Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Chun-Che Huang, Wen-Yau Liang (), Horng-Fu Chuang, Tzu-Liang (Bill) Tseng and Yi-Chun Shen
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Chun-Che Huang: Department of Information Management, National Chi Nan University, 1 University Road, Nantou 545301, Taiwan
Wen-Yau Liang: Department of Information Management, National Changhua University of Education, 1 Jin-De Road, Changhua 500207, Taiwan
Horng-Fu Chuang: Department of Accounting Information, Da-Yeh University, No. 168, University Rd., Changhua 515006, Taiwan
Tzu-Liang (Bill) Tseng: Department of Industrial Manufacturing and Systems Engineering, The University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA
Yi-Chun Shen: Department of Information Management, National Chi Nan University, 1 University Road, Nantou 545301, Taiwan

Sustainability, 2024, vol. 16, issue 9, 1-22

Abstract: The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets.

Keywords: green energy; green investment; incremental technique; rough set; rule induction; multiple outcomes; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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