DOI: 10.3390/systems11080397 ISSN: 2079-8954

Evaluation of Enterprise Decarbonization Scheme Based on Grey-MEREC-MAIRCA Hybrid MCDM Method

Moses Olabhele Esangbedo, Mingcheng Tang
  • Information Systems and Management
  • Computer Networks and Communications
  • Modeling and Simulation
  • Control and Systems Engineering
  • Software

Engineering and technological breakthroughs in sustainability play a crucial role in reducing carbon emissions. An important aspect of this is the active participation of enterprises in addressing carbon reduction as a systemic approach. In response to government incentives in the People’s Republic of China, Chinese enterprises have developed carbon reduction systems to align their organizational goals with national long-term plans. This paper evaluates the carbon reduction schemes employed by six companies as a multi-criteria decision-making (MCDM) problem. To this end, we propose a new hybrid MCDM method called the grey-MEREC-MAIRCA method. This method combines the recently developed method based on the removal effects of criteria (MEREC) for weighting and multi-attribute ideal-real comparative analysis (MAIRCA) based on the grey system theory. The proposed hybrid method provides the additional benefit of accounting for uncertainty in decision making. Notable findings of this research, based on the decision-maker scores, are that the control of direct carbon emissions and energy-saving efficiency are top priorities. In contrast, committing to corporate social responsibility through carbon public welfare and information disclosure are considered lesser priorities. Furthermore, the ranking results obtained using this method are compared with those from the classical weighted sum model and the technique for order preference by similarity to ideal solution (TOPSIS), confirming the selection of the best company. Despite the limitation of the proposed method and the additional steps needed in the evaluation, it opens up opportunities for future research to develop simpler MCDM methods under uncertainty.

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