中国新能源汽车产业供需依赖网络演化机制研究

doi:10.13776j.cnki.resourcesindustries.20230427.002

摘要/Abstract

摘要： 中国新能源汽车产业作为我国战略新兴产业，产业规模不断扩大，成为我国产业升级的重要力量。同时，中国新能源汽车供应链不断细化、延长，所涉及环节不断增多，产业格局不断演化。不确定的外部环境与日益复杂的内部结构，使得理解新能源汽车产业演化机制、把握新能源汽车产业发展规律愈发重要。为了深挖中国新能源汽车产业的内在发展机制，使用2018—2020年中国新能源汽车产业企业级交易数据，构建该产业上市企业之间的供需依赖关系，进而分析中国新能源汽车产业供需依赖网络的结构演化特征。并以网络演化理论为基础，基于财务数据、舆情数据等多源异质数据，通过随机主体导向模型从网络内生结构、情景因素与公司属性3个维度展开实证研究，检验中国新能源汽车产业供需依赖网络形成和演化的影响机制。研究表明：中国新能源汽车产业整体上呈现局部中心化的供需依赖结构，区域性头部企业不断增强自身供应链控制力；产业供需依赖网络结构的形成和演化受到内生机制，如偏好依附效应、三角形闭合效应的影响；共高管关系等情景因素会显著促进上市企业供需依赖关系的形成。基于以上研究结果，从推动区域内企业合作、优化共高管关系和关注弱势企业发展3个方面为中国新能源汽车产业健康有序发展和加速升级提出了政策建议：政府和行业协会应继续大力推动产业集群建设，为区域内产业一体化发展提供政策和标准的支持，促进资金、技术和行业数据的自由流动；相关产业调控政策可以通过限制或促进特定企业间的共高管关系，达到产业供需关系优化的目的，另外，也可以通过追踪企业间舆情关联，识别潜在的依赖关系，完善对产业网络依赖结构的认识，从而更好地指导投资、识别风险；政府监管部门应关注弱势企业发展，警惕产业结构失衡，也应加强对“明星”企业的关注，对其提出更高的经营标准和监管要求，对其财务情况进行充分的披露。

关键词: 中国新能源汽车产业, 供需依赖网络, 内生机制, 随机主体导向模型

Abstract:

China ‘s new energy vehicle industry is expanding as a strategic emerging industry and has become an important force in industrial upgrading. Its supply chain has been prolonged, detailed, and its industrial structure has been developing. Uncertain external environment and increasingly complicated internal structure make it important to understand the evolutionary mechanism and developing law of new energy vehicle industry. This paper, aiming at exploring the endogenous development mechanism of China ‘s new energy vehicle industry, uses China ‘s new energy vehicle trading data from 2008 to 2020 to establish a supply-demand dependance network among listed companies of this industry, and studies its evolutionary characteristics. Empirical research is carried out to verify evolutionary mechanism of the supply-demand dependance network based on financial data and public sentiment data, via stochastic actor-oriented model from three dimensions of endogenous network structure, situation factors and company attributes. China ‘s new energy vehicle industry shows a local-centralized supply-demand dependence structure, and regional leading enterprises are boosting their abilities in controlling supply chain. Formation and evolution of industrial supply-demand dependence network is impacted by endogenous mechanism, such as preference attachment effect and triangle loop effect, and promoted by co-executive relationship. This paper presents suggestions for advancing China ‘s new energy vehicle industry on regional commercial cooperation, optimizing co-executive relationship and caring vulnerable companies.Governments and professional associations should continue boosting industrial cluster construction and supporting regional integration on policies and standards, enhancing free migration of capital, technical and industrial data.Industrial policies are adjusted to optimize industrial demand-supply relation through constraining or promoting co-executive relationship.Tracking commercial sentiment can recognize potential dependance relation, comprehend industrial network dependance structure, helpful in directing investment and recognizing risks. Governments shall take care of vulnerable companies, be vigilant on industrial structural imbalance, and also pay attention to notable companies with higher standard operating code and supervision, fully disclosing their financial information.

Key words: China ‘s new energy vehicle industry, supply-demand dependence network, endogenous mechanism, stochastic actor-oriented model

中图分类号:

F205

李扬, 李华姣, 冯思达. 中国新能源汽车产业供需依赖网络演化机制研究[J]. 资源与产业, 2023, 25(3): 69-81.

LI Yang, LI Huajiao, FENG Sida. EVOLUTIONARY MECHANISM OF SUPPLY-DEMAND DEPENDENCE NETWORK OF CHINA ‘S NEW ENERGY VEHICLE INDUSTRY[J]. Resources & Industries, 2023, 25(3): 69-81.

参考文献

冯菁, 2021. 基于演化博弈理论的新能源汽车产业协同创新机制研究[D]. 重庆: 重庆工商大学.〔FENG J, 2021. Research on collaborative innovation mechanism of new energy automobile industry based on evolutionary game theory [D]. Chongqing: Chongqing Technology and Business University.〕

公丕明, 2022. 中国新能源汽车产业国际竞争力: 影响因素、特征表现与提升路径[J]. 现代管理科学(4): 63-72.〔GONG P M, 2022. International competency of China ‘s new energy vehicle industry: influences, characteristics and promotion [J]. Modern Management Science (4): 63-72.〕

何卓静, 2019. 违约相依的供应链企业信用风险传染与防范研究: 基于Copula方法的探索[D]. 北京: 中央财经大学.〔HE Z J, 2019. The default dependence and its credit risk contagion in supply chain: based on Copula approaches[D]. Beijing: Central University of Finance and Economics.〕

贾楠菲, 2021. 钨产业链产品价格波动传导机制研究 [D]. 北京: 中国地质大学（北京）.〔JIA N F, 2021. Research on the transmission mechanism of product price fluctuation in the tungsten industry chain[D]. Beijing: China University of Geosciences (Beijing).〕

金莉娜, 陆怡雅, 谢婧媛, 等, 2019. 基于 GREET 模型的新能源汽车全生命周期的环境与经济效益分析[J]. 资源与产业, 21(5): 1-8.〔JIN L N, LU Y Y, XIE J Y, et al, 2019. Environment and economy analysis on entire life circle of new energy vehicles based on GREET model[J]. Resources & Industries, 21(5): 1-8.〕

梁子懿, 董志良, 2021. 基于复杂网络的锌矿石国际贸易格局演化特征分析[J]. 资源与产业, 23(4): 63-69.〔LIANG Z Y, DONG Z L, 2021. Evolutional characteristics of zinc ore international trade pattern based on complex network [J]. Resources & Industries, 23(4): 63-69.〕

刘林青, 陈紫若, 田毕飞, 2020. 结构依赖如何影响贸易网络形成及演化: 以“一带一路”为例[J]. 世界经济研究(6): 106-120, 137.〔LIU L Q, CHEN Z R, TIAN B F, 2020. How does structural dependence affect the formation and evolution of trade network: an empirical analysis based on “the Belt and Road” [J]. World Economy Studies (6): 106-120, 137.〕

刘林青, 闫小斐, 杨理斯, 等, 2021. 国际贸易依赖网络的演化及内生机制研究[J]. 中国工业经济(2): 98-116.〔LIU L Q, YAN X F, YANG L S, et al, 2021. Research on the evolution and endogenous mechanism of international trade dependence network[J]. China Industrial Economics (2): 98-116.〕

刘素荣, 谢笑潇, 霍江林, 2022. 补贴退坡对新能源企业研发投资的影响: 基于市场预期调节视角[J]. 资源与产业, 24(4): 65-76.〔LIU S R, XIE X X, HUO J L, 2022. Impacts of subsidy reduction on new energy companies research & development viewing from market expectation adjustment[J]. Resources & Industries, 24(4): 65-76.〕

王涵, 2020. 全产业链视角下新能源汽车产业自主可控均衡机制与引导策略[D]. 镇江: 江苏大学.〔WANG H, 2020. Independent and controllable equilibrium mechanism and guiding strategy of new energy vehicle industry from the whole industry chain[D]. Zhenjiang: Jiangsu University.〕

魏红玉, 高湘昀, 季婷玉, 等, 2021. 战略性金属矿产资源多维价格联动网络研究 [J]. 资源与产业, 23(3): 102-111.〔WEI H Y, GAO X Y, JI T Y, et al, 2021. Multidimensional research on the price linkage network of strategic metal minerals[J]. Resources & Industries, 23(3): 102-111.〕

尹庆民, 诸清, 2022. 基于改进SIR模型的黄河流域产业优化仿真研究[J]. 资源与产业, 24(1): 46-54.〔YIN Q M, ZHU Q, 2022. Simulated industrial optimization of Yellow river basin based on improved SIR model[J]. Resources & Industries, 24(1): 46-54.〕

张锦程, 方卫华, 2022. 政策变迁视角下创新生态系统演化研究: 以新能源汽车产业为例[J]. 科技管理研究, 42(11): 173-182.〔ZHANG J C, FANG W H, 2022. Research on the evolution of innovation ecosystem from the perspective of policy change: taking the new energy vehicle industry as an example [J]. Science and Technology Management Research, 42(11): 173-182.〕

张路蓬, 薛澜, 周源, 等, 2018. 战略性新兴产业创新网络的演化机理分析: 基于中国2000—2015年新能源汽车产业的实证[J]. 科学学研究, 36(6): 1027-1035.〔ZHANG L P, XUE L, ZHOU Y, et al, 2018. Evolution mechanism of the strategic emerging industries ‘ innovation network: based on the new energy automobile empirical data from 2000 to 2015[J]. Studies in Science of Science, 36(6): 1027-1035.〕

周思凡, 郑佳, 赵蕴华, 等, 2017. 产业链视角下我国锂产业发展现状与建议[J]. 资源与产业, 19(6): 22-29.〔ZHOU S F, ZHENG J, ZHAO Y H, et al, 2017. Situation and proposals of China ‘s lithium industry development viewing from perspective of industrial chain[J]. Resources & Industries, 19(6): 22-29.〕

ADAMIC L, BRUNETTI C, HARRIS J H, et al, 2017. Trading networks[J]. The Econometrics Journal, 20(3): S126-S149.

AN P, GUO S, 2022. The impact of information flow by co-shareholder relationships on the stock returns: a network feature perspective[J]. Entropy, 24(9): 1237.

BARABASI A L, ALBERT R, 1999. Emergence of scaling in random networks [J]. Science, 286(5439): 509-512.

CIMINI G, SQUARTINI T, SARACCO F, et al, 2019. The statistical physics of real-world networks [J]. Nature Reviews Physics (1): 58-71.

FORSS T, SARLIN P, 2016. From news to company networks: co-occurrence, sentiment, and information centrality[R]. Athens: Computational intelligence.

GIULIANI E, 2013. Network dynamics in regional clusters: evidence from Chile[J]. Research Policy, 42(8): 1406-1419.

KALISH Y, 2020. Stochastic actororiented models for the co-evolution of networks and behavior: an introduction and tutorial[J]. Organizational Research Methods, 23(3): 511-534.

KIM J Y, HOWARD M, COX PAHNKE E, et al, 2016. Understanding network formation in strategy research: exponential random graph models [J]. Strategic Management Journal, 37(1): 22-44.

KISHIDA N, NISHIURA H, 2018. Demographic supply-demand imbalance in industrial structure in the super-aged nation Japan[J]. Theoretical Biology Medical Modelling, 15(1): 1-10.

LIU Y, PENG G, HU L, et al, 2019. Using Google trends and Baidu index to analyze the impacts of disaster events on company stock prices[J]. Industrial Management & Data Systems, 120(2): 350-365.

LIU Z, HAO H, CHENG X, et al, 2018. Critical issues of energy efficient and new energy vehicles development in China[J]. Energy Policy, 115: 92-97.

OSADCHIY N, GAUR V, SESHADRI S, 2016. Systematic risk in supply chain networks [J]. Management Science, 62(6): 1755-1777.

PAN Z, 2018. Varieties of intergovernmental organization memberships and structural effects in the world trade network[J]. Advances in Complex Systems, 21(2): 1850001.

RESTREPO N, URIBE J M, MANOTAS D, 2018. Financial risk network architecture of energy firms[J]. Applied Energy, 215: 630-642.

SHI Q, SUN X, XU M, et al, 2022. The multiplex network structure of global cobalt industry chain [J]. Resources Policy, 76: 102555.

SUN H, GENG Y, HU L, et al, 2018. Measuring China ‘s new energy vehicle patents: a social network analysis approach[J]. Energy, 153: 685-693.

WANG C, ZHAO L, LIM M K, et al, 2020. Structure of the global plastic waste trade network and the impact of China ‘s import ban[J]. Resources, Conservation and Recycling, 153: 104591.

WANG L, JIA F, CHEN L, et al, 2021. Exploring the dependence structure among Chinese firms in the 5G industry[J]. Industrial Management & Data Systems, 121(2): 409-435.

WU G, FENG L, PERES M, et al, 2020. Do self-organization and relational embeddedness influence free trade agreements network formation?: evidence from an exponential random graph model[J]. The Journal of International Trade & Economic Development, 29(8): 995-1017.

YING Q, KONG D, LUO D, 2015. Investor attention, institutional ownership, and stock return: empirical evidence from China[J].

Emerging Markets Finance and Trade, 51(3): 672-685.

[1]	高孝伟, 张颖昆, 李华, 等. 基于改进Theil指数和Shift-Share模型的2012-2021年中国产业偏离分析[J]. 资源与产业, 2024, 26(1): 15-24.
[2]	王理想, 王建民. 碳中和目标下长三角地区能源结构调整的经济影响及其差异性[J]. 资源与产业, 2024, 26(1): 25-34.
[3]	杨恺钧, 曹安琪, 方慈慧. 新能源汽车产业集聚对区域绿色经济效率的影响——基于长江经济带的实证研究[J]. 资源与产业, 2024, 26(1): 35-49.
[4]	郭素婷, 董书霞, 吴亦宁. 黄河流域水利风景区时空演变及水利旅游空间结构优化[J]. 资源与产业, 2024, 26(1): 61-74.
[5]	李慧涛. 资源型经济转型提升绿色发展效率的作用机制研究——以山西省为例[J]. 资源与产业, 2024, 26(1): 100-112.
[6]	马海良, 高洁, 金瑞琪, 等. 全国能源消费结构低碳化的空间非均衡性及动态演进[J]. 资源与产业, 2024, 26(1): 113-123.
[7]	丁雄军, 王莉, 魏源, 等. 构建“山水林土河微”生命共同体促进酿酒行业高质量发展的思考与实践[J]. 资源与产业, 2024, 26(1): 124-132.
[8]	虞文宝. 基于ARIMA模型的黄土高原河谷城市生态足迹动态模拟及测算——以甘肃省兰州市为例[J]. 资源与产业, 2024, 26(1): 133-140.
[9]	黄馨, 韩玲, 马超群. 陕西省生态系统固碳量与固碳价值的测度及其时空格局演变[J]. 资源与产业, 2024, 26(1): 141-153.
[10]	刘林玲, 刘红琴, 谭丽峰. 基于多利益主体的调水工程生态补偿演化博弈分析[J]. 资源与产业, 2024, 26(1): 154-161.
[11]	何乐天, 杨泳琪, 李蓉, 等. 基于STIRPAT模型的黑龙江省工业碳排放情景分析与峰值预测[J]. 资源与产业, 2024, 26(1): 162-172.
[12]	梁荆花. 山西省省际贸易隐含碳排放时空分析及优化策略[J]. 资源与产业, 2024, 26(1): 173-181.
[13]	潘海英, 陈玲, 任佳佳. 数字经济发展的碳减排效应研究——兼论异质性环境规制的调节作用[J]. 资源与产业, 2023, 25(6): 1-14.
[14]	杨秀平, 刘利利, 杨凯铭. 陕西省经济发展与生态环境压力解耦态势研究 [J]. 资源与产业, 2023, 25(6): 64-76.
[15]	王玉芳, 马晓敏. 污染产业转移背景下的生态补偿与政府监督问题——基于补偿申请的信号博弈 [J]. 资源与产业, 2023, 25(6): 105-114.