An Integrated Approach to Municipal Solid Waste Recycling Performance Evaluation by Incorporating Local Demographic Features

Abstract

Recycling municipal solid waste has become a challenging task for municipalities. Appropriate recycling efficiency evaluations are, thus, essential to find practical benchmark learning targets for inefficient municipal solid waste authorities (MSWAs). This study developed a recycling performance evaluation procedure by subgrouping MSWAs with prominent local demographic features, such as population density, ratio of senior citizens, tourism index etc. Principal recycling relevant factors for MSWAs in each group were then collected, and data envelopment analysis (DEA) was applied for efficiency evaluation and benchmark learning targets. A case study of 181 MSWAs in Taiwan demonstrated the suitability of the proposed procedures. An assessment of the required efforts for efficiency improvements revealed that, in an unsegregated scenario, inefficient MSWAs representing a rural subgroup required maximum efforts to fulfill the efficiency targets, which was on average 61% higher than that determined in their respective subgroup. Furthermore, the unsegregated scenario revealed proximal efficiency results for the urban subgroup. The results indicated that consideration of local demographic features was essential for a fair assessment of recycling efficiency. Additionally, evaluating MSWAs with similar local demographic features was superior in obtaining appropriate benchmark learning targets for the inefficient MSWAs and, consequently, exhibited practicality for improving walkthroughs to achieve the efficiency goal.

How Does Social Capital Affect Residents’ Waste-Separation Behavior? Evidence from China

“…Previous studies have verified the impact of demographic characteristics, such as gender, income, and education level, on individual environmental behavior and household waste-management behavior [ 49 , 50 , 51 ]. This study controlled respondents’ age, gender, marital status, education level, and per capita annual family income.…”

Exploring knowledge benchmarking using time‐series directional distance functions and bibliometrics

“…As highlighted in previous paragraphs, most of the current literature associating Data Envelopment Analysis and Benchmarking are proposals of frameworks, models or methodological contributions to identify non‐parametric frontier peers or targets for benchmarking, developing the most appropriate linear combination (Cook et al, 2004; Rijal et al, 2021; Mazumder et al, 2018; Park & Sung, 2016; Ruiz & Sirvent, 2021; Zhu et al, 2021b) or using the programming formulations for evaluating of ownership structures, human capital, operational or economic determinants (Kumar and Vincent (2011), Lou et al, 2021; Pereira & Soares de Mello, 2021; Sufian, 2011; Zhu et al, 2021). None of the mentioned literature has attempted to properly integrate a benchmark approach to identify not only how much to improve but also how to improve considering different expert information.…”