Drivers of carbon emissions in China’s tourism industry

Luo, Fen; Moyle, Brent D; Moyle, Char-lee; Zhong, Yongde; Shi, Shengyi

doi:10.1080/09669582.2019.1705315

Cited by 50 publications

(29 citation statements)

References 53 publications

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“…Technological effect: Low-carbon technology can improve the eco-efficiency of tourism by reducing carbon emissions. The energy efficiency can reflect the carbon efficiency, and it has been a key index to measure low-carbon technology [ 67 ]. The technology effect is mainly measured with energy input per unit of tourism revenue in this article.…”

Section: Methodsmentioning

confidence: 99%

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Xia

Dong

et al. 2022

IJERPH

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Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input–output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets’ theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery.

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Section: Methodsmentioning

confidence: 99%

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Xia

Dong

et al. 2022

IJERPH

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“…The analysis of the driving factors of tourism carbon emissions is the key to achieving low-carbon development of the tourism industry [ 23 ], which is of great significance to the formulation of emission reduction policies. Scholars have conducted abundant studies on the influencing factors of tourism carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Coupling Coordination and Influencing Factors among Tourism Carbon Emission, Tourism Economic and Tourism Innovation

Pan

Weng

et al. 2021

IJERPH

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To discuss the coupling coordination relationship among tourism carbon emissions, economic development and regional innovation it is not only necessary to realize the green development of tourism economy, but also great significance for the tourism industry to take a low-carbon path. Taking the 30 provinces of China for example, this paper calculated the tourism carbon emission efficiency based on the super-efficiency Slacks based measure and Data envelope analyse (SBM-DEA) model from 2007 to 2017, and on this basis, defined a compound system that consists of tourism carbon emissions, tourism economic development and tourism regional innovation. Further, the coupling coordination degree model and dynamic degree model were used to explore its spatiotemporal evolution characteristics of balanced development, and this paper distinguished the core influencing factors by Geodetector model. The results showed that (1) during the study period, the tourism carbon emission efficiency showed a reciprocating trend of first rising and then falling, mainly due to the change of pure technical efficiency. (2) The coupling coordination degree developed towards a good trend, while there were significant differences among provinces, showing a gradient distribution pattern of decreasing from east to west. Additionally, (3) the core driving factors varied over time, however, in general, the influence from high to low were as follows: technological innovation, economic development, urbanization, environmental pollution control, and industrial structure. Finally, some policy recommendations were put forward to further promote the coupling coordination degree.

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“…The objective function (1) represents the maximal overall satisfaction; constraint (2) denotes the data range of variables; constraint (3) means that all tourists must be served; constraint (4) represents the fact that the tourist groups in each optional route are not larger than the corresponding upper limit of tourist carrying capacity; constraint (5) means that each tourist only visits along a single route and constraint (6) means that each group is selected once at most. Constraint (7) represents the calculation formula of group i satisfaction.…”

Section: Representation Of Variables Of the Problemmentioning

confidence: 99%

“…Reasonable use of tourism resources can not only achieve the purpose of reducing costs and increasing efficiency, but also play a positive role in ecological economy. Analysis of tourism ecology can not only reduce the carbon emission of tourism, but also promote the development of ecological economy [5,6].…”

Section: Introductionmentioning

confidence: 99%

Tourism route optimization based on improved knowledge ant colony algorithm

Luo

Wang

et al. 2022

Complex Intell. Syst.

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With the rapid development of tourism in the economy, popular demand for tourism also increases. Unreasonable distribution arises a series of problems such as reduction of tourist satisfaction and decrease of the income in tourist attractions. Based on consideration of tourism route planning, a mathematical model which takes the maximization of the overall satisfaction of all tourist groups as the objective function is established by taking the age and preferences of tourists, the upper limits of the tourist carrying capacity in various tourism routes, etc. as constraints. It aims to maximize income in tourist attractions while improving tourist satisfaction. Based on the tourist data of a travel agency, the statistical ideas of hierarchical clustering and random sampling are utilized to process the acquired data to obtain the simulation examples in the article. Aiming at this model, a knowledge-based hybrid ant colony algorithm is designed. On this basis, the mechanism of bacterial foraging algorithm is introduced. It improves the performance of the algorithm and avoids the generation of local optimal solution. At the same time, two knowledge models are in addition to improve the solution quality of the algorithm. Typical simulation indicates that the improved ant colony algorithm can find the optimal solution at a higher efficiency when solving the tourism route planning problem. The model can also satisfy the economic benefit of enterprises and achieves favorable path optimization effect under different optional routes, thus further verifying the effect liveness of the model.

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Drivers of carbon emissions in China’s tourism industry

Cited by 50 publications

References 53 publications

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Coupling Coordination and Influencing Factors among Tourism Carbon Emission, Tourism Economic and Tourism Innovation

Tourism route optimization based on improved knowledge ant colony algorithm

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