公众可接受风险标准的确立及应用研究

Establishment and Application of Public Acceptable Risk Criteria

为避免特定情况下,当上级决策者的风险决策结果满足既定标准要求时,由于公众的主观风险感知偏差导致突发性公共事件的发生,基于风险的社会放大框架(SARF)和可接受风险理论,构建了公众可接受风险标准数学模型,并通过AHP和模糊数学等理论研究了模型中5个变量的赋值方法;然后依托“十一五”和“十二五”期间的全国安全生产事故数据,建立公众可接受风险标准;最后以某在役天然气管道泄漏蒸气云爆炸为事故场景,将考虑公众可接受风险标准应用于该事故风险的定量评估中,并对比分析了正常情况和考虑公众风险感知偏差下的风险评估结果。结果表明:①以某在役一般防护目标中的二类防护目标为例,其死亡风险和受伤风险的公众可接受标准建议值范围分别为6.45×10^-7~8.76×10^-5和2.41×10^-6~3.27×10^-4;②公众风险感知将直接影响风险评估的结果,及时、有效地对目标群体进行风险疏导以改善其风险认知,有助于避免公众风险感知偏差引发的负面社会效应。

In order to avoid the occurrence of sudden public events due to the subjective risk perception bias of the public when the risk decision results of the superior decision makers have met the established criteria requirements under special circumstances,this paper first constructs a mathematic model of public acceptable risk criteria on the basis of Social Amplification of Risks Framework(SARF)and acceptable risk theory,and uses AHP and fuzzy mathematics to determine the assignment methods of five variables in the model.Then,the paper establishes the public acceptable risk criteria based on the data of national production safety accidents during the“11th Five-Year Plan”and“12th Five-Year Plan”.Finally,the paper applies the public acceptable risk criteria to the quantitative risk assessment in the case of the vapor cloud explosion caused by leak of a natural gas pipeline in service.Furthermore,the paper compares and analyzes results of the risk assessment under normal conditions considering the deviation of public risk perception.The results are as follows:①Taking the second type of protection target among the general protection targets in service as an example,the recommended values of the public acceptable criteria for death risk and injury risk are 6.45×10^-7~8.76×10^-5 and 2.41×10^-6~3.27×10^-4 respectively.②Public risk perception will directly affect the results of risk assessment.Timely and effective risk counseling for target groups to improve their risk perception will help to avoid negative social effects caused by public risk perception bias.