Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data

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This manuscript aims to construct a decision support tool for the energy policymakers and energy providers to facilitate an analytical decision-making framework where the key drivers, motivators, and barriers are accounted for. The decision support system is designed in the format of a decision tree algorithm, integrating information about the key drivers, motivators, and barriers derived from the results of the ECHOES project and input from decision-makers based on their perceptions regarding the relevance, importance, potential impact, and probability of occurrence for each parameter, in each phase of the process. The input relies on the analysis of 67 in-depth interviews, 15 focus groups, and 12 case studies conducted in seven countries in the energy policy domain. It is exploited to construct patterns, rules, and scenarios as inputs to the decision tree algorithm. The algorithm can be utilized for evaluating the likelihood of success for a particular process or endeavour, conducting scenario analysis concerning various projections of the system under consideration, deciding which projects to prioritize, which schemes to select for implementation, or how to improve the risk management, and assessing the return on the efforts or investments to improve particular key drivers or motivators and alleviate particular barriers. The proposed algorithm also contributes to the alleviation of challenges associated with the exploitation of qualitative data for energy-related decision-making.