Analysis of Early Warning Spatial and Temporal Differences of Tourism Carrying Capacity in China’s Island Cities

Fang Ye, Jaepil Park, Fen Wang and Xihua Hu
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Fang Ye: China (Zhejiang) Pilot Free Trade Zone Research Institute, Zhejiang Ocean University, Zhoushan 316000, China
Jaepil Park: College of International Entrepreneurship, National Kunsan University, Kunsan 54150, Korea
Fen Wang: School of economics and management, Zhejiang Ocean University, Zhoushan 316000, China
Xihua Hu: Institute of Education, Wonkwang University, Iksan-si 54538, Korea

Sustainability, 2020, vol. 12, issue 4, 1-16

Abstract: Tourism is the leading industry of island cities and the tourism carrying capacity is of great significance to the sustainable development of cities. This paper adopts the state-space model to construct an early warning indicator system for tourism carrying capacity from three aspects: nature, economy, and society, explores the early warning status, and spatial and temporal differences of tourism carrying capacity in Chinese island cities, and makes use of the BP(Back Propagation) neural network model to predict the development trend of early warnings. The results show that (1) from 2012 to 2018, the early warning status of China’s island cities’ tourism carrying capacity is generally on the rise, the natural carrying capacity system’s early warning situation has deteriorated, which is in a state of severe warning interval. The economic carrying capacity and social carrying capacity are on the rise, and the warning degree is from the super warning interval to the severe warning interval and then to the moderate warning degree. The forecast of the overall tourism carrying capacity early warning index from 2019 to 2021 presents an upward trend and is in the moderate warning interval. (2) The tourism carrying capacity early warning in China’s island cities shows a large spatial and temporal difference and the early warning values of each island city are different. The early warning value of Putuo tourism carrying capacity always ranks first, and Changdao has the worst performance. (3) In accordance with the contribution status of the subsystem to the total system, the Chinese island cities show regional differences in the northern, central, and southern area, showing two forms of pressure cities and pressure-carrying cities. The government can adopt different policies and measures in accordance with different characteristics of human environmental activities.

Keywords: island city; tourism carrying capacity; early warning of carrying capacity; spatial state model; BP neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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