Induction approach via P-Graph to rank clean technologies.

Area of Science:

• Decision Science
• Industrial Technology Assessment
• Environmental Engineering

Background:

• Evaluating clean technologies for industry requires systematic assessment across economic, technical, and environmental criteria.
• Multiple Attribute Decision-Making (MADM) problems often involve complex trade-offs based on expert priorities.
• Experts frequently rely on tacit knowledge, making traditional MADM approaches insufficient.

Purpose of the Study:

• To develop a novel induction approach for modeling expert decision-making in technology selection.
• To approximate implicit preference rules from expert rankings using the P-graph framework.
• To enhance the systematic evaluation and ranking of industrial clean technologies.

Main Methods:

• An induction approach is proposed to elicit and formalize expert decision-making pathways.
• The P-graph framework is utilized to determine optimal and near-optimal solutions approximating expert preferences.
• Expert knowledge is captured by ranking sample alternatives, and implicit rules are induced for ranking new options.

Main Results:

• The proposed method successfully induces implicit preference rules from expert rankings.
• The P-graph framework enables the approximation of expert decision structures.
• The approach provides a systematic way to rank alternatives, as demonstrated with Negative Emission Technologies (NETs).

Conclusions:

• The induction approach effectively models expert tacit knowledge in MADM problems.
• This method offers a more accurate approximation of expert preferences compared to conventional MADM techniques.
• The study validates the approach's utility in ranking Negative Emission Technologies for industrial implementation.