数实融合赋能黄河流域制造业绿色转型的路径研究

doi:10.12118/j.issn.1000-6060.2025.764

Abstract

Abstract:

The digital-real economy integration is a critical driver for promoting the green transformation of the manufacturing industry and building a modern industrial system in the Yellow River Basin. Based on the balanced panel data of prefecture-level cities in the Yellow River Basin from 2011 to 2022, this study employs the non-radial SBM-ML index method to measure the degree of green transformation in the manufacturing industry. A coupling coordination model is constructed to assess the level of digital-real economy integration. Furthermore, to achieve a comprehensive understanding, this study leverages the fixed effects model, the mechanism testing framework, and the spatial Durbin model to examine the effects, functional mechanisms, and spatial attributes associated with the impact of digital-real economy integration on the green transformation of the manufacturing industry. The findings reveal that: (1) The digital-real economy integration significantly enhances the efficiency of the green transformation of the manufacturing industry in the Yellow River Basin. This conclusion remains robust after addressing endogeneity issues with instrumental variables and conducting various sensitivity tests. (2) Green technology innovation and production structure optimization are two key pathways through which digital-real economy integration empowers the green transformation of the manufacturing industry. (3) The empowering effect of digital-real economy integration exhibits heterogeneity, being more pronounced for non-state-owned manufacturing enterprises, heavily polluting manufacturing enterprises, and regions in the upper reaches of the basin. (4) Digital-real economy integration has a significant local driving effect on the green transformation of the manufacturing industry in the Yellow River Basin. Moreover, the green transformation of the manufacturing industry itself demonstrates a spatial siphon effect. These results provide practical insights for formulating differentiated industrial policies, establishing cross-regional collaborative governance mechanisms, and promoting the green and low-carbon transformation of the manufacturing industry in the Yellow River Basin.

Key words: green technology innovation, production structure optimization, digital-real economy integration, green transformation, Yellow River Basin

FU Jincun, CUI Zihan, JIANG Lisha. Pathways of digital-real economy integration empowering green transformation of manufacturing industry in the Yellow River Basin[J].Arid Land Geography, 2026, 49(10): 2194-2204.

Figures/Tables 12

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变量	一级指标	二级指标	指标说明
制造业绿色转型程度	投入	劳动投入	企业员工人数
		资本投入	企业固定资产净额
		能源投入	企业所在城市工业用电量按企业从业人员占城市城镇人员就业比重
	产出	期望产出	企业营业收入
	产出	非期望产出	工业SO₂、工业废水、工业烟粉尘排放量

变量	一级指标	二级指标	指标说明
数实融合水平	数字经济	互联网普及率	百人中互联网宽带接入用户数
		互联网相关产出	人均电信业务总量
		移动互联网用户数	百人中移动电话用户数
		互联网相关从业人员	计算机服务和软件业从业人员占城镇单位从业人员比重
		数字金融	数字普惠金融指数
	实体经济	发展规模	第二产业占地区生产总值比重
			规模以上工业企业利润总额
			规模以上工业企业数量
			人均社会消费品零售额
		发展结构	实体经济从业人员（除金融业、房地产业从业人员）占比
		发展潜力	规模以上工业企业的R&D投入强度

变量	样本量	均值	标准差	最小值	最大值
GMT	1284	0.997	0.095	0.827	1.176
Mix	1284	0.294	0.050	0.182	0.420
Size	1284	8.452	1.103	6.201	11.115
ROA	1284	0.047	0.055	-0.094	0.228
ROE	1284	0.083	0.101	-0.329	0.388
Board	1284	2.185	0.178	1.792	2.708
GOV	1284	0.137	0.036	0.077	0.257
ECO	1284	7.528	3.536	2.060	20.015

变量	未考虑控制变量	考虑控制变量
Mix	0.096^***	0.109^***
Mix	（3.782）	（4.146）
Size	-	0.001^**
Size	-	（2.245）
ROA	-	0.036^*
ROA	-	（1.863）
ROE	-	-0.022^**
ROE	-	（-2.189）
Board	-	-0.004
Board	-	（-1.571）
GOV	-	0.037
GOV	-	（0.978）
ECO	-	-0.000
ECO	-	（-0.094）
常数项	0.820^***	0.816^***
常数项	（102.815）	（63.904）
city	是	是
year	是	是
N	1284	1284
R²	0.979	0.979

变量	两阶段最小二乘法		剔除省会城市	不同时段		F.Mix
	第1阶段	第2阶段	剔除省会城市	2011—2017年	2018—2022年	F.Mix
	Mix	GMT	GMT	GMT	GMT	GMT
工具变量	0.010^***	-	-	-	-	-
	（5.857）	-	-	-	-	-
Mix	-	0.271^**	0.124^***	0.108^***	0.273^***	-
Mix	-	（2.008）	（3.353）	（3.237）	（3.400）	-
F.Mix	-	-	-	-	-	0.065^**
F.Mix	-	-	-	-	-	（2.085）
控制变量	是	是	是	是	是	是
常数项	-0.459^***	0.779^***	0.806^***	0.813^***	0.961^***	0.827^***
常数项	（-3.872）	（23.186）	（48.850）	（44.585）	（30.328）	（59.513）
city	是	是	是	是	是	是
year	是	是	是	是	是	是
N	1284	1284	900	749	535	1177
R²	0.978	0.979	0.979	0.947	0.881	0.976

Pathways of digital-real economy integration empowering green transformation of manufacturing industry in the Yellow River Basin

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变量	绿色技术创新水平	生产结构优化
Mix	0.460^*	-0.684^***
Mix	（1.766）	（-2.796）
控制变量	是	是
常数项	0.068	-1.005^***
常数项	（0.735）	（-7.708）
city	是	是
year	是	是
N	942	1233
R²	0.153	0.566

变量	产权性质		污染排放程度
变量	国有制造业企业	非国有制造业企业	重污染制造业企业	非重污染制造业企业
Mix	0.092^**	0.108^***	0.122^***	0.099^***
Mix	（2.040）	（3.144）	（2.594）	（3.109）
控制变量	是	是	是	是
常数项	0.835^***	0.812^***	0.823^***	0.819^***
常数项	（47.500）	（46.178）	（39.454）	（55.080）
city	是	是	是	是
year	是	是	是	是
N	693	591	480	804
R²	0.980	0.979	0.981	0.979

变量	上游地区	中游地区	下游地区
变量	GMT	GMT	GMT
Mix	0.137^***	0.113^**	0.118^**
Mix	（2.724）	（2.381）	（2.335）
控制变量	是	是	是
常数项	0.817^***	0.795^***	0.831^***
常数项	（35.172）	（32.975）	（41.243）
city	是	是	是
year	是	是	是
N	324	228	732
R²	0.981	0.983	0.977

变量	地理距离矩阵				经济距离矩阵
变量	GMT	直接效应	间接效应	总效应	GMT	直接效应	间接效应	总效应
Mix	0.098^**	0.087^*	0.639	0.726	0.082^*	0.084^*	-0.032	0.052
Mix	（2.109）	（1.885）	（1.341）	（1.497）	（1.842）	（1.825）	（-0.193）	（0.311）
W×Mix	1.011	-	-	-	-0.017	-	-	-
W×Mix	（1.506）	-	-	-	（-0.092）	-	-	-
ρ	-0.568^*	-	-	-	-0.084	-	-	-
ρ	（-1.834）	-	-	-	（-0.996）	-	-	-
sigma2_e	0.000^***	-	-	-	0.000^***	-	-	-
sigma2_e	（15.101）	-	-	-	（15.091）	-	-	-
控制变量	是	是	是	是	是	是	是	是
city	是	是	是	是	是	是	是	是
year	是	是	是	是	是	是	是	是
N	456	456	456	456	456	456	456	456
R²	0.579	0.579	0.579	0.579	0.312	0.312	0.312	0.312

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