A Machine Learning-Assisted Framework for Prioritizing Electronic-Waste Management Strategies in Developing Countries
Keywords:
Electronic Waste, Machine Learning, Multi-Criteria Decision Analysis, Explainable Artificial Intelligence, Strategy Prioritization, Circular Economy, Developing CountriesAbstract
Electronic waste (e-waste) creates interdependent environmental, occupational-health, economic, and governance pressures in developing countries, yet authorities must often select interventions with incomplete records, heterogeneous institutions, and competing stakeholder priorities. Existing strategy-selection practices are frequently qualitative, single-criterion, or dependent on static expert weights; consequently, they offer limited capacity to anticipate changing waste flows or explain why a policy should be preferred in a particular setting. This paper proposes a conceptual, literature-based framework that combines machine learning (ML), explainable artificial intelligence (XAI), stakeholder preference elicitation, and multi-criteria decision analysis (MCDA). The ML layer is intended to estimate context-dependent quantities such as generation, collection potential, feasibility, adoption, effectiveness, or implementation risk. XAI methods expose the variables influencing those estimates, while MCDA integrates predicted outputs with environmental, economic, social and health, technical, and governance criteria. A hybrid score, feasibility constraints, scenario testing, stakeholder review, and continuous updating translate these components into an auditable strategy ranking. The framework contributes a structured bridge between predictive evidence and normative public choice, an explicit role for informal recyclers and data-quality controls, and a validation plan suitable for data-scarce settings. No model was trained and no country ranking, strategy score, or empirical performance result is reported; future pilots are required to test validity, fairness, robustness, and policy usability.
References
N. Ferronato and V. Torretta, “Waste mismanagement in developing countries: A review of global issues,” Int. J. Environ. Res. Public Health, vol. 16, no. 6, Art. no. 1060, 2019, doi: 10.3390/ijerph16061060.
L. Andeobu, S. Wibowo, and S. Grandhi, “A systematic review of e-waste generation and environmental management of Asia Pacific countries,” Int. J. Environ. Res. Public Health, vol. 18, no. 17, Art. no. 9051, 2021, doi: 10.3390/ijerph18179051.
T. S. Lebbie et al., “E-waste in Africa: A serious threat to the health of children,” Int. J. Environ. Res. Public Health, vol. 18, no. 16, Art. no. 8488, 2021, doi: 10.3390/ijerph18168488.
I. Issah, J. Arko-Mensah, T. P. Agyekum, D. Dwomoh, and J. N. Fobil, “Health risks associated with informal electronic waste recycling in Africa: A systematic review,” Int. J. Environ. Res. Public Health, vol. 19, no. 21, Art. no. 14278, 2022, doi: 10.3390/ijerph192114278.
M. G. K. Houessionon et al., “Environmental heavy metal contamination from electronic waste (e-waste) recycling activities worldwide: A systematic review from 2005 to 2017,” Int. J. Environ. Res. Public Health, vol. 18, no. 7, Art. no. 3517, 2021, doi: 10.3390/ijerph18073517.
A. Borthakur and M. Govind, “How well are we managing e-waste in India: Evidences from the city of Bangalore,” Energy Ecol. Environ., vol. 2, no. 4, pp. 225–235, 2017, doi: 10.1007/s40974-017-0060-0.
A. L. Srivastav et al., “Concepts of circular economy for sustainable management of electronic wastes: Challenges and management options,” Environ. Sci. Pollut. Res., vol. 30, no. 17, pp. 48654–48675, 2023, doi: 10.1007/s11356-023-26052-y.
D. Faibil, R. Asante, M. Agyemang, M. Addaney, and C. Baah, “Extended producer responsibility in developing economies: Assessment of promoting factors through retail electronic firms for sustainable e-waste management,” Waste Manag. Res., vol. 41, no. 1, pp. 117–142, 2023, doi: 10.1177/0734242X221105433.
T. Shevchenko, K. Laitala, and Y. Danko, “Understanding consumer e-waste recycling behavior: Introducing a new economic incentive to increase the collection rates,” Sustainability, vol. 11, no. 9, Art. no. 2656, 2019, doi: 10.3390/su11092656.
C. Wang, F. Zhao, and C. Handwerker, “Transforming and integrating informal sectors into formal e-waste management system: A case study in Guiyu, China,” Clean Technol. Recycl., vol. 2, no. 4, pp. 225–246, 2022, doi: 10.3934/ctr.2022012.
T. Andersen and L. L. Halse, “Product lifecycle information flow in e-waste handling: A means to increase circularity?” Circ. Econ. Sustain., vol. 3, no. 4, pp. 1941–1962, 2023, doi: 10.1007/s43615-023-00258-1.
A.E. Torkayesh, M.A. Rajaeifar, M. Rostom, B. Malmir, M. Yazdani, S. Suh, and O. Heidrich, “Integrating life cycle assessment and multi criteria decision making for sustainable waste management: Key issues and recommendations for future studies,” Renew. Sustain. Energy Rev., vol. 168, Art. no. 112819, 2022, doi: 10.1016/j.rser.2022.112819.
S. S. Sohail et al., “Multi-criteria decision making-based waste management: A bibliometric analysis,” Heliyon, vol. 9, no. 11, Art. no. e21261, 2023, doi: 10.1016/j.heliyon.2023.e21261.
M. Stojčić, E. K. Zavadskas, D. Pamučar, Ž. Stević, and A. Mardani, “Application of MCDM methods in sustainability engineering: A literature review 2008–2018,” Symmetry, vol. 11, no. 3, Art. no. 350, 2019, doi: 10.3390/sym11030350.
A. Mardani, A. Jusoh, K. M. D. Nor, Z. Khalifah, N. Zakwan, and A. Valipour, “Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014,” Econ. Res.-Ekon. Istraž., vol. 28, no. 1, pp. 516–571, 2015, doi: 10.1080/1331677X.2015.1075139.
W. Xia, Y. Jiang, X. Chen, and R. Zhao, “Application of machine learning algorithms in municipal solid waste management: A mini review,” Waste Manag. Res., vol. 40, no. 6, pp. 609–624, 2022, doi: 10.1177/0734242X211033716.
J. K. Solano Meza, D. Orjuela Yepes, J. Rodrigo-Ilarri, and E. Cassiraga, “Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks,” Heliyon, vol. 5, no. 11, Art. no. e02810, 2019, doi: 10.1016/j.heliyon.2019.e02810.
I. Dawar, A. Srivastava, M. Singal, N. Dhyani, and S. Rastogi, “A systematic literature review on municipal solid waste management using machine learning and deep learning,” Artif. Intell. Rev., vol. 58, Art. no. 183, 2025, doi: 10.1007/s10462-025-11196-9.
A. Barredo Arrieta et al., “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion, vol. 58, pp. 82–115, 2020, doi: 10.1016/j.inffus.2019.12.012.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30, 2017, pp. 4765–4774.
M. Arashpour, “AI explainability framework for environmental management research,” J. Environ. Manag., vol. 342, Art. no. 118149, 2023, doi: 10.1016/j.jenvman.2023.118149.
C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nat. Mach. Intell., vol. 1, no. 5, pp. 206–215, 2019, doi: 10.1038/s42256-019-0048-x.
N. Sambasivan, S. Kapania, H. Highfill, D. Akrong, P. K. Paritosh, and L. M. Aroyo, “‘Everyone wants to do the model work, not the data work’: Data cascades in high-stakes AI,” in Proc. CHI Conf. Human Factors Comput. Syst., 2021, pp. 1–15, doi: 10.1145/3411764.3445518.
H. Suresh and J. V. Guttag, “A framework for understanding sources of harm throughout the machine learning life cycle,” in Proc. ACM Conf. Equity Access Algorithms, Mechanisms, Optim., 2021, pp. 1–9, doi: 10.1145/3465416.3483305.
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