Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability

Environmental governance cost prediction can avoid blind investment and waste of resources and achieve effective cost planning for sustainable development of resources and environment. For the sake of solving the problem that most previous studies failed to consider the causal relationship and data reliability of environmental governance inputs and outputs, a new environmental governance cost prediction method is proposed under the framework of the evidential reasoning (ER) rule with three improvements comparing to existing methods: (1) the causal relationship of environmental governance inputs and outputs is embedded into evidence representation for better extracting knowledge from data; (2) the efficiency about the minimum inputs to achieve the maximum outputs is used to evaluate the data reliability of environmental governance inputs and outputs; and (3) a new analytical ER rule is investigated to optimize the process of evidence combination. Hence, the new method includes the calculation of belief distributions, evidence reliabilities, and evidence weights, as well as the combination of evidences to predict environmental governance costs. In the case study, the data of 30 provinces in Mainland China from 2005 to 2020 are collected to verify the effectiveness of the new method. Results show a high level of accuracy of the new method over other existing methods.