PERCEPTION OF UNCERTAINTY AND GREEN INNOVATION: MACHINE LEARNING EVIDENCE FROM PUBLICLY LISTED FIRMS IN CHINA

Abstract

Using a unique panel dataset consisting of 2997 Chinese manufacturing firms publicly listed in the A-share market between 2003 and 2020, we examine whether and to what extent a firm’s perception of uncertainty affects green innovation. After integrating textual analysis with a machine learning approach to measure perception of uncertainty, we find that a firm’s perception of environmental uncertainty negatively affects the number of green patents submitted or approved. The negative effect is weaker for firms followed by more professional analysts, operating in more competitive markets, or located in regions with better institutional settings. In addition, there is significant heterogeneity in the negative effect between non-state-owned versus state-owned firms as well as polluting versus non-polluting firms. The results are robust to different measures of green innovation and perception of uncertainty, and after addressing for potential endogeneity problem. Our study contributes to the literature on behavioral environmental economics by demonstrating that it is not only the environment uncertainty but also how firms perceive the uncertainty matters for green innovation and corporate social responsibility.