摘要
科技成果转化有利于促进国家和地区的经济社会发展,各级政府推出相应政策促进科技成果转化落地,因此探索科学的测度方法来评价政策工具效果是政策信息学研究的重要前沿方向。本文在政策文本计量分析基础上,借助BP神经网络模型,分析科技成果转化政策工具与转化效率的关联关系,评价不同政策工具在促进科技成果转化中的作用。选取重庆市2000-2019年发布的科技成果转化政策文本数据,以技术市场合同成交额表征科技成果转化效率,分析不同政策工具对转化效率的重要程度。结果表明,不同政策工具的使用情况与其对转化效果的相对重要性排序存在明显差异,直接的人力、财力投入并非促进转化的首要因素,公共基础设施与服务对转化的促进效果最明显,金融和税收工具在促进科技成果转化效果方面表现稳定。最后,从提高供给型政策投入精准度、重视环境型政策灵活性和适用性、探索需求型政策合理供给模式、重视政策差异性及政策工具组合效应4个方面提出政策建议。
The transformation of S&T achievements can promote national and regional economic and social development.To this end,governments at all levels have launched corresponding policies to promote the transformation.Therefore,exploring scientific measurement methods to evaluate the effect of policy tools is an important frontier of Policy Informatics.Based on the policy text quantitative analysis,this paper analyzes the correlation between policy tools and transformation efficiency with the help of BP neural network model,and evaluates the effect of different policy tools in promoting the transformation of S&T achievements.The paper selects the S&T achievements transformation policies of Chongqing from 2000 to 2019,represented the transformation efficiency by the contract turnover in technology market,analyzes the importance of different policy tools to the transformation efficiency.The results show there are obvious differences between the utilization of policy tools and their relative importance to the transformation efficiency.The policy tools which directly input human and financial resources are not the most effective ways to promote transformation,while public infrastructures and services have the most obvious effects.In addition,financial and taxation tools have a stable performance in promoting the transformation of S&T achievements.Finally,we put forward policy suggestions from four aspects including improving input precision of supply-oriented policies,emphasizing the flexibility and applicability of environmental-oriented policies,exploring reasonable supply modes for demand-oriented policies,and paying attention to the policy differences and policy tools combination effects.
作者
谢黎
杨华
张志强
Xie Li;Yang Hua;Zhang Zhiqiang(Chengdu Library and Information Center,Chinese Academy of Sciences,Chengdu 610041,China;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100872,China;Library of Northwestern Polytechnical University,Xi'an 710072,China)
出处
《中国科技论坛》
CSSCI
北大核心
2022年第12期55-66,共12页
Forum on Science and Technology in China
基金
中国科学院成都文献情报中心主任基金项目“科技成果转化政策工具对转化效率的影响”(Y9Z0010408)。
关键词
科技成果转化
政策工具
文本量化分析
BP神经网络
Transformation of S&T achievement
Policy tool
Text quantitative analysis
BP neural network
