The spatio-temporal evolution of industrial resilience and its driving factors in the Yangtze River Delta Urban Agglomeration

doi:10.3969/j.issn.1004-9479.2021.03.2019717

Abstract

Abstract:

Industry is an important support for building the development strategic advantages of China in the future. Against the background of increasing uncertainty of the world economy, it is of urgent practical significance to improve the spatial-temporal evolution of industrial resilience development and clarify the impact mechanisms of industrial resilience. Based on the evaluation of industrial resilience by the catastrophe progression model, this paper discusses the spatio-temporal evolution characteristics and driving factors of industrial resilience in the Yangtze River Delta Urban Agglomeration (YRDUA) by using kernel density estimation, Theil entropy index and Tobit spatial lag panel regression model. The results show that:(1)The industrial resilience of most cities in the YRDUA has significantly improved, reaching the medium-high level on the whole. The regional gap of industrial resilience in the YRDUA is narrowing, but there still exists the multi-level 'core-periphery' differentiation pattern at the overall level and the inner metropolitan level.(2)The industrial resilience of central cities with strong comprehensive strength has a significant spatial spillover effect. Shanghai, Hangzhou, Nanjing, Hefei and Ningbo making close intra-industry connections with their neighboring cities have formed agglomeration of high-level industrial resilience.(3)Industrial structure is the main factor affecting the industrial resilience of the YRDUA. In addition, regional financial environment, government public services and regional dependence on foreign trade all have significant impacts on industrial resilience to varying degrees.

Key words: industrial resilience, spatial-temporal evolution, driving factors, the Yangtze River Delta Urban Agglomeration

摘要：

在世界经济不确定性日益增加的背景下，推进优化我国区域工业韧性演变格局和厘清工业韧性影响机制具有重要现实意义。本文以长三角城市群为例，在突变级数模型对工业韧性评价的基础上，利用核密度估计、泰尔熵指数和Tobit空间滞后面板模型，对长三角城市群工业韧性的时空演变特征及其影响因素进行探讨。研究发现：（1）长三角城市群工业韧性提升显著，总体上达到了中高韧性水平。长三角城市群工业韧性的内部差距不断缩小，但仍然存在不同空间尺度的“核心-边缘”分异格局。（2）综合实力较强的中心城市工业韧性具有显著的空间溢出效应。上海、杭州、南京、合肥及宁波等城市周边形成高水平工业韧性的集聚区。（3）工业结构是影响长三角城市群工业韧性的最主要因素。区域金融环境、政府公共服务以及区域外贸依存度均不同程度对工业韧性产生显著影响。

关键词: 工业韧性, 时空演变, 影响因素, 长三角城市群

Feiyu LU, Weihua YIN, Nannan LIU. The spatio-temporal evolution of industrial resilience and its driving factors in the Yangtze River Delta Urban Agglomeration[J]. World Regional Studies, 2021, 30(3): 589-600.

鲁飞宇, 殷为华, 刘楠楠. 长三角城市群工业韧性的时空演变及影响因素研究[J]. 世界地理研究, 2021, 30(3): 589-600.

Figures/Tables 6

Fig.1 The catastrophe model of industrial resilience in the YRDUA

Fig.2 The changes of Kernel density distribution of industrial resilience in the YRDUA

Fig.3 Spatial evolution of industrial resilience in the YRDUA

Tab.1 Theil index on industrial resilience in the YRDUA

指数	2011	2012	2013	2014	2015	2016	2017
$T E W$	0.0089	0.0074	0.0064	0.0064	0.0056	0.0053	0.0054
$T E B$	0.5096	0.4857	0.4534	0.4458	0.4339	0.4173	0.4126
$T E$	0.5185	0.4931	0.4598	0.4522	0.4395	0.4225	0.4180

Tab.1 Theil index on industrial resilience in the YRDUA

指数	2011	2012	2013	2014	2015	2016	2017
$T E W$	0.0089	0.0074	0.0064	0.0064	0.0056	0.0053	0.0054
$T E B$	0.5096	0.4857	0.4534	0.4458	0.4339	0.4173	0.4126
$T E$	0.5185	0.4931	0.4598	0.4522	0.4395	0.4225	0.4180

Tab.2 Descriptive statistics

变量统计量	工业韧性	相关多样性	无关多样性	工业区位熵	金融机构年末存贷款余额/GDP	地方财政一般预算内支出/收入	出口总额/GDP	人均专利数（件）
均值	0.783	0.216	1.735	0.926	2.749	1.433	0.230	49.633
方差	0.093	0.143	0.364	0.450	0.955	0.470	0.204	33.441
变异系数	0.119	0.662	0.210	0.486	0.347	0.328	0.887	0.674
最小值	0.480	0.021	0.922	0.165	1.400	0.896	0.014	1.477
最大值	0.961	0.674	2.329	2.491	6.124	3.161	0.873	159.420
上海均值	0.941	0.604	2.290	0.905	5.266	1.110	0.542	67.735
南京均值	0.860	0.305	1.780	0.815	4.221	1.037	0.235	82.042
无锡均值	0.876	0.338	2.142	1.596	2.466	0.995	0.218	90.712
常州均值	0.838	0.263	1.264	1.484	2.379	1.036	0.252	88.608
苏州均值	0.942	0.549	2.292	2.221	2.765	0.927	0.406	89.642
南通均值	0.856	0.307	1.607	1.018	2.361	1.221	0.138	24.422
盐城均值	0.796	0.221	1.644	0.610	1.680	1.627	0.034	9.588
扬州均值	0.809	0.234	1.471	1.294	1.856	1.320	0.110	28.836
镇江均值	0.797	0.201	1.232	1.335	1.941	1.172	0.074	42.236
泰州均值	0.806	0.247	1.949	0.951	1.992	1.333	0.056	21.745
杭州均值	0.870	0.331	2.234	0.851	4.942	0.953	0.321	81.473
宁波均值	0.867	0.345	1.912	1.420	3.580	1.167	0.570	110.518
嘉兴均值	0.797	0.217	1.756	1.245	2.908	1.101	0.426	67.558
湖州均值	0.751	0.131	1.944	0.954	2.640	1.291	0.280	69.628
绍兴均值	0.806	0.257	1.967	1.099	2.900	1.097	0.418	83.924
金华均值	0.748	0.136	2.072	0.846	3.675	1.397	0.705	46.356
舟山均值	0.612	0.054	1.365	0.560	2.959	1.990	0.446	30.270
台州均值	0.753	0.128	1.557	0.790	3.039	1.410	0.349	34.267
合肥均值	0.829	0.221	1.864	0.541	3.452	1.418	0.082	42.532
芜湖均值	0.778	0.152	1.804	0.837	1.986	1.526	0.090	52.502
马鞍山均值	0.707	0.085	1.735	0.650	1.918	1.363	0.022	29.413
铜陵均值	0.664	0.070	1.253	0.669	1.867	1.690	0.033	32.692
安庆均值	0.698	0.057	1.972	0.318	2.315	2.913	0.033	12.341
滁州均值	0.700	0.081	1.591	0.379	2.096	2.203	0.047	17.539
池州均值	0.581	0.028	1.061	0.301	2.207	2.024	0.019	17.152
宣城均值	0.675	0.065	1.341	0.388	2.072	1.926	0.067	16.732

Tab.3 Results of Tobit spatial lag panel regression model

变量	OLS回归模型		Tobit空间滞后模型
变量	弹性系数	T统计量	弹性系数	T统计量
相关多样性	0.361^***	10.01	0.347^***	10.56
无关多样性	0.0485^***	5.61	0.046^***	5.46
工业区位熵	0.0441^**	3.31	0.0475^***	3.52
金融机构年末存贷款余额/GDP	0.0145^*	2.56	0.0175^***	3.54
地方财政一般预算内支出/收入	-0.0261^**	-3.25	-0.0274^***	-3.39
出口总额/GDP	-0.115^***	-6.28	-0.124^***	-7.12
人均专利申请量（控制变量）	0.000185	1.57	0.000176	1.70
$α$	0.595^***	23.50	0.608^***	22.90
$ρ$			-0.00362^*	-2.31
$σ$			0.0307^***	11.82
R²	0.88		0.88
调整R²	0.88		0.85

Tab.3 Results of Tobit spatial lag panel regression model

变量	OLS回归模型		Tobit空间滞后模型
变量	弹性系数	T统计量	弹性系数	T统计量
相关多样性	0.361^***	10.01	0.347^***	10.56
无关多样性	0.0485^***	5.61	0.046^***	5.46
工业区位熵	0.0441^**	3.31	0.0475^***	3.52
金融机构年末存贷款余额/GDP	0.0145^*	2.56	0.0175^***	3.54
地方财政一般预算内支出/收入	-0.0261^**	-3.25	-0.0274^***	-3.39
出口总额/GDP	-0.115^***	-6.28	-0.124^***	-7.12
人均专利申请量（控制变量）	0.000185	1.57	0.000176	1.70
$α$	0.595^***	23.50	0.608^***	22.90
$ρ$			-0.00362^*	-2.31
$σ$			0.0307^***	11.82
R²	0.88		0.88
调整R²	0.88		0.85

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