The spatio-temporal evolution characteristics and driving factors of digital technology application industry in the Chengdu-Chongqing Double-City Economic Circle

doi:10.3969/j.issn.1004-9479.2024.03.20230426

Abstract

Abstract:

Industrial digitization is an important manifestation of the deep integration of digital economy and real economy. Based on a sample of 4348 digital technology application enterprises in Chengdu-Chongqing double circle from 2014-2023, the average nearest neighbor index, kernel density estimation, standard deviation ellipse and multi-distance spatial cluster analysis are used to portray the characteristics of the evolution of the spatial and temporal patterns of the digital technology application industry in Chengdu-Chongqing double circle, and a geographic probe model was used to explore the driving effects of various factors on the agglomeration development of the digital technology application industry in Chengdu-Chongqing double circle. The study finds that: ① The number of digital technology application enterprises in the Chengdu-Chongqing Double-City Economic Circle has been increasing year by year, with the largest number of small-scale enterprises, followed by micro, medium, and large enterprises. The coverage of enterprise distribution has been expanding year by year, presenting a spatial distribution characteristic of "agglomeration in the central urban area and gradient diffusion in the surrounding areas". The overall distribution is highly consistent with the geographical pattern of industrial parks and higher education institutions. ② The overall level of agglomeration has increased, showing a spatial evolution from "east (slightly south) to west (slightly north)". The transition from "monocentric dominance" to "bicentric radiation" gradually evolves into "polycentric diffusion". ③ Taking Chengdu and Chongqing as the single-core and dual-core respectively, the peak concentration of digital technology application industry is 30 km, 4 km and 50 km, and the optimal values of concentration are 270 km, 130 km and 360 km. ④ The economic base, market potential and policy guarantee factors are generally on the rise, while the human capital factors shows an inverted "U" shape.

Key words: digital technology application industry, digital industry, spatio-temporal evolution, driving factors, Chengdu-Chongqing Double-City Economic Circle

摘要：

产业数字化是数字经济同实体经济深度融合的重要表现，将数字技术应用业的区位及驱动因素纳入其研究框架对成渝双圈实现产业数字化赋能经济高质量发展具有重要意义。基于成渝双圈2014—2023年4 348家数字技术应用企业样本，运用平均最近邻指数、核密度估计、标准差椭圆及多距离空间聚类分析等方法，刻画成渝双圈数字技术应用业的时空格局演化特征，采用地理探测器探究各类要素对成渝双圈数字技术应用业集聚发展的驱动作用。研究发现：①成渝双圈数字技术应用企业数量逐年增加，小型规模的企业数量增加最多，其次为微型、中型和大型企业。企业分布覆盖范围连年扩大，呈现“中心城区集聚，周边梯度扩散”的空间分布特征，总体分布与产业园区和高等院校的地理格局高度契合。②集聚水平总体上升，呈现“东（略偏南）-西（略偏北）”的空间演化态势，由“单中心主导”过渡到“双中心辐射”，逐渐演化为“多中心扩散”。③在分别以成都、重庆中心城区为中心和成渝双中心的观测尺度上，数字技术应用业集聚峰值分别为30 km、4 km和50 km，集聚最优值分别为270 km、130 km和360 km。④经济基础、市场潜力和政策保障因素驱动作用总体均呈上升趋势，人力资本因素驱动作用呈“U”形。

关键词: 数字技术应用业, 数字产业, 时空演化, 驱动因素, 成渝地区双城经济圈

Haoli WANG, Jing XIANG, Qinghua HUANG. The spatio-temporal evolution characteristics and driving factors of digital technology application industry in the Chengdu-Chongqing Double-City Economic Circle[J]. World Regional Studies, 2024, 33(3): 131-146.

王浩力, 向静, 黄庆华. 成渝地区双城经济圈数字技术应用业时空演化特征及驱动因素[J]. 世界地理研究, 2024, 33(3): 131-146.

Figures/Tables 11

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企业规模	2014		2019		2023
企业规模	企业数量/家	收入/十万元	企业数量/家	收入/十万元	企业数量/家	收入/十万元
L(大型)	31	Y≥1 000	89	Y≥1 000	110	Y≥1 000
M(中型)	199	10≤Y＜1 000	641	10≤Y＜1 000	769	10≤Y＜1 000
S(小型)	476	5≤Y＜10	2 153	5≤Y＜10	2 463	50≤Y＜10
XS(微型)	136	Y＜5	837	Y＜5	1 006	Y＜5
总计	842		3 720		4 348

企业规模	2014		2019		2023
企业规模	企业数量/家	收入/十万元	企业数量/家	收入/十万元	企业数量/家	收入/十万元
L(大型)	31	Y≥1 000	89	Y≥1 000	110	Y≥1 000
M(中型)	199	10≤Y＜1 000	641	10≤Y＜1 000	769	10≤Y＜1 000
S(小型)	476	5≤Y＜10	2 153	5≤Y＜10	2 463	50≤Y＜10
XS(微型)	136	Y＜5	837	Y＜5	1 006	Y＜5
总计	842		3 720		4 348

年份	平均观测距离/m	预期平均距离/m	最邻近指数R	Z得分	空间分布类型
2014	1 436	8 530	0.1 683	－62.21	集聚
2015	1 209	7 636	0.1 583	－70.33	集聚
2016	995	6 847	0.1 454	－79.70	集聚
2017	876	6 256	0.1 401	－87.76	集聚
2018	687	5 788	0.1 187	－97.35	集聚
2019	632	5 529	0.1 144	－103.24	显著集聚
2020	651	5 543	0.1 174	－107.63	显著集聚
2021	875	8 497	0.1 030	－112.24	显著集聚
2022	623	5 364	0.1 162	－111.37	显著集聚
2023	623	5 348	0.1 166	－111.66	显著集聚

年份	平均观测距离/m	预期平均距离/m	最邻近指数R	Z得分	空间分布类型
2014	1 436	8 530	0.1 683	－62.21	集聚
2015	1 209	7 636	0.1 583	－70.33	集聚
2016	995	6 847	0.1 454	－79.70	集聚
2017	876	6 256	0.1 401	－87.76	集聚
2018	687	5 788	0.1 187	－97.35	集聚
2019	632	5 529	0.1 144	－103.24	显著集聚
2020	651	5 543	0.1 174	－107.63	显著集聚
2021	875	8 497	0.1 030	－112.24	显著集聚
2022	623	5 364	0.1 162	－111.37	显著集聚
2023	623	5 348	0.1 166	－111.66	显著集聚

变量	双城	重庆	成都
d	－5.279^* (－2.58)	－8.572^*** (－3.45)	－17.88^*** (－3.61)
d²	2.221^** (－2.76)	4.429^** (－3.15)	9.754^*** (－3.50)
d³	－0.273^** (－2.89)	－0.710^** (－3.04)	－1.586^*** (－3.50)
cons	4.057^** (2.79)	5.293^*** (4.40)	9.322^*** (3.85)
R²	0.272	0.538	0.566
样本数	99	99	99