Use of Household Survey Data as a Tool to Assess the Carbon Footprint of Rural Tourist Accommodation and Related Services in China: A Case Study of Mount Qingcheng

Liu, Jun; Lin, Chu; Huang, Li; JiangHuan, Zhu; Wu, Ling; Li, Yunyun

doi:10.3390/su9101680

Cited by 13 publications

(10 citation statements)

References 23 publications

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“…This is because of the greater demand for food, energy, accommodation, and recreational services by tourists. Our findings are aligned with those calculated by Liu et al (2017) in terms of CO 2 emissions per person from tourism‐participating households (e.g., 30.27 kg/person/day). In addition, the difference in CO 2 emissions has mainly resulted from housing, lodging, and transportation, as providing these services to tourists requires a large number of resources, including water, electricity, and energy.…”

Section: Discussion and Recommendationssupporting

confidence: 92%

“…For example, to date literature has failed to analyze CO 2 emissions resulting from the conversion of farming livelihoods to households engaged in tourism operations. Liu et al (2017) surveyed farming households involved in rural tourism operations in Qingcheng Mountain, China. They found that for every RMB 10,000 increase in farm household income, the corresponding accommodation and service sector generates 1412.08 kg of CO 2 emissions.…”

Section: Households Livelihoods Transitions and Environmental Impactsmentioning

confidence: 99%

“…Drawing on Liu et al's (2017) construction life cycle framework diagram, we developed a conceptual framework for our research that integrates all major activities of farmers' households. The resulting framework is represented in Figure 1.…”

Section: Literature Reviewmentioning

confidence: 99%

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Examining the carbon emission impact of livelihood transitions in pro‐poor tourism

Yin,

Xiong,

Cavaliere

et al. 2023

Journal of Tourism Research

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This study examines carbon dioxide (CO2) emission changes from livelihood transitions during tourism development to assess the environmental impact of pro‐poor tourism (PPT) in China. The results indicate that livelihood transitions during PPT increased household income but also produced more CO2 emissions. CO2 emissions vary depending on factors including livelihood types after transition, income and household sizes, etc. The CO2 per unit of income from tourism‐based livelihoods is higher than from non‐tourism‐based livelihoods. Different livelihood strategies post transition also affect emissions. Practical suggestions that may mitigate emissions while not compromising residents’ income levels are provided.

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Section: Discussion and Recommendationssupporting

confidence: 92%

Section: Households Livelihoods Transitions and Environmental Impactsmentioning

confidence: 99%

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Examining the carbon emission impact of livelihood transitions in pro‐poor tourism

Yin,

Xiong,

Cavaliere

et al. 2023

Journal of Tourism Research

View full text Add to dashboard Cite

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“…With the increasing global environmental crisis, society and academia have paid great attention to carbon emissions. Some scholars have evaluated the carbon emissions of scenic spots [ 18 ], tourist transportation [ 19 , 20 ], and tourist accommodation [ 19 , 21 , 22 ]. Because hotels are open 24 h a day, with a wide variety of functions and facilities and the different living habits of residents, they are considered to be the main source of carbon emissions after tourist traffic [ 23 , 24 ].…”

Section: Literature Review and Research Frameworkmentioning

confidence: 99%

Spatial-Temporal Characteristics and Driving Factors of the Eco-Efficiency of Tourist Hotels in China

Zhang

Dong

et al. 2022

IJERPH

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At present, COVID-19 is seriously affecting the economic development of the hotel industry, and at the same time, the world is vigorously calling for “carbon emission mitigation”. Under these two factors, tourist hotels are in urgent need of effective tools to balance economic and social contributions with ecological and environmental impacts. Therefore, this paper takes Chinese tourist hotels as the research object and constructs a research framework for Chinese tourist hotels by constructing a Super-SBM Non-Oriented model. We measured the economic efficiency and eco-efficiency of Chinese tourist hotels from 2000 to 2019; explored spatial-temporal evolution patterns of their income, carbon emissions, eco-efficiency, and economic efficiency through spatial hotspot analysis and center of gravity analysis; and identified the spatial agglomeration characteristics of such hotels through the econometric panel Tobit model to identify the different driving factors inside and outside the tourist hotel system. The following results were obtained: (1) the eco-efficiency of China’s tourist hotels is higher than the economic efficiency, which is in line with the overall Kuznets curve theory, but the income and carbon emissions have not yet been decoupled; (2) most of China’s tourist hotels are crudely developed with much room for improving the economic efficiency, and most of the provincial and regional tourist hotels are at a low-income level, but the carbon emissions are still on the increase; and (3) income, labor, carbon emissions, waste emissions, and water consumption are the internal drivers of China’s tourist hotels, while industrial structure, urbanization rate, energy efficiency, and information technology are the external drivers of China’s tourist hotels. The research results provide a clear path for the reduction in carbon emissions and the improvement of the eco-efficiency of Chinese tourist hotels. Under the backdrop of global climate change and the post-COVID-19 era, the research framework and conclusions provide references for countries with new economies similar to China and countries that need to quickly restore the hotel industry.

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“…En (x i (t)) is the total energy consumption of all sectors in the tth year, i (t) is the energy consumption intensity coefficient (energy consumption needed by intermediate demand, final consumption demand and energy loss per unit output) of the ith sector in the tth year, which can be obtained from the Energy Balance table of China Energy Statistical Yearbook. H (x i (t)) is the total GHG emission in the tth year, i (t), i (t), and i (t) are emission intensity coefficients (GHG emission emitted by domestic and imported products) of three major GHGs (carbon dioxide, methane, and nitrogen oxide) respectively, which can be calculated based on the IPCC [19].…”

Section: Goalmentioning

confidence: 99%

Regional Input–Output Multiple Choice Goal Programming Model and Method for Industry Structure Optimization on Energy Conservation and GHG Emission Reduction in China

Lin¹,

Li²,

Jiang³

et al. 2019

IJCIS

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To assess the potential of China's industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input-output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product (GDP), minimization of energy consumption and GHG emission. They are subjected to the input-output balance, economy development, energy supply, and industry diversity. And four scenarios with different decision preferences are taken into accounted in the solutions of the industrial structure optimization model. The results demonstrate that industrial restructuring has potential in energy saving and emission reducing. First, after optimization, energy consumption intensity and GHG emission intensity can drop by 13.88246% and 5.33767% over 2012, and GDP can grow up at annual growth rate 6.6% from 2013-2020. Second, promoting the development of the low energy-intensive and low GHG emission intensive sectors is an effective method for energy conservation and emission reduction. Three, compared to energy intensity reduction, GHG emission intensity reduction is less effective for four scenarios. Four, there are several difficulties to achieve the amounts and intensity control targets of energy conservation and GHG emission reduction simultaneously. It is suggested that China had better strive to promote progress of technologies of energy conservation and GHG emission reduction while adjusting the industrial structure.

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Use of Household Survey Data as a Tool to Assess the Carbon Footprint of Rural Tourist Accommodation and Related Services in China: A Case Study of Mount Qingcheng

Cited by 13 publications

References 23 publications

Examining the carbon emission impact of livelihood transitions in pro‐poor tourism

Examining the carbon emission impact of livelihood transitions in pro‐poor tourism

Spatial-Temporal Characteristics and Driving Factors of the Eco-Efficiency of Tourist Hotels in China

Regional Input–Output Multiple Choice Goal Programming Model and Method for Industry Structure Optimization on Energy Conservation and GHG Emission Reduction in China

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