Impacts of Environmental Regulations on Tourism Carbon Emissions

Qiao, Chunming; Mao, Yan; Morrison, Alastair M.

doi:10.3390/ijerph182312850

Cited by 32 publications

(23 citation statements)

References 42 publications

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“…Carbon emission assessments are mainly based on a “top-down” perspective on the energy supply side [ 32 ] and a “bottom-up” perspective on the energy consumption side [ 33 ]. Research on carbon emissions in the tourism industry using the “bottom-up” approach focuses on the consumption side and assesses emissions by studying the energy consumption coefficient per unit of the output of various transportation modes [ 34 ], travel hotels [ 35 ], and tourist activities [ 36 ].…”

Section: Literature Review and Research Frameworkmentioning

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: Literature Review and Research Frameworkmentioning

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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“…Based on the STIRPAT model, Shahbaz et al, analyzed the impact of Malaysia's urbanization rate, affluence, and trade openness on energy consumption, and found that the main factor affecting the growth of energy consumption is urbanization [18]. In addition, some scholars have analyzed the impact of foreign trade [19], environmental regulation [20], and financial development [21] on carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

2022

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Since the beginning of the new century, China’s carbon emissions have increased significantly, and the country has become the world’s largest carbon emitter. Therefore, determining the influencing factors of carbon emissions is an important issue for policymakers. Based on the panel data of 30 provinces and cities across the country from 2000 to 2018, this study empirically tested how per capita disposable income, industrial structure, urbanization level, average family size, and technological innovation level impacts carbon emissions at different quantile levels by using the panel quantile STIRPAT model. The results showed that per capita disposable income and industrial structure had significant promoting effects on carbon emissions, while urbanization level, average family size, and technological innovation level had significant inhibitory effects on carbon emissions. The main thing is that the emission distributions of the 10th and 90th quantiles of the independent variables were quite different, which shows that the influence of each factor on carbon emissions has obvious heterogeneity at different levels. Specifically, the impact of per capita disposable income and technological innovation level on carbon emissions in low carbon emission areas were higher than that in high carbon emission areas, and the impact of industrial structure, urbanization level, and average household size on carbon emissions in high carbon emission areas was higher. Finally, specific policy implications are provided based on these results.

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“…Nevertheless, scholars have explored the influence mechanisms of TET from different perspectives, including environment-led, organization-led, and technology-led. Some scholars believe that environmental regulation significantly impacts TET (Chen et al, 2021;Zha et al, 2021). Chen et al (2021) demonstrate that environmental regulation can suppress tourism carbon emissions, promote industrial structure upgrading, and promote TET.…”

Section: Introductionmentioning

confidence: 99%

What configurational conditions promote tourism eco-transformation? a fuzzy-set qualitative comparative analysis based on the TOE framework

Wang

Zheng

Zeng

et al. 2022

Front. Environ. Sci.

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Tourism eco-transformation can provide a long-lasting competitive advantage. However, successful transformation is a challenge for both academia and industry. The literature has focused on the net effect of a single factor. Still, we build on the Technology-Organization-Environment framework and propose that tourism eco-transformation does not depend on a single condition but the configurational effect of organization, environment, and technology. We found six conditions that influence eco-transformation in Chinese provinces. The results show that 1) the tourism eco-transformation in China from 2016 to 2019 “declines first and then rises,” indicating a “U” shape. Spatial distribution is stable; 2) We can classify the high-level transformation model into four configurational types: technology-pulling organization, proactive organization, environmental stress organization, and comprehensive organization. Not-high-level transformation model can be categorized as comprehensive absence and respectable; 3) There are three critical conditions for tourism external connections, environmental regulation, and tourism ecology promotion. In contrast, digital information level, technology innovation capability, and tourism resource endowment are largely determined based on the specific situation. We explore high-level and not-high-level configurational paths of tourism eco-transformation and gain new theoretical insights. We also guide tourism managers to choose or modify high-level tourism eco-transformation paths based on local characteristics to avoid not-high-level tourism eco-transformation situations.

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Impacts of Environmental Regulations on Tourism Carbon Emissions

Cited by 32 publications

References 42 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

Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

What configurational conditions promote tourism eco-transformation? a fuzzy-set qualitative comparative analysis based on the TOE framework

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