Research On The Mechanism Of Environmental Compensation Policy Affecting The Choice Of Corporate Environment Risk Governance Behavior

In recent years,the environmental pollution risks faced by enterprises have become increasingly severe,and environmental risk management has been in great needs.In order to solve the above problems,the central government and many local governments began to explore and implement environmental compensation,and issued a number of laws and regulations on environmental compensation.“Environmental compensation” has become the most popular environmental protection institution in China.This paper analyzes the impact mechanism of government environmental compensation policy on corporate environmental risk governance from the macro and micro perspectives,provides scientific basis for corporate environmental risk governance issues and countermeasure research,and promotes the sustainable development of China's ecological environment.This paper firstly explores the willingness of enterprises to pay for various environment risk governance programs after understanding relevant environmental compensation policies from a macro level.Applying the choice experiment method,this paper identifies the attributes of the corporate environmental risk management scheme based on the existing environmental compensation policy and sets the level value.On the basis of above exploration,the paper designs a variety of alternative environment risk governance scenarios composed of key attributes,conducts questionnaire interviews to enterprises with high pollution risks to assess the choices they make between different environment risk governance scenarios and then calculate the willingness of the company to pay for different environment risk governance projects.Secondly,this paper analyzes the impact mechanism of environmental compensation policy on corporate environment risk governance from the micro level.The Bayesian Reasoning model is constructed to identify and analyze the government environmental compensation behavior in corporate environment risk governance.That is,based on the prior probability of corporate environment risk governance behavior and the conditional probability of government environmental compensation behavior,the posterior probability of corporate environmental risk governance behavior under government environmental compensation behavior is calculated.Furthermore,from the obtained posterior information,this paper infers the function and effect of government environmental compensation behavior.Finally,based on the conclusions of the computational experiments,the optimization strategies of corporate environment risk governance are proposed from the perspective of enterprises and governments.The empirical research shows that:(1)enterprises have a strong willingness to control dust in the air.Respondents are more likely to control dust at occasional levels than water quality and air quality compliance rates,indicating that companies are in urgent need of dust management.(2)The annual sales income of the enterprise affects the willingness to govern the environment risks of the enterprise.The cross-variable of the annual sales and payment of the respondent's company significantly affects the willingness of the respondent to pay,indicating that the greater the annual sales income of the enterprise,the higher the willingness to pay for environment risk governance.(3)Differentiated water prices,sewage discharge charges,and waste disposal charges promote enterprises to carry out wastewater treatment and industrial recycling.After the implementation of the above three environmental compensation policies,the probability of enterprises carrying out wastewater treatment is about 85%,and the proportion has increased.(4)The current environmental compensation policy has a weak effect on the utilization of waste slag and energy-saving optimization of production systems.The current environmental compensation behavior of the government does not effectively encourage enterprises to use waste slag utilization and energy-saving optimization of production systems.