A robust model to enhance organisation boardrooms’ decision-making process towards a low carbon economy

T. Ibn-Mohammed, Lenny Koh, A. Acquaye, S. Taylor

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations


    The role of organisations in efforts to address negative externalities on the natural environment has become urgent. As such, an organisation-focused perspective would be a limited view point of addressing global environmental impacts on the natural environment. We argue that by drawing on resource-based view theory, development of decision support systems (DSS) is an example of how organisational competences which are rooted in technology can be directed and used to not just create value but also enhance environmental sustainability. To this end, we present a decision support model which seeks to provide stakeholders with a better understanding of how to effectively conduct energy savings investment to promote energy conservation and sustainability within organisation. Bridging knowledge domains, the model presented integrates the key variables of economic and net environmental benefits to produce optimal decisions. These variables are used within an optimisation scheme that consists of integrated modules for data input, sensitivity analysis and takes into account the use of a set of energy saving options that satisfies a range of criteria (environmental, demand, cost and resource constraints); hierarchical course of action; and the evaluations of ‘best’ case scenario based on marginal abatement cost methods and Pareto optimisation. The steps involved in the system development are presented and its usefulness is evaluated using a case study application. The results of the application are analysed and presented, verifying the feasibility of the model whilst encouraging further improvements and extensions. The methodology developed provides stakeholders with an efficient and reliable decision process that is informed by both environmentaland financial considerations.

    Original languageBritish English
    Pages (from-to)3-24
    Number of pages22
    JournalGreen Energy and Technology
    StatePublished - 2015


    • Decision making
    • Energy management
    • Natural environment
    • Organisations


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