Impact of intelligent transformation on the green innovation quality of Chinese enterprises: evidence from corporate green patent citation data

ABSTRACTIn the context of the rapid integration of artificial intelligence and the real economy, exploring the effects of intelligent transformation on the quality of green innovation in enterprises is of great practical significance. Therefore, this study aimed to identify the impact mechanism of intelligent transformation on the green innovation quality of enterprises based on panel data of listed enterprises in China from 2007–2019. We found that intelligent transformation promotes the improvement of corporate green innovation quality, and the results were robust. Furthermore, intelligent transformation improves the green innovation quality of enterprises through the mediating effects of human capital, research and development expenditure, information sharing effect and factor allocation efficiency. The development of the Internet, the implementation of the National Big Data Comprehensive Pilot Zone and the Broadband China strategy have all strengthened the green innovation quality improvement effect of intelligent transformation. The green innovation quality enhancement effect of intelligent transformation is heterogenous with regard to region, industry factor intensity, industry pollution level and enterprise ownership. Finally, this study provides important policy implications based on its empirical results. Future research should develop more suitable and comprehensive indicators, and focus on the latest data acquisition status to ensure timeliness.KEYWORDS: Intelligent transformationcorporate green innovation qualityartificial intelligencegreen innovationJEL CLASSIFICATION: I10O14O33 AcknowledgmentsThe authors take sole responsibility for all the views and opinions expressed in the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.Notes1 Due to the limited space, the detailed introduction of the dependent variable is shown in Appendix A in the datasets on line or can be obtained from the authors.2 Due to the limited space, the reason why the robot data published by IFR is not selected as the intelligentisation indicator is shown in Appendix B in the datasets on line or can be obtained from the authors.3 Due to the limited space, the specific keywords related to enterprise intelligentisation are shown in Appendix C in the datasets on line or can be obtained from the authors.4 Due to the limited space, the specific processing of the samples is presented in Appendix F in the datasets on line or can be obtained from the authors.5 In the Hausman test, the statistic value is 8864.72, and the P value is 0.0000.6 This study adopts the cluster-robust standard error at the enterprise level.7 Due to the limited space, the detailed presentation of the KV index is presented in Appendix G in the datasets on line or can be obtained from the authors.8 The data of net fixed assets express capital input; labour input is expressed by the total number of employees of the enterprise; total output is expressed by the data of the enterprise's main business revenue.9 The main environment-related terms used in this study are environmental protection, energy consumption, pollution, environmental protection, emission reduction, green, emission, low carbon, air quality, chemical oxygen demand, carbon dioxide, fine particulate matter, PM2.5, PM10 and sulphur dioxide.Additional informationFundingThis research was funded by the National Natural Science Foundation of China [Grant No. 72073071], the Qing Lan Project of Jiangsu Province [Grant No. D202062045], and the Postgraduate Research & Practice Innovation Program of Jiangsu Province [Grant No. KYCX22_2109].