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中国农村居民生计韧性的时空变化与分解分析。

Spatio-Temporal Variation and Decomposition Analysis of Livelihood Resilience of Rural Residents in China.

机构信息

School of Public Administration, Southwestern University of Finance and Economics, Chengdu 611130, China.

School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China.

出版信息

Int J Environ Res Public Health. 2022 Aug 25;19(17):10612. doi: 10.3390/ijerph191710612.

DOI:10.3390/ijerph191710612
PMID:36078330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9518158/
Abstract

The key to sustainable rural development and coordinated regional development is to properly measure the livelihood resilience of rural residents (LRRR), and investigate its regional differences, distribution characteristics, and evolutionary patterns. This study combined the entropy method, the Dagum Gini coefficient and decomposition, kernel density estimation, and convergence analysis to measure the LRRR in 30 provinces of China from 2006 to 2020, and to analyze its regional differences and sources, dynamic distribution, and characteristics of convergence. The LRRR in China overall declined 2006-2020, with an east-to-west spatial gradient toward lower livelihood resilience. Intra-regional differences in LRRR narrowed in the Eastern and Central Regions, while those in the Western Region widened. Inter-regional differences were the main source of differences in LRRR. The LRRRs in most provinces in China were gradually reaching the same level over time (i.e., σ convergence and β convergence). This research provides a factual reference for policies related to reducing inter-provincial differences in the LRRR in China.

摘要

实现农村可持续发展和区域协调发展的关键在于科学测度农村居民生计韧性(LRRR),并深入探究其区域差异、分布特征及演进规律。本研究结合熵值法、Dagum 基尼系数分解与核密度估计、收敛性分析,测算了 2006—2020 年中国 30 个省份的 LRRR,并对其区域差异与来源、动态分布特征及收敛性进行了分析。结果表明:2006—2020 年中国 LRRR 整体呈下降趋势,呈现出由东向西逐渐降低的空间梯度特征;东部和中部地区 LRRR 的区域内差异呈缩小趋势,西部地区则呈扩大趋势;区域间差异是导致 LRRR 区域差异的主要来源;大部分省份的 LRRR 具有σ收敛和β收敛特征,即时间序列上的趋同特征。本研究为缩小中国各省份间 LRRR 差异的相关政策制定提供了事实参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/74ece9de7c74/ijerph-19-10612-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/9b2ba0cb2844/ijerph-19-10612-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/b9653d51a256/ijerph-19-10612-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/7404a7ca39a3/ijerph-19-10612-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/4da3bb2e5ba6/ijerph-19-10612-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/48982dfe8a3b/ijerph-19-10612-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/74ece9de7c74/ijerph-19-10612-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/9b2ba0cb2844/ijerph-19-10612-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/b9653d51a256/ijerph-19-10612-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/7404a7ca39a3/ijerph-19-10612-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/4da3bb2e5ba6/ijerph-19-10612-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/48982dfe8a3b/ijerph-19-10612-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6956/9518158/74ece9de7c74/ijerph-19-10612-g006.jpg

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