Measuring of tourism eco-efficiency and its comparative research based on carbon emissions

Jun, 刘军 Liu; Ding, 问鼎 Wen; Yun, 童昀 Tong; Yong, 马勇 Ma

doi:10.5846/stxb201712212290

Cited by 11 publications

(12 citation statements)

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“…We discovered that the value of tourism eco-efficiency in the river basin was stable( Fig 2I ), and the regional gap gradually narrowed. Tourism is a vital economic growth point in the economically backward areas, which have a latecomer advantage compared with the economically developed areas [ 60 ]. Apart from the immense tourism eco-efficiency upgrading in the upstream area, the gap among the Basin’s upper, middle, and lower reaches also continues to decrease.…”

Section: Discussionmentioning

confidence: 99%

Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model

Peng,

Liang,

Ding

et al. 2024

PLoS ONE

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With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human–land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

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Section: Discussionmentioning

confidence: 99%

Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model

Peng,

Liang,

Ding

et al. 2024

PLoS ONE

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“…A comparative analysis of the accounting results with existing studies. Liu et al (2019) accounted for Guangdong's total tourism carbon emissions in 2010 of the three tourism sectors of transportation, accommodation, and sightseeing is 4.4530 million tons. In contrast, we also account for the total tourism carbon emissions of these three tourism sectors is 2.534 million tons, nearly 2 million tons less than Liu's result.…”

Section: Discussionmentioning

confidence: 99%

“…This empirical analysis focuses on Guangdong province to test the proposed tourism carbon emissions accounting analytical framework and method based on consumption stripping. Guangdong, a province in southern China, is renowned for its tourism industry; its tourism industry has always been at the forefront of China both in the total tourism revenue and the total number of tourists in the 40 years since China’s reform and opening up (Hu, 2018; Liu et al, 2019). Guangdong was chosen for analysis for several reasons, the first of which was data availability.…”

Section: Empirical Analysismentioning

confidence: 99%

“…Second, academic disputes over the province’s actual tourism carbon emissions must be reconciled; findings regarding Guangdong’s tourism carbon emissions measurement outcomes have been inconsistent. For instance, Hu (2018) and Liu et al (2019) both focused on tourism energy consumption to assess the province’s tourism carbon emissions in 2010 based on three sectors: transportation, accommodation, and sightseeing. Hu’s (2018) calculation was 1.5208 million tons, whereas Liu et al (2019) computed 4.4530 million tons-a roughly threefold discrepancy, because they used different energy consumption coefficients, leading to great differences in their results.…”

Section: Empirical Analysismentioning

confidence: 99%

“…For instance, Hu (2018) and Liu et al (2019) both focused on tourism energy consumption to assess the province’s tourism carbon emissions in 2010 based on three sectors: transportation, accommodation, and sightseeing. Hu’s (2018) calculation was 1.5208 million tons, whereas Liu et al (2019) computed 4.4530 million tons-a roughly threefold discrepancy, because they used different energy consumption coefficients, leading to great differences in their results. A reassessment of carbon emissions in 2010 from the perspective of tourism consumption maybe can resolve the debate.…”

Section: Empirical Analysismentioning

confidence: 99%

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Accounting for tourism carbon emissions: A consumption stripping perspective based on the tourism satellite account

Fang-ming

Liu

2023

Tourism Economics

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Limitations in statistical data and differences in accounting methods have hindered the accuracy of tourism carbon emissions accounting. In this research, based on the Tourism Satellite Account (TSA) and underpinned by the logic of “accounting basis–key coefficient–accounting objective,” a comprehensive decomposition accounting method is built from a consumption stripping perspective. First, it classifies the tourism industry by the “sector–industry–product” structure into seven sectors, 13 industries, and 22 characteristic products/services. Next, it strips the actual tourism consumption from the tourism industry by constructing two key coefficients. Finally, it transforms tourism consumption data into carbon emissions data by introducing tourism ecological efficiency. Taking Guangdong province of China as an example, its tourism carbon emissions are calculated from 2010 to 2020 using the proposed method. The results reveal the distribution structure of tourism carbon emissions and confirm the scientific and accurate nature of this accounting method.

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Measuring of tourism eco-efficiency and its comparative research based on carbon emissions

Cited by 11 publications

References 0 publications

Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model

Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model

Accounting for tourism carbon emissions: A consumption stripping perspective based on the tourism satellite account

Measurement and Analysis of Traffic Carbon Emission in Ningbo City

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