Qin Ji

The perceptions and attitudes of community residents are a “mirror” of tourism development. Little research has been conducted on the effects of place attachment and emotional solidarity on community residents’ attitudes toward tourism in China’s glacier tourism-related areas. In this paper, we selected the southernmost marine glacier in China, Hailuogou Glacier Forest Park, as a case study, and constructed a structural equation model of residents’ tourism perceptions and attitudes based on 358 valid questionnaires obtained from fieldwork. We analyzed the logical connection and influencing relationship between place attachment, residents’ perceptions (residents’ benefits and environmental perceptions), and community residents’ attitudes (security, support, satisfaction), and explored countermeasures and suggestions for building a harmonious host–customer relationship in the Hailuogou area to improve glacier tourism. The results of the study show that the influence of place attachment and residents’ perceptions on emotional solidarity is different from the degree of influence of emotional solidarity on residents’ tourism attitudes, with the most significant positive influence of place attachment on emotional solidarity and the greatest influence of emotional solidarity on sense of security. Emotional solidarity had a certain mediating effect between place attachment, residents’ perceptions, and residents’ tourism attitudes. Significantly, emotional solidarity had the most fully mediated effect between place attachment and support, reaching 73.61%. The moderating effect of place attachment reflects that the higher the place attachment, the weaker the correlation between residents’ perceptions and residents’ tourism attitudes. Meanwhile, residents’ tourism attitudes will weaken their influence with the increase in emotional solidarity. Based on the above results, relevant suggestions are made to provide a theoretical basis and decision-making reference for the development and management of glacier tourism destinations.

The implementation of China’s Beautiful Village Initiative was an extraordinary achievement and aroused extensive public attention. However, existing research mostly focuses on the construction and seldom on public attention towards the Beautiful Village Initiative. For this reason, this paper investigated the spatiotemporal characteristics of public attention based on the Baidu index using time-constrained clustering and the spatial autocorrelation test. Our results showed that the evolutionary process can be divided into three stages: very little national attention (2011–2012), injection of a strong impetus (2013–2015), and rooted in the people’s minds (2016–2020). Spatially, provincial public attention demonstrated obvious spatial differentiation and stable spatial autocorrelation, with Low–Low clusters in Northwest China and High–High Clusters in East, Central, and North China. Spatial econometric models were further utilized to quantify the effects of socioeconomic factors on public attention. The results of the SEM model proved the existence of spatial spillover effects and indicated that the urbanization rate, population density, education level, and network popularity rate all positively affected public attention. The relationship between Beautiful Village construction and public attention was uncoordinated and, in most provinces, advances in public attention were ahead of the construction level. Our findings contribute to the understanding of public attention towards the Beautiful Village Initiative, and policy suggestions we proposed would be applied to increasing public awareness and participation.

Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling–Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products’ to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.

As the “Third Pole” of the Earth, the Tibetan Plateau (TP) has been warming significantly, and the instability of extreme events related to climate and weather has enhanced exceptionally in recent decades. These changes have posed increasingly severe impacts on the population over the TP. So far, however, the impacts on the population have not been assessed systematically and comprehensively from the perspective of risk. In this paper, the hazard of climate change was assessed from a fresh look, not only considering extreme changes of air temperature, precipitation, and wind speed, but also their changes in mean and fluctuation, using daily meteorological data from 1961–2015. The population exposure and vulnerability to climate change were then evaluated using demographic data and considering population scale and structure. Finally, the population risk over the TP to climate change was quantitatively assessed within the framework of the Intergovernmental Panel on Climate Change (IPCC). The results showed that the climate change hazard was mainly at medium and heavy levels, in areas accounting for 64.60% of the total. The population exposure was relatively low; the land area at slight and light levels accounted for 83.94%, but high in the eastern edge area of the TP. The population vulnerability was mostly at medium and heavy levels, and the non-agricultural population rate was the key factor affecting the population vulnerability. Generally, the overall population risk over the TP was not very high: the number of counties with heavy and very heavy risk only accounted for 24.29%, and land area was less than 5%. However, more than 40% of the population was in high-risk areas, located in the eastern edge area of the TP. Population exposure was the decisive factor of the population risk to climate change, and high population exposure might lead to high risk. These findings were potentially valuable to improve cognition of risk, develop proactive risk mitigation strategies, and ensure sustainable development.