Assessment of Zero-Carbon Measures for Sustainable Transportation in Smart Cities: A CRITIC-MARCOS Framework Based on Q-Rung Fuzzy Preferences

Sustainable transportation is an emerging field of research that primarily focuses on eco-friendliness. Smart cities actively attempt to adopt sustainable practices for reducing carbon footprints and improving people’s lives. Developing countries like India intensely concentrate on the zero-carbon theme and promote measures to expedite the process. Mayor’s transport strategy 2018 is a useful document that describes different zero-carbon measures that smart cities could adopt to promote sustainable transport. Due to the heterogeneity of these measures, ranking becomes essential. Driven by the claim, in this article, we develop a new framework using q-rung fuzzy information (qRFI) for rational ranking of zero-carbon measures. QRFI reduces subjective randomness and allows uncertainty modeling from three dimensions: 1) membership; 2) hesitancy; and 3) nonmembership. Specifically, the framework presents criteria importance through intercriteria correlation (CRITIC) with the rank sum (RS) and measurement of alternatives and ranking based on compromise solution (MARCOS) methods for ranking zero-carbon measures. Some notable innovative aspects are: 1) both subjective and objective weights are calculated for criteria; 2) experts’ weights are derived methodically; and 3) zero-carbon measures are ranked with close resemblance to human-driven decisions. To exemplify the usefulness of the proposed tool, a real case example of ranking zero-carbon measures for Coimbatore is demonstrated. Advantages of the innovations are: 1) methodical calculation of weights mitigates biases and inaccuracies; 2) consideration of subjective/objective weights gives better expressibility and understanding of criteria importance; and 3) distance-based human-driven decision scheme offers a flexible and rational ranking. Concerning the findings, the technical aspect criterion receives the highest weight value of 0.1404, followed by documentation (0.1173) and modality shift (0.1125) criteria. Moreover, “start zero-carbon zones,” incentive-based ultralow-carbon vehicles,” and “reduced emissions from on-road machinery” are measures that can be implemented to achieve zero-carbon emissions. The strengths and weaknesses of the tool are inferred via sensitivity and comparison checks.