Pollutant Information Management in Chinese Listed Firms:Measurement and Governance
Environmental performance has become increasingly crucial for enterprises.At the 17th National Congress of the Communist Party of China in 2007,"building an ecological civilization"was identified as a new requirement for at-taining the goal of building a moderately prosperous society in all respects.At the 19th National Congress of the Commu-nist Party of China in 2017,General Secretary Xi Jinping reiterated the importance of environmental protection with the concept that lucid waters and lush mountains are invaluable assets.Concurrently,the China Securities Regulatory Com-mission(CSRC)and the stock exchanges heightened requirements for listed firms to disclose environmental information.However,amid rising public attention to environmental issues,some firms manage,or even manipulate pollutant informa-tion.In February and August 2023,Huang Runqiu,Minister of the Ministry of Ecology and Environment,led a team to Henan,uncovering numerous violations,including falsified production records and manipulated monitoring data.The an-nouncement of these incidents has triggered notable market reactions.For example,on June 25,2018,a China Central Television(CCTV)program reported the issue of Dongjiang Environmental falsifying its pollution data.As a result,the company's stock price plummeted and hit the daily limit.This paper refers to the act of managing and manipulating pol-lutant data as"pollutant information management".In light of anecdotal evidence,this paper attempts to construct a mea-sure of pollutant information management,analyzes its underlying motivation,and investigates whether corporate gover-nance and administrative supervision can serve as effective monitoring mechanisms.Few existing studies,however,have directly discussed pollutant information management.Previous studies have fo-cused on the selective disclosure of environmental information(Cohen et al.,2015)and corporate greenwashing(Beers&Capellaro,1991;Delmas&Montes-Sancho,2010).Some studies have discussed the possibility of pollutant information management,but primarily from the perspective of theoretical models(Grahn,2020)or indirect inference(Greenstone et al.,2022;Karplus et al.,2018).To fill this void in the literature,this paper constructs a measure of pollutant information management of sulfur diox-ide in a Sample of Chinese listed firms from 2015 to 2021.The results show that the measure based on ordinary least squares,elastic net,random forest and neural network can all effectively capture the degree of pollutant information man-agement of listed firms.Specifically,higher values of these measures correlate with a higher likelihood of future regula-tory events related to pollutant data manipulation.In addition,the measure does not predict non-manipulative environmen-tal regulatory events,indicating that the measure specifically reflects the degree of pollutant information management rather than overall environmental performance.Compliance is a primary motivation for pollutant information manage-ment.Those firms whose disclosed pollutant levels just meet the compliance limit have significantly higher values of pol-lutant information management measures.Finally,conventional corporate governance mechanisms cannot effectively re-strain pollutant information management,but administrative supervision can play an effective role,and the pattern contin-ues to hold after we consider the heterogeneous treatment effects of staggered adoption of environmental inspections.The proposed measure remains valid in robustness checks,including using expanding-window estimation,adding predictive variables,constructing more complex predictive models,and excluding alternative explanations.Our paper makes several contributions.First,this paper takes the lead in scientifically measuring,analyzing and em-pirically testing the phenomenon of pollutant information management,which extends the literature on managers'discre-tion over environmental information disclosure(Cohen et al.,2015;Delmas&Montes-Sancho,2010;Lyon&Montgom-ery,2015;Grahn,2020;Greenstone et al.,2022).Second,this paper leverages machine learning techniques to predict the theoretical level of pollutant emissions for Chinese listed firms,which adds to the literature on the application of machine learning techniques in finance and accounting research(Gu et al.,2020;Bao et al.,2020;Brown et al.,2020).Third,this paper discusses the influence of corporate governance and administrative supervision on pollutant information manage-ment,providing new evidence for research on monitoring corporate environmental performance and offering fresh in-sights for government agencies into enhancing environmental regulatory efficiency.
Pollutant Information ManagementMachine LearningCorporate GovernanceEnvironmental Inspections
万方数据
维普
