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Tourism System Resilience and Sustainable Development in Ecologically Fragile Areas: Evidence from Tibet-Related Areas of Sichuan, China

生態学的脆弱地域における観光システムのレジリエンスと持続可能な開発:中国四川省チベット関連地域からのエビデンス (AI 翻訳)

Yuyan Luo, Yong Qin, Xiaojing Yu

Sustainability📚 査読済 / ジャーナル2026-06-24#その他Origin: CN対象セクター: tourism
DOI: 10.3390/su18136448
原典: https://www.mdpi.com/2071-1050/18/13/6448/pdf?version=1782306227
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🤖 gxceed AI 要約

日本語

本研究は、中国四川省のチベット関連地域を対象に、観光システムのレジリエンスを評価し、その影響要因を分析した。エントロピー加重AHPモデル、結合調整モデル、障害度モデルを用いて4つのサブシステム(観光インフラ・規模、経済、社会、生態)を評価。2011~2020年のパネルデータ分析の結果、生態的レジリエンスが最も低く、サブシステム間の連携が不十分であることが明らかになった。持続可能な観光発展には生態系ガバナンスとサブシステムの調整が重要である。

English

This study evaluates tourism system resilience in Tibet-related areas of Sichuan, China, using an integrated entropy weight-AHP model, coupling coordination model, and obstacle degree model. Analyzing panel data from 2011-2020, it finds that ecological resilience is the weakest subsystem and coordination among subsystems is low. Ecological constraints are the main limiting factors. The study emphasizes ecological governance and subsystem coordination for sustainable tourism development.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

本論文は中国の生態学的脆弱地域に焦点を当てており、日本の過疎地や国立公園周辺の観光地にも示唆を与える可能性がある。しかし、SSBJや日本のGX政策との直接的な関連は薄い。

In the global GX context

While focused on Tibetan areas in Sichuan, this study's framework for assessing tourism system resilience and identifying ecological constraints is relevant globally for sustainable tourism in ecologically sensitive regions. However, it does not directly address climate disclosure or transition finance.

👥 読者別の含意

🔬研究者:Researchers in sustainable tourism and regional development can adopt the integrated assessment framework for other ecologically fragile regions.

🏛政策担当者:Policymakers in charge of tourism and ecological conservation in fragile areas can use the findings to prioritize ecological protection and infrastructure development.

📄 Abstract(原文)

Tourism plays an increasingly important role in promoting economic growth and rural revitalization in ecologically fragile regions. However, tourism systems in Tibet–related areas of Sichuan, China, are highly vulnerable to natural disasters, ecological degradation, and regional development imbalances, posing challenges to sustainable tourism development. This study aims to evaluate tourism system resilience and identify its key influencing factors from a sustainability perspective. Based on the regional characteristics of Tibet-related areas in Sichuan, a comprehensive evaluation framework is constructed covering four subsystems: tourism infrastructure and scale, economy, society, and ecology. An integrated entropy weight–analytic hierarchy process (AHP) model, coupling coordination model, and obstacle degree model are employed to assess tourism system resilience and examine subsystem interactions using panel data from 2011 to 2020. The results indicate that: (1) the resilience levels of tourism subsystems show no clear spatial or temporal regularity across the study areas; (2) ecological resilience remains significantly lower than tourism, economic, and social resilience, representing the weakest component of the tourism system; (3) the coupling coordination among subsystems remains at a low level, suggesting insufficient synergy for sustainable regional development; and (4) ecological constraints are the primary limiting factors affecting overall tourism system resilience. This study contributes to sustainable tourism research by revealing the critical role of ecological governance and subsystem coordination in enhancing tourism resilience in ecologically sensitive regions. Policy implications include strengthening ecological protection, improving tourism infrastructure, promoting digital tourism marketing, and advancing rural revitalization to achieve long-term sustainable development. However, this study is limited by data availability and the spatial scope of the selected case-study areas, which may affect the generalizability of the findings.

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