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July 2, 2026Sustainability0 citationsOpen Access

Measuring Tourism Eco-Efficiency and Its Influencing Factors in Anhui Province

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LJLi JBWBin WenJRJianhua Ren

Key Points

  • This research aims to assess tourism eco-efficiency in Anhui Province and identify its influencing factors across different cities.
  • Panel data analysis for 16 cities from 2011 to 2022
  • Utilization of a super-efficiency SBM model to measure eco-efficiency
  • Theil index decomposition to analyze disparities
  • Anhui's tourism eco-efficiency shows an overall upward trend from 1.465 in 2016 to 1.500 in 2022
  • Spatial patterns reveal higher eco-efficiency in southern cities and disparities driven mainly by within-region differences
  • Factors like energy consumption negatively affect eco-efficiency, while R&D investment impacts vary by city

Abstract

Promoting the green development of the tourism industry is a crucial pathway for achieving coordinated progress in ecological civilization development and industrial transformation and upgrading. Based on panel data for 16 prefecture-level cities in Anhui Province from 2011 to 2022, this study constructs an “inputs–desirable outputs–undesirable outputs” indicator system, measures city-level tourism eco-efficiency (TEE) using a super-efficiency SBM model incorporating undesirable outputs, decomposes provincial disparities and their sources using the Theil index and its decomposition, and further identifies city-specific heterogeneity in influencing factors by employing a panel variable-coefficient fixed-effects model. The results show that: (1) Anhui’s TEE exhibited an overall fluctuating upward trend during 2011–2022, with provincial efficiency values ranging from 1.465 (2016) to 1.500 (2022), and a more pronounced rebound after 2017; (2) spatially, TEE displays a pattern of “higher in the south, lower in the north, with a central uplift,” with southern Anhui cities such as Huangshan and Xuancheng performing relatively well, while many northern Anhui cities lag behind; (3) Theil decomposition indicates that overall disparities are driven mainly by within-region differences, whereas between-region differences contribute relatively little; and (4) influencing factors are markedly heterogeneous: scale- and affluence-related variables promote TEE in core cities such as Hefei, but tend to inhibit it in cities such as Bozhou, Anqing, Chuzhou, and Wuhu. The mechanisms associated with technology and structural variables are more complex; in particular, the expansion of energy consumption exerts a significantly negative effect on TEE in most cities and constitutes a common constraint on efficiency improvement, while the effects of R&D investment, digitalization, and the share of the tertiary sector vary across cities. Accordingly, policy efforts should prioritize energy-efficiency improvement and low-carbon substitution at the provincial level while implementing differentiated, city-specific pathways at the municipal level to jointly advance the low-carbon transition and high-quality development of the tourism industry.

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Cite This Study

J et al. (2026) studied this question.

synapsesocial.com/papers/6a4600c99ed1343031310d2fhttps://doi.org/10.3390/su18136625
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