Measurement of Tourism Carbon Emission and Carbon Carrying Capacity in the Yellow River Basin and Analysis of Spatial and Temporal Evolution

Abstract

Abstract To investigate the spatial and temporal evolution characteristics of tourism carbon emissions and carbon carrying capacity in the Yellow River Basin, this paper uses panel data to measure tourism carbon emissions, carbon carrying capacity and net tourism carbon emissions in 65 cities in the Yellow River Basin at three time points, 2009, 2014 and 2019, based on carbon emissions, carbon carrying capacity and net tourism carbon emissions models, and uses Arc GIS software to visually present tourism carbon emissions The spatial and temporal evolution characteristics of tourism carbon emissions and carbon carrying capacity were visualized by Arc GIS software. The study shows that (ⅰ) tourism carbon emissions in the Yellow River Basin show a rapid growth trend from 2009 to 2019, with an increase of 931.95%; the tourism carbon emissions of 65 cities in the Yellow River Basin vary widely, with spatial differentiation showing high in the east, low in the west, high in the middle and lower reaches, and low in the upper reaches, and showing significant urban cluster centrality characteristics. (ⅱ) The carbon carrying capacity of the Yellow River Basin shows growth from 2009-2019, from 29.21 million tons in 2009 to 86.22 million tons in 2019, an increase of 195.17%; the high value area of tourism carbon carrying capacity of the Yellow River Basin is mainly concentrated in the middle and upper reaches of the urban agglomeration, and the regional differences of carbon carrying capacity intensity of the upper, middle and lower reaches of the urban agglomeration in the basin are significant. (ⅲ) The net tourism carbon emissions in the Yellow River Basin from 2009 to 2019 are generally in carbon surplus, showing more obvious local clustering characteristics, the spatial divergence pattern of urban cluster effect is significant, and the carbon surplus area expands from the surrounding to the center, showing spatial clustering characteristics.