智能制造能提高企业环境绩效吗？——基于智能制造试点政策的准实验研究

摘要基于2012—2021年制造业上市公司数据,以智能制造试点示范政策(IMDP)为准自然实验,采用双重差分法(DID)探究智能制造对企业环境绩效的影响。研究表明,智能制造显著提升了企业环境绩效,结论经过系列稳健性、内生性检验后依然成立。机制分析发现,智能制造通过优化人力资本结构、增强环境保护投资以及促进绿色技术创新等渠道提升企业环境绩效。异质性分析发现,智能制造的环境效益在非国有企业、高资本市场关注度以及大规模企业中更为明显。研究从理论上阐释了智能化转型与企业环境绩效之间的内在联系,丰富了智能制造政策的相关研究,为推动制造业智能化和绿色化深度融合提供了有益借鉴。

	Service

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	Email Alert
	RSS
	作者相关文章
	万珠

关键词：智能制造环境绩效人力资本结构环境保护投资绿色技术创新

Abstract： Based on sample data from listed manufacturing companies, the paper uses the Intelligent Manufacturing Pilot Demonstration Policy (IMDP) as a natural experiment and uses the Double Difference Method (DID) to explore the impact of intelligent manufacturing on corporate environmental performance. The study shows that intelligent manufacturing significantly enhances corporate environmental performance. The conclusion remains valid after a series of robustness and endogeneity tests. Mechanism analysis indicates that intelligent manufacturing enhances corporate environmental performance by optimizing human capital structure, increasing environmental protection investments and promoting green technological innovation. Heterogeneous analysis reveals that the environmental benefits of intelligent manufacturing are more pronounced in non-state-owned enterprises, firms with high capital market attention and large-scale companies. The study theoretically explains the intrinsic link between intelligent transformation and corporate environmental performance, enriches research on intelligent manufacturing policies, and provides valuable insights for promoting the deep integration of intelligent and green manufacturing.

Keywords： Intelligent Manufacturing Environmental Performance Human Capital Structure Investment in Environmental Protection Green Technology Innovation

收稿日期 2024-10-11; 接受日期 ;

基金资助:国家社会科学基金重点项目(21AKS014)

作者简介: 万珠(1996— ),女,贵州遵义人,贵州财经大学博士研究生,中共贵州省委党校讲师,贵州发展制度保障高端智库研究人员,主要从事数字经济与绿色经济研究。

引用本文:

万珠.智能制造能提高企业环境绩效吗？——基于智能制造试点政策的准实验研究[J]. 经济经纬, 2025,3: 147-160

WAN Zhu.Whether Intelligent Manufacturing Can Enhance Corporate Environmental Performance: A Quasi-Experimental Study Based on the Intelligent Manufacturing Pilot Policy[J] Economic Survey, 2025,V42(3): 147-160

链接本文:

http://www.jjjw.org.cn/CN/ 或 http://www.jjjw.org.cn/CN/Y2025/V42/I3/147

白景坤,罗晨婧,顾飞,2024.制度合法性压力与企业ESG“漂绿”[J].系统工程理论与实践(7):1-36.
戴翔,杨双至,2022.数字赋能、数字投入来源与制造业绿色化转型[J].中国工业经济(9):83-101.
高杰英,褚冬晓,廉永辉,等,2021.ESG表现能改善企业投资效率吗？[J].证券市场导报(11):24-34,72.
郭孝阳,张秀武,杨静怡,2024.智能制造、人机协作与能源利用效率:基于工业机器人应用的视角[J].环境经济研究(3):43-65.
韩少真,张田,李辉,2024.智能制造能否有效促进企业碳减排[J].财经科学(9):72-88.
何小钢,钟湘菲,2023.数字化赋能企业“绿色升级”的机制和路径研究:基于中国工业企业的经验证据[J].管理学刊(4): 127-145.
侯翠梅,苏杭,2023.智能化转型对企业创新绩效的影响研究:基于数字化能力的视角[J].工程管理科技前沿(2):83-89.
胡海波,毛纯兵,周洁,2023.中国工业数字化转型的演变逻辑与未来展望[J].管理学刊(4): 112-126.
姜富伟,张杉,丁慧,等, 2024.董事会性别多元化、绿色技术创新与企业绿色转型[J].金融评论 (2): 75-96,156-157.
孔东民,韦咏曦,季绵绵,2021.环保费改税对企业绿色信息披露的影响研究[J].证券市场导报 (8): 2-14.
黎文靖,郑曼妮,2016.实质性创新还是策略性创新?——宏观产业政策对微观企业创新的影响[J].经济研究(4):60-73.
李金昌,连港慧,徐蔼婷,2023.“双碳”愿景下企业绿色转型的破局之道:数字化驱动绿色化的实证研究[J].数量经济技术经济研究(9):27-49.
李昆,赵雨婷,杨强,2024.数字化转型对重污染型企业环境绩效的影响研究:基于数据资产化视角[J].经济经纬(3):108-120.
李树文,罗瑾琏,胡文安,2022.从价值交易走向价值共创:创新型企业的价值转型过程研究[J].管理世界(3):125-145.
李增福,冯柳华,2022.企业ESG表现与商业信用获取[J].财经研究(12):151-165.
林熙,刘啟仁,冯桂媚,2023.智能制造与绿色发展:基于工业机器人进口视角[J].世界经济 (8):3-31.
罗进辉,巫奕龙,刘海潮,等,2023.亲清政商关系的绿色治理效应:来自绿色并购的证据[J].财经研究(11):34-49.
罗艳梅,孟敬也,2023.员工学历水平对企业环境绩效影响研究[J].经济论坛(8):120-133.
毛其淋,石步超,2024.通向绿色发展之路:智能制造与企业绿色转型[J].世界经济(9):152-182.
权小锋,李闯,2022.智能制造与成本粘性:来自中国智能制造示范项目的准自然实验[J].经济研究(4):68-84.
申宇,黄昊,赵玲,2018.地方政府“创新崇拜”与企业专利泡沫[J].科研管理(4):83-91.
沈坤荣,乔刚,林剑威,2024.智能制造政策与中国企业高质量发展[J].数量经济技术经济研究(2):5-25.
王嘉鑫,刘雪娜,于鑫雨,等,2024.银企ESG一致性与贷后企业策略性ESG行为[J].财经研究(4):109-123.
王鹏超,韩立彬,2023.多时点双重差分法的潜在问题与解决措施[J].东北财经大学学报(2): 27-39.
谢申祥, 范鹏飞, 宛圆渊, 2021.传统PSM-DID模型的改进与应用[J].统计研究(2): 146-160.
颜逢,赵秀云,2024.智能制造推动了企业绿色全要素生产率提升吗?——基于智能制造示范点企业的研究[J].商业研究(3):41-49.
尹洪英,李闯,2022.智能制造赋能企业创新了吗?——基于中国智能制造试点项目的准自然试验[J].金融研究(10):98-116.
张远,李焕杰,2022.服务型制造如何影响企业环境绩效[J].经济管理(11):167-187.
张志新,蒋欢跃,李成,2024.数字经济赋能长三角制造业高质量发展:空间溢出与机制识别[J].地域研究与开发(2):15-20,34.
竺李乐,吴福象,范衍玮,2021.“专利泡沫”与“创新假象”——国有企业民营化改革及其真实创新产出[J].山西财经大学学报(12):82-95.
ADOMAKO S, AMANKWAH-AMOAH J, FRIMPONG K, 2022. Human capital, reverse engineering and new venture growth: the moderating role of competitive strategy[J]. Technovation, 114(2):20-27.
ANQUETIN T, COQUERET G, TAVIN B, et al, 2022. Scopes of carbon emissions and their impact on green portfolios[J]. Economic modelling, 115: 105951.
ATHEY S, IMBENS G W, 2022. Design-based analysis in Difference-In-Differences settings with staggered adoption[J]. Journal of econometrics, 226(1): 62-79.
FAFALIOU I, GIAKA M, KONSTANTIOS D, et al, 2022. Firms’ ESG reputational risk and market longevity: a firm-level analysis for the United States[J]. Journal of business research, 149: 161-177.
HE J, TIAN X, 2013. The dark side of analyst coverage: the case of innovation[J]. Journal of financial economics,109(3):856-878.
HU X, HUA R, LIU Q, et al, 2023. The green fog: environmental rating disagreement and corporate greenwashing[J]. Pacific-basin finance journal, 78: 101952.
HUANG G, HE L Y, LIN X, 2022. Robot adoption and energy performance:evidence from Chinese industrial firms[J]. Energy economics,107:105837.
KONG D, LIN C, WEI L, et al,2022. Information accessibility and corporate innovation[J]. Management science,68(11):7837-7860.
LIANG T, ZHANG Y J, QIANG W, 2022. Does technological innovation benefit energy firms’ environmental performance? The moderating effect of government subsidies and media coverage[J]. Technological forecasting and social change, 180: 121728.
MU W, LIU K, TAO Y, et al, 2023. Digital finance and corporate ESG[J]. Finance research letters, 51: 103426.
RODGERS W, CARDENAS J A, GEMOETS L A, et al, 2023. A smart grids knowledge transfer paradigm supported by experts’ throughput modeling artificial intelligence algorithmic processes[J]. Technological forecasting and social change, 190: 122373.
SHAPIRO J S, WALKER R, 2018. Why is pollution from US manufacturing declining? The roles of environmental regulation, productivity, and trade[J]. American economic review, 108(12): 3814-3854.
SUN L, ABRAHAM S, 2021.Estimating dynamic treatment effects in event studies with heterogeneous treatment effects[J]. Journal of econometrics,225 (2):175-199.
WEI X, JIANG F, CHEN Y, et al, 2024. Towards green development: the role of intelligent manufacturing in promoting corporate environmental performance[J].Energy economics, 131:107375.
YU Y, ZHANG J Z, CAO Y, et al, 2021. Intelligent transformation of the manufacturing industry for industry 4.0:seizing financial benefits from supply chain relationship capital through enterprise green management[J]. Technological forecasting and social change,172: 120999.
ZHUO C, CHEN J, 2023. Can digital transformation overcome the enterprise innovation dilemma: effect, mechanism and effective boundary[J]. Technological forecasting and social change, 190: 122378.