An Original Information Entropy-Based Quantitative Evaluation Model for Low-Carbon Operations in an Emerging Market

Drawing on the mixed results provided by the existing literature on low-carbon operations management practices, this paper proposes an original evaluation model for CO2 emission reduction practices in Brazil, based on the concept of information entropy. We model the information entropy of different low-carbon operations management practices, such as logistics, manufacturing processes and new product development. Then, in light of the role of stakeholder pressures, motivations and barriers, we take a novel approach to assessing the relative importance of elements of the model by using information entropy to develop probabilistically distinctive weightings for low-carbon managerial practices, computed using a variety of models. These models include (a) the Fuzzy Rasch model, which combines Item Response Theory (IRT) and fuzzy set theory; (b) the Fuzzy AHP (Analytic Hierarch Process) model; and (c) the crisp AHP model, based on eight different judgment scales concerning the relative evolution of each criterion/construct. Our results, both expected and unexpected, suggest that: (i) there is heterogeneity in the ways that different companies perceive the issue of low-carbon practices; (ii) while the firms studied are motivated to reduce CO2 emissions and such reduction is required by various stakeholders, the reduction is implemented solely through low-carbon logistics. Unexpectedly, we find that companies are not adopting a full-range of low-carbon operations practices, which may damage their overall performance. Implications for end-users and policy makers are highlighted. \textcopyright 2021 Elsevier B.V.