Environmental sustainability benchmarking of the U.S. and Canada metropoles: An expert judgment-based multi-criteria decision making approach

摘要

In this paper, environmental sustainability performance assessment of 27 U.S. and Canada metropoles is addressed. A four-step hierarchical fuzzy multi-criteria decision-making approach is developed. In the first step, the proposed methodology is established by determining the sustainability performance indicators (a total of 16 sustainability indicators are considered), collecting the data and contacting experts from academia, U.S. government agencies and within the industry. In the second step, experts are contacted and the entire list is finalized; sustainability performance evaluation forms are delivered; and then expert judgment results are obtained and quantified, respectively. In the third step, the proposed Multi-criteria Intuitionistic Fuzzy Decision Making model is developed and sustainability performance scores are quantified by using the collected data, multi-criteria decision making model and sustainability indicator weights obtained from expert judgment phase. In the final step, the sustainability scores and rankings of the 27 metropoles, results analysis and discussions, and statistical highlights about the research findings are provided. Results indicated that the average sustainability performance score is found to be 0.524 over scale between 0 and 1. The metropole with the greatest sustainability performance score is found to be New York with 0.703 and the poorest performing city is identified as Cleveland with 0.394. The results of the statistical analysis indicate that the greatest significant correlations are obtained with carbon dioxide (CO_2) emissions per person (-0.749 - significant negative correlation with sustainability performance score) and share of workers traveling by public transport (+0.753 - significant positive correlation with sustainability performance score). Therefore, the CO_2 emissions and public transport are found to have the most significant impact on the sustainability scores.