Which energy mix for the UK (United Kingdom)? An evolutive descriptive mapping with the integrated GAIA (graphical analysis for interactive aid)–AHP (analytic hierarchy process) visualization tool. (15th January 2016)

• Record Type: Journal Article
• Authors: Ishizaka, Alessio; Siraj, Sajid; Nemery, Philippe
• Is Part Of: Energy. Volume 95(2016)
• Page Start: 602
• Page End: 611
• Publication Date: 2016-01-15
• Subjects: Multi criteria decision making -- Energy planning -- Group judgements -- AHP -- GAIA -- Visualization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.12.009
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Although Multi-Criteria Decision Making methods have been extensively used in energy planning, their descriptive use has been rarely considered. In this paper, we add an evolutionary description phase as an extension to the AHP (analytic hierarchy process) method that helps policy makers to gain insights into their decision problems. The proposed extension has been implemented in an open-source software that allows the users to visualize the difference of opinions within a decision process, and also the evolution of preferences over time. The method was tested in a two-phase experiment to understand the evolution of opinions on energy sources. Participants were asked to provide their preferences for different energy sources for the next twenty years for the United Kingdom. They were first asked to compare the options intuitively without using any structured approach, and then were given three months to compare the same set of options after collecting detailed information on the technical, economic, environmental and social impacts created by each of the selected energy sources. The proposed visualization method allow us to quickly discover the preference directions, and also the changes in their preferences from first to second phase. The proposed tool can help policy makers in better understanding of the energy planning problems that will lead us towards better planning and decisions in the energy sector. Highlights: We introduce a descriptive visual analysis tool for the analytic hierarchy process. The method has been implemented as an open-source preference elicitation tool. We analyse user preferences in the energy sector using this method. The tool also provides a way to visualize temporal preferences changes. The main negative temporal shift in the ranking was found for the nuclear energy.