IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i18p10446-d639058.html
   My bibliography  Save this article

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

Author

Listed:
  • Sandhya Rijal

    (Department of Environmental Engineering and Management, Chaoyang University of Technology, No. 168, Jifeng E. Rd, Wufeng District, Taichung 413310, Taiwan
    Department of Applied Chemistry, Chaoyang University of Technology, No. 168, Jifeng E. Rd, Wufeng District, Taichung 413310, Taiwan)

  • Yu-Han Huang

    (Department of Environmental Engineering and Management, Chaoyang University of Technology, No. 168, Jifeng E. Rd, Wufeng District, Taichung 413310, Taiwan)

  • Hung-Yueh Lin

    (Department of Environmental Engineering and Management, Chaoyang University of Technology, No. 168, Jifeng E. Rd, Wufeng District, Taichung 413310, Taiwan)

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.

Suggested Citation

  • Sandhya Rijal & Yu-Han Huang & Hung-Yueh Lin, 2021. "An Integrated Approach to Municipal Solid Waste Recycling Performance Evaluation by Incorporating Local Demographic Features," Sustainability, MDPI, vol. 13(18), pages 1-13, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10446-:d:639058
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/18/10446/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/18/10446/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Babaei, Ali Akbar & Alavi, Nadali & Goudarzi, Gholamreza & Teymouri, Pari & Ahmadi, Kambiz & Rafiee, Mohammad, 2015. "Household recycling knowledge, attitudes and practices towards solid waste management," Resources, Conservation & Recycling, Elsevier, vol. 102(C), pages 94-100.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Chang, Dong-Shang & Liu, Wenrong & Yeh, Li-Ting, 2013. "Incorporating the learning effect into data envelopment analysis to measure MSW recycling performance," European Journal of Operational Research, Elsevier, vol. 229(2), pages 496-504.
    4. Fioretti, Guido, 2007. "The organizational learning curve," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1375-1384, March.
    5. Guido Fioretti, 2007. "A connectionist model of the organizational learning curve," Computational and Mathematical Organization Theory, Springer, vol. 13(1), pages 1-16, March.
    6. Bernardino Benito & Francisco Bastida & Jose Garcia, 2010. "Explaining differences in efficiency: an application to Spanish municipalities," Applied Economics, Taylor & Francis Journals, vol. 42(4), pages 515-528.
    7. Juan Aparicio & José Ruiz & Inmaculada Sirvent, 2007. "Closest targets and minimum distance to the Pareto-efficient frontier in DEA," Journal of Productivity Analysis, Springer, vol. 28(3), pages 209-218, December.
    8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yexin Zhou & Hongke Song & Xiaopei Huang & Hao Chen & Wei Wei, 2022. "How Does Social Capital Affect Residents’ Waste-Separation Behavior? Evidence from China," IJERPH, MDPI, vol. 19(6), pages 1-21, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fukuyama, Hirofumi & Sekitani, Kazuyuki, 2012. "Decomposing the efficient frontier of the DEA production possibility set into a smallest number of convex polyhedrons by mixed integer programming," European Journal of Operational Research, Elsevier, vol. 221(1), pages 165-174.
    2. Juan Aparicio & Magdalena Kapelko & Bernhard Mahlberg & Jose L. Sainz-Pardo, 2017. "Measuring input-specific productivity change based on the principle of least action," Journal of Productivity Analysis, Springer, vol. 47(1), pages 17-31, February.
    3. Mustapha Daruwana Ibrahim & Sahand Daneshvar & Hüseyin Güden & Bela Vizvari, 2020. "Target setting in data envelopment analysis: efficiency improvement models with predefined inputs/outputs," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1319-1336, December.
    4. Washio, Satoshi & Yamada, Syuuji & Tanaka, Tamaki & Tanino, Tetsuzo, 2012. "Improvements by analyzing the efficient frontier in DEA," European Journal of Operational Research, Elsevier, vol. 217(1), pages 173-184.
    5. Kao, Chiang, 2020. "Measuring efficiency in a general production possibility set allowing for negative data," European Journal of Operational Research, Elsevier, vol. 282(3), pages 980-988.
    6. Michael Zschille & Matthias Walter, 2012. "The performance of German water utilities: a (semi)-parametric analysis," Applied Economics, Taylor & Francis Journals, vol. 44(29), pages 3749-3764, October.
    7. Aparicio, Juan & Pastor, Jesus T., 2014. "Closest targets and strong monotonicity on the strongly efficient frontier in DEA," Omega, Elsevier, vol. 44(C), pages 51-57.
    8. Aparicio, Juan & Cordero, Jose M. & Gonzalez, Martin & Lopez-Espin, Jose J., 2018. "Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries," Omega, Elsevier, vol. 79(C), pages 9-20.
    9. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).
    10. Kao, Chiang, 2022. "Closest targets in the slacks-based measure of efficiency for production units with multi-period data," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1042-1054.
    11. Ifigeneia-Dimitra Pougkakioti, 2021. "Measuring The Efficiency And Productivity Change Of Municipalities With An Output Oriented Model:Empirical Evidence Across Greek Municipalities Over The Time Period 2012-2016," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 15(1), pages 98-125, JUNE.
    12. Luo Muchen & Rosita Hamdan & Rossazana Ab-Rahim, 2022. "Data-Driven Evaluation and Optimization of Agricultural Environmental Efficiency with Carbon Emission Constraints," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    13. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2022. "Bank production with nonperforming loans: A minimum distance directional slack inefficiency approach," Omega, Elsevier, vol. 113(C).
    14. Ruiz, José L. & Segura, José V. & Sirvent, Inmaculada, 2015. "Benchmarking and target setting with expert preferences: An application to the evaluation of educational performance of Spanish universities," European Journal of Operational Research, Elsevier, vol. 242(2), pages 594-605.
    15. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.
    16. Pérez-López, Gemma & Prior, Diego & Zafra-Gómez, José L., 2018. "Temporal scale efficiency in DEA panel data estimations. An application to the solid waste disposal service in Spain," Omega, Elsevier, vol. 76(C), pages 18-27.
    17. Lee, Chia-Yen, 2014. "Meta-data envelopment analysis: Finding a direction towards marginal profit maximization," European Journal of Operational Research, Elsevier, vol. 237(1), pages 207-216.
    18. Zhu, Qingyuan & Wu, Jie & Ji, Xiang & Li, Feng, 2018. "A simple MILP to determine closest targets in non-oriented DEA model satisfying strong monotonicity," Omega, Elsevier, vol. 79(C), pages 1-8.
    19. Hirofumi Fukuyama & Hiroya Masaki & Kazuyuki Sekitani & Jianming Shi, 2014. "Distance optimization approach to ratio-form efficiency measures in data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 42(2), pages 175-186, October.
    20. Alcaraz, Javier & Anton-Sanchez, Laura & Aparicio, Juan & Monge, Juan F. & Ramón, Nuria, 2021. "Russell Graph efficiency measures in Data Envelopment Analysis: The multiplicative approach," European Journal of Operational Research, Elsevier, vol. 292(2), pages 663-674.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10446-:d:639058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.