Leading System,Driving Context and Role of Government: The Power Engine Model for New Quality Productive Forces
Huang Tao1, Jin Yuhuan2, Mu Lanziwei3, Zhang Sheng3
1. School of Management,Anhui University,Hefei 230601,China; 2. School of Public Policy & Management, China University of Mining and Technology,Xuzhou 221116,China; 3. School of Public Policy and Administration,Xi'an Jiaotong University,Xi'an 710049,China
Abstract：The development of new quality productive forces is a major strategic deployment of the CPC Central Committee.However,existing research lacks a theoretical analysis of the engine for new quality productive forces,and provides insufficient support for the government to identify the vital point and achieve effective governance.This paper critically reviews the insights and limitations of endogenous growth theory and national innovation system theory.By integrating context-driven innovation theory,it deconstructs the production sector into leading and following systems,thereby constructing a power engine model characterized by tripartite synergy among science and technology sectors,enterprises,and users.This engine forms a closed-loop logic of iterative circulation between laboratory knowledge and user knowledge,as well as deep integration of sci-tech innovation with industrial innovation.On this basis,the paper proposes practical pathways to strengthen the power engine through the cultivation of leading enterprises,the cultivation of leading users,and the optimization of the government's context-supply function.This paper represents a localized adaptation of Western economic theories under the conditions of a socialist market economy,and provides important practical implications for optimizing the cultivation paradigm of new quality productive forces.
黄涛, 金宇寰, 牟兰紫薇, 张胜. 领军体系、场景驱动与政府作用——发展新质生产力的动力引擎模型[J]. 中国科技论坛, 2026(9): 64-72.
Huang Tao, Jin Yuhuan, Mu Lanziwei, Zhang Sheng. Leading System,Driving Context and Role of Government: The Power Engine Model for New Quality Productive Forces. , 2026(9): 64-72.
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