以财务会计工作变革赋能新质生产力发展

Core Viewpoint - The article emphasizes the importance of transforming financial accounting practices to support the cultivation of new quality productivity, which is essential for driving innovation and optimizing industrial structures [1]. Group 1: R&D Investment Measurement and Tracking - R&D investment is described as the "fuel" for technological innovation, with its precision and adaptability directly impacting the quality of new productivity cultivation [2]. - A project-based and detailed accounting system for R&D expenditures is proposed, tracking funding flows across various research stages [2]. - A dynamic evaluation mechanism is suggested to identify risks in R&D projects and adjust accounting treatments accordingly [2]. Group 2: Value Realization of Innovation Factors and Capital Allocation - The article discusses the need for a value management system that goes beyond traditional measurements to include new factors like data, technology, and talent [3]. - It highlights the importance of data asset pricing and the establishment of a valuation model for data resources to facilitate market-based allocation [3]. - A strategic human capital measurement system is recommended to assess talent contributions and efficiency, enhancing the visibility of human capital investments [3]. Group 3: Technology Capital Assessment and Securitization - The establishment of differentiated valuation models for intangible assets such as patents and proprietary technologies is emphasized, considering various factors like technological maturity and commercialization costs [4]. - Clear accounting rules for intellectual property are necessary to reflect the value changes and risks associated with these assets [4]. Group 4: Strategic Reconstruction of Value Creation Models - A multi-dimensional value reporting system is proposed to disclose contributions of technological innovation to various societal and environmental goals [5]. - The integration of financial and operational data is encouraged to enhance strategic decision-making capabilities [5][6]. Group 5: Innovation Risk Identification and Quantitative Early Warning - A dynamic risk control system covering the entire lifecycle of R&D to commercialization is recommended, focusing on precise risk identification and quantification [7]. - The establishment of a dashboard for real-time monitoring of core risk indicators is suggested to facilitate timely responses to emerging risks [7]. Group 6: Resilience Mechanism Construction and Risk Response - The article advocates for a multi-layered risk buffer mechanism to optimize resource allocation and ensure stability in innovation efforts [8]. - Regular comprehensive risk reports are recommended to support decision-making processes across various business functions [8].