Environmental investment prediction has attracted much attention in the last few years. However, there are still great challenges in investment prediction modeling, e.g., 1) effective environmental indicators must be accurately selected to avoid the curse of dimensionality; 2) effective environmental data must be reasonably selected to downsize the scale of historical data; 3) the higher interpretability and lower complexity of prediction models must be considered. To address the above three challenges, a new environmental investment prediction model using fuzzy rule-based system (FRBS), evidential reasoning (ER) approach, and subtractive clustering (SC) algorithm is proposed in the present work, called FRBS-ERSC. In this new model, the FRBS is the core component for the modeling of environmental investment prediction and therefore provides good interpretability and complexity to environ-mental managers. Meanwhile, the ER approach is used as an improvement technique of the FRBS to combine the strengths of different feature selection methods for better indicator selection, and the SC algorithm is used as another improvement technique of the FRBS to select effective environmental data. An empirical case of environmental investment prediction is studied based on data on 31 provinces in China ranged from 2005 to 2018. The experimental results show that the proposed FRBS-ERSC not only provides interpretable and scalable environmental investment prediction based on effective indicator selection and data selection, but also produces satisfactory accuracy compared to some existing models.
|Number of pages||18|
|Journal||Fuzzy Sets and Systems|
|Early online date||1 Mar 2021|
|Publication status||Published (in print/issue) - 30 Sep 2021|
Bibliographical noteFunding Information:
This research was supported by the National Natural Science Foundation of China (Nos. 72001043 , 61773123 , and 72001042 ), the Natural Science Foundation of Fujian Province of China (No. 2020J05122 ), the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188 ), the Social Science Foundation of Fujian Province of China (No. FJ2019C032 ), the Chengdu International Science Cooperation Project (No. 2020-GH02-00064-HZ ), and the Research Grants Council, Hong Kong SAR, China (No. 15218919 ).
© 2021 Elsevier B.V.
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- Environmental investment prediction
- Evidential reasoning
- Fuzzy rule-based system
- Subtractive clustering