Spatiotemporal Evolution and Spatial Network Analysis of the Urban Ecological Carrying Capacity in the Yellow River Basin

Abstract

Based on the panel data of 82 cities in the Yellow River Basin (YRB) during 2008–2017, this paper calculated the urban ecological carrying capacity (UECC) index by means of the entropy method, drew a spatiotemporal evolution map using ArcGIS10.3 software, used a spatial cold–hot spot model to explore the spatial characteristics of the UECC index, and used the revised gravity model to construct the spatial network of the UECC. In addition, through social network analysis, we obtained the spatial network correlation characteristics of the UECC of 82 cities in the YRB. The study found the following: (1) The UECC index of the cities in the YRB increased steadily, and showed strong non-stationarity in space. The cold and hot spot patterns both changed greatly. Overall, the changes of the hot and cold spots were very significant. (2) The spatial correlation and linkage effects of the UECC in the YRB were not significant. The central cities with higher point centrality and closeness centrality showed the same spatial distribution, and most of them are located in the midstream and downstream of the YRB. The central cities in the midstream and downstream of the YRB had high betweenness centrality, and stood in the center of the association network. (3) The four plates in the spatial correlation network of the UECC in the YRB all showed their advantages and functions. The first plate was the net spillover plate, which was principally allocated in the upstream and midstream of the YRB. The second plate was the broker plate, which was principally located in the midstream and downstream of the YRB, and a few cities in the upper reaches. The third plate was the net inflow plate, which was distributed sporadically in the upstream and downstream of the YRB. The fourth plate was the broker plate, which was scattered in upstream, midstream, and downstream of the YRB. Therefore, it is necessary to shorten the gap of and promote the improvement of the UECC in the YRB.

Evaluation of Tourism Ecological Security and Its Driving Mechanism in the Yellow River Basin, China: Based on Open Systems Theory and DPSIR Model

“…Therefore, tourism managers should promote the integration of ecotourism with agriculture, forestry, marine, and other related industries, and establish a cooperative relationship with neighboring cities. It is conducive to stimulating the positive driving effect of TES high-level cities and helping TES low-level cities escape from the extensive tourism development model [73].…”

Study on ecological environment carrying capacity of forest community near tourist roads in Lishan Nature Reserve based on tourism disturbance

“…(1) TWINSPAN 将 41 个样地划分为 7 类不同的森林群落类型，分别是第Ⅰ组"无一年 生植物-多幼苗-多地上芽组" 、第Ⅱ组"无一年生植物-幼苗最多-无地上芽组" 、第 Ⅲ组"无一年生植物-无地上芽-地下芽最多组" 、第Ⅳ组为"少一年生植物-无地上芽 -无地下芽组" 、第Ⅴ组"一年生植物最多-幼苗居中-多地上芽组" 、第Ⅵ组"多一年 生植物-少幼苗-无灌木组"以及第Ⅶ组为"少一年生植物-幼苗最少-灌木较低 组" 。从第Ⅰ组到第Ⅶ组，幼苗由"多幼苗"转变为"少幼苗" ，灌木层盖度、灌木层景 表 5 森林群落的生态环境承载力与评价指标之间的相关性 38 卷 自 然 资 源 学 报 与以往生态环境承载力的研究相比，本文所构建的测算模型的侧重点有所不同。已 有学者多倾向于从水 [4,33] 、草地 [34] 、农田 [35] 等不同生态系统的角度，或从省域 [36] 、市域 [37] 、 县域 [38] 等不同空间尺度出发，探索生态环境承载力的评价指标体系与测算方法。而本文 的研究路径则是基于人为干扰对森林群落影响的前提下，从群落的生态功能及其指示特 征的角度，来构建和研究不同群落类型的生态环境承载力。事实上，森林群落中植被是 自然保护区中一种最为敏感、最有生机的生态要素，其对人为干扰的生态响应尤其明 显。因此，从这一生态要素的角度来考虑人为影响下的生态环境承载力具有特殊意义。 此外，本文就森林群落的生态环境承载力与其评价指标、地理因子之间的相关性也展开 研究，并揭示出他们之间存在一定的关联性，由此识别出影响森林群落生态环境承载力 的主要评价指标与关键地理因子。这不仅在理论上拓展了森林群落生态环境承载力的研 究深度，在实践上，也为有效评估及预测自然保护区的生态环境承载能力，加强区域生 态综合管理工作提供了坚实的理论基础和数据支撑。这是本文的突出贡献之一，也是以 往的研究所不曾涉及的 [14,26] Abstract: The present study was carried out in Lishan Nature Reserve. Firstly, the evaluation index system for the ecological environment carrying capacity in forest community near tourist roads was constructed from multiple angles in this paper.…”

Research on Industrial Ecological Efficiency Evaluation and Improvement Countermeasures Based on Data-Driven Evaluations from 30 Provinces and Cities in China

“…The values of China's industrial ecological efficiency in 2011, 2014, 2017, and 2020 were selected, and the natural discontinuity method [47,48] of the ArcGis10.2 software was used to depict the spatial distribution map of China's industrial ecological efficiency (Figure 6).…”