Spatiotemporal Evolution Pattern and Spatial Spillover Effects of China's Lithium Battery Industry Chain and Innovation Chain
Received date: 2024-12-17
Revised date: 2025-05-15
Online published: 2025-08-07
This study investigates China's lithium battery industry using enterprise and patent data from 2000 to 2022, employing spatial coupling analysis and the spatial Durbin model to examine the spatiotemporal evolution, coupling relationships, and innovation drivers of industry and innovation chains across 297 Chinese cities. The findings reveal that: 1) In terms of spatial distribution, upstream lithium battery production concentrates in western regions driven by resource endowments, while innovation clusters in urban agglomerations such as Beijing-Tianjin-Hebei and the Yangtze River Delta, indicating spatial decoupling. Meanwhile, midstream and downstream production and innovation activities are predominantly distributed across eastern coastal cities with robust industrial foundations (e.g., Guangdong, Jiangsu, Zhejiang, Shandong), with a discernible diffusion trend toward central and western regions. 2) In terms of industry-innovation coupling, a pattern of "macro-scale synergy with micro-scale divergence" emerges. At the macro scale, the spatial distance between lithium battery industrial centers and innovation cores progressively narrows, significantly enhancing spatial coupling. At the city level, divergent resource endowments and foundational disparities drive widespread integration challenges, manifested as "production-R&D imbalance", "technological isolation", and "industrial chain fragmentation". 3) In terms of influencing mechanisms, six factors drive the innovative development of China's lithium battery industry: industrial agglomeration intensity, related industrial foundations, government support intensity, economic conditions, openness level, and science-technology innovation ecosystems. The effect strength of these factors exhibits significant heterogeneity. Their synergistic interactions shape four distinct innovation development models: talent and technology-driven, leading enterprise-driven, policy and resource-driven, and cluster-based collaborative innovation. These findings provide empirical evidence and policy references for optimizing industrial layouts and advancing innovation-oriented development.
SONG Jiawen , ZHANG Xiaoping , WU Aiping , LIAN Wenhua . Spatiotemporal Evolution Pattern and Spatial Spillover Effects of China's Lithium Battery Industry Chain and Innovation Chain[J]. Economic geography, 2025 , 45(6) : 109 -120 . DOI: 10.15957/j.cnki.jjdl.2025.06.011
表1 变量选取与描述性统计Tab.1 Variable selection and descriptive statistics |
性质 | 变量类型 | 变量名称 | 符号 | 最小值 | 最大值 |
---|---|---|---|---|---|
被解释变量 | 创新发展水平 | 锂电池创新产出(件) | innovation | 0 | 6894.00 |
核心解释变量 | 产业集聚水平 | 锂电池产业集聚 | agg | 0 | 23.58 |
锂电池产业集聚的平方 | agg2 | 0 | 556.02 | ||
控制变量 | 相关产业基础 | 符合国家《锂离子电池行业规范条件》企业数量(家) | rbc | 0 | 6.00 |
政府支持力度 | 官网政策文件检索数(个) | pfd | 0 | 119.00 | |
经济基础条件 | 制造业从业人员数(万人) | mie | 0.01 | 247.75 | |
固定资产投资总额(万元) | fai | 33504.00 | 4.01e+08 | ||
第二产业增加值占GDP比重(%) | sev | 8.85 | 73.03 | ||
对外开放程度 | 货物进出口总额(万元) | tge | 34.00 | 4.19e+08 | |
实际使用外资金额(万美元) | fdi | 0 | 3.08e+06 | ||
科技创新环境 | 每万人绿色专利申请量(件/万人) | gpa | 0 | 55.38 | |
科学教育支出比重(%) | ses | 4.75 | 34.92 | ||
R&D内部经费支出(万元) | rd | 2.00 | 3.36e+07 | ||
普通高等学校学校数(所) | hes | 1.00 | 93.00 |
表2 空间计量模型适用性检验结果Tab.2 Results of the applicability test of the spatial measurement model |
检验统计量 | 统计值 | P值 | 检验统计量 | 统计值 | P值 | |
---|---|---|---|---|---|---|
LM-Error | 550.017*** | 0.000 | LR Lag | 181.37*** | 0.000 | |
LM-Lag | 194.813*** | 0.000 | Wald Error | 161.94*** | 0.000 | |
Robust LM-Error | 358.782*** | 0.000 | Wald Lag | 87.83*** | 0.000 | |
Robust LM-Lag | 9.218*** | 0.002 | Hausman | 103.29*** | 0.000 | |
LR Error | 146.33*** | 0.000 |
注:***、**、*分别表示在0.01、0.05、0.10的水平上显著。表3同。 |
表3 不同回归模型对比及空间效应分解Tab.3 Comparison of different regression models and spatial effect decomposition |
模型 变量 | 地理邻接 | 经济距离 | 地理距离 | ||||
---|---|---|---|---|---|---|---|
直接效应 | 间接效应 | 直接效应 | 间接效应 | 直接效应 | 间接效应 | ||
lnagg | 3.011*** | -0.430 | 3.316*** | 0.877* | 3.224*** | -3.578 | |
lnagg2 | -1.324*** | -0.111 | -1.549*** | -0.355 | -1.447*** | -1.330 | |
lnrbc | 0.312*** | -0.047 | 0.370*** | 0.021 | 0.323*** | -0.435 | |
lnpfd | 0.302*** | 0.016 | 0.317*** | 0.083 | 0.306*** | 0.184 | |
lnmie | 0.335*** | -0.296*** | 0.323*** | -0.296** | 0.342*** | -2.170*** | |
lnfai | 0.098*** | -0.190*** | 0.073*** | 0.035 | 0.070*** | -1.005** | |
lnsev | 0.006 | -0.066 | -0.019 | 0.083 | 0.051 | -6.045*** | |
lntge | 0.069*** | 0.085*** | 0.132*** | -0.030 | 0.062*** | -0.040 | |
lnfdi | 0.066*** | 0.061*** | 0.065*** | -0.039* | 0.048*** | 0.896*** | |
lngpa | 0.628*** | 0.007 | 0.621*** | -0.185 | 0.576*** | 1.372** | |
lnses | 0.345*** | 0.402** | 0.460*** | 0.583** | 0.137 | 4.622*** | |
lnRD | 0.051*** | 0.176*** | 0.061*** | 0.045 | 0.070*** | 1.673*** | |
lnhes | 0.233*** | -0.118* | 0.225*** | 0.254*** | 0.254*** | -3.385*** | |
样本数 | 2376 | 2376 | 2376 | 2376 | 2376 | 2376 | |
R2 | 0.827 | 0.827 | 0.833 | 0.833 | 0.824 | 0.824 |
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