Article(id=1322239834010276202, tenantId=1146029695717560320, journalId=1146120045056339983, issueId=1322239833028809065, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1002-980X.J25110712, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762444800000, receivedDateStr=2025-11-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793176142456, onlineDateStr=2026-10-28, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793176142456, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793176142456, creator=13701087609, updateTime=1793176142456, updator=13701087609, issue=Issue{id=1322239833028809065, tenantId=1146029695717560320, journalId=1146120045056339983, year='2026', volume='45', issue='4', pageStart='1', pageEnd='158', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793176142222, creator='13701087609', updateTime=1793176142222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=15, endPage=26, ext={EN=ArticleExt(id=1322239835541197163, articleId=1322239834010276202, tenantId=1146029695717560320, journalId=1146120045056339983, language=EN, title=Research on the Impact of Digital Intelligence on Coordinated Regional Development: Causal Inference Based on Double Machine Learning, columnId=1149284496668709835, journalTitle=Journal of Technology Economics, columnName=Industrial Technology Economics, runingTitle=null, highlight=null, articleAbstract=

Digital intelligence, as a core driving force leading the new round of scientific and technological revolution and industrial transformation, is regarded as an important lever for promoting coordinated regional development. Based on panel data from 279 Chinese cities spanning the period from 2011 to 2023, the impact effects and underlying mechanisms of digital intelligence on coordinated regional development were empirically examined using a double machine learning model. It is found that digital intelligence significantly promotes coordinated regional development, and this overall effect is notably stronger than the individual effects of digitalization or intelligence alone. Mechanism tests reveal that digital intelligence indirectly facilitates coordinated regional development by enhancing regional innovation capacity and optimizing regional industrial structures. Moderating effect analysis indicates that government intervention contributes to mitigating the digital divide, thereby strengthening the positive effect of digital intelligence on coordinated regional development. Heterogeneity tests demonstrate that the promotional effect of digital intelligence is influenced by geographical conditions and socioeconomic development levels, and it is found to be more pronounced in the eastern region, the western region, and areas with relatively lower levels of economic development. Further analysis shows that digital intelligence not only promotes overall regional economic growth but also helps narrow the development gap between "core" and "peripheral" cities. The above study reveals the internal logic of empowering coordinated regional development through digital and intelligent transformation, providing empirical evidence and policy references for advancing such transformation in a context-specific manner, optimizing government regulation methods, and addressing regional development imbalances. It holds significant theoretical value and practical implications for achieving high-quality regional economic development.

, authors=Yanting Yao, Jintao Wan, authorsList=Yanting Yao, Jintao Wan, authorCompany=null, correspAuthors=Jintao Wan, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322239835788661100, articleId=1322239834010276202, tenantId=1146029695717560320, journalId=1146120045056339983, language=CN, title=数智化对区域协调发展的影响研究——基于双重机器学习的因果推断, columnId=1148001302568759830, journalTitle=技术经济, columnName=产业技术经济, runingTitle=null, highlight=null, articleAbstract=

数智化作为引领新一轮科技革命和产业变革的核心驱动力，是推动区域协调发展的重要抓手。基于2011—2023年中国279个城市的面板数据，运用双重机器学习模型实证检验数智化对区域协调发展的影响效应及内在机制。研究发现：数智化对区域协调发展具有显著的促进作用，且这一整体效应明显强于数字化和智能化的单独效应。机制检验显示，数智化通过增强区域创新能力与优化区域产业结构，间接推动了区域协调发展。调节效应显示，政府干预有助于缓解数字鸿沟，从而强化数智化对区域协调发展的积极效应。异质性检验发现，数智化的促进作用受到地理区位条件与社会经济发展水平的影响，在东部地区、西部地区及经济发展水平相对较低的区域更为显著。进一步分析发现，数智化不仅能够促进区域整体经济增长，也有助于缩小“中心-外围”城市间发展差距。研究揭示了数智化赋能区域协调发展的内在逻辑，为因地制宜推进数智化建设、优化政府调控方式、破解区域发展不平衡问题提供了经验证据与政策参考，对于实现区域经济高质量发展具有重要的理论价值与现实意义。

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姚延婷（1982—），博士，西北政法大学管理学院副教授，研究方向：数字经济与区域经济；

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姚延婷（1982—），博士，西北政法大学管理学院副教授，研究方向：数字经济与区域经济；

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