Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts

Abstract

The petrochemical industry is a major contributor to carbon emissions, necessitating an urgent shift towards effective emission reduction techniques. However, a lack of essential data has hindered the development of strategies to address this issue, calling for a comprehensive approach. This study seeks to formulate effective approaches for mitigating carbon emissions in the petrochemical sector by assessing their impact and recognizing potential barriers to reduction. The primary objectives revolve around three key aspects: reducing energy intensity, optimizing CO2 emission reduction, and minimizing associated costs. To attain these objectives, we utilized a dataset represented as a Complex Multi-Fuzzy Hypersoft Set (CMFHSS), specifically designed to address data uncertainties through the incorporation of amplitude and phase terms (P-terms) of complex numbers (C-numbers). The research explores three decision-making techniques, namely Similarity Measures (SM), Entropy (ENT) and TOPSIS within CMFHSS. These techniques are applied to identify the most efficient carbon emission reduction strategy, with the goal of maximizing benefits while minimizing costs.

Keywords

• Decision-making
• similarity measure
• entropy
• TOPSIS