生成式人工智能注意力配置如何影响企业绿色技术创新？

——基于技术差距与创新资源配置的视角

张慧　叶邦银

（南京审计大学）

摘要：在数字经济与绿色发展深度融合的背景下，生成式人工智能作为颠覆性技术，正成为推动企业绿色转型的重要力量。企业管理层对生成式人工智能的注意力配置能否有效促进企业绿色技术创新尚待检验。本文以2017—2024年沪深A股上市公司为研究样本，实证分析了生成式人工智能注意力配置对企业绿色技术创新的影响及其作用机制。研究发现，生成式人工智能注意力配置能促进企业绿色技术创新。机制分析表明，生成式人工智能注意力配置通过技术差距和创新资源配置双重机制影响企业绿色技术创新，即通过降低技术创新前沿差距、优化创新财力和人力资源三条途径作用于企业绿色技术创新。异质性分析结果显示，生成式人工智能注意力配置对企业绿色技术创新的促进作用在“宽带中国”试点城市、非资产密集型与非重污染行业、国有企业以及大规模企业中更为明显。本文的研究结论为企业制定数字化绿色转型战略提供了经验参考。

关键词：生成式人工智能；注意力配置；企业绿色技术创新；技术差距；创新资源配置

作者简介：张慧，南京审计大学社会审计学院（中审学院）讲师，通信作者，南京，211815；叶邦银，南京审计大学社会审计学院（中审学院）教授。

基金项目：国家社会科学基金重大项目“发展新质生产力与完善现代化产业体系研究”（24&ZD039）

引用格式：张慧，叶邦银. 生成式人工智能注意力配置如何影响企业绿色技术创新？：基于技术差距与创新资源配置的视角［J］. 经济与管理研究，2026，47（9）：129-145.

How Does GenAI Attention Allocation Affect Firms' Green Technology Innovation?

—From the Perspectives of Technological Gaps and Innovation Resource Allocation

ZHANG Hui, YE Bangyin

(Nanjing Audit University, Nanjing 211815)

Abstract: Accelerating the development of next-generation artificial intelligence is an important strategic pathway to promote the leapfrog development of science and technology, industrial optimization and upgrading, and the overall leap in productivity in China. Against the backdrop of the deep integration of the digital economy and green development, generative artificial intelligence (GenAI), as a disruptive technology, is emerging as a key force to promote corporate green transformation.

This paper takes A-share listed companies in China from 2017 to 2024 as research samples, uses text analysis to measure the attention allocation level of firms' management to GenAI, and discusses the influence of GenAI attention allocation on firms' green technology innovation from a perception-interpretation-action perspective. The main conclusions are as follows. First, GenAI attention allocation can promote firms' green technology innovation, and this conclusion is still valid after endogeneity analysis and a series of robustness tests. Second, this promoting effect operates by narrowing the gaps in technological innovation frontiers and optimizing financial and human resources for innovation. Third, such an effect is more prominent in “Broadband China” pilot cities, non-asset-intensive and non-heavy polluting industries, state-owned enterprises, and large-scale firms. These findings carry clear policy implications, including systematizing GenAI attention allocation, improving the support system for green innovation, and implementing differentiated, categorized, and tiered incentive strategies.

The marginal contributions are threefold. First, this paper focuses on GenAI rather than general AI, constructs a GenAI lexicon, and measures firms' level of GenAI attention allocation through text analysis, providing a valuable supplement to micro-level research on GenAI. Second, by incorporating GenAI into the analysis framework for improving firms' green technology innovation, this paper confirms the value effect of GenAI attention allocation on the quality and efficiency enhancement of firms' green technology innovation. In particular, it identifies the green transmission mechanisms of technological gaps and innovation resource allocation, further enriching the related research on GenAI and firms' green technology innovation, and providing a new perspective for accelerating corporate green transformation. Third, this paper reveals the promoting effect across different situations. Specifically, it identifies how differences in “Broadband China” pilot cities, asset-intensive and heavily polluting industries, ownership structure, and firm size affect the relationship between GenAI attention allocation and firms' green technology innovation. The findings provide references for firms to improve the level of GenAI attention allocation, implement the New Generation Artificial Intelligence Development Plan, and drive corporate green transformation.

Keywords: GenAI; attention allocation; firms' green technology innovation; technological gap; innovation resource allocation

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