IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12405-d929122.html
   My bibliography  Save this article

Identifying the Leverage Points in the Household Solid Waste Management System for Harare, Zimbabwe, Using Network Analysis Techniques

Author

Listed:
  • Phyllis Rumbidzai Kwenda

    (Department of Agricultural Engineering, School of Engineering, University of KwaZulu-Natal, Carbis Road, Scottsville, Pietermaritzburg 3201, South Africa)

  • Gareth Lagerwall

    (Department of Agricultural Engineering, School of Engineering, University of KwaZulu-Natal, Carbis Road, Scottsville, Pietermaritzburg 3201, South Africa)

  • Sibel Eker

    (International Institute of Applied System Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria)

  • Bas van Ruijven

    (International Institute of Applied System Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria)

Abstract

Managing household solid waste (HSW) has gone beyond what the Harare local government can handle. Inadequate knowledge of the interactions existing between issues that affect the efficient running of waste management systems is one of the major hindrances in waste management planning in developing countries like Zimbabwe. The complexity of the waste management system for a given municipal area needs to be identified and understood to generate appropriate and efficient waste management strategies. Network analysis (NA) is a methodology extensively used in research to help reveal a comprehensive picture of the relationships and factors related to a particular phenomenon. The methodology reduces the intricacy of large systems such as waste management to smaller and more understandable structures. In this study, NA, which was done mainly using the R software environment, showed a result of 1.5% for network density, thus signifying that for Harare, waste management strategies need to be ‘seeded’ in various parts of the system. The Pareto principle and the 3Rs (Reduce, Reuse, Recycle) concept were applied to suggest the issues to prioritize and generate strategies that could potentially affect significant change to the city’s waste management system. The key issues identified, in their order of importance, are an increase in uncollected waste, low waste collection efficiency, increase in illegal waste dumping, the deteriorating country’s economy, reduced municipal financial capacity, reduced municipal workforce capacity, inadequate or unreliable waste data, increase in waste volume, increase in the number of street vendors, no waste planning and monitoring unit, no engineered landfills in the city, increase in waste collection pressure, low waste collection frequency, increase in the unemployment rate, reduced municipal technical capacity, few waste collection vehicles, limited vehicles maintenance, distinct socio-economic classes, high vehicles breakdown, and increase in population.

Suggested Citation

  • Phyllis Rumbidzai Kwenda & Gareth Lagerwall & Sibel Eker & Bas van Ruijven, 2022. "Identifying the Leverage Points in the Household Solid Waste Management System for Harare, Zimbabwe, Using Network Analysis Techniques," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12405-:d:929122
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12405/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12405/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duygan, Mert & Stauffacher, Michael & Meylan, Grégoire, 2021. "What constitutes agency? Determinants of actors’ influence on formal institutions in Swiss waste management," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Bakshan, Amal & Srour, Issam & Chehab, Ghassan & El-Fadel, Mutasem & Karaziwan, Jalal, 2017. "Behavioral determinants towards enhancing construction waste management: A Bayesian Network analysis," Resources, Conservation & Recycling, Elsevier, vol. 117(PB), pages 274-284.
    3. Xu, Wanying & Zhou, Chuanbin & Cao, Aixin & Luo, Min, 2016. "Understanding the mechanism of food waste management by using stakeholder analysis and social network model: An industrial ecology perspective," Ecological Modelling, Elsevier, vol. 337(C), pages 63-72.
    4. Contreras, Francisco & Hanaki, Keisuke & Aramaki, Toshiya & Connors, Stephen, 2008. "Application of analytical hierarchy process to analyze stakeholders preferences for municipal solid waste management plans, Boston, USA," Resources, Conservation & Recycling, Elsevier, vol. 52(7), pages 979-991.
    5. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
    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. Abdallah Namoun & Ali Tufail & Muhammad Yasar Khan & Ahmed Alrehaili & Toqeer Ali Syed & Oussama BenRhouma, 2022. "Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges," Sustainability, MDPI, vol. 14(20), pages 1-32, October.

    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. Roberts, Cameron & Greene, Jenna & Nemet, Gregory F., 2023. "Key enablers for carbon dioxide removal through the application of biochar to agricultural soils: Evidence from three historical analogues," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    2. Matteo Vittuari & Fabio De Menna & Silvia Gaiani & Luca Falasconi & Alessandro Politano & Jana Dietershagen & Andrea Segrè, 2017. "The Second Life of Food: An Assessment of the Social Impact of Food Redistribution Activities in Emilia Romagna, Italy," Sustainability, MDPI, vol. 9(10), pages 1-14, October.
    3. Zhai, Li & Yan, Xiangbin, 2022. "A directed collaboration network for exploring the order of scientific collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    4. Sandra Cristina Oliveira & Juliana Cobre & Danilo Florentino Pereira, 2021. "A measure of reliability for scientific co-authorship networks using fuzzy logic," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4551-4563, June.
    5. Alireza Abbasi & Mahdi Jalili & Abolghasem Sadeghi-Niaraki, 2018. "Influence of network-based structural and power diversity on research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 579-590, October.
    6. Sameer Kumar & Jariah Mohd. Jan, 2013. "Mapping research collaborations in the business and management field in Malaysia, 1980–2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 491-517, December.
    7. Yichi Zhang & Zhiliang Dong & Sen Liu & Peixiang Jiang & Cuizhi Zhang & Chao Ding, 2021. "Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries," Sustainability, MDPI, vol. 13(3), pages 1-23, January.
    8. Carlo D'Ippoliti, 2021. "“Many‐Citedness”: Citations Measure More Than Just Scientific Quality," Journal of Economic Surveys, Wiley Blackwell, vol. 35(5), pages 1271-1301, December.
    9. Jungwon Yoon & Joshua SungWoo Yang & Han Woo Park, 2017. "Quintuple helix structure of Sino-Korean research collaboration in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 61-81, October.
    10. Xiaojun Hu & Ronald Rousseau & Jin Chen, 2012. "Structural indicators in citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 451-460, May.
    11. Chao Lu & Yingyi Zhang & Yong‐Yeol Ahn & Ying Ding & Chenwei Zhang & Dandan Ma, 2020. "Co‐contributorship network and division of labor in individual scientific collaborations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1162-1178, October.
    12. Nasirian, Farzaneh & Mahdavi Pajouh, Foad & Balasundaram, Balabhaskar, 2020. "Detecting a most closeness-central clique in complex networks," European Journal of Operational Research, Elsevier, vol. 283(2), pages 461-475.
    13. Zezhou Wu & Ann T.W. Yu & Chi Sun Poon, 2020. "Promoting effective construction and demolition waste management towards sustainable development: A case study of Hong Kong," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(6), pages 1713-1724, November.
    14. Mahmoud Elmezain & Ebtesam A. Othman & Hani M. Ibrahim, 2021. "Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis," Mathematics, MDPI, vol. 9(22), pages 1-14, November.
    15. Konstantin Fursov & Alina Kadyrova, 2017. "How the analysis of transitionary references in knowledge networks and their centrality characteristics helps in understanding the genesis of growing technology areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1947-1963, June.
    16. Guijie Zhang & Luning Liu & Yuqiang Feng & Zhen Shao & Yongli Li, 2014. "Cext-N index: a network node centrality measure for collaborative relationship distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 291-307, October.
    17. He, Bing & Ding, Ying & Tang, Jie & Reguramalingam, Vignesh & Bollen, Johan, 2013. "Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective," Journal of Informetrics, Elsevier, vol. 7(1), pages 117-128.
    18. Yongjun Zhu & Lihong Quan & Pei‐Ying Chen & Meen Chul Kim & Chao Che, 2023. "Predicting coauthorship using bibliographic network embedding," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 388-401, April.
    19. Julia Müller & Thorsten Upmann, 2017. "Eigenvalue Productivity: Measurement of Individual Contributions in Teams," CESifo Working Paper Series 6679, CESifo.
    20. Witting Antje, 2015. "Measuring the Use of Knowledge in Policy Development," Central European Journal of Public Policy, Sciendo, vol. 9(2), pages 54-62, December.

    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:14:y:2022:i:19:p:12405-:d:929122. 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.