Multicriteria-based decision aiding technique for assessing energy policy elements-demonstration to a case in Bangladesh

• Main Authors: Rahman, M. M., Paatero, J. V., Lahdelma, R., Wahid, M. A.
• Format: Article
• Published: Elsevier Ltd 2016
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/id/eprint/73863/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951148369&doi=10.1016%2fj.apenergy.2015.11.091&partnerID=40&md5=9dd50b5107d80be8590d2d406d92fdf2

The adverse environmental consequences and diminishing trend of fossil fuel reserves indicate a serious need for vibrant and judicious energy policy. Energy policy involves a number of stakeholders, and needs to incorporate the interests and requirements of all the key stakeholder groups. This paper presents a methodological technique to assist with formulating, evaluating, and promoting the energy policy of a country in a transparent and representative way with clear scientific justifications and balanced assessments. The multicriteria decision analysis approach has been a widely used technique for evaluating different alternatives based on the interests of a multitude of stakeholders, and goals. This paper utilizes the SMAA (Stochastic Multicriteria Acceptability Analysis) tool, which can evaluate different alternatives by incorporating multiple criteria, in order to examine the preferences of different policy elements. We further extend this technique by incorporating the LEAP model (Long-range Energy Alternatives Planning system) to assess the emission impacts of different policy elements. We demonstrate the application of this evaluation technique by an analysis of four hypothetical policy elements namely Business-as usual (BAU), Renewables (REN), Renewable-biomass only (REN-b), and Energy conservation and efficient technologies (ECET). These are applied to the case of sharing fuel sources for power generation for the Bangladesh power sector. We found that the REN-b and REN policy elements were the best and second best alternatives with 41% and 32% acceptability respectively. This technique gives transparent information for choosing appropriate policy elements that aimed at sustainable energy.