Analysis on the spatial-temporal pattern and influencing factors of regional economic resilience in China under the COVID-19 pandemic

doi:10.3969/j.issn.1004-9479.2023.03.2021987

Abstract

Abstract:

The pandemic (COVID-19) crisis has brought an unprecedented shock on regional economies. The impact object, spatial scale and transmission nature of the shock are different from any previous shocks. Exploring the response and adaptation of regional economy under the new shock provides a new topic for the study of regional economic resilience. Drawing upon the current understanding of the characteristics and developmental trend of COVID-19, this paper measured the economic resilience of China's provinces from the perspective of resistance. It also analyzed the influencing factors of the patial-temporal differences in economic resilience.The results show that: (1) There are differences and complexity in regional economic resilience under different dimensions. The resistance of GDP dimension is high, and the regions with low resistance are concentrated in the central and northeast China. The resistance of import and export dimension is the lowest, and the resistance of import and export in border areas is lower. The resistance of consumption dimension is relatively low. In the long run, the resistance of consumption in the eastern China is high. (2) The attributes of regional industrial structure still play a key role in affecting regional economic resilience. For a long time, a higher degree of specialization significantly promotes resilience. Moreover, related diversification plays a positive role in promoting resilience after the outbreak of the pandemic for a period of time, while unrelated diversification plays a negative role in regional economic resilience for a long time. (3) Governance regulation has a significant effect on regional resilience. Regional external dependence is conducive to the regional economy to actively respond to the pandemic shock.

Key words: COVID-19 pandemic, regional economic resilience, spatial-temporal pattern, influence factors, China

摘要：

新冠疫情对区域经济体带来前所未有的冲击，其影响对象、空间尺度和传播规律均有别于以往任何冲击。探究新冠疫情冲击背景下区域经济的应对与适应，为区域经济韧性研究提供了新的议题。在理解新冠疫情的冲击特征和发展态势基础上，论文从抵抗力角度出发，测度了中国省域经济韧性，分析了造成经济韧性时空差异的影响因素。研究发现：（1）不同维度下区域经济韧性存在差异性与复杂性，GDP维度抵抗力较高，低抵抗力区域集中在中部和东北地区；进出口维度抵抗力最低，边疆地区进出口抵抗力更低；消费维度抵抗力较低，长期来看，东部地区消费抵抗力较高。（2）产业结构属性对区域经济韧性影响显著。从较长时段看，高专业化对韧性有显著促进作用；相关多样化在疫情发生一段时间后对韧性起积极促进作用，非相关多样化长期对区域经济韧性起消极作用。（3）政府调控能力对韧性有显著促进作用，对外依赖有利于区域经济积极应对疫情冲击。

关键词: 新冠疫情, 区域经济韧性, 时空格局, 影响因素, 中国

Liangang LI, Xiaohui HU. Analysis on the spatial-temporal pattern and influencing factors of regional economic resilience in China under the COVID-19 pandemic[J]. World Regional Studies, 2023, 32(3): 64-75.

李连刚, 胡晓辉. 新冠肺炎疫情下中国区域经济韧性时空格局与影响因素分析[J]. 世界地理研究, 2023, 32(3): 64-75.

Figures/Tables 5

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变量	定义	符号
区域经济韧性	抵抗力指数	Resistance
专业化	赫芬达尔—赫希曼指数	spc
多样化	相关多样化指数	rv
多样化	非相关多样化指数	urv
产业结构	二产与三产增加值比值	ind
政府调控	疫情问责官员/人	gov
对外依赖	进出口总额占GDP比重	ope
疫情状况	新冠肺炎确诊人数/人	cof
卫生投入	财政支出中卫生支出比重	hel
人口集聚	人口密度/（人/km²）	pop

变量	定义	符号
区域经济韧性	抵抗力指数	Resistance
专业化	赫芬达尔—赫希曼指数	spc
多样化	相关多样化指数	rv
多样化	非相关多样化指数	urv
产业结构	二产与三产增加值比值	ind
政府调控	疫情问责官员/人	gov
对外依赖	进出口总额占GDP比重	ope
疫情状况	新冠肺炎确诊人数/人	cof
卫生投入	财政支出中卫生支出比重	hel
人口集聚	人口密度/（人/km²）	pop

变量	Q1		Q1—Q2		Q1—Q3		Q1—Q4
常数	10.34^**（6.24^）	5.83^* （1.37）	31.91^*（23.64^*）	15.21^*（6.11）	51.18^*（38.11^*）	20.85^（12.24^）	11.80^*（8.84^*）	5.56^（3.33^）
spc	1.14 （3.08^*）	－1.24（－0.49）	5.76^（11.31^*）	－3.47^*（－1.69）	13.01^*（18.36^*）	－4.16 （－0.85）	2.94^*（4.21^*）	－0.57 （0.05）
rv	3.89 （7.53）		16.44^（27.11^）		32.95^*（39.96^）		6.69^（8.84^）
urv		－6.71^**（－7.75）		－24.00^*（－28.49^）		－40.98^*（－42.21^）		－8.45^*（－9.41^）
ind	－0.04 （－0.69）	－0.21（－0.79）	0.16 （－1.34）	－0.24 （－1.68）	－1.41 （－3.13）	－1.85 （－3.64）	－0.37（－0.78）	－0.46（－0.91）
ope	0.43^**（0.09）	0.45^***（－0.16）	0.95^** （－0.17）	0.97^** （－0.43）	1.66^***（－0.19）	1.62^***（－0.58）	0.29^*（－0.12）	0.28^**（－0.19）
gov	－0.08（－0.13）	－0.06（－0.13）	－0.32^（－0.47^）	－0.26（－0.48^*）	－0.46^（－0.67^*）	－0.35（－0.68^*）	－0.15^（－0.19^）	－0.13^（－0.19^）
cof	－0.56^***	－0.57^***	－1.23^***	－1.28^***	－1.79^***	－1.89^***	－0.38^***	－0.40^***
hel	2.69^***	2.14^***	6.30^***	4.52^**	8.23^***	5.28^**	1.89^***	1.28^**
pop	0.39	0.43	0.76	0.88	1.39	1.53	0.29	0.33
R²	0.75 （0.19）	0.89 （0.20）	0.77 （0.38）	0.82 （0.41）	0.80 （0.39）	0.84 （0.42）	0.78 （0.44）	0.81 （0.48）

变量	Q1		Q1—Q2		Q1—Q3		Q1—Q4
常数	10.34^**（6.24^）	5.83^* （1.37）	31.91^*（23.64^*）	15.21^*（6.11）	51.18^*（38.11^*）	20.85^（12.24^）	11.80^*（8.84^*）	5.56^（3.33^）
spc	1.14 （3.08^*）	－1.24（－0.49）	5.76^（11.31^*）	－3.47^*（－1.69）	13.01^*（18.36^*）	－4.16 （－0.85）	2.94^*（4.21^*）	－0.57 （0.05）
rv	3.89 （7.53）		16.44^（27.11^）		32.95^*（39.96^）		6.69^（8.84^）
urv		－6.71^**（－7.75）		－24.00^*（－28.49^）		－40.98^*（－42.21^）		－8.45^*（－9.41^）
ind	－0.04 （－0.69）	－0.21（－0.79）	0.16 （－1.34）	－0.24 （－1.68）	－1.41 （－3.13）	－1.85 （－3.64）	－0.37（－0.78）	－0.46（－0.91）
ope	0.43^**（0.09）	0.45^***（－0.16）	0.95^** （－0.17）	0.97^** （－0.43）	1.66^***（－0.19）	1.62^***（－0.58）	0.29^*（－0.12）	0.28^**（－0.19）
gov	－0.08（－0.13）	－0.06（－0.13）	－0.32^（－0.47^）	－0.26（－0.48^*）	－0.46^（－0.67^*）	－0.35（－0.68^*）	－0.15^（－0.19^）	－0.13^（－0.19^）
cof	－0.56^***	－0.57^***	－1.23^***	－1.28^***	－1.79^***	－1.89^***	－0.38^***	－0.40^***
hel	2.69^***	2.14^***	6.30^***	4.52^**	8.23^***	5.28^**	1.89^***	1.28^**
pop	0.39	0.43	0.76	0.88	1.39	1.53	0.29	0.33
R²	0.75 （0.19）	0.89 （0.20）	0.77 （0.38）	0.82 （0.41）	0.80 （0.39）	0.84 （0.42）	0.78 （0.44）	0.81 （0.48）

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