Spatio-Temporal Differentiation and Enhancement Path of Tourism Eco-Efficiency in the Yellow River Basin Under the “Dual Carbon” Goals

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin.

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Publication Details

Journal
Sustainability
Published
2025-08-30
DOI
https://doi.org/10.3390/su17177827
Citations
1
Primary Topic
Advanced Technologies in Various Fields
Type
article
Field-Weighted Citation Impact
2.48

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article

Spatio-Temporal Differentiation and Enhancement Path of Tourism Eco-Efficiency in the Yellow River Basin Under the “Dual Carbon” Goals

G. Y. Xiao, Y. Ma, Lu-Yun Li, Dandan Zhao et al.
1 citations
Sustainability
Advanced Technologies in Various Fields
2.48
article

Spatio-Temporal Differentiation and Enhancement Path of Tourism Eco-Efficiency in the Yellow River Basin Under the “Dual Carbon” Goals

G. Y. Xiao, Y. Ma, Lu-Yun Li, Dandan Zhao, Y. T. Liang
article en
1 citations

Abstract

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin.

SustainabilityVol. 17(17)
Inner Mongolia University of Finance and Economics (CN)
National Social Science Fund of China
Openalex Percentile: Top 9%
Advanced Technologies in Various Fields
2.48
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Spatio-Temporal Differentiation and Enhancement Path of Tourism Eco-Efficiency in the Yellow River Basin Under the “Dual Carbon” Goals — G. Y. Xiao, Y. Ma, et al. · Sustainability (2025) | TGRS Research Map | TGRS