| 沈坤荣,金童谣.以场景牵引科技创新加快发展新质生产力[J].数量经济技术经济研究,2026,(5):5-29 | | 以场景牵引科技创新加快发展新质生产力 | | Scene-driven Technological Innovation and the Accelerated Development of New Quality Productive Forces | | | | DOI： | | 中文关键词: 场景 新质生产力 科技创新 创新范式 | | 英文关键词: Scene New Quality Productive Forces Technological Innovation Innovation Paradigm | | 基金项目: | | | 中文摘要: | | 场景牵引科技创新，是在前沿技术高度不确定条件下，推动科技成果转化的新型创新范式。场景将技术在真实运行环境中获得可部署、可复制的可用性过程纳入创新分析框架，为新质生产力理论中“科技成果如何转化为现实生产力”这一命题提供了实践推进路径。以场景牵引科技创新，既是顺应国内外发展条件演变的时代需要，也是高质量发展要求下推动科技创新和产业创新深度融合的必然选择。就作用机制而言，场景通过激活潜在需求引领创新方向，通过嵌入真实环境加速技术验证与迭代，通过降低数据流动摩擦释放数据要素价值，通过汇聚创新资源推动生产要素创新性配置，进而促进科技成果向现实生产力快速转化。当前，中国以场景牵引科技创新的实践探索仍处于起步阶段，存在场景供给不足、协同机制不完善、保障机制不健全等问题。应进一步发挥新型举国体制优势，统筹全国创新资源配置，为新场景大规模开放“聚势能”；健全供需对接和多主体协同机制，为场景牵引创新“提质效”；完善制度供给、风险共担与验证支撑体系，为场景牵引创新生态建设“筑底座”，从而为加快培育和发展新质生产力、推动经济社会高质量发展提供有力支撑。 | | 英文摘要: | | Currently, promoting the deep integration of technological and industrial innovation and accelerating the development of new quality productive forces are major strategic tasks for the 15th Five-Year Plan period. However, the issue of scientific and technological achievements being “transferable but not deeply applicable” remains prominent, as frontier technologies face real-world challenges, including insufficient validation in authentic application environments, unclear transformation pathways, and slow progress toward large-scale diffusion. Therefore, the strategic orientation of “scene-driven technological innovation” has rapidly transitioned from top-level design to concrete policy measures, with the scene serving as a crucial link between technology and industry, connecting research and development with the market. The conceptual connotations and practical logic of this approach urgently require systematic theoretical elaboration.This study focuses on scene-driven technological innovation, and through a review of the theoretical origins of scene studies, defines “scene” in the context of scientific and technological achievement transformation as a concrete situational context that, guided by the application needs of science, technology, and industry, places new technologies and products within real-world production, living, or public service environments and continuously advances technological validation, iterative optimization, and large-scale application through multi-actor collaboration. On this basis, this study proposes “scene-driven technological innovation” as a new innovation paradigm. This is not simply a modification of the “demand-pull” or “technology-push” paradigms but represents a systemic extension and innovation across the dimensions of demand creation, the realization of technological usability, modes of actor coordination, and the logic of the innovation process. Its fundamental distinction from existing paradigms lies in treating technological “usability” not as a prerequisite for application but as the product of continuous validation in real operating environments. It also extends feedback loops from within firms into authentic social environments, generating a flywheel effect through the cycle of “technological breakthrough - scene application - validation feedback - technological iteration - industrial application - system upgrading.”Regarding the mechanisms, this study systematically elaborates the internal logic through which scenes facilitate the accelerated formation of new quality productive forces via four collaborative mechanisms-demand creation, environmental validation, resource aggregation, and data catalysis. First, scenes activate latent demand and guide the direction of technological innovation by converting latent demand into explicit demand, aggregating dispersed demand to form innovation-driving forces, and stimulating continuous demand iteration, while providing sustained traction for original innovation and disruptive breakthroughs. Second, scenes provide authentic validation environments that accelerate technological iteration and application through dynamic feedback mechanisms that reduce transformation uncertainty, shortening the feedback loop from “feasibility” to “usability” and lowering coordination costs through multi-actor collaboration. Third, scenes reduce friction in data flows and accelerate the release of the value of data factors by promoting standardization in data generation, resolving incomplete contract problems through project-based arrangements, and organizing large-scale data utilization to amplify marginal value, thereby enabling data factors to exert a multiplier effect in the process of technological innovation. Fourth, scenes aggregate innovation resources and promote the innovative allocation of production factors by providing an anchor point for innovation coordination, driving the qualitative transformation of production factors from “traditional” to “new quality,” and reshaping conditions for knowledge production to catalyze new knowledge production functions, thereby facilitating the systemic formation of new quality productive forces.Nevertheless, scene-driven technological innovation currently faces three categories of real-world challenges. First, the capacity to supply high-value scenes is insufficient, manifesting in inadequate openness, pronounced fragmentation, and a lack of innovation-leading orientation. Second, collaborative innovation mechanisms are imperfect, with significant constraints, including structural imbalances between supply and demand, disrupted collaborative innovation chains, and insufficient cross-departmental data openness. Third, scene support mechanisms are unsound, with prominent issues, including lagging institutional supply, absent risk-sharing mechanisms, and weak pilot-scale validation infrastructure.This study proposes three policy orientations. The first is to “build momentum” by integrating top-level design with local realities, deepening large-scale scene opening around major national strategic objectives at multiple levels, strengthening cross-regional coordination mechanisms, and planning the strategic deployment of future industries alongside future scenes. The second is to “enhance quality and efficiency” by aligning technology supply with market demand, improving supply-demand matching mechanisms, cultivating a multi-actor collaborative innovation ecosystem, and genuinely embedding data factors into scene operations to strengthen iterative capacity. The third is to “lay the foundation” by combining government guidance with market-led development, advancing forward-looking adaptation of institutional supply, improving risk-sharing and fault-tolerance mechanisms, and addressing gaps in validation support infrastructure by developing pilot-scale platforms. | | 查看全文 |
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