies ranging from 0.275-0.312. Discussion These findings bolster recent research on tourism and FT's environmental impacts, expanding the analysis by incorporating spatial dynamics and investment, and pinpointing over-tourism risks in high-impact areas. The study proposes setting an over-tourism threshold to better manage this issue moving forward.

No abstract

In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon loss area was larger than the carbon increase area. The carbon storage in the ecological priority scenario in 2035 is the highest, reaching 874.76 × 106 t. The aboveground carbon storage (ACG) is the main carbon pool in Guilin. Most of the regions with high carbon storage are located in the northwest and northeast of Guilin. No matter what scenario, the carbon storage in the main urban area is maintained at a low level; (3) In 2035, the distribution of carbon storage in Guilin has a strong spatial positive correlation, with more hot spots than cold spots. The high-value areas of carbon storage are concentrated in the northwest and east, whereas the low-value areas are concentrated in the urban area of Guilin.

Tourism-related carbon emission efficiency is an important indicator that reflects the sustainable development of tourism and can better balance the relationship between negative environmental impact and economic value. According to panel data of 30 provincial regions, "the tourism value added coefficient" (not including the Tibet Autonomous Region) in mainland China from 2000 to 2019, we estimate the tourism of each provincial administrative unit carbon emissions, measure the tourism carbon efficiency value, and analyze the measurement results of the change trend, spatial differentiation characteristics, and influencing factors. The results show that (1) the carbon emission efficiency of regional tourism in China increased significantly from 2000 to 2019, but there was a significant difference in the carbon emission efficiency of tourism among regions, and the sustainable development level of regional tourism was still unbalanced. (2) The spatial pattern of provincial administrative units in China has the adjacent characteristics of High-High agglomeration and Low-Low agglomeration, the difference in the tourism eco-efficiency development level among regions gradually decreases with time, and there is a dynamic convergence characteristic. (3) The <i>q</i> value represents the intensity of the impact factor on tourism carbon emission efficiency. According to the <i>q</i> value, the factors affecting tourism carbon emission efficiency were divided into dominant factors (0.5 ≤ <i>q</i> ≤ 1), inducing factors (0.2 ≤ <i>q</i> < 0.5) and driving factors (0 ≤ <i>q</i> < 0.2), among which the level of technological development was the dominant factor. The level of opening-up to the outside world is the inducing factor; environmental regulation intensity, urbanization level, regional economic development level, tourism industry environment, and tourism infrastructure are the driving factors. (4) The influence degree of influencing factors on the spatial differentiation of tourism carbon emission efficiency is significantly different in different periods. The degree of influence of the urbanization level and tourism industry environment shows an upward trend over time, and the influence degree of other factors shows a "V-shaped" trend. (5) The two-factor interaction will significantly enhance the spatial differentiation of regional tourism carbon emission efficiency, and the interaction between the level of scientific and technological innovation and other influencing factors has a deeper impact on tourism carbon emission efficiency.

No abstract

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。

In the context of global warming, although the coordinated development of tourism has led to regional economic growth, the high energy consumption-driven effects of such development have also led to environmental degradation. This research combines the undesired output of the Super-SBM model and social network analysis methods to determine the eco-efficiency of provincial tourism in China from 2010-2019 and analyzes its spatial correlation characteristics as well as its influencing factors. The aim of the project is to improve China's regional tourism eco-efficiency and promote cross-regional tourism correlation. The results show that (1) the mean value of provincial tourism eco-efficiency in China is maintained at 0.405~0.612, with an overall fluctuating upward trend. The tourism eco-efficiency of eastern China is higher than that of central, western and northeastern China, but the latter three regions have not formed a stable spatial distribution pattern. (2) The spatial network of provincial tourism eco-efficiency in China is multithreaded, dense and diversified. Throughout the network, affiliations are becoming closer, and network structure robustness is gradually improving, although the "hierarchical" spatial network structure remains. In individual networks, Jiangsu, Guangdong and Shandong provinces in eastern China have higher centrality degrees, closeness centrality and betweenness centrality than other provinces, which means they are dominant in the network. Hainan Province, also located in eastern China, has not yet built a "bridge" for tourism factor circulation. In the core-periphery model, the core-periphery areas of China's provincial tourism eco-efficiency are distributed in clusters, and the number of "core members" has increased. (3) The economic development level, information technology development level, and tourism technology level collectively drive the development and evolution of China's provincial tourism eco-efficiency spatial network.

While tourism eco-efficiency has been analyzed actively within tourism research, there is an extant dearth of research on the spatial network structure of provincial-scale tourism eco-efficiency. The Super-SBM was used to evaluate the tourism eco-efficiency of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan). Then, social network analysis was employed to examine the evolution characteristics regarding the spatial network structure of tourism eco-efficiency. The main results are shown as follows. Firstly, tourism eco-efficiency of more than two thirds’ provinces witnessed an increasing trend. Secondly, the spatial network structure of tourism eco-efficiency was still loose and unstable during the sample period. Thirdly, there existed the multidimensional nested and fused spatial factions and condensed subsets in the spatial network structure of tourism eco-efficiency. However, there was still a lack of low-carbon tourism cooperation among second or third sub-groups. These conclusions can provide references for policymakers who expect to reduce carbon emissions from the tourism industry and to achieve sustainable tourism development.

Promoting tourism in China using sustainable practices has become a very important issue. In order to analyze temporal characteristics and spatial regularities of green total factor productivity (GTFP), carbon emissions and the consumption of energy related to tourism in China were estimated using a "bottom-up" method. The construction of a measurement framework (including carbon emissions and energy consumption) of GTFP for the tourism industry was also undertaken. The data envelopment analysis (DEA) model and the Malmquist-Luenberger (ML) index were used to measure and calculate tourism GTFP in China between 2007 and 2018, as well as analyze spatio-temporal differences. Results indicate that: (1) carbon emissions and the consumption of energy are increasing, and they have not yet peaked, with traffic associated with tourism accounting for the largest proportion among tourism sectors; the spatial distribution of carbon emissions and the consumption of energy is not balanced; (2) green development of tourism in China has achieved a good level of performance during the study period, driven by technical efficiency. Since 2014, pure technical efficiency (PE) has been >1, indicating that the tourism industry in China has entered a stage of change and promotion; (3) significant spatial differences exist in tourism GTFP in China. For example, the overall pattern of being strongest in the east and weakest in the west has not changed. Currently, eastern, central, and western regions in China rely on different dynamic mechanisms to promote tourism green development. In addition, some provinces have become the core or secondary growth poles of tourism green development in China.

High-quality development is the theme of China's economic and social development in the new era, and it is also an objective need for tourism development in the 14th Five-Year Plan period. This study presents an investigation of China's patterns of tourism environmental efficiency from the perspective of spatiotemporal interactions. A nested analytical framework of quantile regression and spatiotemporal leaps was used to explore the driving mechanism patterns of tourism environmental efficiency under different leap types. Based on various spatial analysis methods, we posit that the patterns of tourism environmental efficiency differ through spatial associations, dynamic evolutions, and transition mechanisms. Our results indicate that there is a dynamic convergence trend of the overall differences in tourism environmental efficiency in China from 2000 to 2020 where a significant clustering phenomenon is observed in space and the level of spatial clustering gradually tends to be stable. In terms of local spatial structures and the dependence directions of tourism environmental efficiency, China's northwest and northeast regions are more volatile, while eastern coastal regions are relatively stable. Spatiotemporal leaps of tourism environmental efficiency show certain transfer inertia with strong spatial dependence or path-locked characteristics, among which most central and western regions always maintain high carbon emission attributes. These regions are the most limited in the synergy of tourism environmental efficiency. The spatiotemporal network patterns of tourism environmental efficiency are mainly based on positive correlations and show strong spatial integration. However, a few neighboring provinces still have a certain degree of spatiotemporal competition. Driving patterns of the spatiotemporal leaps in tourism environmental efficiency among regions differ greatly. The eastern coastal provinces are driven by population-urbanization constraint patterns, and the northwest, southwest, and northeast regions are driven by technology regulation patterns. From the southeast to the northwest, the leap in the environmental efficiency of China's tourism gradually shows a stepwise pattern of "congruent constraint-reverse development-congruent development." Therefore, the government should not only consider these various driving/constraining factors but also combine different environmentally-efficient tourism clustering types and transition paths to emphasize differentiated environmental tourism measures. This can help avoid the closure of inter-provincial tourism policies through inter-regional synergy.

In the context of ecological priority and green development strategy, accelerating the upgrading of tourism structure and promoting the development of ecotourism is an important guarantee to achieve green and low-carbon economic growth and high-quality development. On the basis of constructing comprehensive evaluation indicators of tourism development (TD) and green development efficiency (GDE), this study analyzed the impulse response relationship between TD and GDE and the impact effect of TD on GDE in the Yangtze River Delta region from 2000-2018. Findings showed that: (1) During the study period, TD generally exhibited a W-shaped fluctuating upward trend and GDE showed a staggered evolution of upward and downward fluctuations, while both regional gaps of TD and GDE continued to decrease. (2) Most cities had made a leap from low to medium, high, and higher levels of tourism development, with tourism development levels decreasing along the Yangtze River basin to the north and south of the delta. The overall green development efficiency was relatively low, showing a spatial pattern of high value in the southern delta and low value in the northwest delta. (3) There was a one-way Granger causality of TD on GDE, and the impact of TD on GDE showed a significant positive cumulative effect. (4) TD exhibited a significant inverted U-shaped impact on GDE. The economic development level and government intervention had a significant positive impact on GDE. The proportion of secondary industry, energy consumption intensity, and foreign direct investment had a significant negative driving effect on GDE. While the impact of environmental regulation on GDE was insignificant positive. This study has great practical significance to alleviate the problems of urban resources and environment, and to realize a green economy and high-quality life.

Star hotels, as the core sector of China's tourism development, the improvement of their economic efficiency is an important way for the tourism industry to operate well. On the other hand, improving eco-efficiency can promote star hotels to take more responsibility for the ecological environment and promote the sustainable development of tourism industry. In this paper, the A Slacks-Based Measure model (SBM), as one kind of data envelopment analysis, is used to construct the economic efficiency and eco efficiency input-output system of Chinese star hotels according to the development characteristics of star hotels in China. By collecting the data in 2014 and using the energy balance sheet to calculate the carbon emission, we further calculate the economic efficiency and eco-efficiency level of the star hotels in 30 provinces in China's main land, and further analysis the input-output spatial pattern and development trend of the eco-economic system in China's star hotels through the Getis-Ord Gi index and coupling analysis. It is found that the economic efficiency and ecoefficiency of Chinese star hotels are consistent with their spatial distribution, and they have a significant linear positive correlation. The spatial distribution of the economic efficiency and eco-efficiency of Chinese star hotels is extremely uneven, and there is no spatial agglomeration. Moreover, the high efficiency area has no obvious positive impact on the periphery.

No abstract