Based on the mixed results found in the existing literature on low-carbon operations management practices, this article proposes an original evaluation model for CO₂ emission reduction practices in Brazil, grounded in the concept of information entropy. The information entropy of different low-carbon operations management practices—such as logistics, manufacturing processes, and new product development—was modeled. Then, considering the role of stakeholder pressures, motivations, and barriers, a new approach was adopted to assess the relative importance of the model’s elements using information entropy to develop probabilistically distinct weightings for low-carbon management 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 Hierarchy Process) model; and
(c) the crisp AHP model, based on eight different judgment scales regarding the relative evolution of each criterion/construct.

The results—both expected and unexpected—suggest that:
(i) there is heterogeneity in how different companies perceive low-carbon practices;
(ii) although the studied companies are motivated to reduce CO₂ emissions and such reduction is demanded by various stakeholders, it is implemented exclusively through low-carbon logistics. Unexpectedly, it was found that companies are not adopting a full range of low-carbon operational practices, which may hinder their overall performance.

Implications for end users and policymakers are highlighted.

Researcher: Peter Wanke et al.
Brazilian School of Public and Business Administration (FGV EBAPE)

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