Fuzzy inference decision model to quantify the effectiveness of incentives on delivering green buildings

The switch to sustainable, or green buildings (GBs) is likely to involve a range of governmental policies, with the mix of incentives dependent on the country. The question then becomes one of creating the mix such that it represents the minimum cost/effort whilst maximizing the delivery rate. This research aimed at quantifying the influence of differing GB incentives using a fuzzy inference system. Twenty-six incentives were identified through literature review and expert interviews (n = 36) using a 5-point pairwise questionnaire. These incentives can be naturally bundled into Technical, Financial and Legal groups. Ranking these suggests the Financial, Technical and Legal groups rank first to third, with weights of 0.42, 0.32 and 0.26, respectively. Then, to quantify the potential influence of individual incentives, a fuzzy inference model was created. The model was then employed to predict the influence of each incentive, individually and in combination, i.e. when part of a policy. Using heat maps and sensitivity analysis, it was shown how to increase the impact of a policy for encouraging GBs, by choosing an appropriate percentage contribution for each incentive. The model introduced here is a tool, showing how policy-makers can change/regulate their policies for GB adoption to receive the best results.