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中国生态福利绩效空间关联网络演变特征与形成机制
引用本文:赵林,曹乃刚,韩增林,高晓彤.中国生态福利绩效空间关联网络演变特征与形成机制[J].自然资源学报,2022,37(12):3183-3200.
作者姓名:赵林  曹乃刚  韩增林  高晓彤
作者单位:1.曲阜师范大学地理与旅游学院,日照 2768262.辽宁师范大学海洋可持续发展研究院,大连 116029
基金项目:国家自然科学基金项目(41701117);国家自然科学基金项目(42071150);山东省高等学校青创科技支持计划项目(2020RWG010)
摘    要:在定量测度生态福利绩效基础上,借助修正的引力模型和社会网络分析方法,研究了中国生态福利绩效空间关联网络演变特征与形成机制。研究发现:(1)中国生态福利绩效空间关联网络呈现“东密西疏”的空间分异规律,关联网络有“扁平化”发展趋势,网络稳定性亟待提升。(2)京津冀、长三角和珠三角处于网络核心位置,具有较强的溢出效应,东北、西北和西南地区处于边缘位置,贵州和甘肃是联系西南和西北省区的关键节点,生态福利绩效在省际间的传输多通过南部省区的中介作用实现。(3)北京、天津和上海构成净溢出板块,珠三角和江浙地区属经纪人板块,长江中下游及西南地区为净溢出板块,东北、黄河中下游及西北地区构成双向溢出板块。(4)资源禀赋差异、市场调节、政府宏观调控和科学技术推动是空间关联网络演变的主要驱动机制。研究成果可为推动生态福利绩效的跨区域协同提升提供参考依据。

关 键 词:生态福利绩效  民生福祉  空间关联网络  形成机制  社会网络分析  中国  
收稿时间:2021-09-27
修稿时间:2021-12-28

Evolution characteristics and formation mechanism of spatial correlation network of ecological well-being performance in China
ZHAO Lin,CAO Nai-gang,HAN Zeng-lin,GAO Xiao-tong.Evolution characteristics and formation mechanism of spatial correlation network of ecological well-being performance in China[J].Journal of Natural Resources,2022,37(12):3183-3200.
Authors:ZHAO Lin  CAO Nai-gang  HAN Zeng-lin  GAO Xiao-tong
Institution:1. School of Geography and Tourism, Qufu Normal University, Rizhao 276826, Shandong, China2. Institute of Marine Sustainable Development, Liaoning Normal University, Dalian 116029, Liaoning, China
Abstract:In this article, the ecological well-being performance of provincial-level regions in China from 2000 to 2019 is measured by using the super EBM model considering undesirable output. At the same time, with the help of the modified gravity model and social network analysis method, the evolution characteristics and formation mechanism of China's ecological well-being performance spatial correlation network are analyzed. The results show that: (1) The spatial correlation network of China's ecological well-being performance shows a spatial pattern of "dense in the east but sparse in the west". During the research period, the hierarchical network structure was gradually changed, and the associated network had a "flat" evolution trend. However, the compactness and stability of spatial association networks need to be improved. (2) The Beijing-Tianjin-Hebei region, Yangtze River Delta and Pearl River Delta are at the core of the spatial correlation network, showing obvious spillover effects, while the Northeast, Northwest and Southwest regions are located at the edge of the network. Guizhou and Gansu are the key nodes between the eastern region and the southwest and northwest provinces in the network. The transmission of ecological well-being performance among provinces is mostly realized through the mediating role of southern provinces. On the whole, the eastern coastal provinces are the "overflow highland" in the spatial correlation network, showing a significant "trickling-down effect" on the Northwest, Southwest and Northeast regions, and the benefit effect of the central provinces is not significant. (3) The results of block model analysis show that China's ecological well-being performance has significant spillover effects between provinces and regions, and the spillover effect between regions is the most important. Among them, Beijing, Tianjin and Shanghai play the role of "engine" in the correlation network, forming the net overflow plate. The Pearl River Delta and Jiangsu and Zhejiang belong to the broker plate, the middle and lower reaches of the Yangtze River and Southwest China are net overflow plates, and Northeast region, the middle and lower reaches of the Yellow River and Northwest region constitute two-way overflow plates. (4) Resource endowment difference, market regulation, government macro-control and science and technology progress are the main driving mechanisms for the evolution of spatial correlation network of ecological well-being performance in China. This study can provide an important reference for promoting the regional collaborative improvement of ecological well-being performance.
Keywords:ecological well-being performance  people's livelihood and well-being  spatial correlation network  formation mechanism  social network analysis  China  
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