A Belief Rule-Based Environmental Responsibility Assessment for Small and Medium-Sized Enterprises

This paper is an extension of [17]. This research proposes the use of a Belief Rule-Based approach to assess an enterprise’s level commitment to environmental issues. Participating companies will have to complete a structured questionnaire. An automated analysis of their responses will determine their environmental responsibility level. This is followed by a recommendation on how to progress to the next level. The recommended best practices will help promote understanding, increase awareness, and make the organization greener. BRB Expert systems consist of two parts: Knowledge Base and Inference Engine, which are used to derive valid conclusions from rules, established by experts with domain-specific knowledge. The knowledge base in this research is constructed after an in-depth literature review, critical analyses of existing environmental performance assessment models and primarily guided by the EU Draft Background Report for the development of an EMAS Sectoral Reference Document on "Best Environmental Management Practice in the Telecommunications and ICT Services Sector". The reasoning algorithm of a selected Drools JBoss BRB inference engine is forward chaining. However, the forward chaining mechanism is not equipped with uncertainty handling. Therefore, a decision is made to deploy an evidential reasoning and forward chaining with a hybrid knowledge representation inference scheme to accommodate imprecision, ambiguity and fuzzy types of uncertainties. It is believed that such a system generates well balanced, sensible and Green ICT readiness adapted results, to help enterprises focus on making improvements on more sustainable business operations.