Search

As an important pioneering city in western China, Chongqing's urbanization and tourism development have brought increasing pressure on the eco-environmental system. This paper presents a comprehensive index system for assessment of the level of tourism based on four aspects: market size, tourism economy, tourism resources, and tourism services. In this paper, we use five indices—economic urbanization, demographic urbanization, social urbanization, spatial urbanization, and cultural urbanization—to establishe a comprehensive index system for urbanization. The developed index system also characterizes the eco-environment based on three factors: eco-environmental pressure, eco-environmental response, and eco-environmental state. A coupling coordination degree model (CCDM) focusing on the degree of coupling coordination between tourism, urbanization, and the eco-environment was established using panel data collected from 2000 to 2017 for Chongqing, China. In addition, we used the gray prediction model to predict the degree of coupling coordination of the three systems in Chongqing from 2018 to 2023. The results showed that (1) overall, despite the twists and turns in the middle of the time period, the degree of coupling coordination of the three systems has become increasingly coordinated over time; (2) the three parameters (α-tourism, ß-urbanization, and d-eco-environmental) that have been widely used in previous studies had little effect on the coupling coordinated system; and (3) according to the prediction results, the degree of coupling coordination of the three systems will continue to rise slowly, and the cooperation between the three systems will be close to perfect by 2023, which means that the development of one system will greatly drive the other two systems. [ABSTRACT FROM AUTHOR]

The classical population growth models include the Malthus population growth model and the logistic population growth model, each of which has its advantages and disadvantages. To address the disadvantages of the two models, this paper establishes a grey logistic population growth prediction model, based on the modeling mechanism of the grey prediction model and the characteristics of the logistic model, which uses the least-squares method to estimate the maximum population capacity. In accordance with the data characteristics of population growth, the weakening buffer operator is used to establish the weakening buffer operator grey logistic population growth prediction model, which improves its accuracy, thus improving the classic population prediction model. Four actual case datasets are used simultaneously, and the two classical grey prediction models are compared. The results of the six evaluation indicators show that the effects of the new model demonstrate obvious advantages. Finally, the new model is applied to the population forecast of Chongqing, China. The prediction results suggest that the population may reach a peak in 2020 and decline in the future. This finding is consistent with the logistic population growth model. [ABSTRACT FROM AUTHOR]