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Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach

Author

Listed:
  • Kanchan Popli

    (Department of Environmental Science and Biotechnology, Hallym University, Chuncheon 24252, Korea
    Authors contributed equally.)

  • Chunkyoo Park

    (United Nations Office for Sustainable Development, Incheon 21983, Korea
    Authors contributed equally.)

  • Sang-Min Han

    (Graduate School of Global Cooperation (GSGC), Hallym University, Chuncheon 24252, Korea)

  • Seungdo Kim

    (Department of Environmental Science and Biotechnology, Hallym University, Chuncheon 24252, Korea
    Environment Strategy Development Institute (Company), Chuncheon 24252, Korea)

Abstract

This paper aims to develop a predictive model for Laos to generate reliable statistics for urban solid waste from 1995 to 2050. The multi-linear regression (MLR) approach is used with six different socio-demographic and economic parameters, i.e., urban population, gross domestic product (GDP) per capita, urban literacy rate, urban poverty incidence, urban household size and urban unemployment rate. Different reliable models are generated under four different scenarios. The value of R 2 (a relative measure of fit) and value of performance indicators (an absolute measure of fit) such as mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are calculated to assure the validity and accuracy of the results. Model 2 of Scenario 4 is estimated as the best model, where population and GDP per capita show statistical significance for estimating urban solid waste generation rate in Laos. The amount of municipal solid waste is estimated to be 0.98 million tons (MT) in the year 2030, 1.26 MT in the year 2040 and 1.52 MT in the year 2050, assuming that the present waste generation trends will be followed in the future. Moreover, the study provides an easy and detailed explanation of the work which will increase the interest of researchers, allow them to understand the MLR approach clearly and inspire them to use it for other developing countries where the scarcity of data is a major obstacle in the field of solid waste management. The drawback of the study is the limited availability of historical official and reliable data statistics in Laos for the dependent and independent variables.

Suggested Citation

  • Kanchan Popli & Chunkyoo Park & Sang-Min Han & Seungdo Kim, 2021. "Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3038-:d:514430
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    References listed on IDEAS

    as
    1. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.
    2. Peter Warr & Sitthiroth Rasphone & Jayant Menon, 2015. "Two Decades of Declining Poverty Despite Rising Inequality in Laos," Departmental Working Papers 2015-13, The Australian National University, Arndt-Corden Department of Economics.
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    Cited by:

    1. Vidas Raudonis & Agne Paulauskaite-Taraseviciene & Linas Eidimtas, 2022. "ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania," Sustainability, MDPI, vol. 14(7), pages 1-16, March.

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