Assessment of comprehensive carrying capacity of urban agglomeration along the Yellow River from the perspective of productive-living-ecology space
Under the background of globalization and urbanization,urban agglomeration has become one of the important engines to promote economic growth and social development.As one of China's important urban agglomerations and a spatial carrier for promoting ecological civilization construction,the Yellow River basin plays a prominent role in China's overall economic development,social stability and opening-up.However,compared with other regions,the resources,environment and economic and social development conditions of the urban agglomeration along the Yellow River are more unbalanced,and the spatial layout of urban construction is unreasonable,which leads to the higher vulnerability of the urban agglomeration and the lower comprehensive carrying capacity.In order to find out the coupling law of production,life and ecological spatial quality of the urban agglomeration along the Yellow River,to provide new ideas for the improvement of its carrying capacity,and to provide decision-making reference for the high-quality and sustainable development of the urban agglomeration.Based on the perspective of productive-living-ecology space,a total of 27 indicators from 9 dimensions were selected to build a comprehensive carrying capacity evaluation index system.The entropy method was used to determine the weight of the indicators,and the carrying capacity along the Yellow River urban Agglomeration from 2001 to 2020 was measured by the comprehensive measurement index model.Finally,the grey relational degree model is used to identify the key influencing factors of the comprehensive bearing capacity.The results show that the spatial differentiation of the comprehensive carrying capacity of the urban agglomeration along the Yellow River is significant,showing a three-level gradient distribution.The first-level gradient includes Shandong Peninsula urban agglomerations and Central Plains urban agglomerations,the second-level gradient includes Hubao and Eyu urban agglomerations,and the third-level gradient includes Jinzhong urban agglomerations,Guanzhong plain urban agglomerations,Lanxi urban agglomerations and Ningxia urban agglomerations.The proportion of cities with moderate carrying capacity and high carrying capacity remained unchanged at 29.31％and 12.07％,while the proportion of cities with high carrying capacity increased from 24.14％to 29.31％,showing a good development trend.There are differences in the factors affecting the comprehensive carrying capacity of urban agglomerations,which are generally concentrated in five dimensions:economic development,life services,social security,ecological pressure and ecological governance.According to the influence factors of different gradient urban agglomerations with comprehensive carrying capacity,the corresponding management measures are proposed.The first gradient urban agglomerations should focus on promoting the optimization and upgrading of industrial structure,the green development of regional industry,strengthening environmental protection and ecological restoration projects,and promoting the development of green circular economy.Urban agglomerations in the second gradient should strengthen the leading role of central cities,encourage the implementation of energy conservation and cleaner production measures,and pay attention to ecological environmental protection and green construction,so as to realize the positive interaction between ecology and economy in urban development.The third level of gradient urban agglomerations should deepen the internal and external opening up,promote industrial transformation and development,increase investment in infrastructure and public services,improve social security measures,improve the quality of living space,and then improve the comprehensive urban carrying capacity,promote high-quality and sustainable development.
productive-living-ecology spacecomprehensive carrying capacityurban agglomeration along the Yellow Riverinfluencing factor
万方数据
维普
