Research on the estimation and spatial pattern of net tourism carbon emissions in the Yellow River Basin from 2009 to 2019.

Based on panel data and remote sensing data of cities in the Yellow River Basin in China from 2009 to 2019, and using the tourism carbon footprint and tourism carbon carrying capacity models, the tourism carbon emissions, tourism carbon carrying capacity, and net tourism carbon of 65 cities in the Yellow River Basin were calculated. The balance and dynamic changes in carbon emissions and carbon fixation of urban tourism in the past ten years were compared. The results show that (1) tourism carbon emissions in the Yellow River Basin are generally on the rise, along with a distribution characteristic of downstream > middle reaches > upstream with obvious characteristics of urban agglomeration centrality within the basin; (2) the carbon carrying capacity of tourism is higher than that of tourism. The growth of carbon emissions is relatively slow, showing a spatial distribution pattern of high in the west and low in the east, which is mainly related to the geographical environment and economic development of the city; (3) the tourism carbon emissions and tourism carbon carrying capacity in the upstream areas can basically maintain a balance, but in the middle and lower reaches of the region, they show a carbon surplus. There is a significant positive spatial correlation in urban net tourism carbon emissions, and the clusters are mainly H-H and L-L.

Spatial distribution pattern and driving mechanism of tourist attractions in Gansu Province based on POI data.

The article utilizes POI (Point of Interest) data of tourist attractions in Gansu Province in 2021, adopts Moran's I and kernel density analysis to study the spatial distribution pattern of tourist attractions in Gansu Province, and uses spatial autoregressive modeling to explore the driving mechanism affecting their spatial distribution pattern. The results show that: (1) Gansu Province has a large number and rich types of tourist attractions, and there are differences in the number of different types of tourist attractions; (2) The spatial distribution pattern of different types of tourist attractions in different cities and towns shows the phenomenon of both agglomeration and dispersion, with a higher degree of agglomeration in the central and northwestern regions of the province and a lower degree of agglomeration in the southwestern and southeastern corners; (3) The overall spatial distribution pattern of tourist attractions shows the distribution characteristics of multi-core decentralized distribution, forming 8 core aggregation areas in the southeast of the province; (4) The article analyzes the driving mechanism of the spatial distribution pattern of tourist attractions in Gansu Province using the buffer zone and OLS models, and the results show that the natural environment, transportation location, national policies and socio-economics all have a positive impact on the distribution of tourist attractions.