Encouraging mitigation behaviour at the personal level is vital to address the issue of global climate change. However, despite numerous climate change communication campaigns, a large percentage of people still feel reluctant to engage in mitigation behaviours. In this paper, we reconsidered the gap between people's objective knowledge on climate change and their mitigation intention in tourism context. By applying protection motivation theory (PMT) and construal level theory (CLT), we accessed the mediating role of threat appraisal and coping appraisal in the context of ski tourism.The results indicate that generally, appraisal of climate change's threat on ski tourism mediates the relationship between knowledge and behavioural intention. Besides, the role of coping appraisal is also partially supported. By contextualizing this study in tourism context and also by separating generally response efficacy from that in terms of ski tourism, we verified the effect of proximising climate change on encouraging mitigation behaviour. This study contributes to existing literature by (i) empirically examining knowledge as an antecedent of PMT and also (ii) incorporating CLT to PMT in a tourism context. The findings have important implications for encouraging personal mitigation behaviours.
Nighttime light (NTL) data have become increasingly practical and are now widely used in studies on urbanization, energy consumption, population estimation, socio-economic evaluation, etc. Based on NTL data and the basic tourism economy (TE) data from 31 provinces of China in 2019, this paper adopted a geographic concentration index, inconsistency index, spatial agglomeration coupling index, global and Local Moran’s index and geographical detector to explore the spatial relationship between NTL and TE. The results of the study were as follows. Firstly, there is a high spatial correlation between NTL and TE. Secondly, the concentration degree, as well as the concentrated distribution area of NTL and TE, are very similar, roughly showing a higher concentration in East and South-Central China. Thirdly, NTL and TE show a type of coordinated development in East and North China, and a TE surpassing NTL in Southwest and South-Central China. The spatial agglomeration coupling index is higher in North China, South-Central China and the coastal regions of East China, and relatively lower in Southwest and Northwest China. Furthermore, in the spatial agglomeration distribution of NTL and TE, there is an obvious high–high and low–low agglomeration. Finally, the geographical detector analysis showed that the driving factor of tourism economy level (TEL) also has a great influence on NTL. The spatial distribution of NTL and TE is integrated to reasonably allocate tourism resources for different areas and promote the sustainable development of NTL and TE among regions.
The study of urban agglomeration boundaries is helpful to understand the internal spatial structure of urban agglomeration, evaluate the development level of urban agglomeration, and thus, assist in the formulation of regional planning and policies. However, previous studies often used only static spatial elements to delineate the boundaries of urban agglomerations, ignoring the spatial connections within urban agglomerations. In this study, night-time light and Tencent user location data were evaluated separately and fused to delineate urban agglomeration boundaries from both static and dynamic spatial perspectives. Additionally, it has been shown in the study results that the accuracy of urban agglomeration boundary delineated by night-time light data is 84.90%, with Kappa coefficient as 0.6348. The accuracy delineated by Tencent user location data is 82.40%, with Kappa coefficient as 0.5637, while the accuracy delineated by data fusion is 92.70%, with Kappa coefficient as 0.7817. Therefore, it can be concluded that the fusion of night-time light and Tencent user location data had the highest accuracy in delineating urban agglomeration boundaries, which verified that the fusion of dynamic spatial elements on a single static spatial element can supplement the spatial connection of urban agglomeration. Our findings enrich the understanding of urban agglomerations, and the accurate delineation of urban agglomerations boundaries can aid urban agglomeration planning and management.
With the increasing demand for diverse ecosystem services, the assessment of ecosystem services has become a hot research topic. Taking Koktokay Global Geopark as the study area, the SoIVES model was used to quantitatively evaluate the various cultural services of the ecosystem in this area from the perspective of social attributes and spatial heterogeneity and to generate corresponding value index (VI) maps. The results show that aesthetic value index is the largest, while entertainment value index is the smallest. With the increase of distance from roads and water bodies, aesthetic value and entertainment value tend to decrease gradually. The value of popular science education still fluctuates slightly in locations far away from roads and water bodies. The value index of health care value fluctuates within a certain distance from the road and gradually decreases as the distance from the water body increases. The application of the SolVES model in a wide range of areas has achieved good results and provided a scientific basis for ecological construction and park planning.
Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning.
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