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. 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.
    2. Hernán P. Guevel & Nuria Ramón & Juan Aparicio, 2025. "Benchmarking and Target Setting in Weight Restriction Context," Mathematics, MDPI, vol. 13(7), pages 1-28, April.
    3. 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.
    4. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Borrás, Fernando & Ruiz, José L. & Sirvent, Inmaculada, 2024. "Planning improvements through data envelopment analysis (DEA) benchmarking based on a selection of peers," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    11. Dalai Ma & Fengtai Zhang & Yaping Xiao & Lei Gao & Hongbo Liao & Na Zhao & Yuedong Xiao & Xingyu Yang & Wenli Wu, 2024. "How industrial water resources green efficiency varies in China: a case study of the Yangtze River Economic Belt considering unexpected output," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 187-213, January.
    12. Fangqing Wei & Junfei Chu & Jiayun Song & Feng Yang, 2019. "A cross-bargaining game approach for direction selection in the directional distance function," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 787-807, September.
    13. Le, Minh Hanh & Afsharian, Mohsen & Ahn, Heinz, 2021. "Inverse Frontier-based Benchmarking for Investigating the Efficiency and Achieving the Targets in the Vietnamese Education System," Omega, Elsevier, vol. 103(C).
    14. Amineh Ghazi & Farhad Hosseinzadeh Lotfı & Masoud Sanei, 2022. "Finding the strong efficient frontier and strong defining hyperplanes of production possibility set using multiple objective linear programming," Operational Research, Springer, vol. 22(1), pages 165-198, March.
    15. Shogo Eguchi & Hirotaka Takayabu & Mitsuki Kaneko & Shigemi Kagawa & Shunichi Hienuki, 2021. "Proposing effective strategies for meeting an environmental regulation with attainable technology improvement targets," Business Strategy and the Environment, Wiley Blackwell, vol. 30(7), pages 2907-2921, November.
    16. Juan Aparicio & Magdalena Kapelko & Juan F. Monge, 2020. "A Well-Defined Composite Indicator: An Application to Corporate Social Responsibility," Journal of Optimization Theory and Applications, Springer, vol. 186(1), pages 299-323, July.
    17. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    18. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    19. Sebastián Lozano & Gabriel Villa, 2010. "Gradual technical and scale efficiency improvement in DEA," Annals of Operations Research, Springer, vol. 173(1), pages 123-136, January.
    20. Maria Basílio & Clara Pires & Carlos Borralho & José Pires Reis, 2020. "Local government efficiency: is there anything new after Troika’s intervention in Portugal?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(2), pages 309-332, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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.