Can artificial intelligence technology improve companies' capacity for green innovation? Evidence from listed companies in China

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics Pub Date : 2025-02-07 DOI:10.1016/j.eneco.2025.108280

Yingji Liu , Fangbing Shen , Ju Guo , Guoheng Hu , Yuegang Song

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引用次数: 0

Abstract

Green innovation in the digital economy is characterized by complex adaptive systems. It is challenging to effectively improve corporate green innovation capacity (CGIC) by relying on traditional technological innovation. The integration of enterprises' green innovation and artificial intelligence technology (AIT) is becoming a significant driver for addressing global environmental challenges and promoting enterprises' energy efficiency and low-carbon development. Combining patent datasets for listed companies in China, this study explores the driving effect of AIT on CGIC from the perspective of “artificial intelligence +”. This study empirically examines how AIT affects CGIC, and further investigates AIT's impact on the duration of enterprises' green innovation. The findings reveal that AIT's intervention can effectively improve CGIC. Enterprises with different characteristics have heterogeneous effects on improving CGIC by applying AI, indicating that the application of AIT has a more prominent impact on heavily polluting, nontechnology-intensive, and highly competitive enterprises. Mechanism analysis demonstrates that AIT can improve CGIC by absorbing high-skilled labor and increasing investment in research and development. Further examination reveals that AIT application can significantly reduce the potential for enterprises interrupting green innovation activities and prolong the duration of green innovation. This study provides valuable insights concerning the effect of enterprises' AIT application on improving CGIC, empowering enterprises to improve energy efficiency and achieve low-carbon development.

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来源期刊

Energy Economics ECONOMICS-

CiteScore

18.60

自引率

12.50%

发文量

524

期刊介绍： Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.