A new environmental governance cost prediction method based on indicator synthesis and different risk coefficients

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@Article{YE:2019:JCP,

• author = "Fei-Fei Ye and Long-Hao Yang and Ying-Ming Wang",
• title = "A new environmental governance cost prediction method based on indicator synthesis and different risk coefficients",
• journal = "Journal of Cleaner Production",
• volume = "212",
• pages = "548--566",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Environmental governance, Cost prediction, Risk preference, Indicator synthesis, Weight calculation",
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2018.12.029",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652618337314",
• abstract = "Environment protection is important for the survival of residents, and the government must improve its governance model on environmental cost prediction methods to address the increasing level of environmental pollution. Therefore, a science-based investment scheme is of great significance. To improve the accuracy and effectiveness of environmental governance cost prediction method, it is important to consider the completeness of the indicators and their degree of contributions-both of which need to be studied further. Considering the influence of a decision-maker's subjectivity on an investment scheme, this paper proposes a prediction method accommodating the risk preferences of different decision-makers. The proposed method is based on the synthesis of evidential reasoning approach. An objective empowerment is carried out according to the standard deviation method of correlation coefficient to highlight the importance degree of different indicators. At the same time, to improve the practical usage of the synthetic cost prediction method, the future cost is predicted by combining the genetic programming models under different risk coefficients, namely, the risk preference, the risk neutrality, and the risk aversion. Finally, a case study involving environmental governance cost prediction of 29 provinces of China is presented. A comparison of the cost predictions and the actual value of different risk coefficients for the different methods are given to evaluate the effectiveness of the proposed method",
• keywords = "genetic algorithms, genetic programming, Environmental governance, Cost prediction, Risk preference, Indicator synthesis, Weight calculation",

}

Genetic Programming entries for Fei-Fei Ye Long-Hao Yang Ying-Ming Wang

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