Urban circular economy performance evaluation: A novel fully fuzzy data envelopment analysis with large datasets

Abstract

This study constructs a fully fuzzy data envelopment analysis (DEA) with large datasets to evaluate the urban circular economy. The proposed fully fuzzy DEA model considers uncertainties of circular economy indicators and introduces fuzzy trigonometric numbers. Additionally, the model provides a modified algorithm to overcome calculation difficulties due to voluminous data and large-scale decision-making units. The proposed model can quickly solve urban circular economy efficiency under uncertainty and with large datasets. An empirical study of 264 Chinese cities over 2009–2018 was conducted. Overall, the average annual fuzzy efficiency scores (theoretically vary from 0˜ to 1˜) of the urban circular economy in these cities are (0.7471, 0.7463, 0.7451), indicating that there is substantial room for improvement. The average efficiency scores and the subitem coordination levels of western cities are the highest compared to those of the other regions. Moreover, Northeast China exhibits the lowest efficiency score, which may be attributed to its decaying industry and unadvanced technical level. The overall urban economy performance presents two distinct trends: in 2009–2015, the urban circular economy exhibited negative growth, whereas it increased in 2015–2018. However, the growth rate declined, and negative growth remains a risk. Based on the results, several policy implications are provided for promoting urban circular economy.