Open Access
Issue
E3S Web of Conf.
Volume 485, 2024
The 7th Environmental Technology and Management Conference (ETMC 2023)
Article Number 05008
Number of page(s) 14
Section Advanced Solid Waste Management and Technology
DOI https://doi.org/10.1051/e3sconf/202448505008
Published online 02 February 2024
  1. Chia, W. Y., Tang, D. Y. Y., Khoo, K. S., Lup, A. N. K., & Chew, K. W. Nature’s fight against plastic pollution: algae for plastic biodegradation and bioplastics production. Environmental Science and Ecotechnology, 4, 100065. (2020). [CrossRef] [Google Scholar]
  2. Rafey, A., & Siddiqui, F. Z. A review of plastic waste management in India–challenges and opportunities. International Journal of Environmental Analytical Chemistry, 1-17. (2021). [Google Scholar]
  3. SIPSN, (2022) [Google Scholar]
  4. World Economic Forum. The World’s plastic problem in numbers. [Online] Available at: https://www.weforum.org/agenda/2018/08/the-world-of-plastics-in-numbers. Accessed on April 6, 2023. (2018) [Google Scholar]
  5. Dauvergne, P. Why is the global governance of plastic failing the oceans?. Global Environmental Change, 51, 22-31. (2018). [CrossRef] [Google Scholar]
  6. Mendenhall, E. Oceans of plastic: a research agenda to propel policy development. Marine Policy, 96, 291-298. (2018). [CrossRef] [Google Scholar]
  7. Prata, J. C., Silva, A. L. P., Da Costa, J. P., Mouneyrac, C., Walker, T. R., Duarte, A. C., & Rocha-Santos, T. Solutions and integrated strategies for the control and mitigation of plastic and microplastic pollution. International journal of environmental research and public health, 16(13), 2411. (2019). [CrossRef] [PubMed] [Google Scholar]
  8. Schnurr, R. E., Alboiu, V., Chaudhary, M., Corbett, R. A., Quanz, M. E., Sankar, K., ... & Walker, T. R. Reducing marine pollution from single-use plastics (SUPs): A review. Marine pollution bulletin, 137, 157-171. (2018). [CrossRef] [PubMed] [Google Scholar]
  9. Mouat, J., Lozano, R. L., & Bateson, H. Economic Impacts of Marine Litter. Kommunenes Internasjonale Miljøorganisasjon. (2010). [Google Scholar]
  10. Litterbase. Species interaction graph. [Online] Available at: https://litterbase.awi.de/interaction_graph. [Accessed on April 6, 2022]. (2020). [Google Scholar]
  11. Worm, B., Lotze, H. K., Jubinville, I., Wilcox, C., & Jambeck, J. Plastic as a persistent marine pollutant. Annual Review of Environment and Resources, 42, 1-26. (2017). [CrossRef] [Google Scholar]
  12. Beaumont, N. J., Aanesen, M., Austen, M. C., Börger, T., Clark, J. R., Cole, M., ... & Wyles, K. J. Global ecological, social and economic impacts of marine plastic. Marine pollution bulletin, 142, 189-195. (2019). [CrossRef] [PubMed] [Google Scholar]
  13. Galloway, T. S., Cole, M., & Lewis, C. Interactions of microplastic debris throughout the marine ecosystem. Nature ecology & evolution, 1(5), 1-8. (2017). [Google Scholar]
  14. Maryanti, D. F. Performance of community-based solid waste management for integrated and sustainable solid waste management: The case of Bogor City, Indonesia (Doctoral dissertation, Unesco-Ihe). (2017). [Google Scholar]
  15. Suardi, L. R., Gunawan, B., Arifin, M., & Iskandar, J. A review of solid waste management in waste bank activity problems. International Journal of Environment, Agriculture and Biotechnology, 3(4), 264433. (2018). [Google Scholar]
  16. Widyarsana, I. M. W., Damanhuri, E., & Agustina, E. Municipal solid waste material flow in Bali Province, Indonesia. Journal of Material Cycles and Waste Management, 22, 405-415. (2020) [CrossRef] [Google Scholar]
  17. Jeuland, M., et al. Preferences for improved piped water access in Beira, Mozambique: A stated preference analysis. Environmental and Resource Economics, 60(4), 679-702. (2015). [Google Scholar]
  18. Mills, J. E., et al. Distance matters: Comparing the spatial decay characteristics of the willingness to pay for water access. Ecological Economics, 70(6), 1168-1179. (2011). [Google Scholar]
  19. Jambeck, J. R., et al. Plastic waste inputs from land into the ocean. Science, 347(6223), 768-771. (2015). [CrossRef] [PubMed] [Google Scholar]
  20. Jain, K., et al.. Application of machine learning for solid waste management: A review and framework for future work. Resources, Conservation and Recycling, 152, 104507. (2020) [CrossRef] [Google Scholar]
  21. Ranzani, O., et al. Machine learning and deep learning techniques for waste management: A comprehensive review. Computers & Industrial Engineering, 158, 107367. (2021) [CrossRef] [Google Scholar]
  22. Salim, A. A., et al. A review of machine learning applications in solid waste management. Environmental Monitoring and Assessment, 194(3), 187. (2022). [CrossRef] [PubMed] [Google Scholar]
  23. Oikarinen, E., Bourassa, S. C., Hoesli, M., & Engblom, J. Revisiting metropolitan house price-income relationships. Journal of Housing Economics, 61, 101946. (2023). [CrossRef] [Google Scholar]
  24. Bennett, J. E., Rashid, T., Zolfaghari, A., Doyle, Y., Suel, E., Pearson-Stuttard, J., ... & Ezzati, M. Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data. The Lancet Regional Health–Europe, 27. (2023). [Google Scholar]
  25. Heo, Y. J. Population aging and house prices: Who are we calling old?. The Journal of the Economics of Ageing, 23, 100417.; (2022). [CrossRef] [Google Scholar]
  26. Zhang, S., Zhou, Y., & Xu, P. Air quality affects house prices—Analysis based on RD of the Huai River policy. Sustainable Cities and Society, 85, 104017. (2022). [CrossRef] [Google Scholar]
  27. Álvarez-Román, L., & Garcia-Posada, M.. Are house prices overvalued in Spain? A regional approach. Economic Modelling, 99, 105499. (2021) [CrossRef] [Google Scholar]
  28. Dai, X., Felsenstein, D., & Grinberger, A. Y. Viewshed effects and house prices: Identifying the visibility value of the natural landscape. Landscape and Urban Planning, 238, 104818. (2023) [CrossRef] [Google Scholar]
  29. McMillen, D., & Singh, R. Land value estimation using teardowns. Journal of Housing Economics, 58, 101874. (2022). [CrossRef] [Google Scholar]
  30. Kim, H. S., Lee, G. E., Lee, J. S., & Choi, Y. Understanding the local impact of urban park plans and park typology on housing price: A case study of the Busan metropolitan region, Korea. Landscape and Urban Planning, 184, 1-11. (2019). [CrossRef] [Google Scholar]
  31. Oikarinen, E., Bourassa, S. C., Hoesli, M., & Engblom, J. Revisiting metropolitan house price-income relationships. Journal of Housing Economics, 61, 101946. (2023). [CrossRef] [Google Scholar]
  32. Bennett, J. E., Rashid, T., Zolfaghari, A., Doyle, Y., Suel, E., Pearson-Stuttard, J., ... & Ezzati, M. Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data. The Lancet Regional Health–Europe, 27. (2023) [Google Scholar]
  33. Berisha, E., Meszaros, J., & Gupta, R. Income inequality and house prices across US states. The Quarterly Review of Economics and Finance, 91, 192-197. (2023) [CrossRef] [Google Scholar]
  34. Heo, Y. J. Population aging and house prices: Who are we calling old?. The Journal of the Economics of Ageing, 23, 100417. (2022). [CrossRef] [Google Scholar]
  35. Anilkumar, P. P., & Chithra, K. Land use based modelling of solid waste generation for sustainable residential development in small/medium scale urban areas. Procedia Environmental Sciences, 35, 229-237. (2016). [Google Scholar]
  36. Kumar, S., & Kumar, R. Forecasting of municipal solid waste generation using non-linear autoregressive (NAR) neural models. Waste Management, 121, 206-214. (2021). [CrossRef] [Google Scholar]
  37. Cubillos, M., Wulff, J. N., & Wøhlk, S. A multilevel Bayesian framework for predicting municipal waste generation rates. Waste Management, 127, 90-100.. (2021) [CrossRef] [Google Scholar]

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