| Title | Green Technology Innovation and Enterprise Performance: An Analysis Based on Causal Machine Learning Models |
|---|---|
| ID_Doc | 30366 |
| Authors | Huang, XA; Wang, YZ; Chen, Y; Hu, ZG |
| Title | Green Technology Innovation and Enterprise Performance: An Analysis Based on Causal Machine Learning Models |
| Year | 2024 |
| Published | Sustainability, 16, 6 |
| Abstract | As increasingly stringent environmental regulations are put into effect, Environmental, Social, and Governance (ESG) concepts are being seamlessly integrated into the core of corporate innovation strategies. Due to the quasi-public product perspective of green innovation, the performance of enterprises as a result of green innovation activities exhibits significant heterogeneity. This heterogeneity exists not only between corporate value and financial performance but also among individual enterprises. This paper is based on a sample of 1510 listed Chinese companies examined from 2013 to 2020 and uses machine learning algorithms and quasi-natural experiments to precisely estimate the causal relationship and mechanisms between green innovation and corporate performance. The findings elucidate several critical aspects of green innovation within the corporate sphere: Firstly, rather than attracting green incentives from financial markets, green innovation activities inadvertently stifle the enhancement of corporate value. Secondly, these activities markedly bolster corporate financial performance, primarily by diminishing operational costs, which in turn elevates the return on assets (ROA). Lastly, of all corporate characteristics examined, enterprise size and equity concentration stand out as key determinants influencing the variability in outcomes of green innovation performance. The above findings provide information on the significant implications of enhancing green technology innovation systems and green incentive mechanisms. |
| https://www.mdpi.com/2071-1050/16/6/2309/pdf?version=1710152847 |
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