中国新质生产力企业时空分布特征及驱动因素

Characteristics and Driving Factors of Spatio-Temporal Distribution of Enterprises with New Quality Productive Forces in China

摘要

在全球产业链重构与中国经济转型背景下,新质生产力企业成为突破技术瓶颈、培育国际竞争优势的关键主体.本文基于2011—2023年中国新质生产力企业数据,运用空间自相关分析及时空地理加权回归(GTWR)模型等方法,系统探究中国新质生产力企业时空分布特征及驱动因素.研究发现:①空间分布集聚程度持续增强,企业分布存在显著空间集聚性,热点区集中于沿海城市群,冷点区分布于西北、东北地区.②政策支持、经济基础、创新资源与环境承载力四维因素对新质生产力企业分布的影响存在时空异质性.数字基础设施、科学技术支出占比、人均收入、工业企业利润、金融发展水平总体上与新质生产力企业密度呈现正相关关系,税收征管强度、传统基础设施、人均产值、高等院校数等在区域影响上呈现差异化,环境规制存在一定抑制作用.于此,提出应优化区域政策设计、用好地区创新资源、营造良好城市环境等建议.

Abstract

Under the background of global industrial chain restructuring and China's economic transformation, enterprises with new quality productive forces have become key carriers in breaking through technological bottlenecks and cultivating international competitive advantages. Based on the data of enterprises with new quality productive forces in China from 2011 to 2023, this paper uses methods such as spatial autocorrelation analysis and geographically and temporally weighted regression (GTWR) model to systematically explore the characteristics and driving factors of spatio-temporal distribution of enterprises with new quality productive forces in China. Research has found that: ① Degree of spatial distribution agglomeration is continuously increasing. There is significant spatial agglomeration in the distribution of enterprises, with hotspots concentrated in coastal urban clusters and coldspots distributed in the northwest and northeast. ② The impact of policy support, economic foundation, innovative resources, and environmental carrying capacity on the distribution of enterprises with new quality productive forces exhibits spatio-temporal heterogeneity. Digital infrastructure, the proportion of science and technology expenditure, per capita income, industrial enterprise profits, and financial development level are generally positively correlated with the density of enterprises with new quality productive forces. The intensity of tax collection and management, traditional infrastructure, per capita output value, and the number of higher education institutions have regional differences in impact, and environmental regulations have a certain inhibitory effect. Based on this, suggestions including optimizing regional policy design, making good use of local innovation resources, and creating a favorable urban environment are put forward.

关键词

新质生产力 / 企业集聚 / 空间分布 / 驱动因素 / 时空地理加权回归

Key words

new quality productive forces / agglomeration of enterprises / spatial distribution / driving factors / GTWR

参考文献

[1] 李正军. 推动产业转型升级是高质量发展的重点工作[J].红旗文稿,2025(10):33-36.

[2] 印剑,夏惟怡,曹向. 新质生产力赋能全球价值链跃升的理论逻辑与推进路径[J].广东财经大学学报,2025,40(2):83-89.

[3] 郭佳宏,周密,李东宇. 中国企业新质生产力的时空格局演化及其地理共聚:基于机器学习算法的测度[J].经济地理,2026,46(1):34-45.

[4] 李婧,谭清美,白俊红. 中国区域创新生产的空间计量分析:基于静态与动态空间面板模型的实证研究[J].管理世界,2010(7):43-55.

[5] 樊杰,陈东,李佳洺,等. 新质生产力的地域性与布局规律研究:因地制宜发展新质生产力的地理科学探讨[J].地理科学,2025,45(1):47-60.

[6] 刘长庚,吴婷. 中国新质生产力水平时空演进及其空间溢出效应[J].经济地理,2025,45(12):13-24.

[7] 李凤娇,刘家明,姜丽丽. 东北地区战略性新兴产业发展水平时空演变与影响因素研究[J].地理科学进展,2022,41(4):541-553.

[8] 李会平,杨一飞. 政策注意力跃升能否驱动新质生产力发展[J].经济问题,2026(5):23-32.

[9] 魏后凯,白玫. 中国企业迁移的特征、决定因素及发展趋势[J].发展研究,2009(10):9-18.

[10] 张玉梅. 企业规模异质性与产业集聚:来自中国制造业的证据[J].产经评论,2012,3(6):84-100.

[11] Cader H A, Crespi J M, Leatherman J C. What factors affect information technology firm location choices in middle America? An examination of regional and industrial variation in Kansas[J].International Regional Science Review, 2013, 36(2): 207-234.

[12] 陈露,刘修岩. 产业空间共聚、知识溢出与创新绩效:兼议区域产业多样化集群建设路径[J].经济研究,2024,59(4):78-95.

[13] 郑江淮,师磊. 本地化创新能力、区域创新高地与产业地理梯度演化路径[J].中国工业经济,2023(5):43-60.

[14] Yurij O, Lee C Y. Technological capability, agglomeration economies and firm location choice[J].Regional Studies, 2014, 48(8): 1337-1352.

[15] 武前波,宁越敏. 中国制造业企业500强总部区位特征分析[J].地理学报,2010,65(2):139-152.

[16] 徐舒扬,姜海宁. 基于吉利汽车企业联系的浙江城市网络演化[J].南京师大学报(自然科学版),2025,48(4):26-38.

[17] 朱彬,李红波,姚柯,等. 江苏省瞪羚企业空间分布及影响因素研究[J].南京师大学报(自然科学版),2024,47(4):48-58.

[18] 薄文广,钱镱,刘璇. 专精特新“小巨人”企业集聚的影响因素及分位异质性:基于分位数回归模型的实证研究[J].经济地理,2024,44(3):55-65.

[19] 楚天骄,宋韬. 中国独角兽企业的空间分布及其影响因素研究[J].世界地理研究,2017,26(6):101-109.

[20] 齐凯,张衡,周杨,等. 综合局部莫兰指数和PageRank算法的网络空间资源节点隐喻可视化表达[J].地球信息科学学报,2024,26(5):1283-1295.

[21] 马晓冬,李全林,沈一. 江苏省乡村聚落的形态分异及地域类型[J].地理学报,2012,67(4):516-525.

[22] 何小芊,刘亚菲. 基于GTWR模型的长江经济带旅游生态安全影响因素时空特征[J].长江流域资源与环境,2026,35(1):156-173.

[23] 沈坤荣,林剑威,傅元海. 网络基础设施建设、信息可得性与企业创新边界[J].中国工业经济,2023(1):57-75.

[24] 张国峰,王永进,李坤望. 开发区与企业动态成长机制:基于企业进入、退出和增长的研究[J].财经研究,2016,42(12):49-60.

[25] Brown J R, Petersen F B C. Financing innovation and growth: cash flow, external equity, and the 1990s R&D boom[J].The Journal of Finance, 2009, 64(1): 151-185.

[26] 谭俊涛,张平宇,李静. 中国区域创新绩效时空演变特征及其影响因素研究[J].地理科学,2016,36(1):39-46.

[27] Becker R, Henderson V. Effects of air quality regulations on polluting industries[J].Journal of Political Economy, 2000, 108(2): 379-421.

[28] 杨洋,乔家君,郭远智,等. 广东省瞪羚企业空间分布特征及驱动机制[J].经济地理,2022,42(8):112-122.

基金资助

江苏高校哲学社会科学研究一般项目(2025SJYB0406)