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各国发展指标的衡量及其起源。

Scaling of development indicators in countries and its origin.

作者信息

Shuai Chenyang, Liao Chuan, Qu Shen, Chen Xi, Zhao Bu, Zou Jian-Ping, Xu Ming

机构信息

School of Management Science and Real Estate, Chongqing University, Chongqing, China.

Department of Global Development, Cornell University, Ithaca, NY, USA.

出版信息

iScience. 2024 Jul 14;27(8):110497. doi: 10.1016/j.isci.2024.110497. eCollection 2024 Aug 16.

DOI:10.1016/j.isci.2024.110497
PMID:39148715
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11325361/
Abstract

Population-normalized indicators (e.g., GDP per capita), under the assumption of the indicators scaling linearly with population, are ubiquitously used in national development performance comparison. This assumption, however, is not valid because it may ignore agglomeration effect resulting from nonlinear interactions in socioeconomic systems. Here, we present extensive empirical evidence showing the sub-linear scaling rather than the presumed linear scaling between population and multiple indicators of national development performance. We then develop a theoretical framework based on the scaling rule observed in cities to explore the origin of scaling in countries. Finally, we demonstrate that urbanization plays a pivotal role in transforming national development from limited sub-linear growth to unlimited super-linear growth. This underscores the significance of urbanization in achieving sustained growth and elevating human living standards at the national level. Our findings have the potential to inform policies aimed at promoting equitable inter-country comparison and achieving sustainable development in countries.

摘要

在指标与人口呈线性比例缩放的假设下,人口归一化指标(如人均国内生产总值)被广泛用于国家发展绩效比较。然而,这一假设并不成立,因为它可能忽略了社会经济系统中非线性相互作用产生的集聚效应。在此,我们给出了大量实证证据,表明人口与国家发展绩效的多个指标之间呈次线性比例缩放,而非假定的线性比例缩放。然后,我们基于在城市中观察到的比例缩放规则构建了一个理论框架,以探究国家层面比例缩放的根源。最后,我们证明城市化在将国家发展从有限次线性增长转变为无限超线性增长过程中起着关键作用。这凸显了城市化在国家层面实现持续增长和提升人类生活水平方面的重要性。我们的研究结果有可能为旨在促进公平的国家间比较以及实现各国可持续发展的政策提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/2470703ba08b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/910ecd5450c9/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/9820b21faf47/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/30b58ef0f71c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/0eb7339608f7/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/5ccf0f5b0dce/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/2470703ba08b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/910ecd5450c9/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/9820b21faf47/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/30b58ef0f71c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/0eb7339608f7/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/5ccf0f5b0dce/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a580/11325361/2470703ba08b/gr5.jpg

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