中国环境违法行为:长期影响评估与未来违法行为预测

C. Lo, Christopher S. Tang, Yi Zhou
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引用次数: 1

摘要

我们采用商业分析的方法来研究如何减少中国制造商所犯的环境事件。我们首先进行描述性分析,以资产收益率(ROA)来衡量环境事件对公司绩效的长期影响。通过对2004年至2013年间在上海/深圳证券交易所上市的418家中国制造业上市公司所发生的1542起环境事件进行分析,我们发现经验证据表明,相对于没有(任何暴露)环境事件的可比公司,(暴露)环境事件的公司“仅在”暴露后连续几年的ROA较低。尽管暴露会带来负面的经济回报,但我们推测,被选中接受检查的可能性低可能是不道德的中国制造商违反环境法规的关键原因之一。这种推测促使我们使用公开的财务数据来识别因素(例如,公司的年龄,总资产,政府所有权的百分比,过去的环境事件),人们可以使用这些因素来预测哪家公司更有可能违反环境法规。通过使用我们的训练样本(从2004年到2012年),我们首先建立了我们的预测模型。然后,我们开发了一个评分系统来描述中国企业在2013年违反环境法规的可能性。通过使用这些风险评分,我们发现政府在2013年可以通过检查风险评分高于前80%的企业中只有21.5%的企业来揭露超过71%的环境违法行为。因此,我们的分析方法有可能被用作中国政府制定更有效的机制来选择公司进行检查以揭露违规公司的基石。随着2014年以来中国政府加大处罚力度,被抓的几率更高,中国环境事件的数量更有可能下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Environmental Violations in China: Evaluating Their Long-Term Impact and Predicting Future Violations
We take a business analytic approach to examine ways to reduce environmental incidents committed by Chinese manufacturers. We first perform descriptive analysis to examine the long-term impact of environmental incidents on a firm’s performance that is measured in terms of Returns On Assets (ROA). By considering all 1542 environmental incidents occurred between 2004 and 2013 committed by 418 public Chinese manufacturing firms listed on Shanghai/Shenzhen Stock Exchange, we find empirical evidence that, relative to comparable firms without (any exposed) environmental incidents, firms with (exposed) environmental incidents have a lower ROA in consecutive years “only after” they were exposed. Despite the negative financial returns for being exposed, we speculate that low probability of being selected for inspection can be one of the key reasons for unethical Chinese manufacturers to violate environmental regulations. This speculation motivates us to use publicly available financial data to identify factors (e.g., age of the firm, total assets, percentage of government ownership, past environmental incidents) that one can use to predict which firm is more likely to violate environmental regulations. By using our training samples (from 2004-2012), we first develop our predictive model. Then we develop a scoring system to characterize the likelihood of a Chinese firm violating environmental regulations in 2013. By using these risk scores, we show the government can expose over 71% of the environmental violations in 2013 by inspecting only 21.5% of the firms with risk scores above the top 80 percentile. Therefore, our analytical approach has the potential to be used as a building block for the Chinese government to develop a more effective mechanism to select firms for inspection to expose non-compliance firms. With a higher chance of getting caught along with a harsher penalty imposed by the Chinese government since 2014, the number of environmental incidents in China is more likely to decline.
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