地方政策是推动区域创新发展的关键因素，科技政策对于提升产业自主创新能力具有至关重要作用。简单的同时增加数量不加区分的政策工具可能会降低政策组合的有效性。研究对地方科技政策组合特征在产业创新中所起的作用进行了实证分析。以四川省2012—2020年科技政策为样本，运用文本分析法将科技政策分为供给型、需求型和环境型政策，构建模型探究不同政策类型组合以及同一政策工具内部组合的综合性、均衡性、一致性对产业创新的影响。研究发现：（1）当地方科技政策组合特征是更均衡地使用需求拉动型、技术推动型和环境支持型工具时，其对产业创新的积极影响往往更大。（2）更全面的地方科技政策组合能够加强产业创新活动，以产生新的技术产出和经济产出。（3）简单的同时增加大量的供给型和需求型政策工具可能会降低政策组合的有效性，往往可能无法对产业创新产生积极影响。本文揭示了地方科技政策组合综合性、均衡性和一致性对产业创新的影响，有助于拓展现有科技政策组合及产业创新的相关研究，且为完善地方科技政策体系，提升产业创新水平提供启示。
Abstract
The policies formulated by the local governments are the key factors to promote the development of regional innovation, and science and technology policies play a vital role in enhancing the independent innovation capability of industries. However, policy is often not a single form, but a combination of multiple policies interacts with innovation. Simply adding an indiscriminate number of policy instruments at the same time may reduce the effectiveness of the policy mix. This paper made an empirical study on the role of the characteristics of local science and technology policy mix in industrial innovation. Taking the science and technology policies in Sichuan Province from 2012 to 2020 as a case, the authors of this paper divided the policies into supply policy, demand policy and environment policy with text analysis method. A model was constructed to study the influence of the comprehensiveness, balance and consistency of different policy mix characteristics and the internal combination of the same policy tool on industrial innovation. The results showed that:First, when the local science and technology policy mix is characterized by a more balanced use of demand-led, technology-driven, and environmentally supportive tools, its positive impact on industrial innovation tends to be greater. The empirical analysis showed that the comprehensiveness, balance and consistency of the three types of policy mixes of supply, demand and environment have significant impacts on patent output and new product output value, indicating that the characteristics of different types of science and technology mixes in Sichuan Province have positive impacts on industrial innovation. As the number of policies increases, the interaction of policies becomes more and more complex. Existing studies have shown that multiple policy tools in different regions will influence and interact with each other, and different policy combinations will have positive or negative impacts on innovation. A more balanced expansion of the use of the three types of science and technology policy tools and improvement of their synergy will effectively drive industrial innovation.Second, a more comprehensive local science and technology policy mix could strengthen industrial innovation activities to generate new technological outputs and economic outputs. The empirical analysis showed that the mix of supply-oriented, demand-oriented and environment-oriented policies has a significant impact on patent output and new product output value, indicating that the science and technology policy mixes of Sichuan Province can help enterprises carry out scientific and technological innovation activities, build a new engine to drive industrial development, and provide solid support for the development. A more comprehensive policy mix could thus promote local innovation.Third, simply adding a large number of supply - and demand-oriented policy tools at the same time may reduce the effectiveness of the policy mix and may often fail to have a positive impact on industrial innovation. The empirical analysis showed that the consistency of the internal tool mix of demand-oriented policies has no significant impact on the output value of patents and new products, and the balance and consistency of the internal tool mix of supply-oriented policies has no significant impact on the output value of new products, indicating that when designing the policy mix, strengthening the market incentive and developing the innovation ability of new products and technologies, the balance and synergy between these two policy instruments becomes a key feature that needs to be addressed, and simply adding an indiscriminate number of policy instruments at the same time may reduce the effectiveness of the policy mix.
关键词
地方科技政策 /
政策组合特征 /
产业创新 /
四川省
Key words
local science and technology policy /
policy mix characteristics /
industrial innovation /
Sichuan Province
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
Claeys P. Policy mix and debt sustainability: evidence from fiscal policy rules[J]. Empirical, 133(23) 2006.: 89-112.
Borras S, Edqvist C. The choice of innovation policy instruments[J]. Technological Forecasting＆Social Change, 2013. 80(8): 1513-1522.
OECD. The innovation policy mix[R]. 2010.Paris: OECD publishing.
Rogge K S. Reichardt K. Policy mixes for sustainability transitions: An extended concept and framework for analysis[J]. Research Policy, 2016.45: 1620－1635.
Costantini V, Crespi F. Alessandro Palma. Characterizing the policy mix and its impact on eco-innovation: A patent analysis of energy-efficient technologies[J]. Research Policy, 2017 (46): 799-819.
Rogge K S, Schleich J. Do policy mix characteristics matter for low-carbon innovation? A survey-based exploration of renewable power generation technologies in Germany[J]. Research Policy, 2018.5(11): 1-16.
Reichardt K, Rogge K. How the policy mix impacts innovation: Findings from Company case studies on offshore wind in Germany[J]. Environmental Innovation and Societal Transitions, 2015. (8): 1-15.
Reichardt K, Negro O S, Rogge K S, et al. Analyzing interdependencies between policy mixes and technologicalinnovation systems: The case of offshore wind in Germany[J]. Technological Forecasting & Social Change, 2016. (106): 11-21.
徐喆,李春艳. 我国科技政策组合特征及其对产业创新的影响研究[J]. 科学学研究，2017.35（01）：45-53.
孟维站，徐喆，刘宇佳等.我国科技政策组合特征对高技术产业创新效率的分阶段影响[J]. 经济问题，2019（06）：49-54.
张宏伟.政策工具及其组合与海上风电技术创新和扩散：来自德国的考察[J]. 科技进步与对策，2017. 34（14）：120-125.
曹建云，李红锦，方 洪.基于目标偏差的政策组合效果评价[J]. 预测，2020. 04：8-15.
雷璇，马文聪，陈修德，翁银娇.我国LED产业创新政策组合特征对创新绩效的影响[J]. 科技管理研究，2020. 40（02）：58-65.
马艳艳，孔梦晗.辽宁创新政策协同对创新绩效影响的实证分析[J]. 理论界，2017.（10）：116-124+48.
樊霞，陈娅，贾建林. 区域创新政策协同——基于长三角与珠三角的比较研究[J]. 软科学，2019.33（03）：70-74+105.
凌媛媛，顾玲琽.上海张江高新技术产业开发区政策协同创新的现状问题分析[J]. 未来与发展，2015. 39（10）：108-113.
Radicic D, Pugh G. R＆D programmes, policy mix, and the “European paradox”: evidence from Euopean SEMS[J]. Science＆public policy, 2016. 44(4): 497-512.
Vitola A. Innovation policy mix in amulti-level context: The case of the Baltic Sea Region countries[J]. Science and Public Policy, 2015. (42): 401-414.
Lanahana L, Feldman M P. Multilevel innovation policy mix: A closer look at state policies that augment the federal SBIR program [J]. Research Policy, 2015. (44): 1387-1402.
Montmaritn B, Herra M, Massard N. The import of French policy mix on business and R＆D:how geography matter[J]. Research Policy, 2018. 47(10): 2010-2017.
Douglas D, Radicic D. Network additionality and policy mix of regional and nationalpublic support for innovation[J]. Informa UK Limited, trading as Taylor & Francis Group, 2020. (5): 1-24.
Cunningham N J. Industrial innovation[J]. Business History, 1960. 2(2): 97-100.
Freeman C. The Economics of Industrial Innovation[M]. 1974. Harmondsworth: Penguin Books.
Storper M. The Regional World: Territorial Development in a Global Economy[M]. 1997.Guildford: New York.
许庆瑞，蒋键，郑刚.各创新要素全面协同程度与企业特质的关系实证研究[J]. 研究与发展管理，2005.（03）：16-21.
Mansfield E. Academic research underlying industrial innovations: sources, characteristics, and financing[J] .The Review of Economics and Statistics , 1995. (1): 55-65 .
Capello R. Spatial transfer of knowledge in Hi -Tech Milieux: Learning versus collective learning progresses[J]. Journal Regional Studies, 1999. (33): 352-365.
曹平，王桂军.产业创新理论国外研究前沿述评——基于Citespace软件的文献挖掘[J]. 管理现代化，2018. 38（04）：113-116.
王欢芳，王娇蕊，宾厚. 战略性新兴产业创新能力影响因素研究综述[J]. 湖南工业大学学报（社会科学版），2020.25（02）：84-92.
李煜华，荣爽，胡兴宾.基于系统动力学的汽车产业技术创新能力影响因素研究[J]. 工业技术经济，2017. 36（02）：50-56.
Gregory N S, Noel P G, William A F, et al. Firm size and dynamic technological innovation[J]. Technovation, 2002.22 (9): 537-549.
王桂军，曹平. 产业创新与产业创新系统:国外理论脉络与国内政策建议[J]. 科技管理研究，2018.38（12）：9-14.
苏竣，张芳. 政策组合和清洁能源创新模式：基于光伏产业的跨国比较研究[J].国际经济评论，2015.（05）：132-142+7.
王昶，卢锋华，左绿水，孙桥. 地方政府发展战略性新兴产业的政策组合研究[J]. 科学学研究，2020.38（06）：1001-1008.
郭元源，葛江宁，程 聪，段 姗.基于清晰集定性比较分析方法的科技创新政策组合供给模式研究[J]. 软科学，2019. 1：45-49.
黄晗，张金隆，熊杰.创新政策对我国制造业转型升级的影响——基于政策组合的政策文本研究[J]. 科技进步与对策，2020. 37（16）：111-119.
刘凤朝,马荣康.公共科技政策对创新产出的影响——基于印度的模型构建与实证分析[J].科学学与科学技术管理,2012,33(05):5-14.
程华.中国技术创新政策演变、测量与绩效实证研究———基于政策工具的研究[M]. 北京：经济科学出版社，2014. 83-141.
张永安，郄海拓. 金融政策组合对企业技术创新影响的量化评价——基于PMC指数模型[J]. 科技进步与对策，2017.34（02）：113-121.
李冬琴.中国科技创新政策协同演变及其效果:2006—2018[J].科研管理,2022,43(03):1-8.
何丽敏,刘海波,许可.知识产权保护对高技术企业创新投入的影响研究——新技术和新产品的中介作用[J].科技管理研究,2021,41(15):170-177.
基金
国家社会科学基金项目：“‘双碳’目标下成渝地区双城经济圈绿色治理的机制及实现路径研究”（22CGL060, 2022.09—2024.12）；四川省软科学研究计划项目：“四川省‘5+1’现代工业的科技支撑体系评价及政策优化研究”（2022JDR0250, 2022.01—2023.12）；成都市软科学研究项目：“成都建设具有全国影响力的科技创新中心的目标定位和主要路径研究”（2021-RK00-00075-ZF, 2022.06—2023.06）。
PDF(1513 KB)
