IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v83y2023ics0957178723001248.html
   My bibliography  Save this article

Understanding municipal solid waste production and diversion factors utilizing deep-learning methods

Author

Listed:
  • Zhao, Yidan
  • Li, Hong

Abstract

We propose a hybrid deep learning neural network model for accurately calculating municipal solid waste (MSW) production and diversions with socioeconomic and demographic factors across 220 municipalities of Ontario, Canada, from 2010 to 2021. The proposed SDAE model is constructed by layering multiple denoising autoencoders (DAEs) and Bootstrap aggregation (bagging). The proposed methodology is tested against benchmark ML techniques, including Decision Trees, ANNs, and SDEs. The findings indicate that deep learning techniques can develop solid waste models with high prediction accuracy. Moreover, it has been statistically confirmed that socioeconomic factors affect solid waste management.

Suggested Citation

  • Zhao, Yidan & Li, Hong, 2023. "Understanding municipal solid waste production and diversion factors utilizing deep-learning methods," Utilities Policy, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:juipol:v:83:y:2023:i:c:s0957178723001248
    DOI: 10.1016/j.jup.2023.101612
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178723001248
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2023.101612?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    2. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    3. Hongda Liu & Haifeng Zhao, 2022. "Upgrading models, evolutionary mechanisms and vertical cases of service-oriented manufacturing in SVC leading enterprises: Product-development and service-innovation for industry 4.0," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-24, December.
    4. Xiuzhen, Xie & Zheng, Wenxiu & Umair, Muhammad, 2022. "Testing the fluctuations of oil resource price volatility: A hurdle for economic recovery," Resources Policy, Elsevier, vol. 79(C).
    5. Sunghyeon Choi & Jin Hur, 2020. "An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting," Energies, MDPI, vol. 13(6), pages 1-16, March.
    6. Li, ChangZheng & Umair, Muhammad, 2023. "Does green finance development goals affects renewable energy in China," Renewable Energy, Elsevier, vol. 203(C), pages 898-905.
    7. Liu, Hongda & Huang, Feipeng & Huang, Jialiang, 2022. "Measuring the coordination decision of renewable energy as a natural resource contracts based on rights structure and corporate social responsibility from economic recovery," Resources Policy, Elsevier, vol. 78(C).
    Full references (including those not matched with items on IDEAS)

    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. Kong, Jiangwei & Gao, Mengxi, 2024. "Sustainable strategies for boosting profitability: Unveiling the connection between fiscal policy and natural resource efficiency," Resources Policy, Elsevier, vol. 88(C).
    2. Tang, Qianning & Shahla, Rzayeva & Leyla, Aliyeva & Sevinj, Huseynova, 2023. "Moderating role of carbon emission and institutional stability on renewable energy across developing countries," Renewable Energy, Elsevier, vol. 209(C), pages 413-419.
    3. Kuang, Hewu & Liang, Yiyan & Zhao, Wenjia & Cai, Jiahong, 2023. "Impact of natural resources and technology on economic development and sustainable environment – Analysis of resources-energy-growth-environment linkages in BRICS," Resources Policy, Elsevier, vol. 85(PB).
    4. Yan, Juan & Haroon, Muhammad, 2023. "Financing efficiency in natural resource markets mobilizing private and public capital for a green recovery," Resources Policy, Elsevier, vol. 85(PB).
    5. Xu, Lan & Wu, Yang, 2023. "Nexus between green finance, renewable energy and carbon emission: Empirical evidence from selected Asian economies," Renewable Energy, Elsevier, vol. 215(C).
    6. Qiu, Kai & Liu, Chunli & Li, Ying, 2023. "The effect of natural resource utilization efficency, financial development for the economic development In Asian Countries," Resources Policy, Elsevier, vol. 86(PA).
    7. Zhang, Shuzhi & Xie, Guangxiong, 2023. "Promoting green investment for renewable energy sources in China: Case study from autoregressive distributed Lagged in error correction approach," Renewable Energy, Elsevier, vol. 214(C), pages 359-368.
    8. Zhao, Shixiu & He, Xiaoyi & Faxritdinovna, Kenjayeva Umriya, 2023. "Does industrial structure changes matter in renewable energy development? Mediating role of green finance development," Renewable Energy, Elsevier, vol. 214(C), pages 350-358.
    9. Su, Yong & Dong, Yue & Ullah, Mirzat & Li, Nanbo, 2024. "Utilizing natural resource efficiency at the core of green economic growth in the Anthropocene," Resources Policy, Elsevier, vol. 90(C).
    10. Xu, Lan, 2023. "Role of belt and road initiative in the development of renewable energy: Mediating role of green public procurement," Renewable Energy, Elsevier, vol. 217(C).
    11. Fang, Ying & Fan, Ying & Haroon, Muhammad & Dilanchiev, Azer, 2023. "Exploring the relationship between global economic policy and volatility of crude futures: A two-factor GARCH-MIDAS analysis," Resources Policy, Elsevier, vol. 85(PA).
    12. Yin, Qingwei, 2023. "Nexus among financial development and equity market on green economic finance: Fresh insights from European Union," Renewable Energy, Elsevier, vol. 216(C).
    13. Sun, Gebing & Li, Guozhi & Dilanchiev, Azer & Kazimova, Asli, 2023. "Promotion of green financing: Role of renewable energy and energy transition in China," Renewable Energy, Elsevier, vol. 210(C), pages 769-775.
    14. Wang, Zheng & Feng, NaNa & Zuo, WenJin & Jia, YanHuai, 2024. "Sustainable recovery through efficient resource markets: Innovations and solutions," Resources Policy, Elsevier, vol. 88(C).
    15. Tao, Nan & Wu, Tiantian & Yan, Guo, 2024. "To the test of economic recovery: The swings in energy resource prices," Resources Policy, Elsevier, vol. 89(C).
    16. zhang, Ling & Niu, Guangli, 2023. "Role of financial performance and natural resources development on economic recovery: Empirical evidence from an Asian perspective," Resources Policy, Elsevier, vol. 85(PA).
    17. Liu, Fang & Chen, Houqun & Zhang, Shuwen, 2023. "Nexus among corruption, political instability and natural resources on economic recovery in Vietnam," Resources Policy, Elsevier, vol. 85(PB).
    18. Li, Na & Dilanchiev, Azer & Mustafa, Ghulam, 2023. "From oil and mineral extraction to renewable energy: Analyzing the efficiency of green technology innovation in the transformation of the oil and gas sector in the extractive industry," Resources Policy, Elsevier, vol. 86(PA).
    19. Cao, Zhenzi & Tao, Ling, 2023. "Green finance and economic resilience: Investigating the nexus with natural resources through econometric analysis," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 929-940.
    20. Cui, Kai & Li, Xinxue & Li, Gang, 2023. "What kind of fiscal policies and natural resources efficiency promotes green economic growth? Evidence from regression analysis," Resources Policy, Elsevier, vol. 85(PB).

    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:eee:juipol:v:83:y:2023:i:c:s0957178723001248. 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: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    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.