Spatiotemporal Evolution and Trend Prediction of Tourism Economic Vulnerability in China’s Major Tourist Cities

Abstract

The evaluation and trend prediction of tourism economic vulnerability (TEV) in major tourist cities are necessary for formulating tourism economic strategies scientifically and promoting the sustainable development of regional tourism. In this study, 58 major tourist cities in China were taken as the research object, and an evaluation index system of TEV was constructed from two aspects of sensitivity and adaptive capacity. On the basis of the entropy weight method, TOPSIS model, obstacle diagnosis model, and BP neural network model, this study analyzed the spatiotemporal patterns, obstacle factors, and future trends of TEV in major tourist cities in China from 2004 to 2019. The results show three key findings: (1) In terms of spatiotemporal patterns, the TEV index of most of China’s tourist cities has been on the rise from 2004 to 2019. Cities throughout the coast of China’s Yangtze River Delta and the Pearl River Delta urban agglomeration show high vulnerability, whereas low vulnerability has a scattered distribution in China’s northeast, central, and western regions. (2) The proportion of international tourists out of total tourists, tourism output density, urban industrial sulfur dioxide emissions per unit area, urban industrial smoke and dust emission per unit area, and discharge of urban industrial wastewater per unit area are the five major obstacles affecting the vulnerability degree of the tourism economy. (3) According to the prediction results of TEV from 2021 to 2030, although the TEV of many tourist cities in China is increasing year by year, cities with low TEV levels occupy the dominant position. Research results can provide reference for tourist cities to prevent tourism crises from occurring and to reasonably improve the resilience of the tourism economic system.

Analisis Pengaruh Gender dalam Pemilihan Jurusan Pariwisata di Universitas di Kota Bandung

“…Sebuah studi oleh (Romadhan et al, 2023) menemukan ketidakseimbangan gender yang signifikan dalam penelitian kolaboratif di sektor pariwisata. Sementara itu, penelitian (Suwarti, 2020) dan (Putra, 2023) menggali persepsi masyarakat dan implementasi kebijakan gender dalam konteks pariwisata, memberikan wawasan penting mengenai faktor-faktor yang mempengaruhi pilihan jurusan (C. Huang, 2021b).…”

Vulnerability of Tourist Cities’ Economic Systems Amid the COVID-19 Pandemic: System Characteristics and Formation Mechanisms—A Case Study of 46 Major Tourist Cities in China

“…For instance, the vulnerability of the tourism economy has been addressed in terms of environment-structure integration [24], industrial reliance [25], and other features. Commonly adopted research methods include entropy evaluation, set pair analysis, TOPSIS, and back propagation neural networks [26][27][28][29]. Studies have considered the tourism economy in addition to the urban ecological environment, society, and culture.…”

“…This article is accordingly systematic, scientific, timely, and operable. Our work addresses two prime dimensions of tourism-related vulnerability assessment (i.e., sensitivity and responsiveness [12,15,16]) and refers to relevant indices from Huang et al [29], Sreya et al [47], Wang et al [48], Liang and Jie [49], Li [7], and Yin et al [50] This paper also accounts for the correlation between vulnerability and resilience in reflecting on relevant indices proposed by Hu et al [51], Zhang and Feng [52], and Lu et al [53] from the perspective of tourism city resilience. The COVID-19 pandemic's attributes are considered as well.…”

Assessment of the impact of island development on water security based on the elements nexus for the water system in Zhoushan archipelago

“…Moreover, we assign an entropy weight for each indicator, and entropy weight has been proven to be an objective method compared with the pairwise comparison method (Wang et al 2014) for giving indicator weight in assessment of tourism economic vulnerability, urban flood hazard and water quality (Zou et al 2006;Sepehri et al 2019;Huang et al 2021).…”