Compulsory liability insurance for environmental pollution is an innovative financial measure that uses market mechanisms to prevent and control environmental pollution,and has important implications for achieving high-quality economic and social development.Taking Shenzhen as the research area,this paper uses the incomplete information dynamic game model to analyze the optimal strategies and equilibrium conditions of the government,enterprises,and insurance companies as decision-makers in compulsory liability insurance for environmental pollution.The research finds that:1)The corporate responsibility sharing formulated by the government is negatively correlated with the risk and loss of environmental accidents,the premium is positively correlated with the liability sharing of insurance companies,and the insurance probability is positively correlated with the corporate responsibility sharing.2)As one of the first pilot demonstration cities of compulsory environmental pollution liability insurance in China,enterprises bear more compensation liability in terms of liability sharing in Shenzhen,insurance companies bear less compensation liability.3)According to the results of model analysis,the insurance willingness of enterprises is limited,which is consistent with the actual insurance probability of only 50%.Based on the above,it is suggested that the government should appropriately subsidize enterprises and insurance companies,for industries with high risk and high loss,a scheme should be designed that allows insurance companies to bear more responsibility and grant them supervision authority,and strict environmental protection standards and fine systems should be formulated for enterprises,so as to encourage them to take proactive insurance while fulfilling their environmental obligations.
compulsory environmental pollution liability insurance/incomplete information dynamic game model/green finance/enterprises and insurance companies/government/high-risk and high-damage industries/environmental risk management
维普
万方数据