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Article(id=1317436297606713759, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1317436203545243877, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1745769600000, receivedDateStr=2025-04-28, revisedDate=1768492800000, revisedDateStr=2026-01-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1792030890132, onlineDateStr=2026-10-15, pubDate=1786723200000, pubDateStr=2026-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792030890132, onlineIssueDateStr=2026-10-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792030890132, creator=13701087609, updateTime=1792030890132, updator=13701087609, issue=Issue{id=1317436203545243877, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='8', pageStart='1569', pageEnd='1792', issueExtLink='null', onlineDate='null', pubDate='1786723200000', pubDateStr='2026-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792030867705, creator='13701087609', updateTime=1792031039088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317436922423792157, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1317436203545243877, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317436922427986462, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1317436203545243877, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1670, endPage=1680, ext={EN=ArticleExt(id=1317436297854177696, articleId=1317436297606713759, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Information consumption and digital technology innovation: Evidence from national information consumption pilot cities, columnId=1309226334803550422, journalTitle=Studies in Science of Science, columnName=Innovation Explorations, runingTitle=null, highlight=null, articleAbstract=

Information consumption, which accelerates the integration of consumption chains with supply chains, industrial chains, and value chains through digital technology, has become a defining feature of digital economic development and a key engine driving high-quality economic growth. To promote information consumption, the State Council issued the “Guiding Opinions on Further Expanding and Upgrading Information Consumption to Continuously Unleash the Potential of Domestic Demand” in 2017. This document emphasized deepening the integration of information consumption with innovation, entrepreneurship, and the “Internet Plus” initiative, while encouraging core technology R&D and service model innovation. The 2021 “Outline of the 14th Five-Year Plan for National Economic and Social Development and the Long-Range Objectives Through the Year 2035” highlighted the need to cultivate new forms of consumption, including information consumption, digital consumption, and green consumption. The 2024 State Council Opinions on Promoting High-Quality Development of Service Consumption called for accelerating the construction and upgrading of information consumption experience centers and launching a series of new information consumption projects. Consequently, advancing information consumption has become a key component of the national innovation strategy. The Ministry of Industry and Information Technology approved 104 national information consumption pilot cities in 2014 and 2015, respectively, deploying pilot initiatives in infrastructure, consumption models, and core technologies to support information consumption development. How, then, do information consumption-driven policies at all government levels simultaneously advance information consumption development and digital technology accumulation while elevating urban digital technology innovation levels? What are their underlying mechanisms? Addressing these questions not only provides a theoretical framework and empirical evidence for evaluating the policy implications of information consumption pilot city development but also offers valuable insights into leveraging demand-driven innovation to enhance urban digital technology innovation capabilities.

Drawing upon demand-driven innovation theory, this paper constructs an analytical framework encompassing foundational elements, core entities, and ecosystem effects. It elucidates the potential mechanisms through which information consumption-driven policies empower urban digital technological innovation. Empirical validation employs a multi-period difference in differences model, utilising multiple datasets including urban datasets, patent databases, national tax survey databases, and listed company databases, alongside information consumption pilot policies. Findings reveal that information consumption-driven policies significantly elevate urban digital technology innovation levels. Mechanism analysis indicates these policies drive innovation through multiple pathways: attracting talent clusters, fostering enterprises as innovation agents, and strengthening governmental external ecosystem safeguards. Heterogeneity analysis demonstrates that such policies primarily enhance digital innovation in regions with high marketisation levels, cities below tier-two status, and areas possessing robust digital industrial foundations. Synergy analysis indicates that digital finance, digital economy policies, and information infrastructure support can generate significant synergistic effects with information consumption-driven policies through financial empowerment, policy guidance, and facility support. The findings of this study provide novel and viable pathways for technological innovation in the digital era, offering important insights for unleashing urban digital technology innovation vitality under information consumption-driven frameworks. Based on the above conclusions, this paper proposes the following policy implications: First, refine top-level institutional design and resource support systems to establish standardized policy closed-loop mechanisms. Second, build collaborative innovation mechanisms among talent, enterprises, and government to stimulate endogenous motivation among micro-level entities. Third, implement tailored, tiered advancement strategies based on the distinct resource endowments of different cities.

, authors=Jing-kui ZHOU, Chang-lei HE, authorsList=Jing-kui ZHOU, Chang-lei HE, authorCompany=null, correspAuthors=Chang-lei HE, 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=1317436298550432163, articleId=1317436297606713759, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=信息消费与数字技术创新——来自国家信息消费试点城市的证据, columnId=1309226334996488408, journalTitle=科学学研究, columnName=创新探索, runingTitle=null, highlight=null, articleAbstract=

信息消费是新发展格局下借助需求引致数字技术创新的重要举措。依据需求引致创新理论,搭建起基座、主体以及生态效应的分析框架,论证信息消费驱动政策赋能数字技术创新的潜在机理,结合城市数据集、专利数据库、全国税收调查数据库、上市公司数据库等多源数据库,并借助信息消费试点政策运用多期双重差分模型进行实证检验。研究发现,信息消费驱动政策能够显著提升数字技术创新水平。机制分析表明,信息消费驱动政策通过吸引人才集聚、培育企业的创新主体作用、增强政府的外部生态保障赋能数字技术创新。异质性分析表明,信息消费驱动政策主要提升了市场化水平高的地区、二线以下城市以及数字产业基础良好的地区的数字技术创新水平。协同效应分析表明,数字金融、数字经济政策、信息基础设施支撑可以从金融赋能、政策引导、设施支撑的角度与信息消费驱动政策发挥显著的协同效应。本文研究结论对于信息消费驱动下释放数字技术创新活力具有重要的启示意义。

, authors=周京奎, 何昌磊, authorsList=周京奎, 何昌磊, authorCompany=null, correspAuthors=何昌磊, authorNote=

周京奎(1974-),男,教授、博士生导师。

, correspAuthorsNote=
何昌磊(1998-),女,博士研究生, E-mail:。
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周京奎(1974-),男,教授、博士生导师。

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周京奎(1974-),男,教授、博士生导师。

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The impact of digital technology innovation on enterprise market value[J]. Journal of Quantitative & Technological Economics, 2023, 40(5):68-91., articleTitle=The impact of digital technology innovation on enterprise market value, refAbstract=null)], funds=[Fund(id=1317436303231275465, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, awardId=22&ZD129, language=CN, fundingSource=国家社会科学基金重大项目(22&ZD129), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1317436298785313188, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, xref=null, ext=[AuthorCompanyExt(id=1317436298797896101, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, companyId=1317436298785313188, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Economics, Nankai University, Tianjin 300071, China), AuthorCompanyExt(id=1317436298806284710, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, companyId=1317436298785313188, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=南开大学经济学院, 天津 300071)])], figs=[ArticleFig(id=1317436301784240569, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Figure 1, caption=Results of pre-event trend and dynamic effect tests, figureFileSmall=VZZYahcUzUNudIe1EVmk1w==, figureFileBig=Wb17LmgmS7HZ/ivjNTr09g==, tableContent=null), ArticleFig(id=1317436301863932346, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=图1, caption=事前趋势与动态效应检验结果, figureFileSmall=VZZYahcUzUNudIe1EVmk1w==, figureFileBig=Wb17LmgmS7HZ/ivjNTr09g==, tableContent=null), ArticleFig(id=1317436302086230459, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 1, caption=

Baseline regression results

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
Diginno Diginno Diginno Diginno
ICPC 0.038*** 0.012*** 0.023*** 0.014***
(0.008) (0.004) (0.005) (0.005)
控制变量 NO YES NO YES
时间效应 NO NO YES YES
个体效应 NO NO YES YES
样本量 4607 4607 4607 4607
R2值 0.120 0.561 0.790 0.824
), ArticleFig(id=1317436302174310844, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表1, caption=

基准回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
Diginno Diginno Diginno Diginno
ICPC 0.038*** 0.012*** 0.023*** 0.014***
(0.008) (0.004) (0.005) (0.005)
控制变量 NO YES NO YES
时间效应 NO NO YES YES
个体效应 NO NO YES YES
样本量 4607 4607 4607 4607
R2值 0.120 0.561 0.790 0.824
), ArticleFig(id=1317436302249808317, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 2, caption=

Robustness test results

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
排除其他政策干扰 工具变量 数字技术创新质量
ICPC 0.010** 0.062*** 0.018** 0.006*
(0.005) (0.006) (0.007) (0.003)
控制变量 YES YES YES YES
时间效应 YES YES YES YES
个体效应 YES YES YES YES
样本量 4607 4607 4607 4607
R2值 0.829 0.756 0.869 0.778
), ArticleFig(id=1317436302321111486, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表2, caption=

稳健性检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
排除其他政策干扰 工具变量 数字技术创新质量
ICPC 0.010** 0.062*** 0.018** 0.006*
(0.005) (0.006) (0.007) (0.003)
控制变量 YES YES YES YES
时间效应 YES YES YES YES
个体效应 YES YES YES YES
样本量 4607 4607 4607 4607
R2值 0.829 0.756 0.869 0.778
), ArticleFig(id=1317436302409191871, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 3, caption=

Test results for the base effect mechanism

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3)
高技能人才 数字人才集聚1 数字人才集聚2
ICPC 0.063* 0.004*** 0.018***
(0.037) (0.001) (0.006)
控制变量 YES YES YES
时间效应 YES YES YES
个体效应 YES YES YES
样本量 4065 3517 2710
R2值 0.951 0.674 0.730
), ArticleFig(id=1317436302493077952, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表3, caption=

基座效应机制检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3)
高技能人才 数字人才集聚1 数字人才集聚2
ICPC 0.063* 0.004*** 0.018***
(0.037) (0.001) (0.006)
控制变量 YES YES YES
时间效应 YES YES YES
个体效应 YES YES YES
样本量 4065 3517 2710
R2值 0.951 0.674 0.730
), ArticleFig(id=1317436302560186817, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 4, caption=

Test results for the main effect mechanism

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
数字企业进入 获得政府补贴 企业研发人员数量占比 企业研发投入
ICPC 1.225** 0.096** 6.065* 1.966***
(0.475) (0.047) (3.265) (0.761)
控制变量 YES YES YES YES
时间效应 YES YES YES YES
个体效应 YES YES YES YES
样本量 4607 39472 18423 107743
R2值 0.650 0.934 0.902 0.671
), ArticleFig(id=1317436302639878594, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表4, caption=

主体效应机制检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
数字企业进入 获得政府补贴 企业研发人员数量占比 企业研发投入
ICPC 1.225** 0.096** 6.065* 1.966***
(0.475) (0.047) (3.265) (0.761)
控制变量 YES YES YES YES
时间效应 YES YES YES YES
个体效应 YES YES YES YES
样本量 4607 39472 18423 107743
R2值 0.650 0.934 0.902 0.671
), ArticleFig(id=1317436302723764675, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 5, caption=

Test results of the ecological effect mechanisms

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
政府数字化关注 数字发展环境 风险投资(金额) 风险投资(案例数)
ICPC 3.030*** 0.011** 0.046* 0.041**
(1.165) (0.005) (0.025) (0.017)
控制变量 YES YES YES YES
时间效应 YES YES YES YES
个体效应 YES YES YES YES
样本量 4549 2710 3744 3744
R2值 0.653 0.874 0.563 0.748
), ArticleFig(id=1317436302803456452, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表5, caption=

生态效应机制检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4)
政府数字化关注 数字发展环境 风险投资(金额) 风险投资(案例数)
ICPC 3.030*** 0.011** 0.046* 0.041**
(1.165) (0.005) (0.025) (0.017)
控制变量 YES YES YES YES
时间效应 YES YES YES YES
个体效应 YES YES YES YES
样本量 4549 2710 3744 3744
R2值 0.653 0.874 0.563 0.748
), ArticleFig(id=1317436302883148229, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 6, caption=

Heterogeneity test results

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4) (5) (6)
市场化水平低 市场化水平高 二线及以上城市 二线以下城市 数字产业基础薄弱 数字产业基础良好
ICPC -0.004 0.019*** 0.005 0.004** 0.004 0.025***
(0.004) (0.007) (0.013) (0.002) (0.002) (0.008)
控制变量 YES YES YES YES YES YES
时间效应 YES YES YES YES YES YES
个体效应 YES YES YES YES YES YES
样本量 3830 4124 833 3774 3959 3974
R2值 0.836 0.879 0.831 0.700 0.812 0.846
), ArticleFig(id=1317436302979617222, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表6, caption=

异质性检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3) (4) (5) (6)
市场化水平低 市场化水平高 二线及以上城市 二线以下城市 数字产业基础薄弱 数字产业基础良好
ICPC -0.004 0.019*** 0.005 0.004** 0.004 0.025***
(0.004) (0.007) (0.013) (0.002) (0.002) (0.008)
控制变量 YES YES YES YES YES YES
时间效应 YES YES YES YES YES YES
个体效应 YES YES YES YES YES YES
样本量 3830 4124 833 3774 3959 3974
R2值 0.836 0.879 0.831 0.700 0.812 0.846
), ArticleFig(id=1317436303059308999, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=EN, label=Table 7, caption=

Synergy effect test results

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3)
金融赋能 政策引导 设施支撑
ICPC×数字金融 0.020***
(0.005)
ICPC×数字经济政策 0.000***
(0.000)
ICPC×移动电话基站数量 0.001***
(0.000)
控制变量 YES YES YES
时间效应 YES YES YES
个体效应 YES YES YES
样本量 3523 4607 2120
R2值 0.898 0.841 0.927
), ArticleFig(id=1317436303126417864, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1317436297606713759, language=CN, label=表7, caption=

协同效应检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 (1) (2) (3)
金融赋能 政策引导 设施支撑
ICPC×数字金融 0.020***
(0.005)
ICPC×数字经济政策 0.000***
(0.000)
ICPC×移动电话基站数量 0.001***
(0.000)
控制变量 YES YES YES
时间效应 YES YES YES
个体效应 YES YES YES
样本量 3523 4607 2120
R2值 0.898 0.841 0.927
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信息消费与数字技术创新——来自国家信息消费试点城市的证据
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周京奎 , 何昌磊
科学学研究 | 创新探索 2026,44(8): 1670-1680
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科学学研究 |创新探索 2026 , 44 (8) : 1670 -1680
信息消费与数字技术创新——来自国家信息消费试点城市的证据
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周京奎(1974-),男,教授、博士生导师。

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周京奎(1974-),男,教授、博士生导师。

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周京奎, 何昌磊
作者信息
  • 南开大学经济学院, 天津 300071
通讯作者:
何昌磊(1998-),女,博士研究生, E-mail:。
作者简介:

周京奎(1974-),男,教授、博士生导师。

Information consumption and digital technology innovation: Evidence from national information consumption pilot cities
Jing-kui ZHOU, Chang-lei HE
Affiliations
  • School of Economics, Nankai University, Tianjin 300071, China
出版时间: 2026-08-15
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摘要
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信息消费是新发展格局下借助需求引致数字技术创新的重要举措。依据需求引致创新理论,搭建起基座、主体以及生态效应的分析框架,论证信息消费驱动政策赋能数字技术创新的潜在机理,结合城市数据集、专利数据库、全国税收调查数据库、上市公司数据库等多源数据库,并借助信息消费试点政策运用多期双重差分模型进行实证检验。研究发现,信息消费驱动政策能够显著提升数字技术创新水平。机制分析表明,信息消费驱动政策通过吸引人才集聚、培育企业的创新主体作用、增强政府的外部生态保障赋能数字技术创新。异质性分析表明,信息消费驱动政策主要提升了市场化水平高的地区、二线以下城市以及数字产业基础良好的地区的数字技术创新水平。协同效应分析表明,数字金融、数字经济政策、信息基础设施支撑可以从金融赋能、政策引导、设施支撑的角度与信息消费驱动政策发挥显著的协同效应。本文研究结论对于信息消费驱动下释放数字技术创新活力具有重要的启示意义。

信息消费  /  数字技术创新  /  国家信息消费试点政策  /  多期双重差分模型

Information consumption, which accelerates the integration of consumption chains with supply chains, industrial chains, and value chains through digital technology, has become a defining feature of digital economic development and a key engine driving high-quality economic growth. To promote information consumption, the State Council issued the “Guiding Opinions on Further Expanding and Upgrading Information Consumption to Continuously Unleash the Potential of Domestic Demand” in 2017. This document emphasized deepening the integration of information consumption with innovation, entrepreneurship, and the “Internet Plus” initiative, while encouraging core technology R&D and service model innovation. The 2021 “Outline of the 14th Five-Year Plan for National Economic and Social Development and the Long-Range Objectives Through the Year 2035” highlighted the need to cultivate new forms of consumption, including information consumption, digital consumption, and green consumption. The 2024 State Council Opinions on Promoting High-Quality Development of Service Consumption called for accelerating the construction and upgrading of information consumption experience centers and launching a series of new information consumption projects. Consequently, advancing information consumption has become a key component of the national innovation strategy. The Ministry of Industry and Information Technology approved 104 national information consumption pilot cities in 2014 and 2015, respectively, deploying pilot initiatives in infrastructure, consumption models, and core technologies to support information consumption development. How, then, do information consumption-driven policies at all government levels simultaneously advance information consumption development and digital technology accumulation while elevating urban digital technology innovation levels? What are their underlying mechanisms? Addressing these questions not only provides a theoretical framework and empirical evidence for evaluating the policy implications of information consumption pilot city development but also offers valuable insights into leveraging demand-driven innovation to enhance urban digital technology innovation capabilities.

Drawing upon demand-driven innovation theory, this paper constructs an analytical framework encompassing foundational elements, core entities, and ecosystem effects. It elucidates the potential mechanisms through which information consumption-driven policies empower urban digital technological innovation. Empirical validation employs a multi-period difference in differences model, utilising multiple datasets including urban datasets, patent databases, national tax survey databases, and listed company databases, alongside information consumption pilot policies. Findings reveal that information consumption-driven policies significantly elevate urban digital technology innovation levels. Mechanism analysis indicates these policies drive innovation through multiple pathways: attracting talent clusters, fostering enterprises as innovation agents, and strengthening governmental external ecosystem safeguards. Heterogeneity analysis demonstrates that such policies primarily enhance digital innovation in regions with high marketisation levels, cities below tier-two status, and areas possessing robust digital industrial foundations. Synergy analysis indicates that digital finance, digital economy policies, and information infrastructure support can generate significant synergistic effects with information consumption-driven policies through financial empowerment, policy guidance, and facility support. The findings of this study provide novel and viable pathways for technological innovation in the digital era, offering important insights for unleashing urban digital technology innovation vitality under information consumption-driven frameworks. Based on the above conclusions, this paper proposes the following policy implications: First, refine top-level institutional design and resource support systems to establish standardized policy closed-loop mechanisms. Second, build collaborative innovation mechanisms among talent, enterprises, and government to stimulate endogenous motivation among micro-level entities. Third, implement tailored, tiered advancement strategies based on the distinct resource endowments of different cities.

information consumption  /  digital technology innovation  /  national information consumption pilot policy  /  multi-period difference in differences model
周京奎, 何昌磊. 信息消费与数字技术创新——来自国家信息消费试点城市的证据. 科学学研究, 2026 , 44 (8) : 1670 -1680 .
Jing-kui ZHOU, Chang-lei HE. Information consumption and digital technology innovation: Evidence from national information consumption pilot cities[J]. Studies in Science of Science, 2026 , 44 (8) : 1670 -1680 .
正文
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以数字技术为基础,促进消费链与供应链、产业链、价值链加速融合的信息消费,已成为数字经济发展的重要特征,是推动经济高质量发展的重要引擎[1]。为推动信息消费发展,工业和信息化部分别于2014年和2015年批复了104个国家信息消费试点城市,并在基础设施、消费模式、核心技术等方面部署了试点城市工作,以支持信息消费发展。那么,信息消费驱动政策如何在推动信息消费发展和数字技术积累的同时,促进城市数字技术创新水平提升?其影响机理和作用边界如何?回答上述问题,不仅为评估信息消费试点城市建设的政策内涵提供了理论框架和经验证据,也为如何依托需求引致创新推动城市数字技术创新水平提升提供有益参考。
数字技术创新是企业或组织以大数据、云计算、人工智能等数字技术为底层基础,重塑产品和商业模式的组合创新过程[2,3]。与传统技术创新相比,数字技术创新具有更强的灵活性、去中心化以及跨界融合等特征,能够降低创新门槛与边际成本,引致规模经济和创造性破坏效应[4]。随着学术界对数字技术创新的广泛关注,其对经济社会发展的重大价值逐渐达成共识[5,6],并致力于探讨数字技术创新的实现路径。已有文献从数据资产、数字知识共享和流动以及外部投资者等供给侧视角研究如何推动数字技术创新[7-9]。从需求引致创新的视角看,消费需求创新被视为技术创新的起点和归宿[10]。新生代消费升级通过潜在收益激发、适应性匹配和问题激发等机制,对创新形成持续的激励作用[11]。作为承载新型消费形态的重要载体,信息消费在扩大内需、挖掘市场潜力的同时,也为新产品和新技术应用提供了更加多元的需求场景。相关研究表明,信息消费在推动城市活力提升、促进数字化与绿色化协同以及激发创业活力等方面发挥了重要作用[12-14]。在信息消费与技术创新关联性问题上,研究发现信息消费政策通过促进创新链升级、数实融合等渠道,显著促进城市技术创新[15,16]。尽管已有部分研究关注到消费需求对技术创新的积极影响,但从需求侧系统考察信息消费如何作用于数字技术创新,特别是识别其作用机制与情境边界的研究仍相对不足。鉴于此,在需求引致创新的理论基础上,本文尝试构建基座、主体以及生态效应的分析框架,识别信息消费驱动政策赋能数字技术创新的影响机理。
本文的边际贡献如下:一是,拓展了信息消费驱动政策创新效应的相关研究。尽管既有研究考察了数字技术创新的影响因素[7-9],但未能从需求侧的角度,依据需求引致创新理论对数字技术创新的影响作出更为详细的探讨。本文不仅能拓展信息消费驱动政策的研究边界,也能从需求侧视角丰富数字技术创新影响因素的研究。二是,构建了基座、主体、生态效应的多维机制链条,从吸引人才集聚、培育企业的创新主体作用、增强政府的外部生态保障等角度,解析信息消费驱动政策推动数字技术创新的传导路径。三是,从市场化水平、城市规模、产业基础差异等角度对信息消费驱动政策影响城市数字技术创新的异质性特征进行分析,并从金融赋能、政策引导、设施支撑等方面考察其对信息消费驱动政策可能产生的协同效应,对于理解信息消费驱动政策的创新效应、完善政策设计以提升城市数字技术创新水平具有重要的政策价值。
1 理论分析与研究假说
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1.1 信息消费驱动政策与数字技术创新
需求引致创新理论表明技术进步的核心驱动力在于创新需求所构建的利润激励[17],更强调市场需求动态对技术研发方向与强度的牵引作用。市场规模优势与需求异质性中蕴含的价值势能,通过边际收益预期引导研发要素重组,构成技术创新的内生性激励[18]。信息消费是以信息产品和服务为核心对象的经济活动,作为传统消费的延伸与升级形态,其动态演化不断催生新型消费需求。信息消费驱动政策通过构建互联网、大数据、人工智能等数字技术赋能的消费情境,不仅提升传统消费的规模效应,更通过需求结构升级倒逼技术适配性改进。信息消费需求增长促使企业以功能迭代和性能突破为导向开展数字技术创新。此外,政策引导下的规模化信息消费形成稳定的市场信号,降低企业创新投入的沉没成本风险和利润不确定性,从而增强数字技术研发的可持续性。基于此,本文提出如下假说:
假说1:信息消费驱动政策能够提高城市数字技术创新水平。
1.2 信息消费驱动政策推动数字技术创新的机制分析
基座效应层面。人才集聚构成数字技术创新的核心要素禀赋,是城市开展数字技术创新的底座支撑[19]。一方面,新型信息消费场景的拓展提高了数据服务、智能终端、平台应用等相关产品的预期收益,释放数字技能偏向型市场需求信号,引导具备敏锐市场洞察能力和专业技能的人才实现更高效的岗位匹配,推动了城市人才集聚。另一方面,信息消费驱动政策通过税收减免、研发补助、人才培养等措施,推动企业增加数字技术研发和应用投入,以成本补偿效应降低数字技术创新活动的边际成本,提升人力资本与数字技术的组合效率。人才集聚拓宽了企业与研发人员的双向选择空间,提升匹配质量与生产效率,引导高技能人才更多投向具有竞争优势的数字技术领域,有效激发了城市数字技术创新潜能。同时,人才集聚通过强化近距离学习与隐性知识交换,降低知识扩散摩擦,使数字技术创新活动更容易在人才与研发活动密集的城市空间内重组和扩散,从而提升城市数字技术创新水平[20]。
主体效应层面。企业能够识别数字技术创新机会,分析潜在创新需求,从技术、产品、服务模式等方面开展数字技术创新[21]。信息消费驱动政策依托成本共担和竞争筛选双重机制培育企业的创新主体地位。成本共担方面,政策实施能够通过财政补贴、研发支持等公共资金安排分担试错成本、缓解融资约束,推动研发投入上升,形成安全可信的数字技术发展体系,降低数字技术创新门槛和成本,为开展数字技术创新提供基础条件。然而,创新补贴在实践中往往具有择优分配特征,创新能力较强的企业更易获得支持,补贴上升本身并不必然等同于创新提升。同时,研发投入增加也未必在当期转化为专利等可观测成果,其效果仍取决于企业的吸收能力、知识积累及与商业化相关的互补资产配置。而信息消费驱动政策能够改善外部数字基础设施和制度环境、强化知识获取与学习条件,提高研发投入的边际创新产出。竞争筛选方面,竞争是激发企业进行数字技术创新的关键。信息消费驱动政策催生新型信息消费需求,引致大量新兴数字企业进入,叠加存量企业数字化转型压力,形成面向数字消费场景的激烈竞争格局,倒逼企业强化创新主体作用,通过产品和服务的持续迭代打造差异化竞争优势,从而提升城市数字技术创新水平。
生态效应层面。依据公共品供给理论,政府在出台信息消费发展的相关政策文件时,会增加在新型基础设施建设项目上的战略性投资。例如,石家庄市《关于以新业态新模式引领新型消费加快发展的实施意见》强调要推进新型消费基础设施和服务保障能力建设,实施“网络强市”建设工程等。在信息消费政策驱动下,政府加速推动信息基础设施迭代升级和公共数字平台建设,形成以高速网络、云计算和数据资源为支撑的数字发展环境,为数字技术创新创造良好的基础设施与要素保障。另一方面,信息消费驱动政策实施带动信息化平台发展,完善“互联网+督查”机制,促使行政审批、公共资源配置等行政流程的标准化、在线化运作机制。不仅提升了政府市场治理效能,更通过建立规范透明的制度环境,重塑数字技术创新活动所依托的制度环境,有效降低企业数字技术创新的制度性交易成本。最终形成数字技术创新与制度创新的良性互动格局,有效提升城市数字技术创新水平。基于此,本文提出如下假说:
假说2:信息消费驱动政策能够通过促进人才集聚、培育企业的创新主体作用、增强政府的外部生态保障,提高城市数字技术创新水平。
2 研究设计
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2.1 模型设定
由于信息消费试点城市的批复时间不同,本文采用多期双重差分模型评估信息消费驱动政策对城市数字技术创新的影响。构建基准回归模型如下:
$Diginn{o}_{it}={\alpha }_{0}+{\alpha }_{1}ICP{C}_{it}+{\alpha }_{2}{X}_{it}+{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$
其中,i代表城市;t代表年份;Diginnoit为城市数字技术创新水平;ICPCit为信息消费驱动政策;Xit为控制变量;μi为城市固定效应;λt为年份固定效应;εit为误差项。
2.2 变量说明
(1)被解释变量:城市数字技术创新水平。参考陶锋等[22]的做法,依据《数字经济及其核心产业统计分类(2021)》和《国际专利分类与国民经济行业分类参照关系表(2023)》,对数字经济核心产业对应的国际专利分类号进行统计,加总得到各地级市数字技术专利数据,采用当年每百人的数字经济相关发明专利申请数量来衡量城市数字技术创新水平,并用当年每百人的数字经济相关发明专利授权数量和数字经济相关专利申请总量作为稳健性检验。
(2)核心解释变量:信息消费驱动政策。剔除数据缺失较为严重的地级市,最终将74个地级市作为处理组,197个地级市作为对照组,对设立信息消费试点的当年及之后年份赋值为1,反之为0。
(3)控制变量。为降低其他因素对实证结果可能存在的影响,选取以下控制变量:地区经济增长水平、外资引入情况、政府规模、金融发展情况、人口密度、基础设施水平①(①限于篇幅,控制变量的具体衡量方式备索。)。
(4)数据来源与处理。为保证试点前后有足够长的时间,选取2007-2023年中国271个地级市作为研究对象(剔除铜仁市、毕节市等数据缺失较为严重的城市)。其中,城市数据主要来源于《中国城市统计年鉴》《中国统计年鉴》、EPS数据库等。数字技术创新数据来源于国家知识产权局。企业层面数据主要来源于2007-2016年全国税收调查数据库和CSMAR数据库中的上市公司数据,剔除总资产、营业收入、固定资产、职工人数小于0的样本,并在数据处理过程中进行1%缩尾处理。对于部分缺失数据采用线性插值法补全②(②限于篇幅,主要变量描述性统计结果备索。)。
3 实证分析
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3.1 基准回归分析
表1汇报了基准回归的估计结果。列(1)是在不考虑控制变量和固定效应的回归结果,列(2)、(3)分别为仅加入控制变量和仅加入固定效应时的回归结果,列(4)将控制变量、年份固定效应以及城市固定效应同时加入基准回归。回归结果显示信息消费驱动政策的系数在1%的水平上为0.014,证实了信息消费驱动政策能够提升城市数字技术创新水平。从经济意义上来看,依据第(4)列估计系数计算了城市数字技术创新的标准差变化后发现,信息消费驱动政策实施促进城市数字技术创新水平上升约0.20个标准差。由此,假说1得到验证。
3.2 事前趋势与动态效应检验
使用多期双重差分模型进行因果识别的前提是满足共同趋势假设。采用事件研究法来进一步考察信息消费驱动政策影响城市数字技术创新的动态效应,构建动态效应回归模型如下:
$Diginn{o}_{it}={\alpha }_{0}+\sum _{k=-7}^{8}{\beta }_{k}ICP{C}_{it}^{k}+{\alpha }_{1}{X}_{it}+{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$
其中,ICP${C}_{it}^{k}$为信息消费驱动政策相对实施时间为k的虚拟变量。特别地,k=-1表示政策实施前的第1年,k=1表示政策实施后的第1年。以政策实施前第7期作为基期,并为避免多重共线性将-7期的虚拟变量剔除。
图1展示了事前趋势与动态效应检验结果。可以看出,在政策实施前ICPC的各个系数均在统计意义上不显著,且分布在零值附近。这表明试点和非试点城市的数字技术创新水平并不存在明显差异,满足事前趋势假设。动态效应方面,在政策实施后的前两年,ICPC估计系数虽为正,但在统计意义上尚不显著,表明短期内存在一定的时滞效应。可能是由于政策落地初期,各地主要围绕信息基础设施补短板和扩容升级,培育信息消费和数实融合的应用场景,难以及时反映在专利等最终创新产出上。自政策实施第三年起,估计系数显著为正,表明随着政策持续推进,市场主体对信息消费场景的适应程度提高,数字要素配置效率和创新生态得到改善,政策促进城市数字技术创新水平提升的红利逐渐显现。
3.3 异质性处理效应检验与处理
考虑到双向固定效应框架下采用多期双重差分模型进行估计可能存在偏误,本文对DID估计量进行Bacon分解。晚入选试点城市与早入选试点城市之间得到的处理效应均占总双向固定效应处理效应的1%;时变处理与从未处理的样本得到的处理效应为正,且占总双向固定效应处理效应的98.2%。这表明在双向固定效应框架下,政策效应存在的偏误较小。为提高本文估计量的稳健性,进一步采用两阶段双重差分法评估政策效应。由回归结果可知,在考虑异质性处理效应后,估计系数仍显著为正,验证了本文核心结论的可靠性③(③限于篇幅,两阶段双重差分法结果备索。)。
3.4 稳健性检验
(1)排除其他政策干扰
在考察期内还存在着一些可能影响数字技术创新的政策冲击,主要包括数据要素政策、政府数字治理政策、数字基础设施建设、创新扶持政策、开放型政策等。本文整理了国家大数据综合试验区、公共数据开放、信息惠民、宽带中国、创新型城市、自贸试验区等政策的设立信息,采用与解释变量同样的方式生成6个政策变量,纳入基准回归重新进行估计。表2列(1)结果表明,信息消费驱动政策对数字技术创新的影响系数与基准估计结果并无明显差异。
(2)内生性分析
选取地级市到1998年建成的“八纵八横”光缆传输骨干网节点城市的最小距离作为信息消费驱动政策的工具变量,进行内生性分析。一方面,“八纵八横”光缆传输骨干网构成中国通信网络的基本骨架,距骨干网节点城市越近,越有可能具备更高水平的信息基础设施,信息消费规模预期越大,被设立为试点城市概率也越高。另一方面,该变量属于历史地理变量,对现阶段城市数字技术创新水平的影响甚微。因此,工具变量满足相关性和外生性假设。由于该变量是截面数据,对其取对数处理后与年份虚拟变量进行交互,作为工具变量。由表2列(2)结果可知,在考虑内生性问题后,核心结论依然成立④(④Kleibergen-Paap rk LM统计量P值为0.000;Kleibergen-Paap rk Wald F值为13.207,大于Stock-Yogo弱识别检验10%水平上的临界值(11.50),拒绝工具变量识别不足和弱识别假设,且一阶段F值大于10。)。
(3)考虑数字技术创新质量
以数字经济相关发明专利总量衡量创新水平,容易在数量扩张而质量未同步提升时高估部分城市的真实创新能力。以人工智能、工业软件、第五代移动通信等为代表构建的关键数字技术专利,更能刻画技术含量高、难度大的“卡脖子”领域创新。为此,采用当年每百人的关键数字技术发明专利申请和授权数量衡量数字技术创新质量⑤(⑤限于篇幅,关键数字技术发明专利的识别方式备索。),重新估计。由表2列(3)、(4)的结果可知,核心结论并非由低质量专利堆积所驱动,而是与数字时代国家提升关键数字技术创新能力的战略取向高度一致。
(4)其他稳健性检验
本文还进行了如下稳健性检验:①控制潜在遗漏变量;②考虑政策强度;③双重机器学习因果推断;④联合固定效应;⑤剔除直辖市和替换被解释变量;⑥PSM-DID;⑦安慰剂检验(限于篇幅原因,备索)。这些稳健性检验结果均表明本文核心结论是可靠的。
3.5 机制检验
(1)基座效应
从高技能人才和数字技术人才两个方面来检验人才集聚的基座效应。第一,将大学专科及以上学历视为高技能人才。依据地级市从业人数分解各省不同技能人才的从业总数得到各地级市的高技能人才并取对数处理。第二,利用信息传输、计算机服务和软件业从业人数与城镇单位从业人员期末人数之比来衡量城市数字技术人才集聚。第三,选取2014-2023年网络招聘数据,据此筛选与算法开发、数据分析、人工智能工程等直接相关的数字技术岗位,将数字技术人才需求量加总至城市层面来刻画数字技术人才集聚。由表3可知,信息消费驱动政策实施促进了城市高技能人才和数字技术人才集聚。而专业化的高技能人才和数字技术人才能够快速掌握和适应数字时代的要求,是影响数字技术创新的重要因素。因此,人才集聚能够为提高城市数字技术创新水平打牢基座效应。
(2)主体效应
从数字企业进入情况、企业获得政府补贴情况、企业研发投入情况三个方面来检验企业在提升城市数字技术创新水平中的主体作用。第一,将信息传输、软件与信息技术服务业新增注册企业数与总人口的比值作为衡量数字企业进入的代理变量。其中,新增注册企业数来源于天眼查数据库。第二,选取全国税收调查数据库中企业获得的补贴收入衡量政府补贴水平。第三,从企业研发人员数量占比、研发投入两个方面考察企业研发投入情况。鉴于全国税收调查数据库未包含研发人员数量相关信息,因此研发人员占比指标基于上市公司数据构造;研发投入则采用全国税收调查数据库中的研究新产品、新技术、新工艺发生的费用加以衡量。由表4可知,信息消费试点城市更能吸引数字企业进入、获得政府补贴支持、增加企业研发投入,从而强化了企业在开展数字技术创新过程中的主体作用⑥(⑥限于篇幅原因,企业层面控制变量的选取及衡量方式备索。)。
(3)生态效应
从政府数字化关注、数字发展环境、风险投资情况三个方面来检验政府在提升城市数字技术创新水平中的生态效应。第一,利用文本分析法来测量政府数字化关注度。依据数字技术和数字应用两个方面的关键词,统计政府工作报告中的数字关注词频数,作为政府数字化关注度的代理变量。第二,选取移动电话渗透率、互联网用户渗透率、计算机服务和软件业从业人员占比、数字普惠金融指数以及人均电信业务量,并利用主成分分析法得到数字发展环境指数。第三,从清科数据库中搜集整理各类风险投资机构的投资数据,得到城市层面的风险投资额和案例数,并分别采用二者与总人口的比重衡量风险投资。由表5可知,信息消费驱动政策有助于增强政府的数字化关注度、优化数字发展环境、增加风险投资规模,从而为企业开展数字技术创新营造良好环境,提升城市数字技术创新水平。
4 拓展分析
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4.1 异质性分析
(1)市场化水平
以市场化指数的中位数为界,将试点城市样本分为两组,再分别与非试点城市样本进行回归。由表6列(1)、(2)可知,在市场化水平高的地区,信息消费驱动政策更有助于提升数字技术创新水平。作为正式制度的市场化水平越高,越能够打破制度路径约束,发挥完善的筛选功能,缓解要素错配。在市场化水平高的地区,信息消费驱动政策实施使得企业更容易获得人才、资金、资源支持以及政策倾斜,有更充裕的资源投入到数字技术创新活动中,从而导致需求引致创新效应更为突出。
(2)城市规模
将样本城市划分为二线及以上城市组与二线以下城市组⑦(⑦依据《第一财经周刊》发布的2025年中国城市等级排名。),分别进行估计。表6列(3)、(4)的结果表明,在二线以下城市,信息消费驱动政策能够显著提升城市数字技术创新水平。这可能是因为,在二线及以上城市,信息基础设施较为完备,平台经济发育成熟,数字技术创新更多受制于前沿研发能力、全球竞争约束和制度环境,单一的需求侧刺激对其促进作用相对有限。而二线以下城市的信息基础设施和平台生态相对薄弱,政策实施能够通过补贴、试点和应用场景建设,有效扩大本地信息消费市场规模,降低数字化改造和新技术应用的不确定性,使更多企业有动力围绕线上服务、数据应用和本地生活场景开展技术改造和产品创新。
(3)产业基础
以电信业务收入作为城市数字产业基础水平的代理变量,以其中位数为界将试点城市划分为数字产业基础薄弱组和良好组,并分别与非试点城市配对进行回归。表6列(5)、(6)的结果表明,在数字产业基础良好的地区,信息消费驱动政策对城市数字技术创新具有显著促进作用。这可能是由于,在数字产业基础良好的城市,专业人才和配套服务体系相对完备,创新主体已具备较强的技术积累与吸收能力。政策通过扩大本地数字产品和服务的潜在市场规模,强化预期需求,对企业数字技术研发和应用创新形成明显的需求拉动。相较之下,数字产业基础薄弱地区的主要约束在于创新主体数量有限、人才和资本供给不足以及产业链不完整。尽管政策同样改善了信息消费环境,但需求信号难以及时转化为研发投入和技术突破,其创新效应具有更强的潜在性和滞后性。
4.2 协同效应检验
(1)金融赋能
数字金融安全和高效的服务能够为消费群体缓解信息不对称问题和信贷约束问题,提升信息消费的可达性。采用北京大学数字金融研究中心的中国数字金融发展指数来衡量数字金融水平,构建信息消费驱动政策与数字金融的交乘项,将其加入基准回归模型。表7列(1)的回归结果显示,交乘项系数显著为正,这表明随着城市数字金融发展水平提升,信息消费驱动政策的数字技术创新效应越明显,即数字金融可与信息消费驱动政策发挥显著的协同效应。
(2)政策引导
数字经济政策的实施能够打破数据资源壁垒,为加速信息消费转型、推动信息消费规模扩张,创造适宜的市场环境。利用《“十四五”数字经济发展规划》《数字经济及其核心产业统计分类(2021)》等确定数字经济关键词谱。通过数字经济关键词谱爬取北大法宝数据库中各地级市颁布的地方规范性文件和工作文件等数字经济政策文本。以年份—城市的数字经济政策累计数量衡量数字经济政策的供给强度,构建信息消费驱动政策与数字经济政策的交乘项,将其加入基准回归模型。表7列(2)回归结果显示,交乘项系数显著为正,这表明随着数字经济政策的深入实施,信息消费驱动政策的数字技术创新效应也越显著,即数字经济政策可与信息消费驱动政策发挥显著的协同效应。
(3)设施支撑
移动电话基站数反映了地区信息基础设施建设情况,移动电话基站数覆盖度高的地区,信息基础设施水平高,信息接入便捷和使用成本低,能够为信息消费发展提供良好的设施支撑。本文采用移动电话基站数来衡量信息基础设施水平,构建信息消费驱动政策与信息基础设施水平的交乘项,将其加入基准回归模型。表7列(3)的回归结果显示,交乘项系数显著为正,这表明随着信息基础设施水平的提高,信息消费驱动政策的数字技术创新效应也越显著,即信息基础设施支撑可与信息消费驱动政策发挥显著的协同效应。
5 结论与政策启示
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汇聚创新要素最多、发展应用前景最广的创新领域在数字技术创新[3]。本文基于城市数据集、专利数据库、全国税收调查数据库、上市公司数据库等多源数据库,以入选信息消费试点城市为外生冲击,深入研究了信息消费驱动政策对城市数字技术创新的影响机理。研究发现信息消费驱动政策能够有效提升城市数字技术创新水平。机制分析表明,信息消费驱动政策通过吸引人才集聚、培育企业的创新主体作用、增强政府的外部生态保障三个重要途径,提升城市数字技术创新水平。异质性分析表明,信息消费驱动政策主要提升了市场化水平高的地区、二线以下城市以及数字产业基础良好的地区的数字技术创新水平,体现了政策资源在不同区位下的效果差异。协同效应分析表明,数字金融、数字经济政策、信息基础设施支撑能够与信息消费驱动政策发挥显著的协同效应。
基于以上结论,本文提出如下政策启示:
第一,完善顶层制度设计与要素保障体系,构建标准化的政策闭环。在省级层面建立统筹协调机构,制定以数字技术创新转化、应用场景拓展为核心的量化考评标准,实施遴选、培育、评估的全流程动态管理。在此基础上,强化金融与基础设施的硬支撑,深化金融供给侧结构性改革,完善数字金融产品风险定价机制,探索数据资产质押融资模式,缓解中小数字企业信贷约束。同时,推动基础设施由广覆盖向高质量跃升,加大“双千兆”网络及工业互联网投资力度。通过制度软约束与设施硬支撑的协同,加速数据要素跨部门流动,确保政策资源精准引导至高创新效率区域。
第二,构建人才—企业—政府协同创新机制,激发微观主体内生动力。在人才端,通过设立专项数字技能提升基金与区域联合培养计划,推动人才结构与产业结构适配。在企业端,重构激励相容机制,摒弃普惠式补贴,建立与数字化改造绩效、研发投入强度直接挂钩的精准奖补制度,倒逼企业通过技术创新而非寻租行为获取收益。在政府端,重点建设数字公共服务平台并明晰数据产权界定,为数字技术创新提供外部性保障,形成政府搭台、企业主导、人才支撑的良性生态系统。
第三,立足城市资源禀赋差异,实施因地制宜的梯度推进策略。针对市场化程度高、数字产业基础雄厚的中心城市,赋予其更大的改革自主权,聚焦关键核心数字技术攻关与高端信息消费场景培育,强化其作为增长极的溢出效应。针对市场化程度较低、产业基础薄弱的城市,政策重点应置于各类基础设施的补齐与营商环境的优化,提升数字要素承载能力。对于二线及中小城市,应侧重于培育特色化的中小数字服务企业,利用数字金融工具与孵化基地,深耕本地细分市场潜力,构建与中心城市优势互补、错位发展的产业格局。
基金
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  • 国家社会科学基金重大项目(22&ZD129)
文献
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2026年第44卷第8期
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  • 接收时间：2025-04-28
  • 首发时间：2026-10-15
  • 出版时间：2026-08-15
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  • 收稿日期：2025-04-28
  • 修回日期：2026-01-16
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国家社会科学基金重大项目(22&ZD129)
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    南开大学经济学院, 天津 300071

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何昌磊(1998-),女,博士研究生, E-mail:。
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Number of
species		 占总种数比例
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total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
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species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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