丁一, 郭青霞, 秦明星. 黄河流域资源型城市土地绿色利用效率时空演变及影响因素[J]. 农业工程学报, 2021, 37(19): 250-259. DOI: 10.11975/j.issn.1002-6819.2021.19.029
    引用本文: 丁一, 郭青霞, 秦明星. 黄河流域资源型城市土地绿色利用效率时空演变及影响因素[J]. 农业工程学报, 2021, 37(19): 250-259. DOI: 10.11975/j.issn.1002-6819.2021.19.029
    Ding Yi, Guo Qingxia, Qin Mingxing. Temporal-spatial evolution and influencing factors of land green use efficiency of resource-based cities in the Yellow River Basin, China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(19): 250-259. DOI: 10.11975/j.issn.1002-6819.2021.19.029
    Citation: Ding Yi, Guo Qingxia, Qin Mingxing. Temporal-spatial evolution and influencing factors of land green use efficiency of resource-based cities in the Yellow River Basin, China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(19): 250-259. DOI: 10.11975/j.issn.1002-6819.2021.19.029

    黄河流域资源型城市土地绿色利用效率时空演变及影响因素

    Temporal-spatial evolution and influencing factors of land green use efficiency of resource-based cities in the Yellow River Basin, China

    • 摘要: 如何以最小的城市土地资源投入获得最大的社会经济生态效益是区域可持续利用和高质量发展关注的重点之一。以黄河流域资源型城市为研究对象,构建土地绿色利用效率测算指标体系,利用SSBM(Super Slack Based Measure,SSBM)模型测度2009-2018年黄河流域资源型城市土地绿色利用效率,选取空间自相关模型分析土地绿色利用效率的时空演变特征,借助时空地理加权回归(Geographically and Temporally Weighted Regression,GTWR)模型揭示影响土地绿色利用效率因素。研究结果表明:1)从时序变化来看,2009-2018年,黄河流域资源型城市土地绿色利用效率整体变化趋势不明显。2)从空间差异来看,黄河流域资源型城市整体空间关联性不强,集聚态势不显著,局部表现出"小集聚大分散"的空间分布特征。3)土地绿色利用效率影响因素具有空间异质性特征,经济和产业结构始终是影响区域土地绿色利用效率的核心因素,科技作用逐渐凸显,同时不同类型资源型城市主导因素存在明显差异。研究结果对于促进土地绿色利用效率驱动机制的深入研究具有指导意义,也可为黄河流域资源型城市土地高效可持续利用提供科学参考。

       

      Abstract: The Yellow River Basin has been one of the most important energy bases in China. The land green use efficiency of resource-based cities directly dominates the sustainable and high-quality development in this region. In this study, taking the resource-based cities in the Yellow River Basin as the case study area, a novel index system of land green use efficiency was developed, where the resource and environmental consumption were as input indicators, while the economic, social, and environmental benefits as output indicators. SSBM (Super Slack Based Measure) model was also used to measure the land green use efficiency of resource-based cities in the study area from 2009 to 2018. A spatial autocorrelation and GTWR (Geographically and Temporally Weighted Regression) model were selected to analyze the spatial evolution characteristics and driving factors of land green use efficiency, respectively. The results showed that: 1) There was a trend of fluctuation on the land green use efficiency of resource-based cities in the study areas from 2009 to 2018. There was also a relatively low proportion of high-value cities in the land green use efficiency, where there was not a significant change over the past 10 years. Furthermore, there was a low growth rate of land green use efficiency with the insufficient rising power in the cities, due mainly to the regional restrictions and economic development. Specifically, the land green use efficiency increased the fastest in Henan and Shaanxi Provinces from 2009 to 2018. Ningxia, Shandong, and Inner Mongolia were in the slow growth stage, while Shanxi and Gansu showed a downward trend. 2) The Global Moran's I index of land green use efficiency was between -0.179 and 0.192 for the resource-based cities in the study area, indicating that the global spatial evolution was ranging from the relatively strong to weak correlation from 2009 to 2018. There were also the small-scale agglomeration and large-scale dispersion in the spatial distributions of land green use efficiency for the resource-based cities in the study area, according to the LISA index. As such, it is necessary to further strengthen the coordinated development among upper, middle, and lower reaches in the study areas. 3) There was spatial heterogeneity in the driving factors of land green use efficiency. The economic and industrial structure factors were consistently dominated the land green use efficiency in the study area. The most critical factor in the main driving factors was gradually shifted to the level of science and technology for different types of resource-based cities in 2018. Furthermore, the growing or declining resource-based cities depended strongly on the economy, industrial structure, and policy. The mature resource-based cities were mainly influenced by technology and urbanization. More importantly, technology and industrial structure posed a strong impact on renewable resource-based cities. Consequently, the finding can widely be used to guide the decision-making for better efficiency of land green use in various resource-based cities.

       

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