A new environmental governance cost prediction method based on indicator synthesis and different risk coefficients
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- @Article{YE:2019:JCP,
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author = "Fei-Fei Ye and Long-Hao Yang and Ying-Ming Wang",
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title = "A new environmental governance cost prediction method
based on indicator synthesis and different risk
coefficients",
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journal = "Journal of Cleaner Production",
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volume = "212",
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pages = "548--566",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Environmental
governance, Cost prediction, Risk preference, Indicator
synthesis, Weight calculation",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2018.12.029",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652618337314",
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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",
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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
Citations