IJEP 45(3): 231-239 : Vol. 45 Issue. 3 (March 2025)
Onukogu Humphrey Francisco1, Amir Hamzah Bin Sharaai1*, Latifah Binti Abd Manaf1, Nitanan Koshy A/L Matthew1 and Abdullahi Adamu2
1. Universiti Putra Malaysia, Department of Environment, Faculty of Forestry and Environment, Selangor, Malaysia
2. Usmanu Danfodiyo University, Faculty of Engineering and Environmental Design, Department of Environmental and Resources Management, Sokoto, Nigeria
Abstract
Waste is a significant economic drain in developing countries, with poor waste collection services in many African cities. The government struggles to establish sustainable waste management policies to manage the waste stream in the municipality effectively. This study aims to establish a clear rationale for making waste management a priority by exploring the economic benefit of improved services of solid waste collection in the Lagos metropolis through econometrics. Using multi-stage probability sampling techniques and face-to-face questionnaire administration, the study used choice modelling to elicit households’ preferences for improved waste collection options. Data was analyzed using SPSS version 25 and Nlogit version 5.0 econometric software. Two models were run and the results revealed that all the attributes of the waste collection services were significant in models (conditional logit model basic and interactive). More so, the result of the marginal rate of substitution has indicated that FCL_3 (frequency of waste collection) is the most preferred attribute, while the least preferred is the WCA_2 (waste collection agency). The study results guide policy on residents’ willingness to pay for solid waste collection services and propose the best policy options for effective, environmentally friendly, economically viable and socially acceptable waste management in Lagos.
Keywords
Choice modelling, Lagos, Solid waste, Collection services, Conditional logit model
References
- Kaza, S., et al. 2018. What a waste 2.0: A global snapshot of solid waste management to 2050. Urban Development, World Bank, Washington, DC. accessed on 26 May 2021.
- Adamoviæ, V.M., et al. 2018. An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries. Waste manage., 78: 955-968.
- Ahmad, M., et al. 2021. Do rural-urban migration and industrial agglomeration mitigate the environmental degradation across China’s regional development levels? Sustain. Prod. Consumption. 27: 679-697.
- Gallardo, A., et al. 2014. Analysis of refuse-derived fuel from the municipal solid waste reject fraction and its compliance with quality standards. J. clean. prod., 83: 118-125.
- Munguía-López, A.D.C., et al. 2020. Optimization of municipal solid waste management using a coordinated framework. Waste Manage., 115: 15-24.
- Fernando, S. J. and A. Zutshi. 2023. Municipal solid waste management in developing economies: A way forward. Clean. Waste Systems. 5(June): 100103. DOI: 10.1016/j.clwas.2023.100103.
- Bjelic, D., et al. 2024. Waste to energy as a driver towards a sustainable and circular energy future for the Balkan countries. Energy Sustain. Soc., 14(1): 3.
- Balogun-Adeleye, R. M., E. O. Longe and K. O. Aiyesimoju. 2019. Environmental assessment of municipal solid waste (MSW) disposal options: A case study of Olushosun landfill, Lagos state. IOP Conf. Series: Mater. Sci. Eng., 640: 012091. DOI: 10.1088/1757-899X/640/1/012091.
- Onuminya, T. O. 2018. An appraisal of waste management in the lagos metropolis: A case study of lagos state waste management agency. Nigerian J. Pure Appl. Sci., 30(3): 3104-3108. DOI: 10.192 40/njpas.2017.C07.
- Morgan, K. 1970. Sample size determination using Krejcie and Morgan table. Kenya Projects Organization. 38: 607-610.
- Israel, G.D. 1992. Determining sample size. A series of the programme evaluation and organizational development. University of Florida.
- Blamey, R.K., et al. 2002. Attribute causality in environmental choice modelling. Env. Resour. Eco., 23: 167-186.
- Hanley, N., S. Mourato and R.E. Wright. 2001. Choice modelling approaches: A superior alternative for environmental valuatioin? J. eco. surveys. 15(3): 435-462.
- Hanley, N., R. E. Wright and V. Adamowicz. 1998. Using choice experiments to value the environment. Env. resour. eco., 11: 413-428.
- Özdemiroglu, E., et al. 2002. Economic valuation with stated preference techniques: Summary guide. Department for Transport, Local Government and the Regions, London.
- 16 McFadden, D. 1999. Computing willingness-to-pay in random utility models. In Trade, theory and eco-nometrics. Ed J.R. Melvin, J.C. Moore and R.G Riezman. pp 275-296.
- Lancaster, K. J. 1966. A new approach to consumer theory. J. political eco., 74(2): 132-157.
- Hanley, N., R. E. Wright and G. Koop. 2002. Modelling recreation demand using choice experiments: Climbing in Scotland. Env. resour. Eco., 22: 449-466.
- Petrin, A. and K. Train. 2010. A control function approach to endogeneity in consumer choice models. J.marketing res., 47(1): 3-13.
- Adamowicz, V. and P. Boxall. 2001. Future directions of stated choice methods for environment valuation. University of Alberta, Alberta, Canada.
- Hensher, D.A., J.M. Rose and W.H. Greene. 2005. Applied choice analysis: a primer. Cambridge university press.
- Arabamiry, S., et al. 2013. Choice modelling stated preference valuation technique in Perhentian island marine park environmental goods. Int. J. Busi. Social Sci., 4(6): 179-188.
- Birol, E. and J. Bennett. 2010. Concluding remarks and recommendations for implementing choice experiments in developing countries (chapter 17). InChoice experiments in developing countries. Edward Elgar Publishing.
- Birol., E., K. Karousakis and P. Koundouri. 2006. Using a choice experiment to account for preference heterogeneity in wetland attributes: The case of Cheimaditida wetland in Greece. Ecol. eco., 60(1): 145-156.